Bibliographic record
Abstract
Cytokines elicit responses in target cells by inducing changes in gene expression. For interferons (IFNs), this involves receptor-mediated activation of specific transcription factors, which then translocate into the nucleus to bind to cognate gene elements in the promoters of IFN-inducible genes. The prototypic IFN-inducible transcription factors are the signal transducer and activator of transcription (STAT) proteins. IFN-receptor interactions invoke Janus kinase activation via phosphorylation events, which in turn leads to the recruitment and phosphorylation of STAT proteins on tyrosine residues. Activated STATs then dimerize to form STAT complexes. IFNs-alpha/beta will activate STAT-1, STAT-2, STAT-3 ,and STAT-5, whereas IFN-gamma will predominantly activate STAT-1. In this chapter, we describe a procedure to identify IFN-inducible deoxyribonucleic acid (DNA) binding factors independently of any knowledge of their target DNA sequences. This procedure permits the identification of IFN-inducible STAT complexes as well as any other IFN-inducible DNA binding factors. This biochemical technique uses genomic DNA affinity chromatography to isolate DNA binding factors from IFN-inducible cytoplasmic or nuclear extracts.
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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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.041 |
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".