Records on final BW (kg), backfat depth (cm), and LM area (cm(2)) of pigs from a University of Nebraska Large White/Landrace composite population were analyzed to estimate the effects of pen mates. Measurements were at approximately 180 d of age for 3,524 pigs in 351 pens (9 to 11 pigs per pen) farrowed from 1999 to 2005. The area of each pen was 8.13 m(2). The full model (M1) included the fixed effects of contemporary group, sex, line, and the covariates of age and inbreeding coefficient, and included random direct genetic, genetic pen-mate, permanent environmental, pen, litter, and residual effects. A derivative-free algorithm was used to obtain REML estimates of variance components for final BW adjusted to 180 d of age with M1 and 7 reduced models, and with 4 reduced models for the carcass traits. For final BW, likelihood ratio tests showed that M1 did not fit the data better than model 2 (permanent environmental effect omitted from M1) or model 3 (pen omitted from M1). Model 2 was not significantly (P > 0.05) better than model 3, which shows that variance attributable to pen effects and permanent environmental effects cannot be separated. Large sampling variances of estimates of the pen component of variance for models with pen-mate effects also indicate an inability to separate pen effects from the effects of pen mates. When pen-mate genetic effects were not in the model, estimates of components of variance and the fit of the data were the same for models 4 (included both permanent environmental and pen effects), 6 (included pen effects), and 7 (included permanent environmental effects), which shows that including both pen and permanent environmental effects was no better than including one or the other. Models 4, 6, and 7 were significantly better than model 8, which did not include pen-mate effects and pen effects, implying that pen effects are important. The estimate of pen variance with model 2 was approximately (number of pen mates - 1) times the estimate of variance of pen-mate permanent environmental effects with model 3. Patterns of estimates of variance components with models 2, 5, 6, and 8 for backfat depth and LM area were similar to those for final BW. Estimates of direct genetic variance and phenotypic variance were similar for all models. Estimates of heritability for direct genetic effects were approximately 0.40 for final BW, 0.45 for backfat depth, and 0.27 for LM area. Estimates of heritability for pen-mate genetic effects were 0.001 for the 3 traits for models including either pen or permanent environmental effects. Under the management conditions for this experiment, the conclusion is that the model for genetic evaluation should include litter effects and either pen effects or pen-mate permanent environmental effects and possibly genetic pen-mate effects, in general agreement with the results of studies of different populations at other locations.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.2527/jas.2009-1879 | DOI Listing |
Ecology
January 2025
Securing Antarctica's Environmental Future, School of Biological Sciences, Monash University, Melbourne, Victoria, Australia.
Antarctica is one of Earth's most untouched, inhospitable, and poorly known regions. Although knowledge of its biodiversity has increased over recent decades, a diverse, wide-ranging, and spatially explicit compilation of the biodiversity that inhabits Antarctica's permanently ice-free areas is unavailable. This absence hinders both Antarctic biodiversity research and the integration of Antarctica in global biodiversity-related studies.
View Article and Find Full Text PDFPopul Environ
January 2025
Center for Comparative and International Studies (CIS), ETH Zurich, 8092 Zurich, Switzerland.
Unlabelled: Various studies predict large migration flows due to climatic and other environmental changes, yet the ex post empirical evidence for such migration is inconclusive. To examine the causal link between environmental changes and migration for a population residing along the Jamuna River in Bangladesh, an area heavily affected by floods and riverbank erosion, I relate the respondents' self-reported affectedness by environmental changes, their migration aspirations, and their capability to move to their migration likelihood. The analysis relies on a unique quasi-experimental research design based on original survey panel data of 1604 household heads.
View Article and Find Full Text PDFMaterials (Basel)
January 2025
School of Civil Engineering, Central South University, Changsha 410075, China.
Geopolymer, as a promising inorganic binding material, holds potential for use in constructing base layers for highway pavements. This study aims to evaluate the mechanical properties of geopolymer-stabilized macadam (GSM) at both the micro- and macro-scale by a series of tests, demonstrating that high-Ca GSM is a high-quality material for pavement base layers. The results demonstrated that GSM exhibits outstanding mechanical and fatigue properties, significantly surpassing those of cement-stabilized macadam (CSM).
View Article and Find Full Text PDFMaterials (Basel)
January 2025
Department of Civil and Environmental Engineering, University of Nevada, Reno, NV 89557, USA.
Spent nuclear fuel (SNF) from the United States' nuclear power plants has been placed in dry cask storage systems since the 1980s. Due to the lack of a clear path for permanent geological repository for SNF, consolidated and long-term storage solutions that use durable concrete and avoid current aging and licensing challenges are becoming indispensable. Ultra-high-performance concrete (UHPC) is a rapidly growing advanced concrete solution with superior mechanical and durability properties that can help realize future resilient nuclear storage facilities.
View Article and Find Full Text PDFInt J Mol Sci
January 2025
School of Environmental Science and Engineering, Hainan University, Haikou 570228, China.
Hepatocellular carcinoma (HCC), a leading liver tumor globally, is influenced by diverse risk factors. Cellular senescence, marked by permanent cell cycle arrest, plays a crucial role in cancer biology, but its markers and roles in the HCC immune microenvironment remain unclear. Three machine learning methods, namely k nearest neighbor (KNN), support vector machine (SVM), and random forest (RF), are utilized to identify eight key HCC cell senescence markers (HCC-CSMs).
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!