Automated quantification of vomeronasal glomeruli number, size, and color composition after immunofluorescent staining
Bibliographic record
Abstract
Glomeruli are neuropil-rich regions of the main or accessory olfactory bulbs (AOB) where the axons of olfactory or vomeronasal neurons and dendrites of mitral/tufted cells form synaptic connections. In the main olfactory system, olfactory sensory neurons (OSNs) expressing the same receptor innervate 1 or 2 glomeruli. However, in the accessory olfactory system, vomeronasal sensory neurons (VSNs) expressing the same receptor can innervate up to 30 different glomeruli in the AOB. Genetic mutation disrupting genes with a role in defining the identity/diversity of olfactory and vomeronasal neurons can alter the number and size of glomeruli. Interestingly, 2 cell surface molecules, Kirrel2 and Kirrel3, have been indicated as playing a critical role in the organization of axons into glomeruli in the AOB. Being able to quantify differences in glomeruli features, such as number, size, or immunoreactivity for specific markers, is an important experimental approach to validate the role of specific genes in controlling neuronal connectivity and circuit formation in either control or mutant animals. Since the manual recognition and quantification of glomeruli on digital images is a challenging and time-consuming task, we generated a program in Python able to identify glomeruli in digital images and quantify their properties, such as size, number, and pixel intensity. Validation of our program indicates that our script is a fast and suitable tool for high-throughput quantification of glomerular features of mouse lines with different genetic makeup.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".