Image Defocus Modulates Activity of Bipolar and Amacrine Cells in Macaque Retina
Bibliographic record
Abstract
PURPOSE: To demonstrate regulation of ocular growth and refractive development by image quality and processing in the retina in higher primates. Focus-sensitive retinal neurons were labeled with immunocytochemical markers after briefly altering image quality in infant monkeys. METHODS: Six rhesus monkeys (Macaca mulatta) 20- to 30-days-old were fitted with goggles after 6 hours in the light. Three wore a +3 D lens and three a diffuser on the treated eye; contralateral control eyes wore plano lenses matched in transmission to the goggles on treated eyes. After 30, 60, or 240 minutes exposure, the animals were killed, the eyes opened and fixed in 4% formaldehyde, and cryosections labeled with antibodies to inducible activity markers (transcription factors Egr-1 and Fra-2) and type-specific amacrine cell markers. Labeled cells were identified and counted in a fluorescence microscope, and the spatial density of activity-labeled nuclei and the frequency of activity-labeling of specific amacrine cells were determined, without knowing treated eye or duration. RESULTS: Focus-sensitive immunoreactivity was demonstrated for Egr-1 and Fra-2 in a GAD65-immunoreactive (IR) subpopulation of GABAergic amacrine cells, and for Egr-1 alone in PKC alpha-, 115A10-, and CD15-IR ON-bipolar cells. Activity of ON-bipolar and GABAergic amacrine cells, as indicated by Egr-1 induction, was stimulated more by in-focus or myopically-defocused images than by hyperopically-defocused or diffusely blurred images, regardless of exposure duration. CONCLUSIONS: This was the first evidence of focus-dependent activation of bipolar as well as amacrine cells in a primate retina. Focus-sensitive neurons are candidates for roles in vision-dependent regulation of eye growth.
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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".