Rapid identification of <i>Amaranthus caudatus</i> and <i>Amaranthus hypochondriacus</i> by sequencing and PCR–RFLP analysis of two starch synthase genes
Bibliographic record
Abstract
The objective of this study was to develop a PCR-RFLP method to identity the cultivated species of grain amaranth based on variations in the sequences of their starch synthase genes. We sequenced the SSSI and GBSSI loci in 126 accessions of cultivated grain amaranth collected from diverse locations around the world. We aligned the gene sequences and searched for restriction enzyme cleavage sites specific to each species for use in the PCR-RFLP analysis. Our analyses indicated that EcoRI would recognize the sequence 5'-GAATT/C-3' in the SSSI gene from Amaranthus caudatus L., and TaqI would recognize the sequence 5'-T/CGA-3' in the GBSSI gene from Amaranthus hypochondriacus L. The PCR products obtained using gene-specific primers were 423 bp (SSSI) and 627 or 635 bp (GBSSI) in length. These products were cut with different restriction enzymes resulting in species-specific RFLP patterns that could be used to distinguish among the cultivated grain amaranths. The results clearly showed that A. caudatus and A. hypochondriacus were easily differentiated at the species level using this method. Therefore, the PCR-RFLP method targeting amaranth starch synthase genes is simple and rapid, and it will be a useful tool for the identification of cultivated species of grain amaranth.
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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) | 0.000 | 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) | 0.001 | 0.000 |
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".