Effects of nickel toxicity on expression of genes associated with nickel resistance in white spruce (Picea glauca): Nickel translocation in plant tissues.
Bibliographic record
Abstract
The main objectives of this study were to determine a) nickel accumulation and \ntranslocation within tissues and b) nickel effects on gene expression in white spruce (Picea \nglauca). The results of the study revealed that even at the highest dose of 1,600 mg of Ni /kg of \nsoil, there was no physical evidence of toxicity to P. glauca seedlings screened. P. glauca was \nfound to be a nickel avoider, as the bioaccumulation factor as well as translocation factors for \nroots to aerial tissues were less than 1.0. Expression of SAT, GR, ACC, NAS, Nramp, and \nAT2G16800 genes in roots and needles were investigated. Expression of ACC and NRAMP were \nupregulated in the presence of nickel, whereas GR was downregulated at the lowest dose (150 \nmg/kg) and upregulated at the highest dose (1,600 mg/kg). There were significant differences \nbetween ACC expression in roots and needles. The results of the present study also show that \nthat potassium nitrate (a common plant fertilizer) does have an effect on gene expression and can \nlead to toxicity in P. glauca plants at high concentrations. Overall, the findings of this research \nsuggest that the low level of bioavailable nickel in mining sites in Northern Ontario and other \nmining regions can trigger changes in gene expression.
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) | 0.001 | 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".