Two signal half‐century: From negative selection of self‐reactivity to positive selection of near‐self‐reactivity
Bibliographic record
Abstract
With the emergence of clonal selection ideas in the 1950s, the development of immune cell repertoires was seen to require the negative selection of self-reacting cells, with surviving cells exhibiting a broad range of specificities. Thus, confronting a universe of not-self-antigens, a potential host organism spread its resources widely. In the 1960s, the two signal hypothesis showed how this might work. However, in the 1970s an affinity/avidity model further proposed that anticipating a pathogen strategy of exploiting "holes" in the repertoire created by negative selection, hosts should also positively select near-self-reacting cells. A microbe mutating an antigen from a form foreign to its host to a form resembling that host should prevail over host defences with respect to that antigen. By mutating a step towards host self, along the path from non-self to self, it should come to dominate the microbe population. By progressive stepwise mutations, such microbes would become better adapted, to the detriment of their hosts. But they would lose this advantage if, as they mutated closer to host self, they encountered progressively stiffer host defences. Thus, as described in the affinity/avidity model, positive selection of lymphocytes for specificities that were very close to, but not quite, anti-self (ie, "anti-near-self") should be an important host adaptation. While positive selection affects both B and T cells, mechanisms are uncertain. Converging evidence from studies of lymphocyte activation, either polyclonally (with lectins as "antigen-analogs") or monoclonally (by specific antigen), supports the original generic affinity/avidity model for countering mutations towards host self.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".