Negative T cell selection on non-random peptides promotes robust self-nonself discrimination
Bibliographic record
Abstract
Our adaptive immune system has the remarkable ability to distinguish previously unseen foreign peptides from harmless self. This self-foreign discrimination was long thought to arise from the silencing of self-reactive T cells during negative selection in the thymus, but recent data show that negative selection is far from complete. Here we ask how a repertoire containing many self-reactive T cells can nevertheless discriminate self from foreign. We address this question using realistic-scale computational models of the T cell repertoire. Our models show that moderate T cell cross-reactivity automatically skews the post-selection repertoire towards peptides that differ systematically from self. But even when no systematic differences between self and foreign exist, discrimination remains possible if the peptides presented in the thymus are chosen in a way that minimizes the co-occurrence of similar, redundant self peptides. Thus, our model predicts that negative selection on a well-chosen subset of self peptides biases the resulting repertoire towards better detection of both self-similar and -dissimilar pathogens. This effect would allow the immune system to "learn self by example", an ability shared with cognitive systems.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".