Measurements of Antibacterial Activity of Seed Crude Extracts in Cultivated Rice and Wild Oryza Species
Bibliographic record
Abstract
Seeds are continuously exposed to a wide variety of microorganisms in the soil. In addition, seeds contain large amounts of carbon and nitrogen sources that support initial growth after germination. Thus, seeds in the soil can easily promote microbial growth, and seeds are susceptible to decay. Therefore, seed defense against microorganisms is important for plant survival. Seed-microbe interactions are also important issues from the perspective of food production, in seed quality and shelf life. However, seed-microbe interactions remain largely unexplored. In this study, we established a simple and rapid assay system for the antibacterial activity of rice seed crude extracts by colorimetric quantification methods by the reduction of tetrazolium compound. Using this experimental system, the diversity of effects of rice seed extracts on microbial growth was analyzed using Escherichia coli as a bacterial model. We used collections of cultivated rice, comprising 50 accessions of Japanese landraces, 52 accessions of world rice core collections, and of 30 wild Oryza accessions. Furthermore, we attempted to find genetic factors responsible for the diversity by genome-wide association analysis. Our results demonstrate that this experimental system can easily analyze the effects of seed extracts on bacterial growth. It also suggests that there are various compounds in rice seeds that affect microbial growth. Overall, this experimental system can be used to clarify the chemical entities and genetic control of seed-microbe interactions and will open the door for understanding the diverse seed-microbe interactions through metabolites.
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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) | 0.000 | 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".