Characterizing patterns of selection pressure on mammalian antiviral immune response
Bibliographic record
Abstract
Abstract Immune response is known to be under constant pressure evolutionary pressure from different factors including pathogens. Although different selection regimes are expected to act on the magnitude of immune response, there are limited studies that investigated the different patterns of selection pressures on the immune response quantitatively. I employed evolutionary models (Ornstein-Uhlenbeck models) to identify different patterns of selection on the antiviral immune response of fibroblasts derived from 18 mammalian species and one vertebrate stimulated by viral ligand, poly I:C, or Interferon alpha cytokine. I found stabilizing selection to be the dominant form of selection on the immune response. Out of 59 genes that were found to be responding in at least 15 species, 50 genes were found to be under stabilizing selection. Moreover, evolutionary variance was found to differ among these conservatively responding genes implicated in fighting viruses. For instance, ADAR was found to have low evolutionary variance while TRIM14 response showed opposite trend suggesting different evolutionary pressures acting on the magnitude of response. Directional selection was also detected specific infraorders of primates such as apes and old-world monkeys on response of innate immune effectors.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".