Phytohormonal Roles in Plant Responses to Heavy Metal Stress: Implications for Using Macrophytes in Phytoremediation of Aquatic Ecosystems
Why this work is in the frame
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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.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it