eKhSACIR інституційному репозитарії Харківської державної академії культури
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How to manage the impossible (HMI)? Anthropological research: understanding parents facing diagnosis of dravet syndrome in their child
No full textInternational audienc
Impact of organic amendments on plant biomass and carbon transfer in the soil
No full textInternational audienc
Experience from large scale use of the EuroGenomics custom SNP chip in cattle
No full textInternational audienc
Human in the loop for modelling food and biological systems: a novel perspective coupling artificial intelligence and life science
No full textSince centuries, agriculture, food and biological systems are strongly linked to human expertise, albeit such knowledge has been capitalized and shared often at a local level, only. Since the beginning of the last century, swept away by productivism, modern agriculture and food production have put cumulated human knowledge aside. Facing new challenges like sustainability in a changing context, holistic approaches cannot be managed “manually” ab initio and there is a clear need for computing decision-support tools to tackle these new issues. Moreover, new approaches should be built centred on humans and for humans. The heart of our purpose is to shift the focus again on human and local expertise, guided by powerful computing interactive systems
Could ferulic acid be an efficient natural antimicrobial against Listeria monocytogenes in model food systems?
No full textInternational audienc
Dose effects of linseed and rapeseed oils on bovine rumen microbial metabolism in continuous culture
No full textInternational audienc
The INRA 2018 updated calculation of fermentable organic matter intake improves the prediction of net portal appearance of volatile fatty acids in ruminants
No full textNational audienc
Effect of long-term heat stress on production, egg quality and physiological traits in four experimental lines of layers differing in heat tolerance and feed efficiency
No full textConference Information and Proceedings World's Poultry Science JournalInternational audienc
Imputation of high density genotypes from medium density genotypes in various French equine breeds.
No full textInternational audienceThe objective of the study was to estimate the efficiency of genotypes imputation of the Affymetrix Axiom Equine genotyping array (670 806 SNPs), from genotypes of the Illumina Equine SNP50 BeadChip (54 602 SNPs) and the Illumina Equine SNP-74k-chip (65 157 SNPs). Genotypes from 5 populations: Arabs (AR, 1 207 horses), Trotteurs Français (TF, 979 horses), Selle Français (SF, 1 979 horses), Anglo-Arabs (AA, 229 horses), and various foreign sport horses (FH, 209 horses) were available. In AR, 15% of horses were genotyped with the high density chip, in SF 57%, in TF 30%, in AA 10% and FH 15%. A validation set equal to the third of the sample was drawn in horses genotyped with high density chip and their genotypes suppressed. Two strategies were compared, one with a reference population with only the horses of the same breed as the validation set and another one with horses from multi-breeds. The software FImpute was used. For the first strategy, the mean error rates were 0.97% in TF, 1.29% in SF and 2.16% in AR populations. For the second strategy, the mean error rates ranged from 1.11% (TF) to 2.63% (AR). SF, FH and AA populations had intermediate error rates: 1.35%, 1.55% and 1.87% respectively. For each population and each strategy, the mean error rates were inferior to 3% which indicates that imputation of high density genotypes from medium density genotypes is feasible and accurate. The imputation accuracy depended on the size of the reference population, the genetic diversity of the breed, the importance of relationship between validation and reference populations and the linkage disequilibrium (LD). The comparison between the two imputation strategies showed that the addition of horses from different populations in the reference population did not improve the imputation accuracy