Heat stress poses a significant challenge to livestock farming, particularly affecting the health and productivity of high-yield dairy cows. This study develops a machine learning framework aimed at predicting the core body temperature (CBT) of dairy cows to enable more effective heat stress management and enhance animal welfare. The dataset includes 3005 records of physiological data from real-world production environments, encompassing environmental parameters, individual animal characteristics, and infrared temperature measurements. Employed machine learning algorithms include elastic net (EN), artificial neural networks (ANN), random forests (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and CatBoost, alongside several optimization algorithms such as Bayesian optimization (BO) and grey wolf optimizer (GWO) to refine model performance through hyperparameter tuning. Comparative analysis of various feature sets reveals that the feature set incorporating the average infrared temperature of the trunk (IRTave_TK) excels in CBT prediction, achieving a coefficient of determination (R²) value of 0.516, mean absolute error (MAE) of 0.239 °C, and root mean square error (RMSE) of 0.302 °C. Further analysis shows that the GWO–XGBoost model surpasses others in predictive accuracy with an R² value of 0.540, RMSE as low as 0.294 °C, and MAE of just 0.232 °C, and leads in computational efficiency with an optimization time of merely 2.41 s—approximately 4500 times faster than the highest accuracy model. Through SHAP (SHapley Additive exPlanations) analysis, IRTave_TK, time zone (TZ), days in lactation (DOL), and body posture (BP) are identified as the four most critical factors in predicting CBT, and the interaction effects of IRTave_TK with other features such as body posture and time periods are unveiled. This study provides technological support for livestock management, facilitating the development and optimization of predictive models to implement timely and effective interventions, thereby maintaining the health and productivity of dairy cows.

Skeletal muscle development and growth are closely associated with efficiency of poultry meat production and its quality. We performed whole transcriptome profiling based on RNA sequencing of breast muscle tissue obtained from Shouguang chickens at embryonic days (E) 12 and 17 to post-hatching days (D) 1, 14, 56, and 98. A total of 9,447 differentially expressed genes (DEGs) were filtered (Q < 0.01, fold change > 2). Time series expression profile clustering analysis identified five significantly different expression profiles that were divided into three clusters. DEGs from cluster I with downregulated pattern were significantly enriched in cell proliferation processes such as cell cycle, mitotic nuclear division, and DNA replication. DEGs from cluster II with upregulated pattern were significantly enriched in metabolic processes such as glycolysis/gluconeogenesis, insulin signaling pathway, calcium signaling pathway, and biosynthesis of amino acids. DEGs from cluster III, with a pattern that increased from E17 to D1 and then decreased from D1 to D14, mainly contributed to lipid metabolism. Therefore, this study may help us explain the mechanisms underlying the phenotype that myofiber hyperplasia occurs predominantly during embryogenesis and hypertrophy occurs mainly after birth at the transcriptional level. Moreover, lipid metabolism may contribute to the early muscle development and growth. These findings add to our knowledge of muscle development in chickens.

Controlling low ambient temperatures and ammonia levels is critical for effective environmental management in poultry houses during winter, as both represent persistent stressors affecting bird health and productivity. However, evidence regarding their combined long-term effects on the physiological responses of laying hens remains limited. In this study, 576 eighteen-week-old Hy-Line Brown hens were randomly assigned to six treatments (8 replicates with 12 birds per replicate each treatment) and housed in environmentally controlled chambers for 20 weeks: T1 (8 °C, ≤5 ppm ammonia), T2 (8 °C, 20 ppm ammonia), T3 (8 °C, 45 ppm ammonia), T4 (20 °C, ≤5 ppm ammonia; control), T5 (20 °C, 20 ppm ammonia), and T6 (20 °C, 45 ppm ammonia). Plasma samples were collected at 22, 26, 30, 34, and 38 weeks to evaluate physiological stress biomarkers (corticosterone, CORT; total antioxidant capacity, T-AOC), immunoglobulins (IgG, IgM, and IgA), and reproductive hormones (luteinizing hormone, LH; follicle-stimulating hormone, FSH; estradiol, E2). At 38 weeks, hypothalamus, pituitary, and spleen tissues were collected to assess the relative mRNA expression of gonadotropin-releasing hormone (GnRH), FSH, tumor necrosis factor-α (TNF-α), and interleukins (IL-1β, IL-6, and IL-10). Results showed that both cold and ammonia stress reduced antioxidant capacity, disrupted immune homeostasis, and altered reproductive hormone profiles. Cold exposure induced acute immunoendocrine alterations with partial physiological adaptation over time, whereas ammonia exerted progressive and cumulative damage, including elevated immunoglobulins (IgG and IgM) and downregulation of GnRH and FSH expression. Combined exposure significantly upregulated TNF-α and IL-1β expression, suggesting a synergistic inflammatory response. These results highlight complex, parameter-specific interactions between cold and ammonia stressors, emphasizing the need for targeted environmental strategies. Stage-specific interventions—thermal regulation in early laying and ammonia control in later phases—are recommended to safeguard hen health and optimize productivity under winter conditions.

Producing translucent eggs has been found to reduce the quality and safety of the eggs, as well as the demand from consumers. However, the intestinal function and the molecular mechanism for the production of translucent eggs remain uncertain. A total of 120 eggs from 276-day-old Jining Bairi were divided into two groups based on eggshell translucence: the translucent egg group (group T) and the normal group (group C). Group T exhibited thicker eggshells and a lower egg yolk color. Subsequently, we divided the chickens into translucent and normal groups based on their egg quality. We then assessed the plasma biochemical index, intestinal morphology and structure, enzyme activity, and antioxidant capacity of the hens producing translucent eggs compared to those producing normal eggs. The results showed that the ratio of duodenal villus length to crypt depth, succinate dehydrogenase (SDH) activity, chymotrypsin, total ATPase (T-ATPase), alkaline phosphatase (AKP), and glutathione peroxidase (GSH-Px) activities were decreased in the hens produced translucent eggs (p < 0.05), but malondialdehyde (MDA) content was increased (p < 0.05); jejunal lipase activity, Na⁺K⁺-ATPase activity, total antioxidant capacity (T-AOC), and GSH-Px activities were decreased (p < 0.05) in group T; ileal amylase and Ca²⁺Mg²⁺-ATPase activities were also decreased (p < 0.05) in group T. In addition, we identified a total of 471 differentially expressed genes (DEGs) in duodenal tissue, with 327 up-regulated genes and 144 down-regulated genes (|log₂FC| ≥ 1 and p < 0.05). Enrichment analysis showed that the up-regulated genes, such as GSTT1, GSTO2, and GSTA3, were mostly enriched in metabolism of xenobiotics by cytochrome P450, drug metabolism-cytochrome P450, and oxidative phosphorylation pathways. The results of our study indicate that plasma lipid metabolism disorder, decreased intestinal antioxidant capacity, and altered intestinal metabolism capabilities may influence the formation of translucent eggs.

High-density SNP chips have been demonstrated to be effective instruments for simultaneously genotyping large numbers of loci, thereby facilitating genome-scale analyses and advancing genomic selection (GS) in poultry and livestock. The meat-type duck, an economically valuable poultry species in China, has so far lacked precise and high-throughput genotyping systems, which has constrained the broader implementation of GS and genome-wide association analyses (GWASs) and consequently slowed genetic progress. In this study, we developed and validated a novel SNP array based on Genotyping-by-Targeted-Sequencing (GBTS) technology. The array comprises 40,875 SNP markers evenly distributed across 32 duck chromosomes. Using data generated from this array, genomic heritability estimates were obtained for six economic traits in a cultured duck population (n = 400), with values of 0.61 ± 0.09, 0.69 ± 0.08, 0.80 ± 0.08, 0.11 ± 0.09, 0.14 ± 0.08, 0.31 ± 0.09 for age at first egg (AFE), egg production number at 38 weeks (EN38w), egg weight at 38 weeks (EW38w), body weight at 35 days (BW35d), shank length at 35 days (SL35d) and thickness of breast muscle at 40 days (TB40d). A total of 163 significant SNPs associated with economic traits were identified through GWAS, and annotation revealed 28 candidate genes related to five of these traits. Moreover, the prediction accuracy of ssGBLUP for AFE, EN38w, EW38w, BW35d, SL35d, and TB40d reached 0.55 ± 0.16, 0.56 ± 0.12, 0.57 ± 0.08, 0.25 ± 0.19, 0.31 ± 0.19, and 0.47 ± 0.17, respectively—values that exceeded those obtained using BLUP. Population genomic analyses of 400 ducks demonstrated that this SNP array provides improved genomic prediction accuracy over pedigree-based BLUP for most analyzed traits. Overall, the developed SNP array provides a robust, high-efficiency, and cost-effective genotyping platform that will accelerate genetic progress and promote the sustainable development of the meat-type duck industry.

Producing translucent eggs has been found to reduce the quality and safety of the eggs produced., as well as the demand from consumers. However, the intestinal function and the molecular mechanism for the production of translucent eggs remain uncertain. A total of 120 eggs were allocated into two groups, one for the translucent egg group (group T) and one for the normal group (group C), according to the degree of eggshell translucence. Translucent eggs showed thicker eggshell and lower egg yolk color. We also detected the plasma biochemical index, intestinal morphology and structure, enzyme activity, and antioxidant capacity of the hens’ producing translucent eggs and normal eggs. The results showed that the ratio of duodenal villus length to crypt depth, succinate dehydrogenase (SDH) activity, chymotrypsin, total ATPase (T-ATPase), alkaline phosphatase (AKP) and glutathione peroxidase (GSH-Px) activities were decreased in the hens produced translucent eggs (P < 0.05), but malondialdehyde (MDA) content was increased (P < 0.05); jejunal lipase activity, Na+K+-ATPase activity, total antioxidant capacity (T-AOC), GSH-Px activities were decreased (P < 0.05) in group T; ileal amylase and Ca2+Mg2+-ATPase activities were also decreased (P < 0.05) in group T. In addition, we identified a total of 471 differentially expressed genes (DEGs) in duodenal tissue, with 327 up-regulated genes and 144 down-regulated genes (|log2FC| ≥ 1 and P < 0.05). Enrichment analysis showed that the up-regulated genes, such as GSTT1, GSTO2, GSTA3, were mostly enriched in metabolism of xenobiotics by cytochrome P450, drug metabolism-cytochrome P450 and oxidative phosphorylation pathways. The results of our study indicate that plasma lipid metabolism disorder, decreased intestinal antioxidant capacity, and altered intestinal metabolism capabilities may influence the formation of translucent eggs.