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Record W2134295005 · doi:10.1039/c4lc01245c

Microfluidic devices for imaging neurological response of Drosophila melanogaster larva to auditory stimulus

2014· article· en· W2134295005 on OpenAlexafffund
Reza Ghaemi, Pouya Rezai, Balaji Iyengar, P. Ravi Selvaganapathy

Bibliographic record

VenueLab on a Chip · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology and Insect Physiology Research
Canadian institutionsYork UniversityMcMaster University
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsDrosophila melanogasterStimulus (psychology)LarvaNeuroscienceMicrofluidicsBiologyNanotechnologyMaterials sciencePsychologyEcologyGenetics

Abstract

fetched live from OpenAlex

Two microfluidic devices (pneumatic chip and FlexiChip) have been developed for immobilization and live-intact fluorescence functional imaging of Drosophila larva's Central Nervous System (CNS) in response to controlled acoustic stimulation. The pneumatic chip is suited for automated loading/unloading and potentially allows high throughput operation for studies with a large number of larvae while the FlexiChip provides a simple and quick manual option for animal loading and is suited for smaller studies. Both chips were capable of significantly reducing the endogenous CNS movement while still allowing the study of sound-stimulated CNS activities of Drosophila 3rd instar larvae using genetically encoded calcium indicator GCaMP5. Temporal effects of sound frequency (50-5000 Hz) and intensity (95-115 dB) on CNS activities were investigated and a peak neuronal response of 200 Hz was identified. Our lab-on-chip devices can not only aid further studies of Drosophila larva's auditory responses but can be also adopted for functional imaging of CNS activities in response to other sensory cues. Auditory stimuli and the corresponding response of the CNS can potentially be used as a tool to study the effect of chemicals on the neurophysiology of this model organism.

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)0.002
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 score0.154
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.029
GPT teacher head0.301
Teacher spread0.272 · 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

Citations27
Published2014
Admission routes2
Has abstractyes

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