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Record W1991482730 · doi:10.3389/fpls.2015.00156

Cd and Ni transport and accumulation in the halophyte Sesuvium portulacastrum: implication of organic acids in these processes

2015· article· en· W1991482730 on OpenAlexaff
Mejda Mnasri, Rim Ghabriche, Emna Fourati, Hanen Zaier, Kebba Sabally, Suzelle Barrington, Stanley Lutts, Chédly Abdelly, Tahar Ghnaya

Bibliographic record

VenueFrontiers in Plant Science · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsMcGill University
Fundersnot available
KeywordsXylemHalophyteShootChemistryMalic acidAscorbic acidCitric acidCadmiumBotanyHorticultureFood scienceBiologySalinity

Abstract

fetched live from OpenAlex

The implication of organic acids in Cd and Ni translocation was studied in the halophyte species Sesuvium portulacastrum. Citric, fumaric, malic, and ascorbic acids were separated and quantified by HPLC technique in shoots, roots and xylem saps of plants grown on nutrient solutions added with 50 μM Cd, 100 μM Ni and the combination of 50 μM Cd + 100 μM Ni. Results showed that Cd had no significant impact on biomass production while Ni and the combination of both metals drastically affected plant development. Cadmium and Ni concentrations in tissues and xylem sap were higher in plants subjected to individual metal application than those subjected to the combined effect of Cd and Ni suggesting a possible competition between these metals for absorption. Both metals applied separately or in combination induced an increase in citrate concentration in shoots and xylem sap but a decrease of this concentration in the roots. However, a minor relationship was observed between metal application and fumaric, malic, and ascorbic acids. Both observations suggest the implication of citric acid in Cd, Ni translocation and shoot accumulation in S. portulacastrum. The relatively high accumulation of citric acid in xylem sap and shoot of S. portulacastrum could be involved in metal chelation and thus contributes to heavy metal tolerance in this species.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.249
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations78
Published2015
Admission routes1
Has abstractyes

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