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Record W1974015550 · doi:10.1021/bi602627d

Regulation of Energy Dissipation in Photosystem I by the Redox State of the Plastoquinone Pool

2007· article· en· W1974015550 on OpenAlexafffund
David L. Joly, Robert Carpentier

Bibliographic record

VenueBiochemistry · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhotosynthetic Processes and Mechanisms
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPlastoquinoneChemistryPhotochemistryP700Photosystem IIQuenching (fluorescence)Photosystem IRedoxNon-photochemical quenchingThylakoidFluorescencePhotosynthesisChloroplastBiochemistryInorganic chemistry

Abstract

fetched live from OpenAlex

The effect of exogenous plastoquinone (PQ) on the different deexcitation pathways of photosystem I (PSI) was investigated. Addition of oxidized decyl-plastoquinone (dPQ) and PQ-2 strongly quenched the chlorophyll (Chl) emission spectra of PSI submembrane fractions over all wavelengths. This quenching increased with the concentration of exogenous PQ added and followed the modified Stern-Volmer law. The Stern-Volmer constants found for dPQ and PQ-2 were 1.25 x 10(6) M-1 and 0.55 x 10(6) M-1, respectively, and the fraction of fluorescence accessible to the quencher was 0.7 for both exogenous PQ. dPQ and PQ-2 also retarded the P700 photooxidation measured under limiting actinic light irradiances. Photoacoustic measurements showed that addition of dPQ increased the heat dissipation and decreased the photochemical capacity of PSI. From these results, exogenous oxidized PQ were shown to efficiently quench the Chl excited state in the PSI antenna and change the balance between Chl deexcitation pathways. Moreover, reduction of the endogenous PQ pool in whole thylakoid membranes by NADPH increased PSI fluorescence by 65%, indicating the importance of the redox state of the PQ pool on PSI energy dissipation.

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 score0.003
Threshold uncertainty score0.252

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 head0.003
GPT teacher head0.206
Teacher spread0.203 · 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

Citations22
Published2007
Admission routes2
Has abstractyes

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