A comprehensive guide to genome<b>-</b>wide DNA methylation research in neuropsychiatric disorders and its implications for deep<b>-</b>space environments.
Bibliographic record
Abstract
Neuropsychiatric disorders arise from complex interactions between genetic and environmental factors. DNA methylation, a reversible and environmentally responsive epigenetic regulatory mechanism, serves as a crucial bridge linking environmental exposure, gene expression regulation, and neurobehavioral outcomes. During long-duration deep-space missions, astronauts face multiple stressors-including microgravity, cosmic radiation, circadian rhythm disruption, and social isolation, which can induce alterations in DNA methylation and increase the risk of neuropsychiatric disorders. Genome-wide DNA methylation research can be divided into 3 major methodological stages: Study design, sample preparation and detection, and data analysis, each of which can be applied to astronaut neuropsychiatric health monitoring. Systematic comparison of the Illumina MethylationEPIC array and whole-genome bisulfite sequencing reveals their complementary strengths in terms of genomic coverage, resolution, cost, and application scenarios: the array method is cost-effective and suitable for large-scale population studies and longitudinal monitoring, whereas sequencing provides higher resolution and coverage and is more suitable for constructing detailed methylation maps and characterizing individual variation. Furthermore, emerging technologies such as single-cell methylation sequencing, nanopore long-read sequencing, and machine-learning-based multi-omics integration are expected to greatly enhance the precision and interpretability of epigenetic studies. These methodological advances provide key support for establishing DNA-methylation-based monitoring systems for neuropsychiatric risk in astronauts and lay an epigenetic foundation for safeguarding neuropsychiatric health during future long-term deep-space missions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.070 | 0.043 |
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".