Perception of Bradyrhizobium japonicum Nod factor by soybean [Glycine max (L.) Merr.] root hairs under abiotic stress conditions
Bibliographic record
Abstract
Suboptimal growth conditions, such as low rhizosphere temperature, high salinity, and low pH can negatively affect the rhizobia-legume symbioses, resulting in poor nodulation and lower amounts of nitrogen fixed. Early stages of the Bradyrhizobium japonicum-soybean [Glycine max (L.) Merr.] symbiosis, such as excretion of genistein (the plant-to-bacteria signal) and infection initiation can be inhibited by abiotic stresses; however, the effect on early events modulated by Nod factors (bacteria-to-plant signalling), particularly root hair deformations is unknown. Thus, the objective of this study was to evaluate the perception of Nod factor by soybean root hairs under three stress conditions: low temperature, low pH, and high salinity. Three experiments were conducted using a 1:1 ratio of Nod Bj-V (C(18:1), MeFuc) and Nod Bj-V (Ac, C(16:0), MeFuc). Nod factor induced four types of root hair deformation (HAD), wiggling, bulging, curling, and branching. Under optimal experimental conditions root hair response to the three levels of Nod factor tested (10(-6), 10(-8), and 10(-10) M) was dose-dependent. The highest frequency of root hair deformations was elicited by the 10(-6) M level. Root hair deformation decreased with temperature (25, 17, and 15 degrees C), low pH, and high salinity. Nod factor concentration did not interact with either low temperature or pH. However, salinity strongly inhibited HAD responses to increases in Nod factor concentration. Thus, the addition of higher levels of Nod factor is able to overcome the effects of low pH and temperature stress, but not salinity.
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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".