Bibliographic record
Abstract
In all forms of life, the amount of cellular protein present at any given time is tightly regulated by a combination of mechanisms of gene expression, namely transcription, mRNA degradation, translation, and protein degradation. A variety of events can trigger changes in protein abundance throughout the cell cycle by affecting the different steps of gene expression. Although the mechanisms that regulate gene expression in prokaryotes and eukaryotes are well known, the way in which protein abundance is controlled at each regulatory step remains unclear. The quantitation of protein levels is essential to understand the different aspects of gene expression, while a solid knowledge of the mechanisms and causes that affect protein levels should enable the development of new technologies, whether for human needs or environmental care. In the past, considerable effort has been invested in quantifying protein abundances in different situations such as stress, disease progression, or changing environmental conditions. More recently, significant advances have been made in the methods used to quantify protein abundance on the genome scale, in specific cells at certain time points, and/or under different environmental conditions. In this chapter, the different mechanisms that control protein abundance variation in prokaryotes and eukaryotes – that is, the biochemical features of mRNAs and proteins, as well as external factors such as environmental changes, stress, diseases, or developmental stages – are reviewed. Details are also be provided of the most relevant methodologies for the qualitative and quantitative analyses of protein abundance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.014 |
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".