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Record W4385476780 · doi:10.1111/nph.19171

Drought affects both photosystems in <i>Arabidopsis thaliana</i>

2023· article· en· W4385476780 on OpenAlexaff
Chen Hu, Eduard Elias, Wojciech J. Nawrocki, Roberta Croce

Bibliographic record

VenueNew Phytologist · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhotosynthetic Processes and Mechanisms
Canadian institutionsMcGill University
FundersNederlandse Organisatie voor Wetenschappelijk Onderzoek
KeywordsArabidopsis thalianaPhotosystemBiologyBotanyPhotosynthesisPhotosystem IIGeneGenetics

Abstract

fetched live from OpenAlex

Summary Drought is a major abiotic stress that impairs plant growth and development. Despite this, a comprehensive understanding of drought effects on the photosynthetic apparatus is lacking. In this work, we studied the consequences of 14‐d drought treatment on Arabidopsis thaliana . We used biochemical and spectroscopic methods to examine photosynthetic membrane composition and functionality. Drought led to the disassembly of PSII supercomplexes and the degradation of PSII core. The light‐harvesting complexes (LHCII) instead remain in the membrane but cannot act as an antenna for active PSII, thus representing a potential source of photodamage. This effect can also be observed during nonphotochemical quenching (NPQ) induction when even short pulses of saturating light can lead to photoinhibition. At a later stage, under severe drought stress, the PSI antenna size is also reduced and the PSI‐LHCI supercomplexes disassemble. Surprisingly, although we did not observe changes in the PSI core protein content, the functionality of PSI is severely affected, suggesting the accumulation of nonfunctional PSI complexes. We conclude that drought affects both photosystems, although at a different stage, and that the operative quantum efficiency of PSII (Φ PSII ) is very sensitive to drought and can thus be used as a parameter for early detection of drought stress.

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 score0.097
Threshold uncertainty score0.713

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 head0.016
GPT teacher head0.260
Teacher spread0.244 · 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

Citations100
Published2023
Admission routes1
Has abstractyes

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