The concentrations and distributions of phytic acid phosphorus and other mineral nutrients in wild-type and <i>low phytic acid</i> Js-12-LPA wheat (<i>Triticum aestivum</i>) grain parts
Bibliographic record
Abstract
Concentrations of P, phytic acid (myo-inositol hexakisphosphate, IP 6 ), and other mineral storage elements were studied in wild-type and low phytic acid (lpa) genotype Js-12-LPA wheat (Triticum aestivum L.) embryos and rest-of-grain fractions. Environmental scanning electron microscopy images revealed a decreased average size and an increased number of aleurone layer globoids in lpa grains compared with the wild type. Energy-dispersive X-ray analyses of unfixed aleurone layer and scutellum cell cytoplasm revealed mainly C, O, P, K, and Mg in both grain types. The starchy endosperm contained virtually no P, K, or Mg, demonstrating no shift of mineral nutrients to that compartment. Scanning transmission electron microscopy – energy-dispersive X-ray analyses of scutellum and aleurone layer globoids in both genotypes revealed that P, K, and Mg were the main mineral nutrients in globoids with low amounts of Ca, Fe, and Zn. Traces of Mn were only in scutellum globoids. Total P was similar between genotypes for the rest-of-grain fractions, which are 97% of grain mass. The main inositol phosphate was IP 6 , but a small amount of IP 5 was present. Both lpa grain fractions exhibited major reductions in IP 6 compared with the wild type and a threefold increase in inorganic P. The concentration of K decreased in both fractions, while Ca increased 25% in the Js-12-LPA rest-of-grain compared with the wild type. The lack of large differences in mineral concentration and distribution between the wild type and Js-12-LPA indicates that there is no direct role of localization of IP 6 synthesis in mineral distribution.
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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".