Effect of common bean seed exudates on growth, lipopolysaccharide production, and lipopolysaccharide transport gene expression of <i>Rhizobium anhuiense</i>
Bibliographic record
Abstract
Lipopolysaccharide (LPS) is essential for successful nodulation during the symbiosis of rhizobia and legumes. However, the detailed mechanism of the LPS in this process has not yet been clearly elucidated. In this study, the effects of common bean seed exudates on the growth, lipopolysaccharide production, and lipopolysaccharide transport genes expression (lpt) of Rhizobium anhuiense were investigated. Rhizobium anhuiense exposed to exudates showed changes in LPS electrophoretic profiles and content, whereby the LPS band was wider and the LPS content was higher in R. anhuiense treated with seed exudates. Exudates enhanced cell growth of R. anhuiense in a concentration-dependent manner; R. anhuiense exposed to higher doses of the exudate showed faster growth. Seven lpt genes of R. anhuiense were amplified and sequenced. Sequences of six lpt genes, except for lptE, were the same as those found in previously analyzed R. anhuiense strains, while lptE shared low sequence similarity with other strains. Exposure to the exudates strongly stimulated the expression of all lpt genes. Approximately 6.7- (lptG) to 301-fold (lptE) increases in the transcriptional levels were observed after only 15 min of exposure to exudates. These results indicate that seed exudates affect the LPS by making the cell wall structure more conducive to symbiotic nodulation.
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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) | 0.000 | 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".