Adapting clinical practice guidelines to local context and assessing barriers to their use
Bibliographic record
Abstract
Abstract Extensive efforts have been made to understand the phenotypic diversity of lipopolysaccharide (LPS) structures through deletion and complementation experiments. However, this approach likely underestimates the available phenotypic diversity. To better explore LPS diversity, we generate LPS mutants in Escherichia coli C by selecting for resistance to ΦX174, a bacteriophage that relies solely on binding to core LPS to infect its host. An analysis of 31 E. coli C mutants that are resistant to ΦX174 reveals that each mutant carries at least one mutation in genes linked to core LPS biosynthesis or assembly. Based on which genes are mutated, we predict the core LPS structures of each bacterial mutant, and test our predictions by evolving phages to recognize each evolved LPS structure. We find that phages that evolved to infect the same predicted LPS structure were not always able to cross-infect each other’s host, suggesting that core LPS structure diversity is higher than predicted. Similarly, phage genotype-phenotype maps can be constructed using the bacterial LPS mutant classes. For example, we demonstrate that a combination of two phage mutations leads to loss of the ability to infect wildtype E. coli C. Our results show that phages are a useful tool to study LPS structures, and conversely that the study of LPS structures helps to understand phage evolution and biology.
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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 | 0.058 | 0.243 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".