Abstract 2348 Deployment of Quantitative Proteomics Approaches in Defining Dynamic Changes the Diel Plant Proteome
Bibliographic record
Abstract
Plants carefully regulate their daily cellular and physiological processes through a combination of circadian (anticipatory) and light-responsive (reactive) mechanisms in order to adapt to their changing daily environment and to maximize growth. To date, our understanding of diel plant cell regulation has largely been driven by a combination of genetic and transcriptomic approaches, where substantial portions of protein encoding genes have been found to possess diel fluctuations in abundance, including 100's of nuclear encoded, chloroplast-targeted proteins involved in diverse biological processes. The advancements made in quantitative proteomic technologies and workflows over the past 10 years has created an exciting new frontier of discovery that now allows us to define the diel plant proteome directly. However, substantial challenges remain when considering the assessment of dynamic temporal (e.g. daily) and/or spatial (e.g. organelle) changes. Here, we discuss our efforts to overcome the challenges that exist when endeavoring to assess dynamic changes in the chloroplast proteome, and further explore quantitative proteomic approaches as a means to help us better understand how the circadian clock intersects with chloroplast cell signaling and metabolic processes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".