BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Machine learning and data mining advance predictive big data analysis in precision animal agriculture

Precision animal agriculture is poised to rise to prominence in the livestock enterprise in the domains of management, production, welfare, sustainability, health surveillance, and environmental footprint. Considerable progress has been made in the use of tools to routinely monitor and collect information from animals and farms in a less laborious manner than before. These efforts have enabled the animal sciences to embark on information technology-driven discoveries to improve animal agriculture. However, the growing amount and complexity of data generated by fully automated, high-throughput data recording or phenotyping platforms, including digital images, sensor and sound data, unmanned systems, and information obtained from real-time noninvasive computer vision, pose challenges to the successful implementation of precision animal agriculture. The emerging fields of machine learning and data mining are expected to be instrumental in helping meet the daunting challenges facing global agriculture. Yet, their impact and potential in “big data” analysis have not been adequately appreciated in the animal science community, where this recognition has remained only fragmentary. To address such knowledge gaps, this article outlines a framework for machine learning and data mining and offers a glimpse into how they can be applied to solve pressing problems in animal sciences

Functional and structural impact of 10 ACADM missense mutations on human medium chain acyl-Coa dehydrogenase

Funding Information: This work was supported by FEDER and Fundação para a Ciência e a Tecnologia , I. P. through iMed.ULisboa (Projects UIDB/04138/2020 and UIDP/04138/2020 ), iNOVA4Health ( UIDB/04462/2020 , UIDP/04462/2020 ) and LS4FUTURE Associated Laboratory ( LA/P/0087/2020 ) and research project PTDC/BIA-BQM/29570/2017 . Publisher Copyright: © 2023 The Author(s)Medium chain acyl-CoA dehydrogenase (MCAD) deficiency (MCADD) is associated with ACADM gene mutations, leading to an impaired function and/or structure of MCAD. Importantly, after import into the mitochondria, MCAD must incorporate a molecule of flavin adenine dinucleotide (FAD) per subunit and assemble into tetramers. However, the effect of MCAD amino acid substitutions on FAD incorporation has not been investigated. Herein, the commonest MCAD variant (p.K304E) and 11 additional rare variants (p.Y48C, p.R55G, p.A88P, p.Y133C, p.A140T, p.D143V, p.G224R, p.L238F, p.V264I, p.Y372N, and p.G377V) were functionally and structurally characterized. Half of the studied variants presented a FAD content <65 % compared to the wild-type. Most of them were recovered as tetramers, except the p.Y372N (mainly as dimers). No correlation was found between the levels of tetramers and FAD content. However, a correlation between FAD content and the cofactor's affinity, proteolytic stability, thermostability, and thermal inactivation was established. We showed that the studied amino acid changes in MCAD may alter the substrate chain-length dependence and the interaction with electron-transferring-flavoprotein (ETF) necessary for a proper functioning electron transfer thus adding additional layers of complexity to the pathological effect of ACADM missense mutations. Although the majority of the variant MCADs presented an impaired capacity to retain FAD during their synthesis, some of them were structurally rescued by cofactor supplementation, suggesting that in the mitochondrial environment the levels and activity of those variants may be dependent of FAD's availability thus contributing for the heterogeneity of the MCADD phenotype found in patients presenting the same genotype.publishersversionpublishe

Fine mapping of genomic regions associated with female fertility in Nellore beef cattle based on sequence variants from segregating sires

Impaired fertility in cattle limits the efficiency of livestock production systems. Unraveling the genetic architecture of fertility traits would facilitate their improvement by selection. In this study, we characterized SNP chip haplotypes at QTL blocks then used whole-genome sequencing to fine map genomic regions associated with reproduction in a population of Nellore (Bos indicus) heifers.https://doi.org/10.1186/s40104-019-0403-

Building the genomic nation: ‘Homo Brasilis’ and the ‘Genoma Mexicano’ in comparative cultural perspective

This article explores the relationship between genetic research, nationalism and the construction of collective social identities in Latin America. It makes a comparative analysis of two research projects – the ‘Genoma Mexicano’ and the ‘Homo Brasilis’ – both of which sought to establish national and genetic profiles. Both have reproduced and strengthened the idea of their respective nations of focus, incorporating biological elements into debates on social identities. Also, both have placed the unifying figure of the mestizo/mestiço at the heart of national identity constructions, and in so doing have displaced alternative identity categories, such as those based on race. However, having been developed in different national contexts, these projects have had distinct scientific and social trajectories: in Mexico, the genomic mestizo is mobilized mainly in relation to health, while in Brazil the key arena is that of race. We show the importance of the nation as a frame for mobilizing genetic data in public policy debates, and demonstrate how race comes in and out of focus in different Latin American national contexts of genomic research, while never completely disappearing

Exacerbated leishmaniasis caused by a viral endosymbiont can be prevented by immunization with Its viral capsid

Recent studies have shown that a cytoplasmic virus called Leishmaniavirus (LRV) is present in some Leishmania species and acts as a potent innate immunogen, aggravating lesional inflammation and development in mice. In humans, the presence of LRV in Leishmania guyanensis and in L. braziliensis was significantly correlated with poor treatment response and symptomatic relapse. So far, no clinical effort has used LRV for prophylactic purposes. In this context, we designed an original vaccine strategy that targeted LRV nested in Leishmania parasites to prevent virus-related complications. To this end, C57BL/6 mice were immunized with a recombinant LRV1 Leishmania guyanensis viral capsid polypeptide formulated with a T helper 1-polarizing adjuvant. LRV1-vaccinated mice had significant reduction in lesion size and parasite load when subsequently challenged with LRV1+ Leishmania guyanensis parasites. The protection conferred by this immunization could be reproduced in naïve mice via T-cell transfer from vaccinated mice but not by serum transfer. The induction of LRV1 specific T cells secreting IFN-γ was confirmed in vaccinated mice and provided strong evidence that LRV1-specific protection arose via a cell mediated immune response against the LRV1 capsid. Our studies suggest that immunization with LRV1 capsid could be of a preventive benefit in mitigating the elevated pathology associated with LRV1 bearing Leishmania infections and possibly avoiding symptomatic relapses after an initial treatment. This novel anti-endosymbiotic vaccine strategy could be exploited to control other infectious diseases, as similar viral infections are largely prevalent across pathogenic pathogens and could consequently open new vaccine opportunities

EXPLORING ANTICORRELATIONS AND LIGHT ELEMENT VARIATIONS IN NORTHERN GLOBULAR CLUSTERS OBSERVED BY THE APOGEE SURVEY

We investigate the light-element behavior of red giant stars in northern globular clusters (GCs) observed by the SDSS-III Apache Point Observatory Galactic Evolution Experiment. We derive abundances of 9 elements (Fe, C, N, O, Mg, Al, Si, Ca, and Ti) for 428 red giant stars in 10 GCs. The intrinsic abundance range relative to measurement errors is examined, and the well-known C–N and Mg–Al anticorrelations are explored using an extreme-deconvolution code for the first time in a consistent way. We find that Mg and Al drive the population membership in most clusters, except in M107 and M71, the two most metal-rich clusters in our study, where the grouping is most sensitive to N. We also find a diversity in the abundance distributions, with some clusters exhibiting clear abundance bimodalities (for example M3 and M53) while others show extended distributions. The spread of Al abundances increases significantly as cluster average metallicity decreases as previously found by other works, which we take as evidence that low metallicity, intermediate mass AGB polluters were more common in the more metal-poor clusters. The statistically significant correlation of [Al/Fe] with [Si/Fe] in M15 suggests that 28Si leakage has occurred in this cluster. We also present C, N, and O abundances for stars cooler than 4500 K and examine the behavior of A(C+N+O) in each cluster as a function of temperature and [Al/Fe]. The scatter of A(C+N +O) is close to its estimated uncertainty in all clusters and independent of stellar temperature. A(C+N+O) exhibits small correlations and anticorrelations with [Al/Fe] in M3 and M13, but we cannot be certain about these relations given the size of our abundance uncertainties. Star-to-star variations of a-element (Si, Ca, Ti) abundances are comparable to our estimated errors in all clusters

Strategies for genotype imputation in composite beef cattle

Genotype imputation has been used to increase genomic information, allow more animals in genome-wide analyses, and reduce genotyping costs. In Brazilian beef cattle production, many animals are resulting from crossbreeding and such an event may alter linkage disequilibrium patterns. Thus, the challenge is to obtain accurately imputed genotypes in crossbred animals. The objective of this study was to evaluate the best fitting and most accurate imputation strategy on the MA genetic group (the progeny of a Charolais sire mated with crossbred Canchim X Zebu cows) and Canchim cattle. The data set contained 400 animals (born between 1999 and 2005) genotyped with the Illumina BovineHD panel. Imputation accuracy of genotypes from the Illumina-Bovine3K (3K), Illumina-BovineLD (6K), GeneSeek-Genomic-Profiler (GGP) BeefLD (GGP9K), GGP-IndicusLD (GGP20Ki), Illumina-BovineSNP50 (50K), GGP-IndicusHD (GGP75Ki), and GGP-BeefHD (GGP80K) to Illumina-BovineHD (HD) SNP panels were investigated. Seven scenarios for reference and target populations were tested; the animals were grouped according with birth year (S1), genetic groups (S2 and S3), genetic groups and birth year (S4 and S5), gender (S6), and gender and birth year (S7). Analyses were performed using FImpute and BEAGLE software and computation run-time was recorded. Genotype imputation accuracy was measured by concordance rate (CR) and allelic R square (R(2)). The highest imputation accuracy scenario consisted of a reference population with males and females and a target population with young females. Among the SNP panels in the tested scenarios, from the 50K, GGP75Ki and GGP80K were the most adequate to impute to HD in Canchim cattle. FImpute reduced computation run-time to impute genotypes from 20 to 100 times when compared to BEAGLE. The genotyping panels possessing at least 50 thousands markers are suitable for genotype imputation to HD with acceptable accuracy. The FImpute algorithm demonstrated a higher efficiency of imputed markers, especially in lower density panels. These considerations may assist to increase genotypic information, reduce genotyping costs, and aid in genomic selection evaluations in crossbred animals