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Record W2360009226

Effect of zinc on proline metabolism under iron stress in wheat seedlings

2014· article· en· W2360009226 on OpenAlexaff
Yang Ying-l

Bibliographic record

VenueJournal of Northwest Normal University · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Micronutrient Interactions and Effects
Canadian institutionsScience North
Fundersnot available
KeywordsProlineProline dehydrogenaseChemistryEnzyme assayMetabolismEnzymeZincHorticultureBiochemistryFood scienceAmino acidBiology
DOInot available

Abstract

fetched live from OpenAlex

The effects of different ZnCl2concentrations(50,250μmol·L-1 and 2 mmol·L-1)on proline content and the activities of proline related to metabolism enzymes are investigated in Xi Han wheat seedlings when exposed to 300μmol·L-1 FeCl3.The results show that proline content and ornithineδ-aminotransferase(OAT)activity due to single Fe stress significantly elevate as compared with the control,but both proline dehydrogenase(PDH)and glutamate kinase(GK)activities decrease in wheat roots and leaves under Fe treatment.The application of different Zn concentrations results in reduction of proline accumulation as well as inhibition of OAT and GK activities in roots and leaves of Fe-treated wheat;50or250μmol·L-1 Zn together with Fe induce an increase in PDH activity as compared with the single Fetreated wheat roots and leaves,while this enzyme in wheat leaves is inhibited in response to 2mmol·L-1Zn combined with Fe treatment.In conclusion,these results indicate that the elevation of proline content induced by Fe stress is correlated with the increase of OAT activity and the decrease of PDH activity in wheat roots and leaves;Zn applied leads to the decrease of proline content under Fe stress,which may be associated with the decrease of OAT activity and the elevation of PDH activity in wheat seedlings under Zn in combination with Fe treatment.

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)0.000
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 score0.314
Threshold uncertainty score0.156

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.005
GPT teacher head0.186
Teacher spread0.181 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

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