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The Intracellular Distribution of Folate Derivatives in Pea Leaves

2003· article· en· W4244902085 on OpenAlexafffund
Sherwin Y. Chan, Edwin A. Cossins

Bibliographic record

VenuePteridines · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Micronutrient Interactions and Effects
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsChloroplastBiochemistryPisumPhotorespirationGlycineSerineBiologyMetabolismChemistryAmino acidPhotosynthesisEnzyme

Abstract

fetched live from OpenAlex

Abstract After hydrolysis of polyglutamate derivatives, leaf extracts of pea (Pisum sativum L.) seedlings were examined for individual folates using high Performance liquid chromatography and microbiological assays employing Lactobacillus rhamnosus and Pediococcus acidilactici. 14-day seedlings contained 0.17±0.01 nmol folate mg -1 protein that was predominantly methylated and associated with the cytosolic fraction. Percoll gradient-purified mitochondria and chloroplasts contained 11.0±2.3% and 8.4±0.6% of cellular folate, respectively. Mitochondrial folates (0.47±0.11 nmol folate mg -1 protein) were predominately (90%) formylated and unsubstituted. In contrast, chloroplasts contained a less concentrated (0.02±0.001 nmol folate mg -1 protein) folate pool consisting of approximately 30% methylated folate. The total folate content of pea leaves increased by 40% when dark-grown (etiolated) 9-day seedlings were exposed to light for 48 hours. During this light treatment, the mitochondrial folate pool, on a per mg protein basis, increased 10-fold. This light treatment also changed the composition of the mitochondrial folate pool with a shift occurring from methylated to formylated and unsubstituted derivatives. These changes in the folates of greening tissues are discussed in relation to the metabolism of glycine and serine that accompanies photorespiration in leaf tissues.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.340
Threshold uncertainty score0.070

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 head0.012
GPT teacher head0.205
Teacher spread0.193 · 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 designBench or experimental
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

Citations5
Published2003
Admission routes2
Has abstractyes

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