Investigating the Silencing Mechanism of the X-Chromosome Meiotic Driver, Ste, by the Y-Chromosome lncRNA, Su(Ste), in Drosophila melanogaster
Bibliographic record
Abstract
Long non-coding RNAs (lncRNAs) are over 200 nucleotide long transcripts that are not translated into protein. As the most diverse and abundant class of non-coding RNA (ncRNA), lncRNAs are involved in different biological processes across species. This project aims to investigate the function of a Drosophila melanogaster Y-linked lncRNA, Su(Ste), in regulating the expression of an X-chromosome meiotic driver, Stellate (Ste). By using a novel FISH method called HCR FISH, I re-examine and validate the spatial and temporal expression of Su(Ste) and Ste transcripts during early spermatogenesis. My preliminary results suggest that the sense Su(Ste) transcripts, which were previously hypothesized as non-functional, could have important roles in regulating the expression of both antisense Su(Ste) and Ste. To test the hypothesized functions of Su(Ste), I started using the HyPro proximity labelling approach to identify the potential interactors of Su(Ste). So far, I have successfully expressed and purified the active HyPro enzyme. To economically generate digoxigenin-labelled probes, I expressed and purified homemade TdT enzyme and showed that its labelling efficiency is similar to the commercially available TdT enzyme.
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 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".