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Record W2981413824 · doi:10.1073/pnas.1912354116

Quantifying cellulose accessibility during enzyme-mediated deconstruction using 2 fluorescence-tagged carbohydrate-binding modules

2019· article· en· W2981413824 on OpenAlexafffund
Vera Novy, Kevin Aïssa, Fredrik Nielsen, Suzana K. Straus, Peter N. Ciesielski, Christopher G. Hunt, Jack Saddler

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2019
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaAustrian Science FundUniversity of Wisconsin-MadisonU.S. Department of Agriculture
KeywordsCelluloseHydrolysisEnzymeChemistrySubstrate (aquarium)Enzymatic hydrolysisBiochemistryCellulasePolysaccharideFluorescenceCarbohydrateBiology

Abstract

fetched live from OpenAlex

C NMR, scanning electron microscopy (SEM) imaging, and fiber length distribution analysis, showed that enzymatic degradation predominates at these zones during the initial phase of the reaction, resulting in rapid fiber fragmentation and an increase in cellulose surface crystallinity. By applying this method to elucidate the differences in the enzyme-mediated deconstruction mechanisms, this work further demonstrated that drying decreased the accessibility of enzymes to these disorganized zones, resulting in a delayed onset of degradation and fragmentation. The use of fluorescence-tagged CBMs with specific recognition sites provided a quantitative way to elucidate supramolecular substructures of cellulose and their impact on enzyme accessibility. By designing a quantitative method to analyze the cellulose ultrastructure and accessibility, this study gives insights into the degradation mechanism of cellulosic substrates.

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)0.000
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.067
GPT teacher head0.343
Teacher spread0.276 · 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

Citations53
Published2019
Admission routes2
Has abstractyes

Explore more

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