Spatial distribution of isoflavones and isoflavone-related gene expression in high- and low-isoflavone soybean cultivars
Bibliographic record
Abstract
Chen, H., Seguin, P., Jabaji, S. and Liu, W. 2011. Spatial distribution of isoflavones and isoflavone-related gene expression in high and low isoflavone soybean cultivars. Can. J. Plant Sci. 91: 697–705. Soybean contains high concentrations of isoflavones that have putative health-beneficial effects. A study was conducted to document the spatial distribution of isoflavones and the expression of 14 key genes and gene homologs encoding enzymes involved in isoflavone synthesis using quantitative reverse transcription (QRT)-PCR. Genes studied included phenylalanine ammonia lyase (PAL), chalcone synthase (CHS), chalcone isomerase (CHI), chalcone reductase (CHR), and isoflavone synthase (IFS). Two cultivars were used, AC Proteina and AC Orford, high- and low-isoflavone cultivars, respectively. Plants were harvested at the seed filling stage (R5) and were separated into leaf, stem, root, flower, pod, and seed parts. The present study revealed that at the R5 stage the expression of 14 key genes and gene homologs involved in isoflavone synthesis is observed in all plant parts, being, however, minimal in pods and seeds and greatest in leaves and roots. Gene expression data parallel isoflavone concentrations, which are also minimal in pods and seeds. In most cases, expression of all homologs of a given gene family was comparable in specific parts, the expression of homologs not being part-specific. Finally, gene expression differences between cultivars also paralleled differences observed in their isoflavone concentrations.
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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) | 0.000 | 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".