PSII-11 Supplementation of Non-Protein Nitrogen to a Nitrogen-Deficient Diet Increases Lysine Requirement for Nitrogen-Retention
Bibliographic record
Abstract
Abstract Low protein (LP) diets have improved nutrient utilization while maintaining animal performance. Regardless, these diets may be limiting in nitrogen (N) content to meet non-essential amino acid (NEAA) requirements, potentially altering essential amino acid (EAA) utilization and requirements. Inclusion of a non-protein nitrogen (NPN) may be beneficial for improving utilization of EAA in LP diets. The objective of this study was to determine the impact of NPN in an N-deficient diet, as indicated by EAA-N:total N ratio (EAA-N:TN) on the Lys requirement for nitrogen retention (NR). A total of 90 growing barrows (initial BW of 20.4 ± 0.46 kg) were randomly assigned to 1 of 10 dietary treatments (n = 9 pigs/treatment) in 9 blocks in a 2 × 5 factorial design. Diets contained no ammonium phosphate (NAP; EAA-N:TN of 0.56) or were supplemented with 1.7% ammonium phosphate (AP; EAA-N:TN of 0.50) with graded levels of dietary lysine [Lys; 0.8%, 0.9%, 1.0%, 1.1% and 1.2% standardized ileal digestible (SID)]. Diets were corn and soybean meal-based and formulated to meet or exceed nutrient requirements according to NRC (2012). Diets were fed at 2.8 × maintenance metabolizable energy requirements in two equal meals per day. A 4-d nitrogen balance collection period was conducted following a 7-d dietary adaptation period. Urine was collected quantitatively, and fresh fecal samples were obtained daily. Nitrogen retention was determined as the difference between N intake and output (fecal and urinary). Linear breakpoint modeling with PROC NLIN was used to estimate Lys requirement. Nitrogen retention was optimized at 1.00% SID Lys (15.6 g/d NR; R2 = 0.68) in pigs fed NAP diets and 1.09% SID Lys (16.4 g/d NR; R2 = 0.61) in pigs fed AP diets. Overall, deficient dietary N reduced NR and Lys requirement, which were increased with NPN supplementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".