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Simple Summary: Angus cattle are highly valued by breeders due to their outstanding adaptability, superior meat quality, and excellent reproductive performance. In recent years, the Ningxia region has imported Angus cattle and frozen semen from Australia, the United States, and New Zealand. To better understand the genetic improvement in the selection history of the Ningxia Angus population, we performed whole-genome resequencing of 282 Angus cattle from the Ningxia region to identify selection signatures associated with improved productivity and adaptability in this study. These findings may provide valuable insights for future livestock breeding programs. The genetic improvement of beef cattle breeds is crucial for the advancement of the beef cattle industry. Whole-genome resequencing technology has been widely applied in genetic breeding as well as research on selection signatures in beef cattle. In this study, 20× whole-genome resequencing was performed on 282 Angus cattle from the Ningxia region, and a high-quality dataset encompassing extensive genomic variations across the entire genome was constructed. The iHS test identified 495 selection signal regions, which included pregnancy-associated glycoprotein (PAG) family genes and immune-related genes such as UL16-binding protein 21 (ULBP21), CD1b molecule (CD1B), and tumor necrosis factor ligand superfamily member 11 (TNFSF11). A quantitative trait locus (QTL) enrichment analysis revealed that several economic traits, including longissimus muscle area, marbling score, carcass weight, average daily gain, and milk yield, were significantly enriched in cattle with these selection signatures. Although the enrichment of QTLs for health traits was low, immune-related genes may indirectly contribute to improvements in production performance. These findings show the genetic basis of economic and adaptive traits in Ningxia Angus cattle, providing a theoretical foundation and guidance for further genetic improvement and breeding strategies. [ABSTRACT FROM AUTHOR]

Leveraging whole-genome sequencing (WGS) that includes the full spectrum of genetic variation provides a better understanding of the biological mechanisms involved in the economically important traits of farm animals. However, the effectiveness of WGS in improving the accuracy of genomic prediction (GP) is limited. Recent genetic analyses of complex traits, such as genome-wide association study (GWAS), have identified numerous genomic regions and potential genes, which can provide valuable prior information for the improvement of genomic selection (GS). In this study, we applied different genome prediction methods to integrate GWAS results and gene feature annotations, which significantly improved the accuracy of GS for beef production traits. The Bayesian models incorporating genomic features showed the highest prediction accuracy, particularly for average daily gain (ADG) and bone weight (BW). Compared to prediction models based on WGS data, GP including biological prior can optimize the prediction accuracy by up to 11.56% for ADG and 14.60% for BW. Also, GP using GBLUP and Bayesian methods integrating biological priors for single-trait GWAS can significantly increase the prediction accuracy. Bayesian methods generally outperformed GBLUP models, with average improvements of 2.25% for ADG, 5.04% for BW, and 3.44% for live weight (LW). Our results indicate that leveraging biological prior knowledge can significantly refine GS models and underline the potential of combining WGS data with biological prior knowledge to further enhance the breeding process. [ABSTRACT FROM AUTHOR]

To prospective research the efficacy of dual-energy computed tomography (DECT) in predicting contrast medium extravasation and secondary cerebral hemorrhage after stent thrombectomy in acute ischemic cerebral infarction. Ninety-two patients with acute ischemic stroke who underwent intra-arterial thrombolysis in our hospital from December 2019 to January 2022 have opted as the study subjects. DECT was performed immediately after stent thrombectomy. Images were generated through the image workstation and routine diagnosis was performed 24 hours after the operation. To analyze the diagnostic value of To analyze the diagnostic value of DECT, and to explore the diagnostic status of lesions with hemorrhagic transformation or increased hemorrhage and their correlation with iodine concentration. (1) 68 situations were confirmed, 56 positive and 12 negative with detection rates of 10.71% for hemorrhage, 75.00% for contrast agent extravasation, and 14.29% for extravasation combined with hemorrhage; (2) DECT diagnosed 8 cases of postoperative bleeding and 44 cases of extravasation of contrast media and 4 cases of extravasation of contrast media with hemorrhage ; The accuracy of DECT in diagnosing postoperative hemorrhage was 96.43%. The accuracy of diagnosis of extravasation was 96.43%. (3) The mean iodine concentration of lesions with increased hemorrhage or hemorrhagic transformation was higher compared to those without; (4) There was a correlation between hemorrhagic transformation or increased hemorrhage and iodine concentration. Dual-energy CT (DECT) can accurately distinguish the extravasation of contrast agent and secondary cerebral hemorrhage, and can predict the increased bleeding and bleeding transformation, with good diagnostic value and good predictive efficacy. [ABSTRACT FROM AUTHOR]

Present study focuses on a clean energy replacement for an oilfield hot water station and develops a combined solar and ground source heat pump (GSHP) heating system with a latent heat storage tank (LHST). The effects of LHST and heat replenishment on energy and coefficient of performance (COP) are quantitatively analyzed during short-term or long-term operation of the system. The suitability of three different phase change materials for the system is compared, and the optimal one is identified. Results show that the LHST with 70 °C melting point can increase the collector efficiency to 53.91 % and reduce the total energy consumption of the system by 52344 kWh. Heat replenishment could effectively mitigate the ground temperature drop to 0.19 °C/year, reducing the annual energy consumption of GSHP by more than 4230 kWh and providing an additional thermal energy of 72 GJ. Compared to the initial heating system, the novel system efficiency increases to 51.25 %. Gas consumption and total system energy consumption decreased by 81.7 % and 75.2 %, respectively. Annual reduction of CO 2 emission is about 1150 tons, showing satisfactory performance. Outcomes of this study prove the application of clean energy in oil field industrial processes, and help in achieving the goals of emission reduction and carbon neutrality. • Designed a new combined heating system of solar energy and ground source heat pump for an oilfield hot water station. • Effects of latent heat storage tank and soil heat replenishment on temperature and performance of the system. • Selection of the optimal melting temperature for phase change materials. • Improvement of the annual average COP to 7.49 and of efficiency of the system to 51 %. [ABSTRACT FROM AUTHOR]

Simple Summary: In order to evaluate the potential application of genomic selection (GS) for Chinese indigenous cattle, we assessed the influence of combining multiple populations on the reliability of genomic predictions for 10 indigenous breeds of Chinese cattle using simulated data. We found the predictive accuracies to be low when the reference and validation populations were sampled from different breeds. When using multiple breeds for the reference population, the predictive accuracies were higher if the reference was comprised of breeds with close relationships. In addition, the accuracy increased in all scenarios when the heritability increased, and the genetic architecture of the QTL can affect genomic prediction. Our study suggested that the application of meta-populations can increase accuracy in scenarios with a reduced size of reference populations. Genomic selection (GS) has been widely considered as a valuable strategy for enhancing the rate of genetic gain in farm animals. However, the construction of a large reference population is a big challenge for small populations like indigenous cattle. In order to evaluate the potential application of GS for Chinese indigenous cattle, we assessed the influence of combining multiple populations on the reliability of genomic predictions for 10 indigenous breeds of Chinese cattle using simulated data. Also, we examined the effect of different genetic architecture on prediction accuracy. In this study, we simulated a set of genotype data by a resampling approach which can reflect the realistic linkage disequilibrium pattern for multiple populations. We found within-breed evaluations yielded the highest accuracies ranged from 0.64 to 0.68 for four different simulated genetic architectures. For scenarios using multiple breeds as reference, the predictive accuracies were higher when the reference was comprised of breeds with a close relationship, while the accuracies were low when prediction were carried out among breeds. In addition, the accuracy increased in all scenarios with the heritability increased. Our results suggested that using meta-population as reference can increase accuracy of genomic predictions for small populations. Moreover, multi-breed genomic selection was feasible for Chinese indigenous populations with genetic relationships. [ABSTRACT FROM AUTHOR]

The genetic improvement of beef cattle breeds is crucial for the advancement of the beef cattle industry. Whole-genome resequencing technology has been widely applied in genetic breeding as well as research on selection signatures in beef cattle. In this study, 20× whole-genome resequencing was performed on 282 Angus cattle from the Ningxia region, and a high-quality dataset encompassing extensive genomic variations across the entire genome was constructed. The iHS test identified 495 selection signal regions, which included pregnancy-associated glycoprotein ( PAG ) family genes and immune-related genes such as UL16-binding protein 21 ( ULBP21 ), CD1b molecule ( CD1B ), and tumor necrosis factor ligand superfamily member 11 ( TNFSF11 ). A quantitative trait locus (QTL) enrichment analysis revealed that several economic traits, including longissimus muscle area, marbling score, carcass weight, average daily gain, and milk yield, were significantly enriched in cattle with these selection signatures. Although the enrichment of QTLs for health traits was low, immune-related genes may indirectly contribute to improvements in production performance. These findings show the genetic basis of economic and adaptive traits in Ningxia Angus cattle, providing a theoretical foundation and guidance for further genetic improvement and breeding strategies.