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Simple Summary: Investigating the proliferation and differentiation of skeletal muscle satellite cells (MuSCs) in vitro is crucial for elucidating the underlying mechanisms of skeletal muscle development in goats. Real-time quantitative PCR (RT-qPCR) is a widely utilized technique for quantifying the expression levels of target genes. A significant challenge associated with this method is the identification of optimal reference genes for accurate normalization. In this study, we evaluated ten candidate reference genes for the standardization of gene expression. Our results indicated that RPL14 and RPS15A constituted the most stable reference gene combination during the proliferation and differentiation of goat MuSCs. The process of skeletal muscle development is intricate and involves the regulation of a diverse array of genes. Accurate gene expression profiles are crucial for studying muscle development, making it essential to choose the right reference genes for real-time quantitative PCR (RT-qPCR). In the present study, eight candidate reference genes were identified from our previous transcriptome sequencing analysis of caprine skeletal muscle satellite cells (MuSCs), and two traditional reference genes (ACTB and GAPDH) were assessed. The quantitative levels of the candidate reference genes were determined through the RT-qPCR technique, while the stability of their expression was evaluated utilizing the GeNorm, NormFinder, BestKeeper, and RefFinder programs. Furthermore, the chosen reference genes were utilized for the normalization of the gene expression levels of PCNA and Myf5. It was determined that conventional reference genes, including ACTB and GAPDH, were not appropriate for normalizing target gene expression. Conversely, RPL14 and RPS15A, identified through RNA sequencing analysis, exhibited minimal variability and were identified as the optimal reference genes for normalizing gene expression during the proliferation and differentiation of goat MuSCs. Our research offers a validated panel of optimal reference genes for the detection of differentially expressed genes in goat muscle satellite cells using RT-qPCR. [ABSTRACT FROM AUTHOR]

Domestic animals have multiple phenotypes of skin and coat color, which arise from different genes and their products, such as proteins and metabolites responsible with melanin deposition. However, the complex regulatory network of melanin synthesis remains to be fully unraveled. Here, the skin and tongue tissues of Liangshan black sheep (black group) and Liangshan semi-fine-wool sheep (pink group) were collected, stained with hematoxylin–eosin (HE) and Masson–Fontana, and the transcriptomic and metabolomic data were further analyzed. We found a large deposit of melanin granules in the epidermis of the black skin and tongue. Transcriptome and metabolome analysis identified 744 differentially expressed genes (DEGs) and 443 differentially expressed metabolites (DEMs) between the pink and black groups. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses revealed the DEGs and DEMs were mainly enriched in the pathways of secondary metabolic processes, melanin biosynthesis processes, melanin metabolism processes, melanosome membranes, pigment granule membranes, melanosome, tyrosine metabolism, and melanogenesis. Notably, we revealed the gene ENSARG00020006042 may be a family member of YWHAs and involved in regulating melanin deposition. Furthermore, several essential genes (TYR, TYRP1, DCT, PMEL, MLANA, SLC45A2) were significantly associated with metabolite prostaglandins and compounds involved in sheep pigmentation. These findings provide new evidence of the strong correlation between prostaglandins and related compounds and key genes that regulate sheep melanin synthesis, furthering our understanding of the regulatory mechanisms and molecular breeding of pigmentation in sheep. [ABSTRACT FROM AUTHOR]

Optimal coordination is essential for multi‐automated guided vehicle (AGV) systems, particularly in logistic transportation cases, where the system task completion time needs to be minimized, with the guarantee of safe operation. This is because an optimal coordination strategy (OCS), if achieved, can significantly improve the transportation system's efficiency. In this paper, to deal with the dynamic interaction process among AGVs, and sensing and communication range limits, we formulate the optimal coordination problem into a distributed differential game (DDG) framework, where individual AGVs only use information communicated from nearby AGVs to design their optimal operation trajectories. This helps to significantly reduce the computational and communication requirements for the multi‐AGV logistic transportation systems. Targeting operation safety and working efficiency requirements, we incorporate collision avoidance and trajectory optimization objectives into the proposed framework. It is shown that local OCS, obtained by solving the DDG problem for each AGV, will converge to the global Nash equilibrium, which represents the most efficient operating condition for the entire logistic transportation system. Finally, the efficacy of the proposed method is demonstrated, based on simulations and experiments, benchmarked with existing logistic warehousing planning and differential game methods. Compared with conventional methods, the proposed framework successfully helps reduce the task completion time by up to 16%. [ABSTRACT FROM AUTHOR]

Gene trap locus 2 (GTL2), a long non-coding paternal imprinting gene, participates in various biological processes, including cell proliferation, differentiation, and apoptosis, by regulating the transcription of target mRNA, which is tightly related to the growth of the organic and maintenance of function. In this study, DNA methylation patterns of CpG islands (CGI) of GTL2 were explored, and its expression level was quantified in six tissues, rumen epithelium cells, and skeletal muscle cells in goats. GTL2 expression levels were measured by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR), and the methylation model was confirmed by bisulfite-sequencing PCR (BSP). CGI methylation of GTL2 indicated a moderate methylation (ranging from 81.42 to 86.83%) in the brain, heart, liver, kidney, lung, and longissimus dorsi. GTL2 is most highly expressed in brain tissues, but there is no significant difference in the other five tissues. In addition, in the rumen epithelium cell proliferation, GTL2 expression was highest at 60 h, followed by 72 h, and almost unchanged at 12–48 h. In the skeletal muscle cell differentiation, GTL2 expression was highest at 0 and 24 h, significantly decreasing at 72 and 128 h. Pearson correlation analysis did not indicate a clear relationship between methylation and GTL2 expression levels, suggesting that other regulatory factors may modulate GTL2 expression. This study will provide a better understanding of the expression regulation mechanism of genes in the delta-like homolog 1 gene (DLK1)-GTL2 domain. [ABSTRACT FROM AUTHOR]