INVESTIGATING THE MORTALITY CAUSES OF 1-59 MONTHS BABIES OF VILLAGE FROM 2012 TO 2015, ZAHEDAN, IRAN

Since the mortality of 1-59 babies is the most serious challenges facing human society and an important indicator of the health and development of countries, providing solutions to reduce this rate as much as possible is of paramount importance. The main objective of the present study is to examine mortality causes of 1-59 months babies in villages under the protection of Zahedan University of Medical Sciences from 2012 to 2015. The present cross-sectional study was conducted through census. The study included the death of all babies registered in Zahedan villages from 2012 to 2015. Required data was collected using raw information form and Child Health Bureau of the Ministry of Health standard questionnaire; SPSS, version 18, was used to process and analyze collected data. The present study examined the death of an overall number of 1252 Zahedan village babies aged between 1 to 59 months; 752 infants aged less than month and this age group turned out to have highest mortality rate; i.e. 60; the lowest mortality rate, 16.7, happened in the age group of 1 to 4 years. The most common causes of death included perinatal disease (29.8), unintentional injuries (17.5), respiratory system diseases (13.2), infectious and parasitic diseases (6.5), congenital and chromosome diseases (6), and cardiovascular disease (2.5). Since the highest mortality rate, 29.8, is related to perinatal diseases of infants, couples pre-pregnancy care and counselling can play crucial role in decreasing the rate of baby, and specially infants, mortality

Accuracy of genotype imputation with different low density panels in Braford and Hereford cattle.

The main objective of this research was to test alternative low density SNP panels to impute Illumina 50K SNP panel genotypes in Braford and Hereford cattle. Genotypes from 3,768 Hereford, Braford and Nellore animals were used for testing imputation from low density SNP panels (3K, 6K, 8K, 15K and 20K) to the Illumina 50K SNP panel, under four different scenarios: including or not Nellore genotypes in the reference population in combination with the use or not of pedigree information. There were no significant differences in imputation accuracy among these four scenarios within each panel. However, significant differences between panels were found. The best accuracy was given by a customized 15K SNP panel, with an overall genotype concordance rate of 0.977, with 93.3% of the animals imputed with a concordance rate above 0.95. The concordance rates for the other SNP panels were 0.872, 0.952, 0.957 and 0.958 for 3K, 6K, 8K and 20K SNP panel, respectively. Therefore, in the Braford/Hereford population considered in this study, all the alternative panels denser than 3K could be used for imputing to the 50K SNP panel with an overall high imputation accuracy. However, the best results were obtained with the customized 15K SNP instead of the alternative commercial panels. The use of Nellore sire genotypes and pedigree information did not increase accuracy of imputation in this population

Genomic predictions for economically important traits in Brazilian Braford and Hereford beef cattle using true and imputed genotypes.

Genomic selection (GS) has played an important role in cattle breeding programs. However, genotyping prices are still a challenge for implementation of GS in beef cattle and there is still a lack of information about the use of low-density Single Nucleotide Polymorphisms (SNP) chip panels for genomic predictions in breeds such as Brazilian Braford and Hereford. Therefore, this study investigated the effect of using imputed genotypes in the accuracy of genomic predictions for twenty economically important traits in Brazilian Braford and Hereford beef cattle. Various scenarios composed by different percentages of animals with imputed genotypes and different sizes of the training population were compared.Article 2

Accuracy of genome-wide imputation in Braford and Hereford beef cattle.

Article 157

Role of defects and disorder in the half-metallic full-Heusler compounds

Half-metallic ferromagnets and especially the full-Heusler alloys containing Co are at the center of scientific research due to their potential applications in spintronics. For realistic devices it is important to control accurately the creation of defects in these alloys. We review some of our late results on the role of defects and impurities in these compounds. More precisely we present results for the following cases (i) doping and disorder in Co$_2$Cr(Mn)Al(Si) alloys, (ii) half-metallic ferrimagnetism appeared due to the creation of Cr(Mn) antisites in these alloys, (iii) Co-doping in Mn$_2$VAl(Si) alloys leading to half-metallic antiferromagnetism, and finally (iv) the occurrence of vacancies in the full-Heusler alloys containing Co and Mn. These results are susceptible of encouraging further theoretical and experimental research in the properties of these compounds.Comment: Chapter intended for a book with contributions of the invited speakers of the International Conference on Nanoscale Magnetism 2007. Revised version contains new figure

Comparison of analyses of the QTLMAS XII common dataset. I: Genomic selection

<p>Abstract</p> <p>A dataset was simulated and distributed to participants of the QTLMAS XII workshop who were invited to develop genomic selection models. Each contributing group was asked to describe the model development and validation as well as to submit genomic predictions for three generations of individuals, for which they only knew the genotypes. The organisers used these genomic predictions to perform the final validation by comparison to the true breeding values, which were known only to the organisers. Methods used by the 5 groups fell in 3 classes 1) fixed effects models 2) BLUP models, and 3) Bayesian MCMC based models. The Bayesian analyses gave the highest accuracies, followed by the BLUP models, while the fixed effects models generally had low accuracies and large error variance. The best BLUP models as well as the best Bayesian models gave unbiased predictions. The BLUP models are clearly sensitive to the assumed SNP variance, because they do not estimate SNP variance, but take the specified variance as the true variance. The current comparison suggests that Bayesian analyses on haplotypes or SNPs are the most promising approach for Genomic selection although the BLUP models may provide a computationally attractive alternative with little loss of efficiency. On the other hand fixed effect type models are unlikely to provide any gain over traditional pedigree indexes for selection.</p

Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

Tool wear detection is a key issue for tool condition monitoring. The maximization of useful tool life is frequently related with the optimization of machining processes. This paper presents two model-based approaches for tool wear monitoring on the basis of neuro-fuzzy techniques. The use of a neuro-fuzzy hybridization to design a tool wear monitoring system is aiming at exploiting the synergy of neural networks and fuzzy logic, by combining human reasoning with learning and connectionist structure. The turning process that is a well-known machining process is selected for this case study. A four-input (i.e., time, cutting forces, vibrations and acoustic emissions signals) single-output (tool wear rate) model is designed and implemented on the basis of three neuro-fuzzy approaches (inductive, transductive and evolving neuro-fuzzy systems). The tool wear model is then used for monitoring the turning process. The comparative study demonstrates that the transductive neuro-fuzzy model provides better error-based performance indices for detecting tool wear than the inductive neuro-fuzzy model and than the evolving neuro-fuzzy model

Genomic evaluations with many more genotypes

<p>Abstract</p> <p>Background</p> <p>Genomic evaluations in Holstein dairy cattle have quickly become more reliable over the last two years in many countries as more animals have been genotyped for 50,000 markers. Evaluations can also include animals genotyped with more or fewer markers using new tools such as the 777,000 or 2,900 marker chips recently introduced for cattle. Gains from more markers can be predicted using simulation, whereas strategies to use fewer markers have been compared using subsets of actual genotypes. The overall cost of selection is reduced by genotyping most animals at less than the highest density and imputing their missing genotypes using haplotypes. Algorithms to combine different densities need to be efficient because numbers of genotyped animals and markers may continue to grow quickly.</p> <p>Methods</p> <p>Genotypes for 500,000 markers were simulated for the 33,414 Holsteins that had 50,000 marker genotypes in the North American database. Another 86,465 non-genotyped ancestors were included in the pedigree file, and linkage disequilibrium was generated directly in the base population. Mixed density datasets were created by keeping 50,000 (every tenth) of the markers for most animals. Missing genotypes were imputed using a combination of population haplotyping and pedigree haplotyping. Reliabilities of genomic evaluations using linear and nonlinear methods were compared.</p> <p>Results</p> <p>Differing marker sets for a large population were combined with just a few hours of computation. About 95% of paternal alleles were determined correctly, and > 95% of missing genotypes were called correctly. Reliability of breeding values was already high (84.4%) with 50,000 simulated markers. The gain in reliability from increasing the number of markers to 500,000 was only 1.6%, but more than half of that gain resulted from genotyping just 1,406 young bulls at higher density. Linear genomic evaluations had reliabilities 1.5% lower than the nonlinear evaluations with 50,000 markers and 1.6% lower with 500,000 markers.</p> <p>Conclusions</p> <p>Methods to impute genotypes and compute genomic evaluations were affordable with many more markers. Reliabilities for individual animals can be modified to reflect success of imputation. Breeders can improve reliability at lower cost by combining marker densities to increase both the numbers of markers and animals included in genomic evaluation. Larger gains are expected from increasing the number of animals than the number of markers.</p

Dissecting the susceptibility/resistance mechanism of Vitis vinifera for the future control of downy mildew

23CO.NA.VI. 2020 – 8° Convegno Nazionale di Viticoltura, Udine, Italy, July 5-7, 2021openInternationalBothThe Eurasian grapevine (Vitis vinifera), a species cultivated worldwide for high-quality wine production, is extremely susceptible to the agent of downy mildew, Plasmopara viticola. Nevertheless, germplasm from Georgia (Southern Caucasus, the first grapevine domestication centre), characterized by a high genetic variability, showed resistance traits to P. viticola. The cultivar Mgaloblishvili exhibited the most promising phenotype in terms of resistance against P. viticola. Its defence response results in: i) low disease intensity; ii) low sporulation; iii) damaged mycelium; iv) production of antimicrobial compounds such as volatile organic compounds (VOCs), whose effectiveness on the pathogen was evaluated by leafdisc assays. At the transcriptomic level, its resistance mechanism is determined by the differential expression of both resistance and susceptible genes. The resistance genes are related to: i) pathogen recognition through PAMP, DAMP and effector receptors; ii) ethylene signalling pathway; iii) synthesis of antimicrobial compounds (VOCs) and fungal wall degrading enzymes; iv) development of structural barriers (cell wall reinforcement). The first putative susceptible gene was the transcription factor VviLBDIf7 gene, whose validation was carried out by dsRNA (double-stranded RNA) assay. In this work, these unique results on plant-pathogen interaction are reviewed with the aim of developing new strategies to control the disease.openRicciardi, Valentina; Marcianò, Demetrio; Sargolzaei, Maryam; Marrone Fassolo, Elena; Fracassetti, Daniela; Brilli, Matteo; Moser, Mirko; Vahid, Shariati J.; Tavakole, Elahe; Maddalena, Giuliana; Passera, Alessandro; Casati, Paola; Pindo, Massimo; Cestaro, Alessandro; Costa, Alex; Bonza, Maria Cristina; Maghradze, David; Tirelli, Antonio; Failla, Osvaldo; Bianco, Piero Attilio; Quaglino, Fabio; Toffolatti, Silvia Laura; De Lorenzis, GabriellaRicciardi, V.; Marcianò, D.; Sargolzaei, M.; Marrone Fassolo, E.; Fracassetti, D.; Brilli, M.; Moser, M.; Vahid, S.J.; Tavakole, E.; Maddalena, G.; Passera, A.; Casati, P.; Pindo, M.; Cestaro, A.; Costa, A.; Bonza, M.C.; Maghradze, D.; Tirelli, A.; Failla, O.; Bianco, P.A.; Quaglino, F.; Toffolatti, S.L.; De Lorenzis, G