Genetic Decoding of the African Malaria Mosquito Olfactory System: New Insights into Responses to Human Odors
Bibliographic record
Abstract
The paper "An expanded neurogenetic toolkit to decode olfaction in the African malaria mosquito Anopheles gambiae," authored by Diego Giraldo, Andrew M. Hammond, Jinling Wu, et al., was published in Cell Reports Methods on March 27, 2024, from institutions such as Johns Hopkins Malaria Research Institute, Johns Hopkins Bloomberg School of Public Health, and Department of Life Sciences, Imperial College London. In this research, Giraldo and colleagues developed a neurogenetic toolkit to decode the olfactory system of the African malaria mosquito, Anopheles gambiae . By creating cell-type-specific driver lines, the research team successfully encoded genetic access to specific olfactory sensory neuron populations and validated the application of these tools in decoding mosquito responses to human odors. The method integrates the driver-responder-marker (DRM) system using CRISPR-Cas9 technology, thereby enabling rapid identification of the expression patterns of target chemoreceptor genes by screening GFP+ olfactory sensory neurons.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".