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Record W3129032456 · doi:10.1115/1.4050054

Motility Response to Hydrodynamic Stress During the Growth Cycle in Active Fluid Suspensions

2021· article· en· W3129032456 on OpenAlexaff
Hadi Fadlallah, Hassan Peerhossaini, Christopher De Groot, Mojtaba Jarrahi

Bibliographic record

VenueJournal of Fluids Engineering · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsWestern University
Fundersnot available
KeywordsChlamydomonas reinhardtiiMotilityChlamydomonasSynechocystisBiophysicsShear stressBiologyBioreactorAlgaeBotanyCell biologyCyanobacteriaBiochemistryMechanicsPhysicsMutantBacteria

Abstract

fetched live from OpenAlex

Abstract In this work, we focus on the motility behavior of two model micro-organisms widely used in the study of active fluids: Chlamydomonas reinhardtii micro-alga and Synechocystis sp. cyanobacterium. Understanding the physiological responses of micro-organisms under variable environmental conditions is essential for bioreactor engineering. Yet, most of the previous studies focused on the observation of cellular motility regardless of the growth process. Here, we measure the motility of Chlamydomonas reinhardtii and Synechocystis sp. during their growth when subjected to different intensities of hydrodynamic shear stress. The results demonstrate a significant difference in the motility response of the two species against the applied hydrodynamic shear stress. Mechanical agitation appears to affect the motility of Chlamydomonas reinhardtii micro-algae by stimulating the growth process and increasing the magnitude of the cellular swimming velocity. This effect is described using an empirical model for the time variation of the motility. Synechocystis cells show a high endurance to the applied shear such that the global effect of agitation intensity on their motility is insignificant. However, it seems that the peak of the swimming velocity always occurs in the middle of exponential phase of growth.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.475
Threshold uncertainty score0.383

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 integrity0.0000.000
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 head0.003
GPT teacher head0.205
Teacher spread0.201 · 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 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

Citations7
Published2021
Admission routes1
Has abstractyes

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