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Effect of Allium mongolicum Regel and Its Extracts on the Growth Performance, Carcass Characteristics, Meat Quality and Serum Biochemical Indices of Captive Small-Tailed Han Sheep
Scientia Agricultura Sinica 2022, 55(17): 3461-3472
Published: 01 September 2022
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【Objective】

The aim of this experiment was to investigate the effects of Allium mongolicum Regel and its extracts on the production performance, carcass characteristics, meat quality and serum biochemical indexes of Small Tailed-Han Sheep, and to provide an experimental basis for the development of feed additives for captive sheep.

【Method】

Sixty 3-month-old, male, Small-Tailed Han sheep were selected and allocated into four groups in a randomized block design. There were four treatments with 3 replications each: 1) a basal diet as the control group; 2) the basal diet supplemented with 10 g·lamb-1·d-1Allium mongolicum Regel powder as the AMR group; 3) the basal diet supplemented with 3.4 g·lamb-1·d-1Allium mongolicum Regel water extract as the AWE group; and 4) the basal diet supplemented with 2.8 g·lamb-1·d-1Allium mongolicum Regel ethanol extract as the AFE group. The experiment lasted for 75 days, including a 15-day preliminary feeding period for adaptation and a 60-day experimental feeding period. The production performance of small-tailed Han sheep was measured during experimental feeding period, and the serum biochemical indexes were measured by fasting jugular blood collection at the end of experimental feeding period, followed by slaughter of 2 sheep randomly selected in each replication and carcass characteristics (Hot carcass weight, Carcass yield, perirenal adipose tissue weight, dorsal subcutaneous adipose tissue thickness and firmness) were measured immediately, meat quality was determined by taking Longissimus Dorsi muscle on the left side of the carcass, and fat color was determined by taking perirenal adipose tissue, tail adipose tissue, dorsal subcutaneous adipose tissue, omental adipose tissue.

【Result】

The results showed that: compared to the control group, ① the mean daily feed intake was significantly lower (P=0.001), feed conversion ratio was significantly higher (P=0.039) and mean daily weight gain did not differ (P>0.05) for AFE group; ② the addition of Allium mongolicum Regel powder and water extracts was able to significantly reduce the shear force of lamb compared to the control group (P=0.029); ③ there was no significant difference (P>0.050) in the b* values of perirenal adipose tissue in other groups, while the b* values of perirenal adipose tissue (P=0.096) and omental adipose tissue (P=0.087) in the alcohol extract group tended to be higher compared with Allicum mongolicum Regel powder group, while the a* (P=0.044) and C* (P=0.021) values were significantly higher than those of Allicum mongolicum Regel powder group; ④ the addition of Allium mongolicum Regel and its extract significantly increased the serum total protein (P=0.006), Allium mongolicum Regel and its alcoholic extract significantly increased the serum albumin (P=0.006), and Allium mongolicum Regel alcoholic extract significantly decreased the serum urea nitrogen (P=0.024).

【Conclusion】

In conclusion, Allium mongolicum Regel and its extracts had no significant effect on carcass characteristics and fat color of captive Small Tailed-Han Sheep. The addition of Allium mongolicum Regel and its alcoholic extract to the diet significantly improved the feed conversion rate of Small-Tailed Han sheep, and Allium mongolicum Regel powder and water extract significantly reduced the shear force of meat. Meanwhile, Allium mongolicum Regel and its alcoholic extract can significantly increase the content of total protein and albumin in serum and promote the absorption of protein of meat sheep.

Open Access Issue
TV-SAM: Increasing Zero-Shot Segmentation Performance on Multimodal Medical Images Using GPT-4 Generated Descriptive Prompts Without Human Annotation
Big Data Mining and Analytics 2024, 7(4): 1199-1211
Published: 04 December 2024
Abstract PDF (7.5 MB) Collect
Downloads:40

This study presents a novel multimodal medical image zero-shot segmentation algorithm named the text-visual-prompt segment anything model (TV-SAM) without any manual annotations. The TV-SAM incorporates and integrates the large language model GPT-4, the vision language model GLIP, and the SAM to autonomously generate descriptive text prompts and visual bounding box prompts from medical images, thereby enhancing the SAM’s capability for zero-shot segmentation. Comprehensive evaluations are implemented on seven public datasets encompassing eight imaging modalities to demonstrate that TV-SAM can effectively segment unseen targets across various modalities without additional training. TV-SAM significantly outperforms SAM AUTO (p < 0.01) and GSAM (p < 0.05), closely matching the performance of SAM BBOX with gold standard bounding box prompts (p = 0.07), and surpasses the state-of-the-art methods on specific datasets such as ISIC (0.853 versus 0.802) and WBC (0.968 versus 0.883). The study indicates that TV-SAM serves as an effective multimodal medical image zero-shot segmentation algorithm, highlighting the significant contribution of GPT-4 to zero-shot segmentation. By integrating foundational models such as GPT-4, GLIP, and SAM, the ability to address complex problems in specialized domains can be enhanced.

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