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Record W2774691437 · doi:10.1002/smll.201702982

Magnetotaxis Enables Magnetotactic Bacteria to Navigate in Flow

2017· article· en· W2774691437 on OpenAlexafffund
Saeed Rismani Yazdi, Reza Nosrati, Corey A. Stevens, David Vogel, Peter L. Davies, Carlos Escobedo

Bibliographic record

VenueSmall · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGeomagnetism and Paleomagnetism Studies
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Excellence Research Chairs, Government of CanadaCanada Foundation for Innovation
KeywordsMagnetotactic bacteriaFlow (mathematics)NanotechnologyBacteriaComputer scienceMaterials scienceBiologyPhysicsMechanicsPaleontology

Abstract

fetched live from OpenAlex

Abstract Magnetotactic bacteria (MTB) play an important role in Earth's biogeochemical cycles by transporting minerals in aquatic ecosystems, and have shown promise for controlled transport of microscale objects in flow conditions. However, how MTB traverse complex flow environments is not clear. Here, using microfluidics and high‐speed imaging, it is revealed that magnetotaxis enables directed motion of Magnetospirillum magneticum over long distances in flow velocities ranging from 2 to 1260 µm s −1 , corresponding to shear rates ranging from 0.2 to 142 s −1 —a range relevant to both aquatic environments and biomedical applications. The ability of MTB to overcome a current is influenced by the flow, the magnetic field, and their relative orientation. MTB can overcome 2.3‐fold higher flow velocities when directed to swim perpendicular to the flow as compared to upstream, as the latter orientation induces higher drag. The results indicate a threshold drag of 9.5 pN, corresponding to a flow velocity of 550 µm s −1 , where magnetotaxis enables MTB to overcome counterdirectional flow. These findings bring new insights into the interactions of MTB with complex flow environments relevant to aquatic ecosystems, while suggesting opportunities for in vivo applications of MTB in microbiorobotics and targeted drug delivery.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.766
Threshold uncertainty score0.981

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.245
Teacher spread0.230 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations45
Published2017
Admission routes2
Has abstractyes

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