What’s self got to do with it: Sources of heterogeneity among naive T cells
Bibliographic record
Abstract
T cells are largely homogeneous populations despite the extraordinary diversity of their T cell receptors (TCR). The self-immunopeptidome plays a key role in the selection of the naive T cell repertoire in the thymus, and self-peptides are also an important driver of differences between individual naive T cells with regard to their subsequent functional contributions to an immune response. Accumulating evidence suggests that as early as the β-selection stage of T cell development, when only one of the recombined chains of the mature TCR is expressed, signaling thresholds may be established for positive selection of immature thymocytes. Stochastic encounters subsequently made with self-ligands during positive selection in the thymus imprint functional biases that a T cell will carry with it throughout its lifetime, although ongoing interactions with self in the periphery ensure a level of plasticity in the gene expression wiring of naive T cells. Identifying the sources of heterogeneity in the naive T cell population and which functional attributes of T cells can be modulated through post-thymic interventions versus those that are fixed during T cell development, could enable us to better select or generate T cells with particular traits to improve the efficacy of T cell therapies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".