In Situ Distribution and Speciation of Toxic Copper, Nickel, and Zinc in Hydrated Roots of Cowpea 1(W)(OA)
Bibliographic record
Abstract
The phytotoxicity of trace metals is of global concern due to contamination of the landscape by human activities. Using synchrotron-based x-ray fluorescence microscopy and x-ray absorption spectroscopy, the distribution and speciation of copper (Cu), nickel (Ni), and zinc (Zn) was examined in situ using hydrated roots of cowpea (Vigna unguiculata) exposed to 1.5 mM Cu, 5 mM Ni, or 40 mM Zn for 1 to 24 h. After 24 h of exposure, most Cu was bound to polygalacturonic acid of the rhizodermis and outer cortex, suggesting that binding of Cu to walls of cells in the rhizodermis possibly contributes to the toxic effects of Cu. When exposed to Zn, cortical concentrations remained comparatively low with much of the Zn accumulating in the meristematic region and moving into the stele; approximately 60% to 85% of the total Zn stored as Zn phytate within 3 h of exposure. While Ni concentrations were high in both the cortex and meristem, concentrations in the stele were comparatively low. To our knowledge, this is the first report of the in situ distribution and speciation of Cu, Ni, and Zn in hydrated (and fresh) plant tissues, providing valuable information on the potential mechanisms by which they are toxic. Trace metals are natural components of the environment, but elevated and potentially toxic levels sometimes occur. The toxicity of trace metals to plants is an important environmental and economic problem. The release of copper (Cu), nickel (Ni), or zinc (Zn) from anthropogenic sources (such as mining and refining, fungicide and manure use, and the disposal of biosolids) is of concern due to their potentially detrimental effects on the environment. It is estimated that hundreds of thousands of sites are contaminated globally (Canadian Environmental Industries, 2005). The mechanisms by which trace metals are toxic to plants remain unclear, despite their toxic effects having been researched for .100 years in the case of
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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 | 0.000 | 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".