Distribution, number, and certain neurochemical identities of infracortical white matter neurons in the brains of three megachiropteran bat species
Bibliographic record
Abstract
A large population of infracortical white matter neurons, or white matter interstitial cells (WMICs), are found within the subcortical white matter of the mammalian telencephalon. We examined WMICs in three species of megachiropterans, Megaloglossus woermanni, Casinycteris argynnis, and Rousettus aegyptiacus, using immunohistochemical and stereological techniques. Immunostaining for neuronal nuclear marker (NeuN) revealed substantial numbers of WMICs in each species-M. woermanni 124,496 WMICs, C. argynnis 138,458 WMICs, and the larger brained R. aegyptiacus having an estimated WMIC population of 360,503. To examine the range of inhibitory neurochemical types we used antibodies against parvalbumin, calbindin, calretinin, and neural nitric oxide synthase (nNOS). The calbindin and nNOS immunostained neurons were the most commonly observed, while those immunoreactive for calretinin and parvalbumin were sparse. The proportion of WMICs exhibiting inhibitory neurochemical profiles was ~26%, similar to that observed in previously studied primates. While for the most part the WMIC population in the megachiropterans studied was similar to that observed in other mammals, the one feature that differed was the high proportion of WMICs immunoreactive to calbindin, whereas in primates (macaque monkey, lar gibbon and human) the highest proportion of inhibitory WMICs contain calretinin. Interestingly, there appears to be an allometric scaling of WMIC numbers with brain mass. Further quantitative comparative work across more mammalian species will reveal the developmental and evolutionary trends associated with this infrequently studied neuronal population.
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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 | 0.001 | 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".