Remodeling of root morphology by CuO and ZnO nanoparticles: effects on drought tolerance for plants colonized by a beneficial pseudomonad
Bibliographic record
Abstract
Formulations that include nanoparticles of CuO and ZnO are being considered for agricultural applications as fertilizers because they act as sources of Cu or Zn. Currently, few studies of the effects of these nanoparticles (NPs) consider the three-way interactions of NPs with the plant plus its microbiome. At doses that produced root shortening by both nanoparticles (NPs), CuO NPs induced the proliferation of elongated root hairs close to the root tip, and ZnO NPs increased lateral root formation in wheat seedlings (Triticum aestivum L.). These responses occurred with roots colonized by a beneficial bacterium, Pseudomonas chlororaphis O6 (PcO6), originally isolated from roots of wheat grown under dryland farming in calcareous soils. The PcO6-induced tolerance to drought stress in wheat seedlings was not impaired by the NPs. Rather, growth of the PcO6-colonized plants with NPs resulted in systemic increases in the expression of genes associated with tolerance to water stress. Increased expression in the shoots of other genes related to metal stress was consistent with higher levels of Cu and Zn in PcO6-colonized shoots grown with the NPs. This work demonstrates that plants grown with CuO or ZnO NPs showed cross-protection from different challenges such as metal stress and drought.
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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) | 0.001 | 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".