Identification and Expression Characteristic Analysis of CML Gene Family of Melon
Bibliographic record
Abstract
Calmodulin-like ( CML ) is one of the important Ca 2+ sensors, which plays an important role in plant growth and development and stress response. In this study, melon ( Cucumismelo L.) genomic data was used to identify the melon CML protein family using bioinformatics methods, and its physical and chemical properties, location information, gene structure, phylogeny, and promoter were analyzed. The results showed that 60 CmCML protein genes were identified in the melon genomic data, containing 1~4 EF-hand domains, which were unevenly distributed on 12 chromosomes. By analyzing the evolutionary treewith Arabidopsis CMLs , CmCML could It is divided into 8 groups, and the number of various groups is different. It has certain similarities with other plant CML family genes in group classification. In the promoter analysis, abscisic acid, jasmonic acid, auxin, and gibberellin were identified. And drought, low temperature, mechanical damage and other signal response homeopathic elements. Real-time fluorescence quantitative results showed that gene expression of melon CML protein family was induced by the specialization of Fusarium oxysporum, these results predicted that the transcriptional regulation of melon CML protein family may participate in the resistance response of melon wilt, which is the gene family. The functional identification of each member laid a theoretical foundation.
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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".