Mapping Transcript Cell-Specific Localization and Protein Subcellular Localization in the Adult Mosquito<i>Aedes aegypti</i>
Bibliographic record
Abstract
This introduction reviews techniques used to examine the distribution and expression of gene transcripts and proteins in a variety of tissues/organs in the medically important global disease vector mosquito,Aedes aegypti. Specifically, these methods allow the detection of cell-specific transcript expression by fluorescent in situ hybridization; facilitate immunohistochemical mapping of a protein of interest in whole-mount small tissue/organ samples; examine the subcellular localization of proteins, such as membrane transporters, through sectioning of paraffin-embedded tissue/organ samples; and finally, enable the efficient separation of cytosolic and membrane proteins for western blot analysis without the need for specialized equipment (e.g., ultracentrifuge) in the mosquitoAe. aegypti. Such techniques are useful to help answer fundamental questions in mosquito scientific research including (but not limited to) the identification of specific cells in an organ responsible for expressing a receptor of particular interest and necessary for eliciting a response to exogenous signals, including hormones. Moreover, changes in the subcellular localization of specific targets of interest can be assessed both qualitatively and quantitatively, providing insight into transient or long-term physiologically relevant regulation necessary for activity under experimental treatments or varied internal (e.g., development) or external (e.g., environmental stress) factors that might be normally experienced by the organism.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".