Sex-sorting Aedes aegypti for sterile insect technique (SIT) by knocking down four different gene targets, expressed in the guts of female larvae
Bibliographic record
Abstract
Mosquitoes are vectors for diseases that affect approximately 0.7 billion people globally. Most of these diseases have a high incidence in tropical often developing countries, where they prove either fatal or severely debilitating. Despite huge investments of time and money to find solutions, we have been unable to control this disease vector. Additionally, some of the techniques previously and currently in use to prevent these diseases, have serious drawbacks such as detrimental effects on non-target species, humans and the environment. We need eco-friendly alternatives to these harmful chemicals, that are just as efficient while also being species-specific to avoid harmful effects on other beneficial insect species. Sterile insect technique (SIT) is a creative technology that does just that. It involves the mass release of sterile males of a species to outcompete wild males and mate with wild females, eventually resulting in the decline of a targeted insect population. However, before these sterile males are released, they must be separated from females. An efficient way to sex-sort males from females can use RNA interference (RNAi). RNAi is a natural defence mechanism that exists in eukaryotic cells, that can be used to knock down female-biased or female-specific genes. Targeted females will develop slower than their male counterparts due to this knockdown and this will enable their separation from a pool of male individuals. For my honours thesis, I targeted four such female-biased genes - AAEL014604, AAEL014797, AAEL009313 and AAEL005884 and measured development (in days), survival and pupal size for each of these treatments for both male and female mosquitoes. None of my treatments resulted in significant results but one of these (AAEL014797) showed great promise for an improved SIT.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".