Functional Genomics of Social Recognition
Bibliographic record
Abstract
Although various types of group living are widespread in mammals, including humans, the study of the hormonal and genetic underpinnings of nonsexual social behaviour, is in its infancy compared to the analysis of sexual behaviour mechanisms. Oxytocin, vasopressin and gonadal hormones certainly play an important role. Social recognition, where animals identify and recognize other individual conspecifics, is a crucial prerequisite for the occurrence of a wide range of social behaviours. Social recognition is also important for coping with one major cost of life in a group: the increased risk of exposure to parasites and infection. We review recent functional genomic studies on the involvement of oxytocin and oestrogen-receptor genes in the regulation of social recognition in mice and in the ecologically relevant context of parasite recognition and avoidance. Based on quantitative studies of social recognition with gene-knockout mice and with antisense DNA, we propose a four-gene micronet contributing to social recognition. This micronet involves the genes coding for oestrogen receptors alpha (ER-alpha), beta (ER-beta), oxytocin and the oxytocin receptor. In this model, circulating oestrogens promote transcription of (i) oxytocin in the paraventricular nucleus of the hypothalamus through ER-beta and (ii) oxytocin receptor in the amygdala through ER-alpha. This model forms the core around which increasingly complex genetic, hormonal and neural interactions associated with social behaviours and recognition can be organized.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".