m. h. moradi

Today, in the poultry industry, food additives are used to achieve the highest production at the lowest cost. Growth stimulants and food additives are chemical, biological, or natural compounds added to water and feed. They are used to improve growth and feed efficiency and obtain the highest and most economical production. The use of medicinal plants in poultry nutrition showed that, in addition to stimulating feed consumption, these plants also have antibiotic and anti-coccidiosis properties. This experiment aimed to investigate the effects of different levels of the herbal Bilhar (Dorema aucheri Boiss.) on the Production Parameters, hematology, and thigh and breast meat quality traits of broiler chickens in a completely randomized design.

In this study, 240 one-day-old Ross 308 were exposed to five treatments (four replicates), and 12 chickens in each replicate were raised for 42 days. The chickens had free access to water and feed during the rearing period. The necessary care was following scientific rearing principles and commercial catalog recommended methods. The experimental treatments included 1) control-common diet with no additive 2) control diet + 0.1% flavophospholipol antibiotic, 3) control diet + Bilhar (0.1 % in starter and grower, 0.05 % in finisher phase), 4) control diet + Bilhar (0.3 % in starter and grower, 0.15 % finisher phase), 3) control diet + Bilhar (0.5 % in starter and grower,0 .25 % in finisher phase). The experiment measured body weight and food consumption at the end of the initial, growth, and final periods. On the 42nd day, one chicken from each replicate was randomly selected and blood was collected through the wing vein. Two blood samples, one into the venoject tubes containing 0.5 cc of the anticoagulant ethylenediaminetetraacetic acid (EDTA), to collect and measure blood hematological parameters (the amount of red blood cells, white blood cells, hemoglobin, hematocrit and the subtracted population of white blood cells) and the other part of the blood into tubes free of anticoagulants in order to separate the blood serum, to measure the metabolites Serum biochemical tests were transferred.

Chick's body weight at 24 and 42 d was higher in the first Bilhar group and antibiotic level than in the control group. Different experimental groups significantly affected feed intake at the starter and grower phases (P> 0.05). Adding bilhar to the diet did not change red blood cell numbers but impacted hemoglobin, hematocrit percentages, and white blood cell count. Different levels of Bilher powder and antibiotics significantly influenced villus height, villus thickness, and villus area in the duodenum (P<0.05). But the depth of the crypt and the ratio of the height of the villi to the depth of the crypt in the duodenum did not show a statistically significant effect between the treatments (P<0.05). Despite no significant improvement in breast water holding capacity, dripping loss, or cooking loss, dietary billiards significantly decreased breast PH. Experimental treatments significantly affected thigh water holding capacity, dripping loss, and pH but did not affect thigh cooking loss.

It can be concluded that in the case of most traits, especially functional traits, the first level of bilhar (treatment 3) improved compared to the control treatment. Therefore, this plant can be used at the indicated level as a plant additive in a broiler chicken diet. This will improve functional and histological traits.

The principal aim of the sheep industry worldwide is to produce high-quality meat. In addition to the meat, milk, and wool production are the economic traits in sheep breeding programs. Wool production is one of the most important economic characteristics of sheep with a complex physiological and biochemical process that is influenced by genetics, environment, and nutrition. Almost all Iranian sheep breeds are double-coated and produce carpet wool. Therefore, considering the role of wool on the country's economy, it is necessary to conduct a study to identify the genetic factors affecting this trait. Identifying the genomic regions under selection is effective in understanding the processes involved in the evolution of the genome and also in identifying the genomic regions involved in the emergence of economic traits. Selective signatures in the whole genome can help us to understand the mechanisms of selection and to identify the genomic regions that have been under natural or artificial selection for many years. Since Selective signatures are usually associated with major effect genes and important economic traits, they can provide suitable information sources to improve the performance of selection programs in the future. The objective of this study was to identify the genomic regions that have been under selection in skin and wool sheep breeds.

 In the present study, Illumina ovine SNP600K BeadChip genomic arrays of 80 sheep from six breeds were used, three breeds were bred for their skin (Karakul, SiahKabud, and Gray Shiraz) and three breeds were bred for their wool (Sanjabi, Kermani and Baluchi). Unbiased methods of Weir and Cockerham’s FST (Theta) and hapFLK were used to detect the selection signatures. Also, to check the genes and QTLs in the selected regions, the Biomart database, OAR 3.1 version of the sheep genome, was used, and the function of the identified genes was analyzed through a wide search in different databases such as Genecards and OMIM. Finally, the list of genes related to the selected regions was reported. For this purpose, the chromosomal position of SNPs with high numerical values of theta and hapFLK, as well as the 250 kbp region around these markers, were further investigated. Then, the DAVID database online search was used to investigate the biological and functional processes of genes and to study the ontology. Finally, Cytoscape software was used to determine gene networks.Then, DAVID database online search was used to investigate the biological and functional processes of genes and to study the ontology.سپس از جستجوی آنلاین پایگاه داده DAVID برای بررسی فرآیندهای بیولوژیکی و عملکردی ژن ها و مطالعه هستی شناسی استفاده شد.Then, to investigate the biological and functional processes of genes and to study the ontology, DAVID database was used to search online. سپس برای بررسی فرآیندهای بیولوژیکی و عملکردی ژن ها و مطالعه هستی شناسی از پایگاه داده DAVID برای جستجوی آنلاین استفاده شد. Can't load full results Try again Retrying…

 The results of the Theta analysis revealed 26 genomic regions on 1, 2, 6, 19, and 24 chromosomes, and the results of hapFLK revealed seven genomic regions on 6 and 19 chromosomes. Bioinformatics analysis demonstrated that some of these genomic regions overlapped with known genes related to pigment traits and characteristics of wool (KIT on chr 6, MITF on chr 19, IGSF10 on chr 1, PDGFRA on chr 6), muscles (MICALL2 on chr 24), vasodilation and immune response (P2RY on chr 1) and cancer (MIR339 on chr 24, ELFN1 on chr 24, MAD1L1 on chr 24, GPR87 on chr 1). The investigation of reported QTLs showed that these regions are related to QTLs of important economic traits, including traits related to meat, carcass, milk, body weight, bone density, and the total number of lambs born. Also, the analysis of Gene Ontology and Enriched pathway terms in regions under positive selection were related to the pathways involved in the differentiation of melanocytes and pigments, differentiation of stem cells, cellular processes, development of the immune system, blood system, reproductive and cellular processes. The results of the gene networks with the information obtained from Theta and hapFLK statistics showed that the genes identified were significantly active in the development and morphogenesis networks of the embryonic digestive tract, the networks related to pigment and melanocyte differentiation, and the networks related to Purine and G protein. However, to identify the exact function of the identified genes and QTLs, it is recommended to carry out more investigations.نتایج شبکه های ژنی با اطلاعات به دست آمده از آمار تتا و hapFLK نشان داد که ژن های شناسایی شده به طور معنی داری در شبکه های رشد و مورفوژنز دستگاه گوارش جنینی، شبکه های مربوط به تمایز رنگدانه و ملانوسیت و شبکه های مربوط به The results of the gene networks with the information obtained from theta and hapFLK statistics showed that the genes identified were significantly in the development and morphogenesis networks of the embryonic digestive system, the networks related to the differentiation of pigment and melanocytes and the networks related to نتایج شبکه های ژنی با اطلاعات به دست آمده از آمار تتا و hapFLK نشان داد که ژن های شناسایی شده به طور معنی داری در شبکه های رشد و مورفوژنز دستگاه گوارش جنینی، شبکه های مربوط به تمایز رنگدانه ها و ملانوسیت ها و شبکه های مربوط بهCan't load full results Try again Retrying…

 The results of the present study and the identified genomic regions can play an important role in the study of the effect of the selection on population differentiation in two sheep breeds that bred for skin and sheep production. Subsequently, this would direct us to identify the genomic regions associated with traits that differentiate these groups. However, these areas need to be confirmed in other independent studies with more samples. In general, the data of this research can be used in research related to genomic selection, design of mating systems, and additional reviews and evaluations to improve skin and wool production in sheep.

In this paper, a novel risk-based, two-objective (technical and economical) optimal reactive power dispatch method in a wind-integrated power system is proposed which is more consistent with operational criteria.  The technical objective includes the minimization of the new voltage instability risk index. The economical objective includes cost minimization of reactive power generation and active power loss. The proposed voltage instability risk employs a hybrid possibilistic (Delphi-Fuzzy)-probabilistic approach that takes into consideration the operator’s experience, the wind speed and demand forecast uncertainties when quantifying the risk index. The decision variables are the reactive power resources of the system. To solve the problem, the modified multi-objective particle swarm optimization algorithm with sine and cosine acceleration coefficients is utilized. The method is implemented on the modified IEEE 30-bus system. The proposed method is compared with those in the previously published literature, and the results confirm that the proposed risk index is better at estimating the voltage instability risk of the system, especially in cases with severe impact and low probability. In addition, according to the simulation results compared to typical security-based planning, the proposed risk-based planning may increase the security and economy of the system due to better utilization of system resources.

Identifying genes with large effects on economically important traits, has been one of the important goals in sheep breeding. A method to identify new loci and confirm existing quantitative trait loci (QTL) is through genome-wide association studies (GWAS). QTL-assisted selection and genomic regions affecting the production traits have been considered to increase the efficiency of selection and improve production performance. GWAS typically focuses on genetic markers with the strongest evidence of association. However, single markers often explain only a small component of the genetic variance and hence offer a limited understanding of the trait under study. A solution to tackle the aforementioned problems, and deepen the understanding of the genetic background of complex traits, is to move up the analysis from the single nucleotide polymorphism (SNP) to the gene and gene-set levels. In a gene-set analysis, a group of related genes that harbor significant SNP previously identified in GWAS is tested for over-representation in a specific pathway. The present study aimed to conduct a GWAS based on gene-set enrichment analysis for identifying the loci associated with economic traits using the high-density SNPs.

In this research, to identify genes and biological pathways associated with some economic traits, GWAS based on gene-set enrichment analysis was conducted in a F2 population derived from a reciprocal cross by using Illumina iSelect 4K Japanese quail SNP Bead chip. For each bird, traits including body weight gain, feed intake, feed conversion ratio, tibia ash, and foot ash were measured. The SNPs that were associated with traits were identified based on mixed linear models using GCTA software and no correction was made to adjust the error rate. The gene-set analysis consisted of three different steps: (1) the assignment of SNPs to genes, (2) the assignment of genes to functional categories, and (3) the association analysis between each functional category and the phenotype of interest. In brief, for each trait, nominal P<0.005 from the GWAS analyses were used to identify significant SNPs. Using the biomaRt R package, the SNPs were assigned to genes if they were within the genomic sequence of the gene or a flanking region of 15 kb up- and downstream of the gene, to include SNP located in regulatory regions. For the assignment of the genes to functional categories, the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway databases were used. The GO database designates biological descriptors to genes based on attributes of their encoded products and it is further partitioned into three components: biological process, molecular function, and cellular component. The KEGG pathway database contains metabolic and regulatory pathways, representing the actual knowledge of molecular interactions and reaction networks. Finally, a Fisher’s exact test was performed to test for overrepresentation of the significant genes for each gene set. The gene enrichment analysis was performed with the goseq R package. In the next step, bioinformatics analysis was implemented to identify the biological pathways performed in BioMart, Panther, DAVID, and GeneCards databases.

Gene-set enrichment analysis has proven to be a great complement to GWAS. Among available gene set databases, GO is probably the most popular, whereas KEGG is a relatively new tool that is gaining ground in livestock genomics. We hypothesized that the use of gene-set information could improve prediction. It is likely that a better understanding of the biology underlying meat production specifically, plus an advance in the annotation of the quail genome, can provide new opportunities for predicting production using gene-set information. 11 SNPs on chromosomes 2, 3, 4, 5, 10, 18, 20, 24, and 27 located in NPY, DRD2, PTPRN2, BMPR1B, MYF5, IGF2BP1, MYO1E, FGF2, LDB2, BMP4, ACOX1, PCK1, PLCB4, PLCB, and PLCG1 genes were identified. According to gene-set enrichment analysis, 23 categories from gene ontology and the KEGG pathway were associated with the traits (P˂0.05). Among those categories, Protein glycosylation, Myoblast differentiation, Positive regulation of muscle cell differentiation and Biological MAPK signaling pathway, and Calcium signaling pathway have a significant association with skeletal muscle fiber, feed intake, and availability utilization.

This study supported previous results from GWAS and revealed additional regions associated with these economically important traits. Using the findings of this study could potentially be useful for genetic selection to improve production in Japanese quail.

The number of lambs per lambing is one of the most important reproductive traits in sheep. Many studies have reported that genetic mechanisms play an important role in the variation of litter size in sheep. Selection for higher litter size in sheep has led to a variation of this trait within and across different breeds. Natural and artificial selection related to adaptation and economic traits, such as litter size, results in changes at the genomic level which leads to the appearance of selection signatures. Detection of these regions provides an opportunity for a better understanding of genetic mechanisms underlying the phenotypic variation of litter size in sheep. Several tests including the linkage disequilibrium-based approach, site frequency spectrum, and population differentiation-based approach have been developed to explore the footprints of selection in the genome. This study aimed to identify selection signatures in Baluchi sheep to investigate the genes annotated in these regions as well as the biological pathways involved. For this purpose, XP-EHH and FST analyses were conducted using the genome-wide single nucleotide polymorphisms (SNPs).

In this study, data from 96 Baluchi ewes genotyped using Illumina Ovine SNP50K BeadChip were used to identify genomic regions under selection associated with litter size in sheep. Phenotypic and pedigree data were collected at the Abbasabad Sheep Breeding Station. Based on records on different litters, ewes were divided into two groups: the case (two lambs per litter) and control (one lamb per litter).Quality control was conducted using the Plink software. The markers or individuals were excluded from the further study based on the following criteria: unknown chromosomal or physical location, call rate <0.95, missing genotype frequency >0.05, minor allele frequency (MAF) < 0.05, and a P-value for Hardy–Weinberg equilibrium test less than 10-6. To identify the signatures of selection, two statistical methods of FST and XP-EHH were used under FST and EHH software packages, respectively. We also calculated unbiased estimates of FST. Because the results were strongly correlated with the FST results, unbiased estimates have not been reported. In our study, all the SNPs ranking above 0.1 percentile of the distribution of test statistics were selected as candidates for the signature of selection. Gene ontology analysis for identified genes was performed using DAVID online database.

We used the FST and XP-EHH statistics to identify genomic regions that have been under positive selection associated with litter size in Baluchi sheep. Using FST approach, we identified 14 genomic regions on chromosomes 1 and 2 (two regions per chromosome), 3, 7, 9, 14, 18, 22, 23 (one region per chromosome), and X chromosome (three regions). Also, XP-EHH analysis identified nine genomic regions on chromosomes 2, 12, 13, and 22 (one region per chromosome), 7 (three regions), and X (two regions). Some of the genes located in identified regions under selection were associated with the number of lambs per lambing (ACVR1 and TGIF1), ovarian and follicle growth (DDX24), and fertility (FOXH1). Bone morphogenetic proteins are critical regulators of chondrogenesis during development which transduce their signals through three type I receptors namely BMPR1A, BMPR1B, and ACVR1/ALK2. TGIF1 is highly expressed in sheep ovaries suggesting that TGIF1 plays an important role in ewe reproduction. Also, TGF-β/SMAD signaling is critical in reproductive processes such as follicular activation, ovarian follicle development, and oocyte maturation. It has been evidenced that Ddx24 is highly expressed in sheep uterus affecting the development of ovaries and follicles. Foxh1 was first introduced as a transcriptional partner for Smad proteins and has been reported to play an important role in embryonic development. Results of gene ontology analysis identified two biological pathways namely defense response and cell motility which play an important role in the ovulation rate and the number of lambs per lambing. Reproductive activity and immune defenses can be mutually constraining, with increased reproductive activity limiting immune function and immune system activation leading to decreased reproductive function.

The results of this study identified candidate genes involved in the regulation of litter size in sheep suggesting that ACVR1 and TGIF1 genes can be considered as candidate genes related to the number of lambs per litter in sheep breeding programs to improve reproductive performance.

The goal of genome-wide association (GWA) studies of quantitative traits is to identify genomic regions that explain a substantial proportion of the genetic variation for the trait, with the ultimate goal to identify causal mutations underlying the genetic basis of the trait. The standard GWA approach is to genotype a population that has been phenotyped for the trait(s) of interest and genotyped for many genetic markers across the genome and to analyze these data by estimating and testing the effects of marker genotypes on phenotypes using a regression-type of analysis for each single nucleotide polymorphism (SNP), one at a time. Bayesian methods such as Bayes A and Bayes B assume a heavy tail prior distribution for SNP effects and use Markov Chain Monte Carlo (MCMC) to sample from the posterior distribution. Although the objective of these methods was to predict the breeding value of selection candidates (genomic breeding values), they do that by estimating the effects of all SNPs. The estimated SNP effect, the proportion of variance explained by a SNP, or the number of times the SNP fits in the model with non–zero effect can be used as criteria to identify locations or genomic regions that affect the trait of interest. Results have shown that these Bayesian methods can effectively detect QTL in simulated and real data. Recently, a new methodology has been developed to address this limitation and allow for a better understanding of the genetic architecture of complex traits through a gene network analysis. For this purpose, to identify genomic regions and candidate genes associated with egg weight (EW), a genome-wide association study (GWAS) was performed in the present study using Affymetrix 600 K high density SNP array in 1,078 hens of 11th generation of Rhode Island Red. Data available for 1,078 pedigree-recorded hens were used to collect phenotypic EW-related data. Seven traits, including egg weight at the first laying of hens, and egg weight at 28, 36, 56, 66, 72, and 80 weeks of age were collected for each bird.  The analyses were performed using GenSel v4.73R, by fitting covariates for haplotype alleles in BayesA and BayesB models. A single Markov chain Monte Carlo (MCMC) chain of length 41,000, including burn-in of 1,000 first iterations, was computed for each analysis to obtain posterior estimates of covariate effects. These were used to obtain a direct genetic variance for animals. The primary analysis showed that correlations and regression coefficients had converged at this chain length. Annotation terms and pathway analyses were conducted using protein analysis through evolutionary relationships of PANTHER software version 10.0. The results showed that the BayesA method performed better in explaining additive genetic variance compared to BayesB method. Nine markers obtained from BayesA with the highest additive genetic variance were located on chromosomes 1, 3, 5, and 20. Genes that overlap in regions of interest were identified with the Ensembl BioMart data mining (http://www.ensembl.org/biomart/) based on the Galgal6 assembly and the Ensembl Genes 96 database. The detected SNPs were located close to 35 genes, among which, the candidate genes of BPIFB2, OCX36, CPT1A, TCF15, CECR2, SIAH3, FADS1, FADS2, and SGK1 play important functions in the egg production process through the albumen protein formation, fatty acids metabolism, and eggshell formation. It is noteworthy that the present study has detected an association in regions different from that reported by previous studies. This can be because of flock particularities, such as the extent of linkage disequilibrium, allelic frequencies, and statistical approaches. The results of the present study showed that when the genetic architecture of studied traits follows infinitesimal model assumptions, the BayesA method usually performs better than BayesB. Moreover, considering the identification of new genome regions and the key role of the mentioned genes on the development of egg weight, the efficiency of the BayesA method can be confirmed for GWAS in egg weight traits.

Zebu cattle are highly adapted to tropical regions. However, females reach puberty after taurine heifers, which affects the economic efficiency of beef cattle breeding in tropical regions. A method to identify new loci and confirm existing quantitative trait loci (QTL) is through genome-wide association studies (GWAS). QTL-assisted selection and genomic regions affecting the production and reproduction traits have been considered to increase the efficiency of selection and improve production performance. The GWAS typically focuses on genetic markers with the strongest evidence of association. However, single markers often explain only a small component of the genetic variance and hence offer a limited understanding of the trait under study. A solution to tackle the aforementioned problems, and deepen the understanding of the genetic background of complex traits, is to move up the analysis from the single nucleotide polymorphism (SNP) to the gene and gene-set levels. In a gene-set analysis, a group of related genes that harbor significant SNP previously identified in GWAS is tested for over-representation in a specific pathway. The present study aimed to conduct a GWAS based on a gene-set enrichment analysis for identifying the loci associated with age at first calving trait using the high-density SNPs.

A total of 2273 Nelore cattle (995 males and 1278 females) genotyped using the Illumina BovineHD BeadChip were used in the current study. The association analysis included females with valid first calving records as well as open heifers. The association analyses were carried out by considering deregressed estimated breeding values (dEBV) for age at first calving as response variables. Before deregression, the estimated breeding values (EBV) were obtained for the dataset by considering both the calved and non-calved heifers. Variance components and EBV were obtained using the DMU software. In the analysis of AFC, a single-trait animal model was run. The gene-set analysis consists of three different steps: the assignment of SNPs to genes, the assignment of genes to functional categories, and finally the association analysis between each functional category and the phenotype of interest. The GWAS was evaluated using the GHap package in the R program. Using the biomaRt2 R package, the SNPs were assigned to genes if they were within the genomic sequence of the gene or within a flanking region of 15 kb up- and downstream of the gene. For the assignment of the genes to functional categories, the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway databases were used. The GO database designates biological descriptors to genes based on attributes of their encoded products and it is further partitioned into three components: biological process, molecular function, and cellular component. The KEGG pathway database contains metabolic and regulatory pathways, representing the actual knowledge of molecular interactions and reaction networks. Finally, a Fisher’s exact test was performed to test for overrepresentation of the significant genes for each gene-set.

The block sizes of five SNPs were chosen to perform association studies. Gene-set enrichment analysis has proven to be a great complement to GWAS. Among available gene-set databases, GO is probably the most popular, whereas KEGG is a relatively new tool that is gaining ground in livestock genomics. We hypothesized that the use of gene set information could improve prediction. However, neither of the gene set SNP classes outperformed the standard whole-genome approach. Gene-sets have been primarily developed using data from model organisms, such as mice and flies; therefore, some of the genes included in these terms may be irrelevant for reproduction. Gene-set enrichment analysis identified candidate genes related to estrogen metabolic process (HSD17B12), synapse organization (PPFIA2 and PPFIA2), sensory perception of mechanical stimulus (MYO3A and KCNMA1), protein tyrosine kinase activity (IGF1R), the cell-cell junction (FRMD4A), GnRH signaling pathway (ADCY5), and focal adhesion (PPP1R12A). Some of the genes which were found are consistent with some previous studies and are involved in biological pathways related to fertilization, age at first calving, estrogen biosynthesis, heifer conception rate, early development of the fetus, puberty, and glucose homeostasis in the ovary.  

This study supported previous results from GWAS of reproductive traits, and also revealed additional regions in the cattle genome associated with these economically important traits. These findings could potentially be useful for genetic selection in cows.

Optimal reactive power dispatch (ORPD), is one of the most challenging problems of power system operation. This problem deals with both system security and the economy. The problem’s objective functions are usually decreasing the voltage profile deviation of the system, improving the voltage stability margin, and decreasing system loss. ORPD is modeled as an optimization problem with nonlinear functions and constraint and mixed continuous/discrete decision variables. The decision variables are the reactive power of the generators and compensators and the tap number of the transformers. To consider the security of the system, the effect of unscheduled events must be modeled and investigated in the ORPD problem. This type of ORPD is called security constraint ORPD (SC-ORPD). However, the uncertain nature of power systems makes it inevitable to consider them in planning issues. These uncertainties are the source of risk in power grids and must be addressed in planning issues, including ORPD. Risk is the possibility of something bad happening. In other words, risk involves uncertainty about the effects of an activity. Therefore, risk functions include a combination of the probability of an event and its impact. Considering deterministic objective functions (not risk-based ones) in ORPD problems may decrease system economy because most of the events are rare in power system operation. In addition, due to low event probability, the calculated risk may not reflect the true risk that operators and networks are facing. These uncertainties are classified into two categories: technical and economic. Technical uncertainties, which affect the system security, include network topology and operation (like demand and generation) whereas economic uncertainties include micro and macro economies. This paper presents, security-constrained optimal reactive power dispatch by considering the technical uncertainties of power systems. Maximizing the voltage stability margin based on the new voltage instability risk index and minimizing losses are the technical and economic objective functions, respectively. The proposed index is more compatible with practical criteria than existing indicators because it excludes the effect of the low events outage probability of power system components on the risk index. The SPEA-II optimization algorithm is then used to find non-dominant solutions, and finally, the fuzzy decision-maker is used to select the dominant solution. The proposed method is applied to standard 30 IEEE-bus system. The results show, that the proposed algorithm is superior to existing methods and increases the voltage stability margin for postulated contingencies and at the same time reduces operating costs.

In this paper, a coordinated control method for LFC and SMES systems based on a new robust controller is designed. The proposed controller is used to compensate for frequency deviations related to the power system, to prevent excessive power generation in conventional generators during load disturbances, and to reduce power fluctuations from wind power plants. The new robust controller does not require the measurement of all the power system states and it only uses the output feedback. It also has a higher degree of freedom than the conventional robust controllers (conventional output feedback) and thus it helps improve the system control. The proposed control method is highly robust against load and distributed generation resources (wind turbine) disturbances and it is also robust against the uncertainty of the power system parameters. The proposed method is compared under several scenarios with the coordinated control method for LFC and SMES systems based on Moth Swarm Algorithm-optimized PID controller, the LFC system based on Moth Swarm Algorithm-optimized PID controller with SMES, the coordinated control method for LFC and SMES systems based on Robust Model Predictive Control, and the LFC system based on optimized PID controller without SMES and it puts on satisfactory performance. The simulation was performed in MATLAB.

This study was performed to investigate the effect of dietary supplementation with Manna of Alhaji Maurum (Tarangabin) powder on performance, carcass traits, serum biochemistry, and jejunum histology parameters in broilers. The experiment was arranged in two separate sections, in the first section, 400, day-old male broilers (Ross 308) were randomly assigned to five treatments with four replicates. The dietary treatments consisted of the basal diet as control, control + 0.5% Manna in starter, control + 0.5% Manna in starter and grower, control + 1% Manna in starter, control + 1% Manna in starter and grower. In this experiment, supplementing the diet with 0.5% Manna in starter and grower periods increased the body weight of broilers at 10, 24, and 42 days of age (P<0.05). The higher average daily gain was observed at 1-10, 11-24, and 1-42 days of age with 1% Manna treatment in starter and grower periods. The treatment of 1% Manna in the starter period had the greatest villus height that had a significant difference with control treatment (P<0.05). It can be concluded that 0.5% Manna in starter and grower periods can be used as an additive for improving performance traits and 1% Manna in starter for jejunum morphology traits in broilers.

The aim of the present study was to conduct a genome wide association studies (GWAS) based on gene set enrichment analysis for identifying the loci associated with body weight in four chicken breeds including Chahua, Silkie, Langshan, and Beard using the high throughput single nucleotide polymorphisms (SNPs). Phenotypic and genotypic data were obtained from the Frontiersin online public repository. In this study, the 402 cocks and hens were used with body weight from 1 to 15 weeks using GenABEL software. The gene enrichment analysis was performed with the goseq R package. In the next step, a bioinformatics analysis was implemented to identify the biological pathways performed in GO, KEEG, DAVID and PANTHER databases. Ten SNP markers on  chromosomes 1, 2, 5, 7, 10, 14, 18, 19, 20 and 27 located in ABCG1, MYOD1, MYH10, MYH11, MYO1B, MYO1C, MYO1E, MYL1, MYL2, MYL3, SLC2A8, ACACA, ACOX1, ACOX2, and PNPLA2 genes were identified. Some of the genes were found are consistent with some previous studies and to be involved in biological pathways related to body weight. According to pathway analysis, 17 pathways from gene ontology and KEGG pathway were associated with the body weight (P˂0.01). Among those pathways, the cytoskeletal protein binding, anatomical structure development and tricarboxylic acid cycle had significant association with skeletal muscle fiber and metabolism lipid traits. In total, this study supported previous results from GWAS of body weight; also revealed additional regions in the chicken genome associated with these economically important traits. The use of these findings can accelerate the genetic progress in the breeding programs.

Genomic selection has provided the dairy industry with a powerful tool to increase genetic gains on economically important traits such as milk production (Taylor et al. 2016). One way to identify new loci and confirm existing QTL is through genome-wide association analysis (GWAA). In addition, identifying of genes' loci with large effects on economically important traits has been one of the important goals to dairy cattle breeding. Quantitative Trait Loci assisted selection and genomic regions affecting the production traits have been considered to increase the efficiency of selection and improve production performance. Genome wide association studies typically focus on genetic markers with the strongest evidence of association. However, single markers often explain only a small component of the genetic variance and hence, offer a limited understanding of the trait under study. A solution to tackle the aforementioned problems, and deepen the understanding of the genetic background of complex traits, is to move up the analysis from the SNP to the gene and gene-set levels. In a gene-set analysis, a group of related genes that harbor significant SNP previously identified in GWAS, is tested for over-representation in a specific pathway. The aim of the present study was genome wide association studies (GWAS) based on gene set enrichment analysis for identifying the loci associated with milk yield and somatic cell score traits in Holstein cattle breed using the high-confidence SNPs that enable us to study 164312 SNP markers simultaneously.

In this study, the 1092 Holstein cattle and 164312 markers were analyzed with milk yield, fat percentage, and somatic cell score using plink software with no corrections to adjust the error rate. The gene set analysis consists basically in three different steps: (1) the assignment of SNPs to genes, (2) the assignment of genes to functional categories, and finally (3) the association analysis between each functional category and the phenotype of interest.In brief, for each trait, nominal P-values < 0.05 from the GWAS analyses were used to identify significant SNP. Using the biomaRt R package the SNP were assigned to genes if they were within the genomic sequence of the gene or within a flanking region of 20 kb up- and downstream of the gene, to include SNP located in regulatory regions. For the assignment of the genes to functional categories, the gene ontology and Kyoto encyclopedia of genes and genome pathway databases were used. The GO database designates biological descriptors to genes based on attributes of their encoded products and it is further partitioned into three components: biological process, molecular function, and cellular component. The KEGG pathway database contains metabolic and regulatory pathways, representing the actual knowledge on molecular interactions and reaction networks. Finally, a Fisher’s exact test was performed to test for overrepresentation of the significant genes for each gene-set. The gene enrichment analysis was performed with the goseq R package. In the next step, a bioinformatics analysis was implemented to identify the biological pathways performed in BioMart, Panther, DAVID, and GeneCards databases.

Gene set enrichment analysis has proven to be a great complement of genome-wide association analysis (Gambra et al. 2013; Abdalla et al. 2016). Among available gene set databases, GO is probably the most popular, whereas KEGG is a relatively new tool that is gaining ground in livestock genomics (Morota et al. 2015, 2016). It was hypothesized that the use of gene set information could improve prediction. However, neither of the gene set SNP classes outperformed the standard whole-genome approach. Gene sets have been primarily developed using data from model organisms, such as mice and flies; so, it is possible that some of the genes included in these terms are irrelevant for milk production. It is likely that a better understanding of the biology underlying milk production specifically, plus an advance in the annotation of the bovine genome, can provide new opportunities for predicting production using gene set information. Eleven SNP markers  were identified on  chromosomes  5,  6,  7,  8, 14, 19, 22, 24, 25, 27, and 28 located  in  ASIC2,  ANXA3,  CCL2,  CCL11,  CCL24, IL33, TLR3, WWOX, EGFR, PRKCA, CAMK2A, KCNMA1, FABP2, SPP1, THBS4, HSP90B1, and ITPR1 genes. Some of the found genes are consistent with some previous studies and are involved in biological pathways related to milk yield and immune systems. According to pathway analysis, 25 pathways from gene ontology and KEGG pathway were associated with the milk yield and somatic cell score traits (P˂0.01). Among those pathways, the sensory perception of chemical stimulus, positive regulation of inflammatory response, and defense response biological pathway have an important role in the immune system and somatic cell score. Also, the GnRH signaling pathway, PPAR signaling pathway, oxytocin signaling pathway, and focal adhesion had a significant association with milk yield and fat percentage traits. Some of these regulatory regions, such as enhancers, are located far from the genes. Therefore, the gene might be part of the analysis, but the relevant variant would probably not be included in the gene set SNP class. Finally, linkage disequilibrium interferes with the use of biological information in prediction, because irrelevant regions (regions without any biological role) capture part of the information encoded in relevant regions, causing both regions to exhibit similar predictive abilities. The use of very high density SNP data or even whole genome sequence data could alleviate some of these issues. Finally, it worth’s noting that our gene-set enrichment analysis was conducted using a panel of SNP obtained from a single marker regression GWAS, which relies on a simplified theory of the genomic background of traits, without considering for instance the joint effect of SNP. Hence, other approaches (e.g. GWAS exploring SNP by SNP interactions) might provide a better basis for biological pathway analysis.

This study supported previous results from GWAS of milk yield and somatic cell score, also revealed additional regions in the cattle genome associated with these economically important traits. So, using these findings can accelerate the genetic progress in the breeding programs.

Copy number variation (CNV), one of the most important structural changes in the genome, has been known as an important source of genetic and phenotypic variations. The purpose of this study was to compare the two different algorithms in CNV detection consisting PennCNV and QuantiSNP in Baluchi sheep.Data analysis was performed using the Illumina OvineSNP50k BeadChip on 96 Baluchi sheep.After CNV calling, the copy number variation regions (CNVRs) were determined using the CNVRuler program.91 CNVRs with a length range of 18.75 up to 511.7 kbp were identified by the PennCNV algorithm, covering 0.46% of whole sheep genome.Also, 316 CNVRs with the length range of 7.5 up to 1280 kbp were obtained using QuantiSNP algorithm, covering 1.33% of whole sheep genome. The number of loss events was about five and three times more than the number of gain events for QuantiSNP and PennCNV algorithms, respectively.Also, the number of CNVs detected by QuantiSNP was about four times higherthan PennCNV.Also, 86.6% of total CNVs (174 CNVs with average length of 12.62 kb) identified by PennCNV were common with CNVs detected by QuantiSNP. In general, the results showed that the use of several algorithms could improve the accuracy for detecting the structural variation in the genome and led to a better understanding of the sheep genome.

Average daily gain (ADG) is the most economically important trait in sheep industry.
In genome wide association study and genomic selection, determining of extent and level of linkage
disequilibrium (LD) and haplotype block structure are critical in sample size and marker density. Haplotype blocks are defined as long stretches of SNPs along a chromosome that have low recombination rates, which characterized by relatively few haplotypes. Understanding haplotype structure in genome can greatly facilitate LD analysis. Haplotype-based association analysis can offer a powerful approach for mapping functional genes (Gabriel et al., 2002). Therefore, the objective of this research were to study LD pattern, determine haplotype block structure and genome wide haplotype association study in Zandi sheep for identifying the genomic region associated with pre-weaning (AGW) and post-weaning daily gain (PWG). A total of 96 Iranian Zandi sheep were used in the study. The following two traits were analyzed: pre-weaning daily gain and post-weaning daily gain. Animals were genotyped using 50 K SNPChip panel. Quality control of the genotype data consisted in removing SNPs with a call rate less than 95 %, SNPs with a minor allele frequency (MAF) less than 5 %, individual with more than 10 % missing genotypes, and SNPs that deviated strongly from Hardy–Weinberg equilibrium (P < 10−6). LD between all pairs were calculated with r2 by PLINK v1.07. Haplotype blocks were identified on base algorithm Gabriel et al. (2002) for all autosomes, using Haploview software (Barrett et al. 2005), based on estimates of D' for all pairwise combinations of SNPs within each chromosome. Following Gabriel et al. (2002), a pair of SNPs is defined to be in “strong LD” if the upper 95% confidence bound of D' is > 0.98 (consistent with no historical recombination) and the lower bound is > 0.7. Using the Haploview default values for blocks (Gabriel et al. 2002), a haplotype block is defined as a region over which 95 % of informative SNP pairs show “strong LD”. The PLINK was used to generate the matrix using the GLM algorithm. In this analysis, because of the previous selection history of the flock, it was important to identify and correct for population stratification. To evaluate whether estimates were overinflated, we used the genomic inflation factor λ using the PLINK software (Purcell et al., 2007). We also assessed their deviation from the expected distribution of no SNPs being associated with the trait of interest using a quantile-quantile (Q-Q) plot, which is commonly used to analyze population stratification in GWAS. We use SNPEVG tool to show the (Q-Q) plot. The Bonferroni method was used to adjust for multiple testing from the number of SNP loci detected. We declared a significant SNP at the genome-wide significance level if the raw P-value was, 0.05/N, here N is the number of SNP loci tested in the analyses. Therefore, for each trait, the threshold P-value for declaring genome-wide significance was (0.05)/40,879=1.2×10-6. The exact positions of the annotated genes were extracted from the latest sheep genome Oar_v4.0 assembly along with the NCBI annotation release 102 of the sheep genome. To investigate whether the
significant SNPs detected in this study were within the range of previously identified QTL for relevant traits, we searched for meat or production QTL in the Animal QTLdb within a 1-Mb region on both sides of each significant haplotype. After quality control, 2 individuals were excluded, leaving 94 sheep for the association analysis. Additionally, we removed 1070 SNPs with call rates less than 95% and 7717 SNPs with MAF less than 0.05. A total of 40,879 SNPs passed these quality-control filters and were retained in the dataset. These SNPs were distributed across 26 autosomes, with the number of SNPs per chromosome ranging from 747 to 5694, and with a mean distance between adjacent SNPs ranging from 50.4 to 68.7 kb. Also, in this study, the extent of LD was 40 kb with r2=0.2. Overally, 1472 blocks were observed in the 7.58% of all SNPs were classified into haplotype blocks, covering 1.45% of the total autosomal genome size. The results showed a reduction in LD level with the increase in distance between markers. The average pre-weaning daily gain was 0.197±0.04 kg with an individual sheep range of 0.08-0.34 kg. Average post-weaning daily gain was 0.126±0.07 kg with an individual sheep range of 0.03-0.41 kg. The result from genomic control showed weak population stratification for AGW and PGW in between population of Zandi sheep. The genomic inflation factors (λgc) for the two traits were equal to 1.039 and 1.073, for AGW and PGW, respectively. However, as the Q-Q plots clearly show, there is no evidence of any systematic bias (λgc˂1.1) due to population structure or analytical approach in our case. Considering the significant fixed effects in the genomic wide association analysis, four haplotypes on chromosomes 3, 5, 6 and 7 identified to affect significantly AGW and PWG traits. The results of this study could provide a suite of novel SNP markers and candidate genes associated with growth traits and hence, may play an important role for understanding the biology of average daily gain in sheep.

Corn meal is a by-product of corn grain which is a relatively new feedstuff in ruminant nutrition. This feed stuff is a by-product of corn after oil extraction. There is lack of information regarding the chemical analysis as well as its nutrient degradation in rumen. Moreover, the nutritional characteristics did not compare with other grains yet. Therefore, identification of its nutritional value is necessary for ration formulation. The aim of the present study was to evaluate and compare the nutritional value of corn meal with different corn varieties based on gas test technique as well as identification of microbial mass and short chain fatty acids (SCFA) concentration predicted based on this technique. The microbial protein production prediction was estimated as well to evaluate and compare the potential to produce microbial protein in corn meal and other grain sources. Material and Different grain sources which had been used extensively in local dairy farms had been used to compare their potential for gas production with corn meal as new feedstuff. The treatments were as follows 1) corn meal, 2) ground barley grain, 3) ground corn grain (contain semivitreous starch), 4) ground corn grain (contain floury starch), 5) ground corn grain (contain vitreous starch), 6) ground dent corn grain, 7) pure corn starch. Different grain sources were categorized based on starch form content. All the samples were ground to pass 1 mm sieve. The rumen liquor was collected from three non-lactating, non-pregnant dairy cows fed a similar basal diet. The gas production technique was conducted in three periods for and each sample has three subsamples in each period (totally nine repeat for each experimental feedstuff). Gas production was measured on 2, 4, 8, 12, 24, 36, 48, and 72 h after incubation. The measurement was done based on gas produced for fast and slow degradable fractions of feedstuffs and total volume was measured based on total gas produced through 72 h after incubation. In addition to gas production measurement, other fermentation parameters such as partitioning factor (PF), organic matter digestibility (OMD), microbial biomass production (MBP), metabolisable energy (ME), SCFA were estimated as well. In addition, the cumulative gas produced based on ml gas produced per mg of dry matter for different experimental feedstuffs was calculated. Results showed that corn meal has the lowest gas production volume from rapidly degradable pool, and floury starch contained corn had the highest rate of gas production volume. Slowly degradable pool of corn meal, also, has the highest (greatest) gas production volume. Despite these differences among treatments regarding the gas production in different sampled times, however, the total amount of gas produced in the different treatments was not significant. The results show that the proportional ratio of gas production was different based on different incubation times, but total produced gas was similar among treatments. The results show that cumulative gas produced (ml/mg DM) was differed between treatment with the lowest value for corn meal (124.48 ml/mg DM). The prediction results based on gas test method showed that digestibility of dry matter and organic matter from corn meal was lower in comparison with all varieties of grain corn. This may have related to chemical composition of this feedstuff in comparison to other treatments. Partitioning factor was statistically constant among treatments. Microbial biomass production as well as its efficiency was similar among treatments. This shows that corn meal was as same as other grain sources in affecting the microbial fermentation in vitro. Furthermore, metabolizable energy and predicted volatile fatty acids concentration of corn meal had the lowest value among treatments (10.30 Mj/Kg DM). The lower concentration for volatile fatty acid (1.10 mmol) predicted for corn meal clear that this by-product could not supply as adequate energy as supplied by other treatments for animal on farm scale. The lower ME content as well as the lowest SCFA predicted in corn meal treatment may be related to greater fiber content in comparison with other treatments. The greater cell wall content has potential to reduce energy content and total volatile acid production in rumen. Although the predicted SCFA concentration of other grain sources were greater than that of corn meal, all the predicted values were similar among grain sources. This was mostly because similar chemical analysis among these grains. The results of the present study showed corn meal had lower energy content compared to corn grain varieties as well as barley grain. However total gas volume production and microbial biomass produced for this feedstuff was similar to other experimental feedstuffs. Future studies need to evaluate the performance of the animal fed with corn meal when compared with other conventional grains.

Identifying of genes with large effects on economically important traits, has been one of the important goals in sheep breeding. Over the last decade, by the advent of genome-wide panels of single nucleotide polymorphisms (SNPs), it has become possible to identify and localize QTLs for complex traits in many livestock species. One important obstacle in association studies is the confounding effect of population structure. Because this effect generally increases in proportion to population size, population structure remains a major concern in association analyses. To date, 98 QTLs for wool traits have been reported via genome scan based on marker-QTL linkage analyses (http://cn.animalgenome.org/cgi-bin/QTLdb/OA/index, 27 Aug, 2017). Compared with traditional QTL mapping strategies, a GWAS has major advantages both in its power to detect causal variants with modest effects and in defining narrower genomic regions harboring causal variants for economically important traits. In this study, we assessed population stratification and performed a genome-wide association study (GWAS) of wool quality traits in Zandi sheep. Material and

A total of 96 Iranian Zandi sheep was used in the study. The following four traits were analyzed: staple length (SL), mean fiber diameter (MFD), fiber diameter coefficient of variation (FDCV), and the proportion of fiber that was equal or more than 30 µm (F≥30). Animals were genotyped using 50 K SNPChip panel. Quality control of the genotype data consisted in removing SNPs with a call rate less than 95 %, SNPs with a minor allele frequency (MAF) less than 5 %, SNPs with more than x % missing genotypes, and SNPs that deviated strongly from Hardy– Weinberg equilibrium (P < 10−6). The PLINK was used to generate the matrix using the GLM algorithm. In this study, although the resources of this breed were very clear, we still examined the distribution of the test statistics obtained from the numerous association tests. We also assessed their deviation from the expected distribution of no SNPs being associated with the trait of interest using a quantile-quantile (Q-Q) plot, which is commonly used to analyze population stratification in GWAS. We use SNPEVG tool to show the (Q-Q) plot. The Bonferroni method was used to adjust for multiple testing from the number of SNP loci detected. We declared a significant SNP at the genomewide significance level if the raw P-value was0.05/N, here N is the number of SNP loci tested in the analysis. The exact positions of the annotated genes were extracted from the latest sheep genome Oar_v4.0 assembly along with the NCBI annotation release 102 of the sheep genome. To investigate if the significant SNPs detected in this study were within the range of previously identified QTL for relevant traits, we searched for meat or production QTL in the Animal QTLdb within a 1-Mb region on both sides of each significant haplotype.

After quality control, 2 individuals were excluded, leaving 94 sheep for the association analysis. Additionally, we removed 1070 SNPs with call rates less than 95% and 7717 SNPs with MAF less than 0.05. A total of 40,879 SNPs passed these quality-control filters and were retained in the dataset. These SNPs were distributed across 26 autosomes, with the number of SNPs per chromosome ranging from 747 to 5694, and with a mean distance between adjacent SNPs ranging from 50.4 to 68.7 kb. Wool MFD was 29.85±0.03 µm with an individual sheep range of 22.4-39.04 µm. The overall coefficient of variation of fiber diameter was 43.12%±0.7% with an individual sheep range of 19.7%–68.0%. The average percentages of fiber that had equal or more than 30 µm were 27.04±0.03% with an individual sheep range of 12.04-43.10%. Average wool staple length was 11.25±0.03 cm with an individual sheep range of 6-19 cm. The result from genomic control showed weak population stratification for SL, MDF, FDCV and F≥30 between populations of Zandi sheep. The genomic inflation factors (λgc) for the four traits were equal to 1.127, 1.101, 1.059, and 1.009 for MDF, FDCV, FD≥30 and SL, respectively. However, the Q-Q plots clearly showed there was no evidence of any systematic bias due to population structure or analytical approach in our case. Overally, two significant SNPs at the genome-wise level were identified for FD, and FDCV. No significant SNPs was identified for SLor FD≥30. Two Haplotype region within ERBB2 and GNAS genes previously reported in human growth and development hair and skin. Haplotypes were located with previously QTL reported to affect fiber diameter and fiber diameter coefficient of variation in Merino and INRA401 breed sheep. The functions of all of the above genes are directly or indirectly related to skin and hair development. Hair follicles are skin appendages and produce hair; therefore, we hypothesize that these genes control hair follicle development and fiber diameter trait.

The results of this study could provide a suite of novel SNP markers and candidate genes associated with wool traits and hence, may play an important role in understanding the biology of wool traits in fat-tailed sheep.

K-complex is an underlying pattern in the sleep EEG. Due to the role of sleep studies in neurophysiologic and cognitive disorders diagnosis, reliable methods for analysis and detection of this pattern are of great importance. In our previous work, Synchrosqueezing Transform (SST) was proposed for analysis of this pattern. SST is an EMD-like tool, which benefits from wavelet transform and reallocation approaches. This method is able to decompose signals into their time-varying oscillatory ingredients. In addition, it provides a time-frequency representation with less blurring compared to wavelet transform. In this paper, firstly, the ability of SST is investigated by applying the ANOVA test, which is approved by proper p-values. This paper proposes SST for K-complex detection. The proposed method is based on a so-called “detection of K-complexes and sleep spindles” (DETOKS) framework. DETOKS is based on spares optimization and decomposes signals into four components, namely transient, low frequency, oscillatory, and a residual. Applying the Teager-Kaiser energy operator and setting a threshold on the low-frequency component result in K-complex detection. We modify DETOKS using SST. The proposed method is applied to DREAMS dataset. The dataset provides two visual scorings accompanied by an automatic one. As the visual labels were extremely different, the automatic detection is considered as the third expert’s scoring and data is re-labeled by a voting approach among three experts. For DETOKS, DETOKS modified by CWT, and the proposed method, MCC measure is 0.62, 0.71, and 0.76, respectively. It shows superiority of the proposed method.