Screening and Transcriptome Analysis of Different Materials with Low Temperature Tolerance in Eggplant (<i>Solanum melongena</i>)
Bibliographic record
Abstract
The growth and fruit quality of eggplant were seriously affected by low temperature stress. In order to screen low temperature tolerance germplasm of eggplant and reveal its correlation with low temperature tolerance at molecular level. In this study, two eggplant inbred lines with different genotypes were selected on the basis of preliminary experiments. The effects of low temperature stress on the seed germination and seedling chilling injury were studied. The transcriptome of seedling leaves under 4°C low temperature was sequenced, and the differentially expressed genes were classified and enriched. The results showed that CHEN18 was much stronger than 819 in seed germination and seedling low temperature tolerance. The analysis of transcriptome data showed that there were some differences in genetic background between the two eggplant materials, and the differentially expressed genes were mainly concentrated in biological regulation, cell process, metabolic process and single organism process. Up-regulated differentially expressed genes are mainly enriched in cell processes, environmental information processing, genetic information processing and metabolism. The results of this study laid a foundation for the selection of cold tolerance germplasm and the excavation of cold tolerance genes in eggplant.
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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 | 0.000 | 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) | 0.001 | 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".