Changes in Organic and Inorganic Osmolytes of Maize (Zea mays L.) by Sulfur Application Under Salt Stress Conditions
Bibliographic record
Abstract
Mineral nutrients have favourable potential in alleviation of salinity problem in plants. Sulfur has specific functions in regulating plant growth, metabolism, enzymatic reactions and osmolyte homeostasis in plants. Hence, an experiment was carried out to explore the role of sulfur in ameliorating salt toxicity in maize by changes in organic and inorganic osmolyte contents. A range of sulfur levels (40, 80 mM) were used to induce salinity tolerance in maize. Various treatments of salinity (25, 75 mM) were applied by using sodium chloride. Results revealed that glycine betaine, proline, total soluble sugars, total soluble proteins and total free amino acids contents were increased by applying salinity while the application of sulfur lowered the proline and increased other studied organic osmolyte contents in all studied maize organs (leaf, shoot, root). The maximum improvement in organic osmolyte contents were found at 40 mM sulfur, however, at 80 mM sulfur proline contents were reduced. Applied salinity increased leaf tissue concentration of Na+ and decreased that of K+, Ca2+, NO3-, PO43-, SO42- leading to a severely declined in K+/Na and Ca2+/Na+ ratio. However, application of sulfur reduced the Na+ contents and improved K+, Ca2+, NO3-, PO43-, SO42-, K+/Na+ and Ca2+/Na+ ratio in the salinity grown plants. Moreover, 40 mM level of sulfur was greatly effective in osmolyte homeostasis at all levels of salinity. This indicated that use of sulfur (40 mM) ameliorated the effect of salinity by changing organic and inorganic osmolyte contents in maize plants.
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.
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".