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Record W1980227123 · doi:10.1093/femsec/fiu020

Bacterial motion in narrow capillaries

2014· article· en· W1980227123 on OpenAlexafffund
Liyan Ping, Vaibhav Wasnik, Eldon Emberly

Bibliographic record

VenueFEMS Microbiology Ecology · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBiocrusts and Microbial Ecology
Canadian institutionsSimon Fraser University
FundersNational Institute of Allergy and Infectious DiseasesNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthHarvard UniversityNational Science Foundation
KeywordsBiologyMotion (physics)Classical mechanicsPhysics

Abstract

fetched live from OpenAlex

Motile bacteria often have to pass through small tortuous pores in soil or tissue of higher organisms. However, their motion in this prevalent type of niche is not fully understood. Here, we modeled it with narrow glass capillaries and identified a critical radius (Rc) for bacterial motion. Near the surface of capillaries narrower than that, the swimming trajectories are helices. In larger capillaries, they swim in distorted circles. Under non-slip condition, the peritrichous Escherichia coli swam in left-handed helices with an Rc of ~10 μm near glass surface. However, slipping could occur in the fast monotrichous Pseudomonas fluorescens, when a speed threshold was exceeded, and thus both left-handed and right-handed helices were executed in glass capillaries. In the natural non-cylindrical pores, the near-surface trajectories would be spirals and twisted loops. Engaging in such motions reduces the bacterial migration rate. With a given pore size, the run length and the tumbling angle of the bacterium determine the probability and duration of their near-surface motion. Shear flow and chemotaxis potentially enhance it. Based on this observation, the puzzling previous observations on bacterial migration in porous environments can be interpreted.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.945
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.007
GPT teacher head0.175
Teacher spread0.168 · 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 designBench or experimental
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

Citations15
Published2014
Admission routes2
Has abstractyes

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