The Yunling cattle breed, a three-breed crossbreed, which comprises 50% Brahman cattle, 25% Murray Grey cattle and 25% Yunnan Yellow cattle, has several advantageous traits, including rapid growth, superior meat quality, ability to improve tolerance in hot and humid climates, tick resistance and rough feed. It can be rightfully stated that Yunling cattle serve as vital genetic repositories of the local Yunnan cattle. Gaining insights into the genetic information of Yunling cattle plays a significant role in the formulation of sound breeding strategies for this breed, safeguarding genetic resources and mitigating the risks associated with inbreeding depression. In this study, we constructed the Yunling cattle standard reference genome and aligned the whole genomes of 129 Yunling cattle individuals to the constructed reference genome to estimate the current genetic status of Yunling cattle in Yunnan Province, China. The average alignment rate and the average percentage of properly paired are both 99.72%. The average nucleotide diversity in Yunling cattle is 0.000166, which indicates a lower level of diversity. Population structure analysis classified Yunling cattle into two subgroups. Inbreeding analysis revealed that inbreeding events did occur in the Yunling cattle, which may have contributed to the low genetic diversity observed. This study presents a comprehensive assessment of the genetic structure and diversity among the Yunling cattle and provides a theoretical foundation for the preservation and exploitation of these precious germplasm resources.

Copy number variation (CNV) serves as a crucial contributor to genetic diversity, exerting a profound influence on phenotypic diversity, traits of economic significance, and the evolutionary trajectory of livestock species. This study aimed to dissect the genome-wide CNV landscape of the Nanyang cattle line (Nanyang, Pinnan, and Xianan cattle) to identify functionally relevant CNVs associated with key economic traits and breed differentiation. In this study, 27 resequencing datasets were utilized to analyze the genome-wide distribution of CNVs in three breeds of Nanyang cattle (Nanyang cattle, Pinnan cattle, and Xianan cattle) based on the latest reference genome ARS-UCD2.0. This study identified a total of 97,564 CNVs, and after merging CNVs with overlapping genomic positions, we obtained 10,349 CNV regions (CNVRs), accounting for 1.48% of the reference genome. Functional enrichment analysis showed that CNVR genes were mainly involved in organ development, neural regulation, immune regulation, and metabolism. In addition, 131 CNVRs overlapped with 81 quantitative trait loci (QTLs), such as growth and carcass QTL, multiple birth QTL, tenderness score QTL, and antal follicle number QTL. Additionally, AOX1, KRT72, and ZBTB7C were found to overlap with body weight QTLs. Furthermore, a selective sweep analysis of CNVR revealed that numerous genes (KIF26A, SPINT4, OR5W1, etc.) exhibited divergent copy numbers between breeds. Conclusively, this study facilitates comprehension of the genetic characteristics of the Nanyang cattle line at the CNV level and furnishes valuable information for the advancement of the Nanyang cattle line breeding system.

Machine learning has attracted much attention in the field of genomic prediction due to its powerful predictive capabilities, yet the lack of an explanatory nature in modeling decisions remains a major challenge. In this study, we propose a novel machine learning method, ExAutoGP, which aims to improve the accuracy of genomic prediction and enhance the transparency of the model by combining automated machine learning (AutoML) with SHapley Additive exPlanations (SHAP). To evaluate ExAutoGP’s effectiveness, we designed a comparative experiment consisting of a simulated dataset and two real animal datasets. For each dataset, we applied ExAutoGP and five baseline models—Genomic Best Linear Unbiased Prediction (GBLUP), BayesB, Support Vector Regression (SVR), Kernel Ridge Regression (KRR), and Random Forest (RF). All models were trained and evaluated using five repeated five-fold cross-validation, and their performance was assessed based on both predictive accuracy and computational efficiency. The results show that ExAutoGP exhibits robust and excellent prediction performance on all datasets. In addition, the SHAP method not only effectively reveals the decision-making process of ExAutoGP and enhances its interpretability, but also identifies genetic markers closely related to the traits. This study demonstrates the strong potential of AutoML in genomic prediction, while the introduction of SHAP provides actionable biological insights. The synergy of high prediction accuracy and interpretability offers new perspectives for optimizing genomic selection strategies in livestock and poultry breeding.

Marbling, or intramuscular fat (IMF), is a primary determinant of high-quality beef, defining key sensory attributes and nutritional value. Yunling (YL) cattle, an indigenous breed from Yunnan, China, are renowned for their superior marbling, yet the underlying molecular mechanisms remain unclear. This study employed an integrated transcriptomic, lipidomic, and amino acid metabolomic approach to systematically compare the multi-omics profiles of the longissimus dorsi muscle among YL, Angus (AGS), and Simmental (XMTE) cattle. Transcriptome analysis identified 2053 and 2156 differentially expressed genes (DEGs) in XMTE vs. YL and AGS vs. YL, respectively. These DEGs were primarily enriched in the PI3K-Akt and MAPK signaling pathways, as well as oxidative phosphorylation. Lipidomic analysis revealed a distinct lipid profile in YL cattle, identifying 27 characteristic lipid molecules (e.g., SM(d20:0/24:1), DG(16:0/18:1(11Z)/0:0)) compared to XMTE and 17 differential lipids compared to AGS. The amino acid metabolome showed that Beta-Alanine and L-Aspartic acid levels in YL were 42.6% and 54.8% lower than in XMTE, respectively (p < 0.01), and levels of several functional amino acids were significantly reduced compared to AGS. Weighted Gene Co-expression Network Analysis (WGCNA) constructed a gene-metabolite network, identifying key modules strongly correlated with lipid and amino acid metabolism (|r| > 0.6). Within these modules, energy metabolism-related genes such as NDUFB1, COX7C, and IDH3B, along with signal transduction genes including ITGB3, PDGFRA, and FN1, were found to synergistically regulate marbling formation in YL cattle. This study systematically elucidates the molecular mechanisms underlying both marbling formation and the nutritional characteristics of meat in Yunling cattle. This provides a theoretical foundation for genetic improvement and offers potential molecular targets to enhance both marbling and overall meat quality in other indigenous cattle breeds worldwide.

Accurate detection of blueberries at different maturity stages supports orchard monitoring and harvest planning, but remains difficult under complex field conditions because fruits are small, densely clustered, frequently occluded, and show subtle colour transitions across immature, colour-turning, and ripe stages. A key technical challenge for lightweight detectors is to preserve fine spatial information for small fruits while maintaining sufficient contextual representation for dense clusters and visually ambiguous maturity stages without substantially increasing inference complexity. To address this gap, this study develops a lightweight detection framework that jointly targets small-object perception, contextual feature enhancement, and training-stage semantic supervision. A field dataset containing 4680 images and 60,573 ground-truth bounding boxes was constructed. The P3/P4/P5 detection structure of YOLOv12n was redesigned as a P2/P3/P4 architecture to strengthen high-resolution feature representation for small objects. A lightweight feature-enhancement module based on single-head self-attention, termed SHSA2C2f, was introduced to improve contextual representation in dense fruit clusters and visually ambiguous maturity stages. DINOv3 (ViT-S/16)-guided asymmetric knowledge distillation was further evaluated as a training-only semantic supervision strategy that introduces no additional inference cost. Across three independent runs, the accuracy-oriented M4 model achieved 92.68 ± 0.04% mAP@0.5 and 86.38 ± 0.10% mAP@0.5:0.95 with 0.79 M parameters, while its colour-turning AP50 reached 89.78 ± 0.28%. For the same student architecture, introducing the asymmetric distillation strategy increased recall from 85.00 ± 0.52% to 85.81 ± 1.12%, while slightly reducing mAP@0.5:0.95 from 86.38 ± 0.10% to 86.19 ± 0.17%, indicating a recall-oriented performance trade-off. After TensorRT FP16 conversion, M4 retained 92.75% mAP@0.5 and 86.24% mAP@0.5:0.95 and ran at 15.9 FPS on a Jetson Orin Nano, supporting its feasibility for edge-device inference under the evaluated conditions.