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Record W1982953521 · doi:10.1063/1.4886771

A high-throughput cellulase screening system based on droplet microfluidics

2014· article· en· W1982953521 on OpenAlexfundno aff
Raluca Ostafe, Radivoje Prodanović, W. Lloyd Ung, David A. Weitz, Rainer Fischer

Bibliographic record

VenueBiomicrofluidics · 2014
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsnot available
FundersDivision of Materials ResearchMaterials Research Science and Engineering Center, Harvard UniversityNatural Sciences and Engineering Research Council of CanadaRWTH Aachen UniversityFraunhofer-GesellschaftHarvard UniversityNational Science Foundation
KeywordsCellulaseMicrofluidicsSortingThroughputPopulationChemistryCell sortingEnzymeMicrofluidic chipHigh-throughput screeningNanotechnologyBiochemistryMaterials scienceComputer scienceCellTelecommunications

Abstract

fetched live from OpenAlex

A new ultra-high-throughput screening assay for the detection of cellulase activity was developed based on microfluidic sorting. Cellulase activity is detected using a series of coupled enzymes leading to the formation of a fluorescent product that can be detected on a chip. Using this method, we have achieved up to 300-fold enrichments of the active population of cells and greater than 90% purity after just one sorting round. In addition, we proved that we can sort the cellulase-expressing cells from mixtures containing less than 1% active cells.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.009
GPT teacher head0.199
Teacher spread0.190 · 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 source (direct Gemma or distilled Codex), 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

Citations76
Published2014
Admission routes1
Has abstractyes

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