Transcriptomic, Proteomic, Metabolomic and Functional Genomic Approaches for the Study of Abiotic Stress in Vegetable Crops
Bibliographic record
Abstract
Vegetables are plants or portion of plants cultivated for food with a savory flavor and considerably nutritional value with little protein or fat. The yield and quality of vegetable crops are affected by various abiotic stresses, such as drought, salinity and low and high temperatures. Higher plants have evolved a series of complex responses in order to adapt to a single or multiple stresses. Recently, high-throughput sequencing has brought powerful and efficient research tools that can lead to a better understanding of the molecular mechanisms behind stress in plants. Many molecular markers, functional and regulatory genes have been discovered based on the genome sequencing. The new technologies, such as transcriptome analysis, digital gene expression, deep sequencing of small RNAs, proteomics, metabolomics, etc. should pave new avenues for studying stress resistance in vegetable crops. Here, we review recent progresses in the transcriptomic, proteomic, metabolomic and functional genomic approaches that have been used in the field of abiotic stress with vegetable crops. The perspectives on future research and improvement of vegetable crops through applied genomics are provided.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".