Rehydration conditions for isolation of high quality RNA from the lichen Lobaria pulmonaria
Bibliographic record
Abstract
BACKGROUND: The poikilohydric nature of lichens enables them to survive repeated episodes of desiccation by utilizing water when it becomes available. During rehydration, RNA-degrading endonucleases may be released, reducing RNA quantity and quality. Re-generation of a steady-state condition where RNA quantity and quality no longer fluctuate establishes a framework for development of new hypotheses for future investigations. Using Lobaria pulmonaria as a model species, the objective of this study was to compare the effect of different rehydration conditions on the quantity and quality of RNA from the rehydrated thallus. FINDINGS: Spectrophotometric measurements of total RNA and cDNA were performed for samples prepared from dry lichen or lichen after rehydration (0.5 h, 1 h, 2 h, 4 h or 24 h), with limited light and dark conditions, and at three temperatures (15°C, 20°C or 32°C) for some of these conditions. The results showed that rehydration of the thallus for 4 h at 20°C in light yielded the highest concentration and quality of RNA. A higher RNA concentration was obtained in light than in dark conditions, but the RNA quality was unaffected. CONCLUSIONS: This study suggests that allowance of 4 h for thallus rehydration should be adequate to ensure complete recovery of transcription. After 4 h at 20°C further studies can be carried out on the RNA in this model species.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".