Modulation of Plant MicroRNA Expression: Its Potential Usability inWheat (Triticum aestivum L.) Improvement
Why this work is in the frame
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Bibliographic record
Abstract
Wheat, a crucial crop for the pursuit of food security, is faced with a plateauing yield projected to fall short of meeting the demands of the exponentially increasing human population. To raise global wheat productivity levels, strong efforts must be made to overcome the problems of (1) climate change-induced heat and drought stress and (2) the genotype-dependent amenability of wheat to tissue culture, which limits the success of recovering genetically engineered plants, especially in elite cultivars. Unfortunately, the mainstream approach of genetically engineering plant protein-coding genes may not be effective in solving these problems as it is difficult to map, annotate, functionally verify, and modulate all existing homeologs and paralogs within wheat's large, complex, allohexaploid genome. Additionally, the quantitative, multi-genic nature of most agronomically important traits furthers the complications faced by this approach. miRNAs are small, noncoding RNAs (sncRNAs) that repress gene expression at the post-transcriptional level, regulating various aspects of plant growth and development. They are gaining popularity as alternative targets of genetic engineering efforts for crop improvement due to their (1) highly conserved nature, which facilitates reasonable prediction of their gene targets and phenotypic effects under different expression levels, and (2) the capacity to target multiple genes simultaneously, making them suitable for enhancing complex and multigenic agronomic traits. In this mini-review, we will discuss the biogenesis, manipulation, and potential applications of plant miRNAs in improving wheat's yield, somatic embryogenesis, thermotolerance, and drought-tolerance in response to the problems of plateauing yield, genotype-dependent amenability to tissue culture, and susceptibility to climate change-induced heat and drought stress.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it