Phytohormonal Roles in Plant Responses to Heavy Metal Stress: Implications for Using Macrophytes in Phytoremediation of Aquatic Ecosystems
Bibliographic record
Abstract
Abstract Heavy metals can represent a threat to the health of aquatic ecosystems. Unlike organic chemicals, heavy metals cannot be eliminated by natural processes such as their degradation into less toxic compounds, and this creates unique challenges for their remediation from soil, water, and air. Phytoremediation, defined as the use of plants for the removal of environmental contaminants, has many benefits compared to other pollution-reducing methods. Phytoremediation is simple, efficient, cost-effective, and environmentally friendly because it can be carried out at the polluted site, which simplifies logistics and minimizes exposure to humans and wildlife. Macrophytes represent a unique tool to remediate diverse environmental media because they can accumulate heavy metals from contaminated sediment via roots, from water via submerged leaves, and from air via emergent shoots. In this review, a synopsis is presented about how plants, especially macrophytes, respond to heavy metal stress; and we propose potential roles that phytohormones can play in the alleviation of metal toxicity in the aquatic environment. We focus on the uptake, translocation, and accumulation mechanisms of heavy metals in organs of macrophytes and give examples of how phytohormones interact with plant defense systems under heavy metal exposure. We advocate for a more in-depth understanding of these processes to inform more effective metal remediation techniques from metal-polluted water bodies. Environ Toxicol Chem 2021;40:7–22. © 2020 SETAC Abstract Heavy metal absorption, translocation, and accumulation within organs of aquatic plants and the changes in phytohormone signaling in response to heavy metal exposure. ABA = abscisic acid; BR = brassinosteroid; CK = cytokinin; ETH = ethylene; GA = gibberellin; JA = jasmonic acid; PA = polyamine; SA = salicylic acid.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".