Theme 4 <i>In Vivo</i> Experimental Models
Bibliographic record
Abstract
Background: Most human genes have multiple polyadenylation sites and the choice of site (alternative polyadenylation; APA) varies developmentally and spatially.ALS-associated TDP-43 proteinopathy has recently been shown to impact APA in human cell lines and patient tissues (1-3).It has not yet been shown whether these changes are recapitulated in Drosophila models of ALS, which could open new avenues of investigation and therapeutics.Objectives: In this study, we computationally investigated disease-associated APA in Drosophila and compared it to human data to determine the extent of flies modeling of this facet of ALS.Methods: RNA-seq data from previous studies of Drosophila models of ALS (4,5) were mined for APA analysis using two programs, TAPAS and QAPA.These studies included Tagged Ribosome Affinity Purifications (TRAP) and RNA immunoprecipitation (RIP) experiments which were used to filter APA genes for ribosome and TDP-43 complex associations.Results: There were 780 genes that exhibited APA in models of TDP-43 proteinopathy based on overexpression of wild-type or mutant TDP-43.Of these, 43 showed differential association with the ribosome.Several of these genes are already linked to ß 2024
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".