Regulation of transcription factors on sexual dimorphism of fig wasps
Bibliographic record
Abstract
Fig wasps exhibit extreme intraspecific morphological divergence in the wings, compound eyes, antennae, body color, and size. Corresponding to this, behaviors and lifestyles between two sexes are also different: females can emerge from fig and fly to other fig tree to oviposit and pollinate, while males live inside fig for all their lifetime. Genetic regulation may drive these extreme intraspecific morphological and behavioral divergence. Transcription factors (TFs) involved in morphological development and physiological activity may exhibit sex-specific expressions. Herein, we detect 865 TFs by using genomic and transcriptomic data of the fig wasp Ceratosolen solmsi. Analyses of transcriptomic data indicated that up-regulated TFs in females show significant enrichment in development of the wing, eye and antenna in all stages, from larva to adult. Meanwhile, TFs related to the development of a variety of organs display sex-specific patterns of expression in the adults and these may contribute significantly to their sexual dimorphism. In addition, up-regulated TFs in adult males exhibit enrichment in genitalia development and circadian rhythm, which correspond with mating and protandry. This finding is consistent with their sex-specific behaviors. In conclusion, our results strongly indicate that TFs play important roles in the sexual dimorphism of fig wasps.
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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".