Bacillin 20, a bacterial derived compound, improves soybean growth, photosynthesis and nutrients content under drought stress conditions
Bibliographic record
Abstract
Water scarcity is a global challenge with profound implications, particularly for agriculture, where it undermines crop production by diminishing yields and heightening vulnerability to environmental stresses. This study investigates the impact of Bacillin 20, a derivative of Bacillus thuringiensis, on soybean plant physiology under drought stress, focusing on growth dynamics, photosynthetic activity, and nutrient assimilation. The experimentation was carried out using a factorial structure within a completely randomized design and four replications. Factors included drought levels (control, -0.75 MPa and -1.5 MPa) and Bacillin 20 concentrations (0, 10-11 M and 10-9 M). Results indicated that drought stress significantly reduced plant height, leaf area, shoot dry weight, photosynthetic rate, stomatal conductance, transpiration, substomatal CO2 concentration, nodulation, and root length and volume. Bacillin 20 application had mixed effects, with no significant impact on plant height but increasing leaf area, enhancing shoot dry weight under moderate drought, and improving photosynthetic rate. The interaction between drought and Bacillin 20 was significant, particularly in terms of shoot dry weight and photosynthetic rate. Additionally, Bacillin 20 at 10-11 M increased root tips by 12.6% and shoot dry weight by 28%; it increased nodule number by 51% only under normal moisture conditions, and decreased it under drought stress. Drought increased leaf N, Mg, Zn, Fe, Mn, and B contents, while Bacillin raised leaf N at -0.75 MPa and decreased Zn and Mn under severe drought (-1.5 MPa). The increased plant N and decreased nodulation under drought suggest enhanced nodule efficiency. Bacillin 20 did not affect P, K, Ca, and S contents, which were influenced solely by drought.
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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".