Immune genomic response associated with preference behavior: an examination in a freshwater fish
Bibliographic record
Abstract
Abstract Immune activity in the brain underlies many social behaviors. This immune-behavior relationship can be viewed under a genomics scope by isolating transcriptomic correlates and molecular pathways present following various social encounters. Here we explore how immune genes correlate with different social exposures: mate-choice and social affiliation. Using an established model for sexual selection studies, we compared the immune gene responses in female brains following exposure to either a potential mate or a conspecific female. Females from the Poeciliidae family of fishes, the sailfin molly (Poecilia latipinna), were profiled for their social preference behavior and brain transcriptomes using RNA-seq. During the 30 minute behavioral assay, females were placed into one of two groups: (n=8) given the choice of a large male and small male (‘mate choice’) or a choice between a large and small female (n=8; ‘social affiliation’). The relationship between immune gene responses (categorized using Gene Ontology analysis) and behavior (preference, social affiliation, activity) will be explored using a linear model approach for differential expression (limma; Ritchie ME, et al, 2015) and WGCNA (weighted gene co-expression network analysis; Langfelder & Horvath, 2008). The RNA-seq transcriptomes from each female (n=16) will be analyzed by comparing differential expression between the two groups (‘mate-choice’ vs ‘social preference’).
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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 | 0.001 | 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) | 0.002 | 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".