Nuclei Release Methods Comparison for Fresh Leaves of Rice (Oryza sativa) for Efficient High Throughput Flow Cytometry Ploidy Studies
Bibliographic record
Abstract
Flow cytometry trituration methods and the efficiency of isolation buffer solutions are compared in this study for extraction of nuclei from fresh leaves of rice. The razor blade sample trituration procedure has been widely used to release nuclei from tissues in many plant species, and combined with different isolation buffers for low throughput analysis. In contrast, the bead beating trituration method has rarely been used for DNA ploidy determination, despite it being proposed as a less tedious alternative procedure to prepare nuclear suspensions. In this study, bead beating was assessed and compared with the traditional chopping procedure. Each trituration method was combined with one of three nuclear isolation buffers (i.e. Hanson’s, Otto’s and LB01 buffer). Bead beating was applied for the first time using all three of the buffers, resulting in a rapid and effective procedure for ploidy determination in fresh rice leaves. In addition, bead beating saved, while reducing the exposure of the user to harmful substances. The best results were obtained when Hanson’s nuclear isolation buffer was combined with the bead beating trituration method.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".