Extractions of High Quality RNA from the Seeds of Jerusalem Artichoke and Other Plant Species with High Levels of Starch and Lipid
Bibliographic record
Abstract
Jerusalem artichoke (Helianthus tuberosus L.) is an important tuber crop. However, Jerusalem artichoke seeds contain high levels of starch and lipid, making the extraction of high-quality RNA extremely difficult and the gene expression analysis challenging. This study was aimed to improve existing methods for extracting total RNA from Jerusalem artichoke dry seeds and to assess the applicability of the improved method in other plant species. Five RNA extraction methods were evaluated on Jerusalem artichoke seeds and two were modified. One modified method with the significant improvement was applied to assay seeds of diverse Jerusalem artichoke accessions, sunflower, rice, maize, peanut and marigold. The effectiveness of the improved method to extract total RNA from seeds was assessed using qPCR analysis of four selected genes. The improved method of Ma and Yang (2011) yielded a maximum RNA solubility and removed most interfering substances. The improved protocol generated 29 to 41 µg RNA/30 mg fresh weight. An A260/A280 ratio of 1.79 to 2.22 showed their RNA purity. Extracted RNA was effective for downstream applications such as first-stranded cDNA synthesis, cDNA cloning and qPCR. The improved method was also effective to extract total RNA from seeds of sunflower, rice, maize and peanut that are rich in polyphenols, lipids and polysaccharides.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".