Bibliographic record
Abstract
Osmotic stress tolerance mechanisms determine whether bacteria survive or grow because osmotic stress profoundly affects the structure, physics, and chemistry of bacterial cells. In vitro studies have shown that K+ glutamate differentially modulates transcription mediated by the σ70 and σS RNA polymerases of Escherichia coli, the latter being central to many stress response. Progress toward understanding the structural changes associated with the opening of representative channels is discussed in this chapter. The study of osmoregulatory proteins is motivated partly by a desire to understand how cells sense osmotic pressure (osmosensing). During the last decade, representative osmoregulatory transporters and mechanosensitive (MS) channels have been shown to both sense osmotic pressure changes (osmosensing) and respond by modulating transmembrane solute distribution (osmoregulation) after purification and reconstitution in proteoliposomes. The osmoregulation of protein activity is discussed by focusing on representative proteins that have been studied. In many bacteria, the proportion of anionic phospholipids increases and the fatty acid composition changes with cultivation at high salinity. For E. coli, growth at high osmolality increases the proportion of CL at the expense of PE without changing the proportion of PG or the fatty acid composition. Interest in the osmoregulation of transcription was stimulated by a desire to understand how osmolality can direct gene expression. Studies focused on promoter identification and the identification of transcriptional regulatory proteins were complicated by multiple factors, including transcription that depends on multiple promoters and σ factors.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.027 |
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".