Seed Viability and RNA Extraction Methods of Erythrina velutina Under Drought Stress
Bibliographic record
Abstract
Abiotic stress caused by rigorous environmental conditions, such as drought, has negative effects on seeds. The species from arid and semiarid areas have mechanisms to maintain seed viability. In this study, the analyses of physical and physiological quality of the seeds of Erythrina velutina and their response to drought stress and RNA extraction methods were carried out. The seeds were collected from mother trees in two different provenances of natural occurrences of the species. The physical quality, viability, and vigor of the seeds were evaluated by morphometric parameters, germination, and electrical conductivity tests. The seeds were submitted to drought stress conditions for 0, 24, 48, and 72 hours on -0.8 MPa polyethylene glycol solution. To evaluate the viable RNA extraction method, the seed embryos were used for the extraction of this molecule and with an assessment of quantity and quality. The seeds from Pernambuco that were harvested in 2008 were bigger and presented lower vigor when in comparison to seeds that were harvested from Sergipe in 2012. The seeds under drought stress conditions presented null germination for all the treatments. After the drought stress, they were germinated on a paper substrate moistened with water, with better responses from the Lot Sergipe, 2012. The imbibition of seeds in the solution for 24 hours provides an increase in germination for less vigorous seeds, being indicated as a pre-germinative treatment. The RNA quality obtained by using a commercial extraction kit has better results, providing materials without contaminants when compared to the TRIzol method.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".