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Record W2160872664 · doi:10.3389/fpls.2012.00144

The beginnings of crop phosphoproteomics: exploring early warning systems of stress

2012· article· en· W2160872664 on OpenAlexafffund
Christof Rampitsch, Natalia V. Bykova

Bibliographic record

VenueFrontiers in Plant Science · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhotosynthetic Processes and Mechanisms
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsAbiotic stressPhosphoproteomicsAbiotic componentBiotechnologyBiologyProteomicsBiotic stressCropComputational biologyEcologyCell biologyGeneticsProtein phosphorylationPhosphorylationProtein kinase A

Abstract

fetched live from OpenAlex

This review examines why a knowledge of plant protein phosphorylation events is important in devising strategies to protect crops from both biotic and abiotic stresses, and why proteomics should be included when studying stress pathways. Most of the achievements in elucidating phospho-signaling pathways in biotic and abiotic stress are reported from model systems: while these are discussed, this review attempts mainly to focus on work done with crops, with examples of achievements reported from rice, maize, wheat, grape, Brassica, tomato, and soy bean after cold acclimation, hormonal and oxidative hydrogen peroxide treatment, salt stress, mechanical wounding, or pathogen challenge. The challenges that remain to transfer this information into a format that can be used to protect crops against biotic and abiotic stresses are enormous. The tremendous increase in the speed and ease of DNA sequencing is poised to reveal the whole genomes of many crop species in the near future, which will facilitate phosphoproteomics and phosphogenomics research.

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)0.001
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 score0.029
Threshold uncertainty score0.229

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.013
GPT teacher head0.215
Teacher spread0.202 · 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

Citations47
Published2012
Admission routes2
Has abstractyes

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