Bibliographic record
Abstract
Transgenerational epigenetic inheritance (TEI) allows the transmission of information through the germline without changing the genome sequence, through factors such as non-coding RNAs and chromatin modifications. The phenomenon of RNA interference (RNAi) inheritance in the nematode Caenorhabditis elegans is an effective model to investigate TEI that takes advantage of this model organism's short life cycle, self-propagation, and transparency. In RNAi inheritance, exposure of animals to RNAi leads to gene silencing and altered chromatin signatures at the target locus that persist for multiple generations in the absence of the initial trigger. This protocol describes the analysis of RNAi inheritance in C. elegans using a germline-expressed nuclear green fluorescent protein (GFP) reporter. Reporter silencing is initiated by feeding the animals bacteria expressing double-stranded RNA targeting GFP. At each generation, animals are passaged to maintain synchronized development, and reporter gene silencing is determined by microscopy. At select generations, populations are collected and processed for chromatin immunoprecipitation (ChIP)-quantitative polymerase chain reaction (qPCR) to measure histone modification enrichment at the GFP reporter locus. This protocol for studying RNAi inheritance can be easily modified and combined with other analyses to further investigate TEI factors in small RNA and chromatin pathways.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.677 | 0.425 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".