Visual input regulates melanophore differentiation
Bibliographic record
Abstract
Introduction Developmental processes continue in organisms in which sensory systems have reached functional maturity, however, little research has focused on the influence of sensory input on cell and tissue development. Here, we explored the influence of visual system activity on the development of skin melanophores in Xenopus laevis. Methods Melanophore number was measured in X. laevis larvae after the manipulation of visual input through eye removal (enucleation) and/or incubation on a white or black substrate at the time when the visual system becomes functional (stage 40). To determine the developmental process impacted by visual input, migration, proliferation and differentiation of melanophores was assessed. Finally, the role of melatonin in driving melanophore differentiation was explored. Results Enucleating, or maintaining stage 40 larvae on a black background, results in a pronounced increase in melanophore number in the perioptic region within 24 h. Time lapse analysis revealed that in enucleated larvae new melanophores appear through gradual increase in pigmentation, suggesting unpigmented cells in the perioptic region differentiate into mature melanophores upon reduced visual input. In support, we observed increased expression of melanization genes tyr, tyrp1, and pmel in the perioptic region of enucleated or black background-reared larvae. Conversely, maintaining larvae in full light suppresses melanophore differentiation. Interestingly, an extra-pineal melatonin signal was found to be sufficient and necessary to promote the transition to differentiated melanophores. Discussion In this study, we found that at the time when the visual system becomes functional, X. laevis larvae possess a population of undifferentiated melanophores that can respond rapidly to changes in the external light environment by undergoing differentiation. Thus, we propose a novel mechanism of environmental influence where external sensory signals influence cell differentiation in a manner that would favor survival.
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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) | 0.002 | 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".