The Role of Major Transcription Factors in Solanaceous Food Crops under Different Stress Conditions: Current and Future Perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Plant growth, development, and productivity are adversely affected by environmental stresses such as drought (osmotic stress), soil salinity, cold, oxidative stress, irradiation, and diverse diseases. These impacts are of increasing concern in light of climate change. Noticeably, plants have developed their adaptive mechanism to respond to environmental stresses by transcriptional activation of stress-responsive genes. Among the known transcription factors, DoF, WRKY, MYB, NAC, bZIP, ERF, ARF and HSF are those widely associated with abiotic and biotic stress response in plants. Genome-wide identification and characterization analyses of these transcription factors have been almost completed in major solanaceous food crops, emphasizing these transcription factor families which have much potential for the improvement of yield, stress tolerance, reducing marginal land and increase the water use efficiency of solanaceous crops in arid and semi-arid areas where plant demand more water. Most importantly, transcription factors are proteins that play a key role in improving crop yield under water-deficient areas and a place where the severity of pathogen is very high to withstand the ongoing climate change. Therefore, this review highlights the role of major transcription factors in solanaceous crops, current and future perspectives in improving the crop traits towards abiotic and biotic stress tolerance and beyond. We have tried to accentuate the importance of using genome editing molecular technologies like CRISPR/Cas9, Virus-induced gene silencing and some other methods to improve the plant potential in giving yield under unfavorable environmental conditions.
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.
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) | 0.001 | 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