Differential Requirements for Semaphorin 3F and Slit-1 in Axonal Targeting, Fasciculation, and Segregation of Olfactory Sensory Neuron Projections
Bibliographic record
Abstract
The formation of precise stereotypic connections in sensory systems is critical for defining accurate internal representations of the external world; however, the molecular mechanisms underlying the formation of sensory maps are poorly understood. Here, we examine the roles of two structurally unrelated repulsive guidance cues, semaphorin 3F (Sema3F) and Slit-1, in olfactory sensory axon fasciculation, targeting, and segregation. Using sema3F-/- mice, we show that Sema3F is critical for vomeronasal sensory neuron axonal fasciculation and for segregation of these sensory afferents from the main olfactory system; however, Sema3F plays only a minor role in targeting of apical vomeronasal neuron axons to the anterior accessory olfactory bulb (AOB). In addition, we show that Sema3F is required for lamina-specific targeting of olfactory sensory axons within the main olfactory system. In contrast to Sema3F, Slit-1 is dispensable for fasciculation of basal vomeronasal neuron axons but is critical for targeting these axons to the posterior AOB. These results reveal discrete and complementary roles for secreted semaphorins and slits in axonal targeting, fasciculation, and segregation of olfactory sensory neuron projections.
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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".