Effects of silencing <i>TT8</i> and <i>HB12</i> on <i>in vitro</i> nutrients degradation and VFA production in relation to molecular structures of alfalfa (<i>Medicago sativa</i>)
Bibliographic record
Abstract
BACKGROUND: Transparent Testa8 (TT8) and Homeobox12 (HB12) are two transcriptional factors in plant phenylpropanoid pathways and were reported to be positively related to lignin content. Alfalfa with silenced TT8 (TT8i) and HB12 (HB12i) was therefore generated using the RNA interference (RNAi) technique. Although lignin was found to be high in HB12i, such gene-silencing of alfalfa resulted in nutrient profiles that might be suitable for grazing. To extend the nutritional evaluation of transformed alfalfa, ground samples of 11 HB12i, 5 TT8i and 4 wild type (WT) were incubated in rumen fluid : buffer solution for 0, 2, 4, 8, 12, 24 and 48 h at 39 °C. Dry matter (DM) and neutral detergent fiber (NDF) degradations at each time point, and production of volatile fatty acids (VFA) at 4, 12, 24 and 48 h were analyzed, as well as degradation and production kinetics. The correlations and regressions between nutritive profiles and attenuated total reflection Fourier transform infrared (ATR-FTIR) spectral parameters were determined. RESULTS: Both transformed genotypes had lower DM degradation and HB12i had lower VFA production compared with WT. Structural carbohydrate (STC) parameters were found to be negatively correlated with DM degradation and VFA production. The kinetics of DM degradation and VFA production were predicted from spectral parameters with good estimation power. CONCLUSION: Silencing of HB12 and TT8 affected fermentation characteristics of alfalfa and some fermentation characteristics were predictable from spectral parameters using ATR-FTIR spectroscopy. © 2019 Society of Chemical Industry.
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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".