Neurogenetic identification of mosquito sensory neurons
Bibliographic record
Abstract
Anopheles mosquitoes, as vectors for the malaria parasite, are a global threat to human health. To find and bite a human, they utilize neurons within their sensory appendages. However, the identity and quantification of sensory appendage neurons are lacking. Here we use a neurogenetic approach to label all neurons in Anopheles coluzzii mosquitoes. We utilize the homology assisted CRISPR knock-in (HACK) approach to generate a T2A-QF2 w knock-in of the synaptic gene bruchpilot . We use a membrane-targeted GFP reporter to visualize the neurons in the brain and to quantify neurons in all major chemosensory appendages (antenna, maxillary palp, labella, tarsi, and ovipositor). By comparing labeling of brp>GFP and Orco>GFP mosquitoes, we predict the extent of neurons expressing ionotropic receptors (IRs) or other chemosensory receptors. This work introduces a valuable genetic tool for the functional analysis of Anopheles mosquito neurobiology and initiates characterization of the sensory neurons that guide mosquito behavior.
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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) | 0.002 | 0.001 |
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".