Understanding thermal sensitivity at the molecular level and developing temperature-based systems using RNA Thermometers
Bibliographic record
Abstract
PURPOSE: Temperature sensitivity is found in all multicelleular organisms, as well as in most primitive life forms. The ubiquity of this temperature sensitivity is an indicator of its effects at the multicellular, cellular and molecular levels [1]. Previous studies have shown that temperature-based regulation is present in the transcriptional process [2]. RNA Thermometers, temperature-sensitive sequences, have been shown to act on heat-shock genes to regulate temperature-dependant systems in many organisms [3,4]. The goal of this study was to characterize the shifts in the functioning of these RNA Thermometers at various temperatures. In addition, using the principle of transcriptional thermoregulation, an automated temperature-responsive system stimulating inverse endothermic and exothermic enzymatic reactions for heat stabilization was proposed. METHODS: The endothermic enzymatic reaction was designated as the breakdown of urea, reflecting the function of urease, and the exothermic reaction was designated as the breakdown of hydrogen peroxide, reflecting the function of catalase [5]. RESULTS: The proposed system was built upon the translation of urease and the inhibition of catalase translation at higher temperatures, and the inverse at lower temperatures. As RNA Thermometers can be used only to drive transcription at higher temperatures, the installation of a lac-regulated 2-way system was suggested. This system would also provide a synthetic solution to thermoregulation and the current systems employed today. This system could be applied where the current thermoregulatory systems prove insufficient and could be further developed and optimized to replace them in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".