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Record W2080124297 · doi:10.1128/jb.01430-10

Anomalous Spatial Redistribution of Competing Bacteria under Starvation Conditions

2011· article· en· W2080124297 on OpenAlexfundno aff
Guillaume Lambert, David Liao, Saurabh Vyawahare, Robert H. Austin

Bibliographic record

VenueJournal of Bacteriology · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicEvolutionary Game Theory and Cooperation
Canadian institutionsnot available
FundersU.S. Department of DefenseNational Defense Science and Engineering GraduateNatural Sciences and Engineering Research Council of CanadaNational Cancer InstituteNational Institutes of HealthNational Science Foundation
KeywordsBiologyRedistribution (election)NutrientBacteriaCell biologyMutantPopulationEcologyGeneticsGene

Abstract

fetched live from OpenAlex

Bacterial cells evolved under prolonged stress often have a growth advantage in stationary phase (GASP); we expect GASP cells to maintain a proliferative state and dominate wild-type cells during starvation, especially when nutrients are limited and the medium has been conditioned. However, when we compete GASP mutants against wild-type cells in a chain of microfluidic microhabitat patches (MHPs) with alternating nutrient-rich and nutrient-limited regions, we observe the reverse effect: wild-type cells achieve maximum relative density under nutrient-limited conditions, while GASP cells dominate nutrient-rich regions. We explain this surprising observation in terms of ideal free distributions, where we show that wild-type cells maximize their fitness at high cell density by redistributing themselves to sparsely populated MHPs. At the microscopic level, we describe how biofilm formation also contributes to the population redistribution. We conclude by discussing the implications of these results for social interactions of more complex organisms.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.916
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.032
GPT teacher head0.285
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2011
Admission routes1
Has abstractyes

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