Distinct type II opsins in the eye decode light properties for background adaptation and behavioural background preference
Bibliographic record
Abstract
Crypsis increases survival by reducing predator detection. Xenopus laevis tadpoles decode light properties from the substrate to induce two responses: a cryptic coloration response where dorsal skin pigmentation is adjusted to the colour of the substrate (background adaptation) and a behavioural crypsis where organisms move to align with a specific colour surface (background preference). Both processes require organisms to detect reflected light from the substrate. We explored the relationship between background adaptation and preference and the light properties able to trigger both responses. We also analysed which retinal photosensor (type II opsin) is involved. Our results showed that these two processes are segregated mechanistically, as there is no correlation between the preference for a specific background with the level of skin pigmentation, and different dorsal retina-localized type II opsins appear to underlie the two crypsis modes. Indeed, inhibition of melanopsin affects background adaptation but not background preference. Instead, we propose pinopsin is the photosensor involved in background preference. pinopsin mRNA is co-expressed with mRNA for the sws1 cone photopigment in dorsally located photoreceptors. Importantly, the developmental onset of pinopsin expression aligns with the emergence of the preference for a white background, but after the background adaptation phenotype appears. Furthermore, white background preference of tadpoles is associated with increased pinopsin expression, a feature that is lost in premetamorphic froglets along with a preference for a white background. Thus, our data show a mechanistic dissociation between background adaptation and background preference, and we suggest melanopsin and pinopsin, respectively, initiate the two responses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".