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Record W2055054814 · doi:10.4161/psb.3.10.5877

A model for the 26S proteasome and ribosome actions in leaf polarity formation

2008· article· en· W2055054814 on OpenAlexaff
Qihua Ling, Yao Yao, Hai Huang

Bibliographic record

VenuePlant Signaling & Behavior · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Molecular Biology Research
Canadian institutionsInstitute for Biological Sciences
FundersChinese Academy of Sciences
KeywordsBiologyRibosomal proteinProteasomeRibosomeGeneMutantRibosome biogenesisRibosomal RNAPhenotypePrimordiumCell biologyProtein subunitTranslation (biology)GeneticsMutationArabidopsisRNAMessenger RNA

Abstract

fetched live from OpenAlex

Leaf morphogenesis requires the establishment of adaxial-abaxial polarity in emerging leaf primordia, and a number of genes participating in this process have been identified in recent years. We previously reported that the 26S proteasome is important in specifying the leaf adaxial fate. More recently, two papers from separate researches showed that several genes encoding ribosomal large subunit proteins also play an important role in leaf adaxial-abaxial patterning. Here we show that plants with a single mutation in the genes encoding either 26S proteasome subunits or ribosomal proteins shared similar abnormalities in some leaves, with an outgrowth formed on the distal part of the leaf abaxial side. Plants harboring these 26S proteasome or ribosome mutations in combination with an additional mutation asymmetric leaves1 or 2 (as1 or as2) demonstrated severely defective leaves, and the phenotypes of these double mutants were very similar. Because activities of the 26S proteasome and ribosome both affect the level of functional proteins, the recent findings suggest that a previously unrecognized regulation, the protein level regulation, is critical in normal leaf patterning. A regulatory model for the 26S proteasome and ribosome actions in leaf patterning is discussed.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.910
Threshold uncertainty score0.314

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 head0.136
GPT teacher head0.289
Teacher spread0.153 · 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

Citations1
Published2008
Admission routes1
Has abstractyes

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