Selection of reference genes for RT-qPCR analysis of rice with Rhizoctonia solani infection and PGPR/KSi application
Bibliographic record
Abstract
Abstract Background: Rhizoctonia solani AG1 IA is an important pathogen of rice (Oryza sativa L.) that causes rice sheath blight (RSB). Since control of RSB by conventional measures has failed, novel strategies like application of plant growth-promoting rhizobacteria (PGPR) can be an efficient alternative. Method and Results: mRNA sequences of rice were retrieving from NCBI for candidate reference genes selction, and seven candidate reference genes (RGs), namely 18SrRNA, ACT1, GAPDH2, UBC5, RPS27, eIF4aand CYP28, were selected for their stability in real-time quantitative PCR (RT-qPCR). Different algorithms were exploited, Delta Ct, geNorm, NormFinder, BestKeeper, and Comprehensive ranking by RefFinder, to evaluate RT-qPCR of rice in tissues infected with R. solani and treated with the PGPR strains, Pseudomonas saponiphilia and Pseudomonas protegens, and potassium silicate (KSi) alone or in combination with each PGPR strain. RGs stability was affected by each treatment and treatment-specific selection was approved and validated for nonexpressor of PR-1(NPR1) for each treatment. Conclusion: Overall, ACT1 was the most stable RG with R. solani infection alone, GAPDH2 with R. solani infection plus KSi, UBC5 with R. solani infection plus P. saponiphilia, and eIF4a with R. solani infection plus P. protegens. Both ACT1 and RPS27 were the most stable with the combination of KSi and P. saponiphilia, while PRS27 was the most stable with the combination of KSi and P. protegens
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".