Morphological Plasticity in the Neural Circuitry Responsible for Seasonal Breeding in the Ewe
Bibliographic record
Abstract
An increase in the response of GnRH neurons to estrogen negative feedback is responsible for seasonal anestrus in the ewe, but the underlying neural mechanisms remain largely unknown. Neural plasticity may play an important role because the density of synaptic input to GnRH neurons changes with seasons. Moreover, the transition from breeding to anestrous season requires thyroid hormones, which are also required for neuronal development. In the first experiment, we examined whether the decrease in synapses on GnRH neurons is critical for the transition to anestrus by comparing synaptic input in thyroidectomized and thyroid-intact controls, using electron microscopic analysis. Thyroidectomized ewes remained in the breeding season, but the number of synaptic contacts on their GnRH cells was not different from those in thyroid-intact ewes that were anestrus. The next experiment tested whether there was a seasonal change in morphology of the A15 dopaminergic neurons that mediate estrogen negative feedback during anestrus by analyzing synapsin-positive close contacts onto A15 neurons with confocal microscopy. There was a 2-fold increase in these close contacts onto dendrites of A15 neurons in anestrus and a corresponding increase in the length of A15 dendrites at this time of year. The increase in dendritic length was blocked by thyroidectomy, but this procedure did not significantly affect synaptic input to A15 neurons. These results provide initial evidence that the seasonal change in synapses on GnRH neurons is not sufficient for the transition into anestrus but that plasticity of the A15 dopaminergic neurons mediating estrogen negative feedback may contribute to this seasonal alteration.
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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".