Establishment and Optimization of cDNA-AFLP Reaction System for Rice Leaves
Bibliographic record
Abstract
In this paper,we analyzed the key factors affecting cDNA-AFLP,established cDNA-AFLP analysis system for rice by employing petioles with high resistance and high susceptibility to petiole spot in rice,and obtained a distinct electrophoretogram of cDNA-AFLP.The results indicated that RNA extracted by centrifugal columnar(RNeasy Plant Mini Kit,Qiagen) method had high purity and integrity;cDNA reversed by M-MLV RTase cDNA Synthesis kit(TAKARA)was digested with EcoRⅠenzyme under 37 ℃ for 2 h and MseⅠ enzyme under 65 ℃ for 2 h and linked under 4 ℃ for 13 h.Optimal results was obtained in following ways:an initial 35-cycle preamplification PCR by using 10-fold dilution of ligation products as template and the preamplification products diluted to 30 times for selective amplification in 20 μL reaction volume.By the separation of 6% PAGE gel and rapid silver staining,50 pairs of AFLP primer with high polymorphic bands were screened from 64 pairs of AFLP primer.This research provided insights into further study of heat-tolerant gene in rice.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".