Effect of organic N source on bacterial growth, lipo-chitooligosaccharide production, and early soybean nodulation by <i>Bradyrhizobium japonicum</i>
Bibliographic record
Abstract
Production of Bradyrhizobium japonicum inoculants is problematic because high inoculation rates are necessary but expensive, while production of rhizobial Nod factors (lipo-chitooligosaccharides (LCOs)), key signal molecules in the establishment of legume-rhizobia symbioses, may be inhibited at high culture cell densities. We conducted experiments to determine the effects of growth medium N source on B. japonicum growth, LCO production, and early nodulation of soybean. We found that 1.57 mmol ammonium nitrate x L(-1) resulted in less rhizobial growth and rhizobial capacity to produce LCOs (on a per cell basis) than did 0.4 g yeast extract x L(-1), which contained the same amount of N as the ammonium nitrate. Increasing yeast extract to 0.8 g x L(-1) increased rhizobial growth and LCO production on a volume basis (per litre of culture) and did not affect cell capacity to produce LCOs; however, at 1.4 g yeast extract x L(-1) per cell, production was reduced. A mixture of 0.8 g yeast extract x L(-1) and 1.6 g casein hydrolysate x L(-1) resulted in the greatest bacterial growth and LCO production on a volume basis but reduced LCO production per cell. Changes in organic N level and source increased production of some of the measured LCOs more than others. LCO production was positively correlated with cell density when expressed on a volume basis; however, it was negatively correlated on a per cell basis. We conclude that although quorum sensing affected Nod factor production, increased levels of organic N, and specific compositions of organic N, increased LCO production on a volume basis. Greenhouse inoculation experiments showed that the medium did not modify nodule number and N fixation in soybean, suggesting that it could have utility in inoculant production.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".