Analysis of iron and zinc homeostasis in barnyard millet through transcriptome and ionome approach
Bibliographic record
Abstract
Iron (Fe) and Zinc (Zn) are the most essential micronutrients needed for the growth and metabolism of higher plants. Fe and Zn has important role as a component of various enzymes that are involved in chlorophyll biosynthesis, photosynthesis and seed development. Plants have evolved with multifaceted Fe and Zn homeostatic mechanisms that regulate its acquisition from the environment and the movement between organelles, cells, tissues, and organs. In addition, Plant establishes a tightly controlled system including metal specific uptake transporters and transcriptional regulators to balance the uptake, utilization and storage of metal ions. Barnyard millet (Echinocloa frumentaceae), one of the minor millets is superior in Fe and Zn content compared to the most widely consumed cereals like rice and wheat. In the present study, ionomic profiling of grains of several barnyard millet accessions revealed that accession ACM-10-145 accumulates high Fe and Zn content (Fe: 14.5 mg/100g; Zn: 2.18 mg/100g). Furthermore, transcriptomic studies are in progress to understand the key factors involved in metal uptake and translocation in barnyard millet. The research outcome could be exploited for biofortification program in cereals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".