The role of maternal factors in epigenetic programming of neurodevelopment
Bibliographic record
Abstract
Early life events are potent determinants of vulnerability and resistance to stressors. In many species, including rodents, the mother is the primary mediator of behavioral and physiological responses to stress in offspring during development. Emerging evidence indicates that epigenetic modifications in the brain of developing offspring are associated with the effects of maternal stress as well as individual neural, physiological, and behavioral responses to adversity. I will discuss research in my lab focused on identifying the relevant genomic targets (in the brain and periphery) of maternal stressors that exert long‐term ‘programming’ effects on stress responses. We have approached this question in several ways. First, we have studied ecologically important stressors applied during gestation. Second, we have investigated factors that co‐occur or interact with maternal care, including variations in ambient temperature and offspring genotype, that influence neurodevelopment and later‐life behavior. Third, we have explored direct exposure to maternal dietary stressors across the developmental period on offspring phenotype and genome‐wide epigenetic modifications in offspring brain. Through these investigations we will explore the significance of the maternal environment in neurodevelopment and the role of epigenetic mechanisms in important signaling pathways involved in susceptibility to stress‐related illness.
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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 | 0.000 | 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".