The objective of the present study was to estimate the genetic parameters for test-day milk yields (TDMY) in the first and second lactations using random regression models (RRM) in order to contribute to the application of these models in genetic evaluation of milk yield in Gyr cattle. A total of 53,328 TDMY records from 7118 lactations of 5853 Gyr cows were analyzed. The model included the direct additive, permanent environmental, and residual random effects. In addition, contemporary group and linear and quadratic effects of the age of cows at calving were included as fixed effects. A random regression model fitting fourth-order Legendre polynomials for additive genetic and permanent environmental effects, with five classes of residual variance, was applied. In the first lactation, the heritabilities increased from early lactation (0.26) until TDMY3 (0.38), followed by a decrease until the end of lactation. In the second lactation, the estimates increased from the first (0.29) to the fifth test day (0.36), with a slight decrease thereafter, and again increased on the last two test days (0.34 and 0.41). There were positive and high genetic correlations estimated between first-lactation TDMY and the remaining TDMY of the two lactations. The moderate heritability estimates, as well as the high genetic correlations between half the first-lactation TDMY and all TDMY of the two lactations, suggest that the selection based only on first lactation TDMY is the best selection strategy to increase milk production across first and second lactations of Gyr cows.
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http://dx.doi.org/10.4238/2015.December.9.22 | DOI Listing |
J Med Internet Res
January 2025
Department of Epidemiology, School of Public Health, Sun Yat-Sen University, Shenzhen, China.
Background: With the rapid expansion of social media platforms, the demand for health information has increased substantially, leading to innovative approaches and new opportunities in health education.
Objective: This study aims to analyze the characteristics of articles published on the "Dr Ding Xiang" WeChat official account (WOA), one of the most popular institutional accounts on the WeChat platform, to identify factors influencing readership engagement and to propose strategies for enhancing the effectiveness of health information dissemination.
Methods: A total of 5286 articles published on the "Dr Ding Xiang" WOA from January 2021 to December 2021 were collected and analyzed.
Oncologist
January 2025
Department of Hepatic Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China.
Background: Peritoneal metastasis (PM) after the rupture of hepatocellular carcinoma (HCC) is a critical issue that negatively affects patient prognosis. Machine learning models have shown great potential in predicting clinical outcomes; however, the optimal model for this specific problem remains unclear.
Methods: Clinical data were collected and analyzed from 522 patients with ruptured HCC who underwent surgery at 7 different medical centers.
Clin Child Fam Psychol Rev
January 2025
School of Psychology, The University of Sydney, Sydney, NSW, 2006, Australia.
This meta-analytic review examined irritability across childhood and adolescence as it relates to symptoms of common mental health disorders in these periods. Of key interest was whether the relationship between irritability and symptom severity varies according to symptom domain. This was tested at the level of broad symptom dimensions (internalizing versus externalizing problems) as well as discrete diagnostic domains (e.
View Article and Find Full Text PDFAnal Bioanal Chem
January 2025
Intercollege Graduate Degree Program in Plant Biology, Pennsylvania State University, University Park, PA, USA.
Species identification of botanical products is a crucial aspect of research and regulatory compliance; however, botanical classification can be difficult, especially for morphologically similar species with overlapping genetic and metabolomic markers, like those in the genus Ocimum. Untargeted LC-MS metabolomics coupled with multivariate predictive modeling provides a potential avenue for improving herbal identity investigations, but the current dearth of reference materials for many botanicals limits the applicability of these approaches. This study investigated the potential of using greenhouse-grown authentic Ocimum to build predictive models for classifying commercially available Ocimum products.
View Article and Find Full Text PDFAnn Emerg Med
January 2025
Department of Emergency Medicine, Indiana University School of Medicine, Indianapolis, IN; Center for Health Services Research, The William M. Tierney Center for Health Services Research, Regenstrief Institute, Indianapolis, IN.
Study Objective: Patient experience is an essential measure of patient-centered emergency care. However, emergency department (ED) patient experience scores may be influenced by patient demographics as well as clinical and operational characteristics unrelated to actual patient-centeredness of care. This study aimed to determine whether there are characteristics associated with patient experience scores that have not yet been proposed for risk adjustment by the Centers for Medicare and Medicaid Services (CMS).
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