Isolation and Purification of Functional Total RNA from Woody Branches and Needles of Sitka and White Spruce
Bibliographic record
Abstract
The isolation of intact, functional RNA from conifer spp. is not easy, especially from those tissues that are heavily lignified and characterized by a low number of living cells. An efficient procedure for isolating RNA from combined wood and bark tissues of conifers was developed based on a protocol optimized for the extraction of RNA from pollen and one for the isolation of RNA from woody stems. This protocol does not involve the use of phenol, and no ultracentrifugation was required. In addition, the protocol overcame the problems of RNA degradation and low yield due to oxidation by polyphenolics and co-precipitation with polysaccharides, both of which are abundant components in conifer bark tissues. The isolated RNA was of high quality and undegraded as gauged by spectrophotometric readings and electrophoresis in denaturing agarose gels. Quality was further assessed through the subsequent use of the RNA in reverse transcription and RT-PCR, indicating that it could be used for a number of downstream purposes including Northern blot hybridization and cDNA library construction. Using this modified protocol, 80-150 micrograms of RNA was routinely obtained from 1 g of fresh material. This protocol was also used for the isolation of RNA from needles of spruce spp., from which 750-950 micrograms RNA per gram of starting material could routinely be obtained.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".