Potassium Nitrate Priming Affects the Activity of Nitrate Reductase and Antioxidant Enzymes in Tomato Germination
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Priming has been used to improve the performance of germination at the field, and potassium nitrate (KNO3) is a promising compound for this purpose. The nitrate (NO3) could be absorbed, being used in the metabolism of the embryo, through the enzyme nitrate reductase (NR). Besides, the priming could also activate the response of the antioxidant system, becoming the primed seeds more prepared for possible stresses. Thus, the aim of this study was to evaluate the metabolic effect of nitrate in tomato seed germination by the quantification of NR activity, and also evaluate the activity of some antioxidant enzymes as superoxide dismutase (SOD), catalase (CAT) and ascorbate peroxidase (APX). Tomato seeds were primed using solutions of polyethylene glycol (PEG 6000) -1,1MPa, 50 mM KNO3 and PEG+KNO3. The variables analyzed were germination (germinability, mean germination time, mean germination rate, coefficient of variation of the germination time, uncertainty and synchrony) nitrogen, total proteins and enzymes. The germination data were analyzed using an ANOVA, comparing the averages by Scott-Knott test (P < 0.05). To analyze the nitrogen, protein and enzymatic activities, we used a Kruskal-Wallis test (P < 0.05). The results show an increase in the NR activity, as well as in the antioxidant enzymes. The germination time (t) and germination rate (v) primed in KNO3 had a better performance compared to the other treatments. In conclusion, the observed benefits in tomato seeds primed with KNO3 were related to the activity of the enzyme nitrate reductase in the production of nitrite/nitric oxide, which acted promoting a faster germination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it