The Effect of Replacement of Fish Oil by Soybean Oil in Practical Diets, on Tissue Fatty Acid and Expression of Related Genes in Pacific White Shrimp Litopenaeus vannamei
Bibliographic record
Abstract
High prices and unsustainable supply have rendered the use of high levels of fish oil in aquafeeds problematic. In the present study, an eight-week feeding trial was conducted to evaluate the replacement of fish oil with less expensive and more sustainable soybean oil in a practical diet containing 17% fish meal, which is widely used in China for Pacific white shrimp Litopenaeus vannamei. Five diets with five levels of fish oil replacement (0%, 25%, 50%, 75%, and 100%) by soybean oil were fed to shrimp for 56 days. At the end of the trial results obtained after analysis of the shrimp showed that shrimp fed diets containing 50% fish oil and 50% soybean oil displayed no significant differences in weight gain, specific growth rate, survival, and feed conversion ratio. Quantitative polymerase chain reactions revealed that the expression levels of fatty acid binding protein and fatty acid synthase, two critical genes in fat metabolism, gradually decreased with increased levels of soybean oil in diets. Fatty acid profiling showed that complete replacement of fish oil with soybean oil affected fatty acid content in shrimp muscles, including monounsaturated fatty acid, polyunsaturated fatty acid, and highly-unsaturated fatty acid, as well as the ratio of n-3/n-6 polyunsaturated fatty acids. The expression of two genes decreased with increased soybean oil level in diets. The growth results indicate
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".