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Robo-2 Controls the Segregation of a Portion of Basal Vomeronasal Sensory Neuron Axons to the Posterior Region of the Accessory Olfactory Bulb

2009· article· en· W2060716852 on OpenAlexafffund
Janet E. A. Prince, Jin Hyung Cho, Émilie Dumontier, William Andrews, Tyler Cutforth, Marc Tessier‐Lavigne, John G. Parnavelas, Jean‐François Cloutier

Bibliographic record

VenueJournal of Neuroscience · 2009
Typearticle
Languageen
FieldNeuroscience
TopicOlfactory and Sensory Function Studies
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaWellcome Trust
KeywordsVomeronasal organBiologySensory systemOlfactory bulbNeuroscienceAxon guidanceAnatomyBasal (medicine)Central nervous systemAxon

Abstract

fetched live from OpenAlex

The ability of sensory systems to detect and process information from the environment relies on the elaboration of precise connections between sensory neurons in the periphery and second order neurons in the CNS. In mice, the accessory olfactory system is thought to regulate a wide variety of social and sexual behaviors. The expression of the Slit receptors Robo-1 and Robo-2 in vomeronasal sensory neurons (VSNs) suggests they may direct the stereotypic targeting of their axons to the accessory olfactory bulb (AOB). Here, we have examined the roles of Robo-1 and Robo-2 in the formation of connections by VSN axons within the AOB. While Robo-1 is not necessary for the segregation of VSN axons within the anterior and posterior regions of the AOB, Robo-2 is required for the targeting of some basal VSN axons to the posterior region of the AOB but is dispensable for the fasciculation of VSN axons. Furthermore, the specific ablation of Robo-2 expression in VSNs leads to mistargeting of a portion of basal VSN axons to the anterior region of the AOB, indicating that Robo-2 expression is required on projecting VSN axons. Together, these results identify Robo-2 as a receptor that controls the targeting of basal VSN axons to the posterior AOB.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.653
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.167
GPT teacher head0.294
Teacher spread0.128 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations54
Published2009
Admission routes2
Has abstractyes

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