The objective of the present study was to estimate the genetic parameters associated with the achievement of desirable weight, conformation, and fat specifications, represented by a series of binary traits. The desired specifications were those stipulated by Irish beef processors, in accordance with the EUROP carcass grading system, and were represented by a carcass weight between 270 and 380 kg, a fat score between 2+ and 4= (between 6 and 11 on a 15-point scale), and a conformation score of O= or better (≥5 on a 15-point scale). Using data from 58,868 beef carcasses, variance components were estimated using linear mixed models for these binary traits, as well as their underlying continuous measures. Heritability estimates for the continuous traits ranged from 0.63 to 0.73; heritability estimates for the binary traits ranged from 0.05 to 0.19. An additional trait was defined to reflect if all desired carcass specifications were met. All genetic correlations between this trait and the individual contributing binary traits were positive (0.38 to 0.87), while all genetic correlations between this trait and the continuous carcass measures were negative (-0.87 to -0.07). The genetic parameters estimated in the present study signify that potential exists to breed cattle that more consistently achieve desirable carcass metrics at harvest.
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http://dx.doi.org/10.1093/jas/skaa158 | DOI Listing |
Alzheimers Dement
December 2024
Social Science Research Institute, Duke University, Durham, NC, USA.
Background: Results of recent analyses indicate that axon demyelination may play an important role in AD pathology. The MBP gene encodes a myelin basic protein involved in axon myelination in the nervous system including the central nervous system. Polymorphisms in this gene, as well as variations in expression, have been associated with multiple sclerosis (MS).
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Cleveland Clinic Lou Ruvo Center for Brain Health, Cleveland, OH, USA.
Background: The emerging tools of protein-protein interactome network offer a platform to explore not only the molecular complexity of human diseases, but also to identify risk genes and drug targets. Integration of the genome, transcriptome, proteome, and the interactome networks are essential for such identification, including Alzheimer's disease (AD), Parkinson disease (PD), and Amyotrophic lateral sclerosis (ALS) METHOD: In this study, we performed multi-modal analyses of cross-species protein interactome networks and human brain functional genomics data to identify risk genes and drug targets for neurodegenerative diseases. We presented a multi-view topology-based deep learning framework to identify disease-associated genes for cross-species interactome (TAG-X).
View Article and Find Full Text PDFBMC Med Educ
December 2024
Department of Infectious and Tropical Diseases, Hôpital Saint-Louis Et Lariboisière, AP-HP, Université Paris Cité, 1 Avenue Claude Vellefaux, Paris, F-75010, France.
Background: Historically, women have been shown to underestimate their abilities, while men often assess themselves more accurately or overestimate. This study aims to determine self-assessment accuracy during online Objective Structured Clinical Examinations (OSCEs) according to gender.
Methods: A prospective study was conducted among fourth-year medical students at Paris Cité University during faculty training OSCEs, utilizing Zoom® software for remote participation.
Sci Rep
December 2024
Department of Psychiatry, Faculty of Medicine Universitas Indonesia, Dr. Cipto Mangunkusumo National General Hospital, Jakarta, Indonesia.
Food craving is a common phenomenon during pregnancy. This behaviour may be influenced by personality traits that have been known to be linked with obesity and addiction affecting pregnancy outcomes. We identified the prevalence of food cravings and evaluated its relationship with personality traits in pregnant women.
View Article and Find Full Text PDFBr J Math Stat Psychol
December 2024
Athens University of Economics and Business, Athens, Greece.
This paper introduces the generalized Hausman test as a novel method for detecting the non-normality of the latent variable distribution of the unidimensional latent trait model for binary data. The test utilizes the pairwise maximum likelihood estimator for the parameters of the latent trait model, which assumes normality of the latent variable, and the maximum likelihood estimator obtained under a semi-non-parametric framework, allowing for a more flexible distribution of the latent variable. The performance of the generalized Hausman test is evaluated through a simulation study and compared with other test statistics available in the literature for testing latent variable distribution fit and an overall goodness-of-fit test statistic.
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