The Effect of Environmental Heterogeniety on the Fitness of Antibiotic Resistant Escherichia Coli
Bibliographic record
Abstract
The cost of antimicrobial resistance (AMR) is the reduction of fitness of a resistant mutant relative to a susceptible strain in the absence of drug.Costs of resistance are usually estimated in a single environment and on one genetic background; these fitness estimates may not be representative of what happens in nature.I measured the fitness of AMR E. coli strains in different environments, including medically and ecologically relevant ones.To do this, a collection of AMR strains of Escherichia coli bearing a single resistance mutation were competed against their ancestral strain in 10 different media.The results of this study indicate that laboratory media does not predict fitness in natural environments.We found environments in which resistance alleles suffered no cost, suggesting that these mutants may persist for long periods of time.Data on the fitness of AMR pathogens across environments will help manage their spread."A [persons]'s friendships are one of the best measures of his worth."Charles Darwin Finishing this thesis would not have been possible without the support of many people.First, I would like to thank my supervisor, Alex Wong, for his kindness and encouragement.I joined Alex's lab in September 2016, and in that short amount of time, he has truly inspired me to never give up on my goals and that anything is possible.During the past 2 years he has guided me, expanded my knowledge base, and has been supportive of my hobbies.He genuinely cares about his students as individuals and serves as a life mentor as well as an academic mentor.The effect of Alex's guidance has been truly transformational.I am a better scientist and person because of his supervision and guidance.My lab members have been my champions, teachers, mentors, and most of all friends throughout this degree.Thanks to Bryn Hazlett and Amanda Carroll, for sharing the good times and the bad times (failed experiments) during this degree, for always being down to go to Burrito Shack, and for taking care of me when I drank too much wine at CSM Waterloo.Thanks to Andrew Low for much help with bioinformatics, and for being my office buddy until you left.Thanks to Nicole Filipow for always lending an ear to talk science
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".