Goose meat is consumed by consumers because it contains a relatively high proportion of polyunsaturated fatty acids (PUFAs). This study was conducted to explore the main differences in production performance, breast meat quality traits, and cecal microbiota compositions between the Zi goose (ZG) and Xianghai flying goose (FG). The production performance and breast meat quality trait analyses showed that compared with the ZG, the FG had a higher right breast muscle index, ileum villi height/crypt depth ratio (VH/CD), and cecum fermentation rate (higher short-chain fatty acid (SFCA) concentration); a lower abdominal fat index; a higher proportion of PUFAs; and a lower shear force. Spearman’s correlation coefficients between the cecal microbiota composition and production performance indexes suggested that the genus Faecalibacterium was positively associated with production performance; in contrast, the genus Candidatus Saccharimonas was negatively correlated with production performance; moreover, the Ruminococcus torques group, Parasutterella, and Methanobrevibacter were negatively related to the VH/CD. Taken together, in this particular trial, FG had better production performance, healthier meat quality traits, and better intestinal digestion and absorption capacities than ZG. These results not only provide a useful data reference for the production of healthy geese for human consumption but can also help guide the utilization of goose breed resources.

Background: Cardiac amyloidosis has poor prognosis, high mortality and is often misdiagnosed as hypertrophic cardiomyopathy, leading to delayed diagnosis.Objective: Machine learning combined speckle tracking echocardiography was proposed to automate differentiating two conditions. Methods: A total of 74 patients with pathologically con rmed cardiac amyloidosis and 64 patients of hypertrophic cardiomyopathy were enrolled from June 2015 to November 2018. Machine learning models utilizing traditional and advanced algorithms were established and determined the most signi cant predictors. The performance was evaluated by receiver operating characteristic curve (ROC) and area under the curve (AUC).Results: With clinical and echocardiography data, all models showed great discriminative performance (AUC > 0.9). Compared with logistic regression (AUC=0.91), machine learning such as support vector machine (AUC=0.95, P=0.477), random forest (AUC= 0.97, P=0.301) and gradient boosting machine (AUC= 0.98, P=0.230) demonstrated varying degrees of improvement of predicting cardiac amyloidosis. With speckle tracking echocardiography, the predictive performance of the voting model was similar to that of LightGBM (AUC were 0.86 for both), while the AUC of XGBoost was slightly lower (AUC=0.84). In 5fold cross validation, the voting model was more robust globally and superior to the single model in some test sets.Conclusions: Data-driven machine learning had shown admirable performance in differentiating two conditions and could automatically integrate abundant variables to identify the most discriminating predictors without making preassumptions. In the era of big data, automated machine learning will help to better identify patients with cardiac amyloidosis, and to timely and effectively intervene, thus improving the outcome.

This study investigated the effects of CCHMA on growth performance, slaughter performance, serum biochemical indicators, intestinal morphology and microbiota of Zi goose. Initially, it was determined the optimal addition concentration of CCHMA to be 3 g/kg by the first feeding experiment. Then, 78 Zi geese were divided into control and CCHMA supplemented groups. The results showed that the body weight (BW) and average daily gain (ADG) of the CCHMA supplemented group was significantly increased (p < 0.05), and the feed/gain (F/G) of the CCHMA supplemented group was significantly decreased (p < 0.05) compared with the control group. The dressed yield percentage in the CCHMA supplemented group significantly increased by 0.78% (p < 0.05). Alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels were significantly lower in the CCHMA fed birds than in the control group (p < 0.05). Further, 16S rDNA gene sequencing conducted for cecal flora composition found that 3 g/kg CCHMA significantly increased the abundance of beneficial bacteria (CHKCI001, Colidextribacter and Subdoligranulum) (p < 0.05; p < 0.01) and suppressing harmful bacteria (Bacteroidetes and Methanobrevibacter) (p < 0.05) in the cecum of Zi goose. In conclusion, adding 3 g/kg of CCHMA in the diet can improve the growth performance, slaughter performance of Zi goose, and optimize the cecum microflora.

The high cost of feed and nitrogen pollution caused by high-protein diets have become major challenges restricting sustainable development in China's animal husbandry sector. Properly reducing protein levels and improving protein utilization in feed are effective approaches to solving this problem. To determine the optimal dose of methionine hydroxyl analogue chelated zinc (MHA-Zn) in broiler diets with a 1.5% reduction in crude protein (CP), a total of 216 1-day-old broilers were randomly assigned into 4 groups (each group consisted of 3 replications with 18 broilers per replicate), and growth and development indexes were assessed after 42 days. The broilers in control group were fed a basic diet, whereas those in the three test groups were fed diets with a 1.5% reduction in CP. The results showed no significant difference in the edible parts of broilers between low-protein (LP) diet group (90 mg/kg MHA-Zn) and normal diet group (p > 0.05), and adding 90 mg/kg MHA-Zn to LP diet significantly improved ileum morphology and apparent total tract digestibility (ATTD) of nutrient (p < 0.01; p < 0.05). A 16S rRNA sequencing analysis indicated that supplementing the LP diet with 90 mg/kg MHA-Zn was adequate for production performance of broilers and promoted beneficial bacteria in the cecum (Lactobacillus, Butyricoccus, Oscillospira, etc.) (p < 0.01). In summary, adding an optimal dose of organic zinc (90 mg/kg MHA-Zn) in low protein diets led to enhanced production performance of broilers and optimized cecum microbiota. Additionally, the reduction of crude protein consumption in broiler production proved to be a cost-effective measure, while also mitigated nitrogen pollutant emissions in the environment.

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