Transcriptional Epigenetic Mechanisms in Aquatic Species
Bibliographic record
Abstract
Epigenetic mechanisms not only are involved in the proper development and differentiation of organisms in general but also allow the organism to respond to environmental changes. In this chapter, we will briefly review molecular epigenetic mechanisms that are capable of modulating mRNA abundance at the transcriptional level (DNA methylation and chromatin remodeling through histone modifications), highlighting the complexity of epigenetic regulation of gene transcription. We will pay special attention to the epigenetic mechanisms that modulate key biological functions in aquatic species. The emphasis of this chapter is placed on teleost (bony) fish, the largest vertebrate group characterized by a high degree of phenotypic variation, and aquatic invertebrates including mollusks, arthropods, and sponges. We provide examples of how environmental signals are capable of causing epigenetic modifications to impact phenotypic traits related to metabolism, growth, development, reproduction, and immune response in aquaculture species. We anticipate that an increase in the knowledge of epigenetic mechanisms modulating the appearance of desired phenotypic traits will potentially help in the development of new aquaculture practices that can significantly benefit aquaculture, making it more sustainable and economically viable.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".