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Record W639334613

The Tannase Gene: Metaphylogenomics, Global Distribution and Presence in the Midgut Flora of the Forest Tent Caterpillar Malacosoma disstria Hübner

2014· dissertation· en· W639334613 on OpenAlexaboutno aff
Michael Gasse

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2014
Typedissertation
Languageen
FieldImmunology and Microbiology
TopicTannin, Tannase and Anticancer Activities
Canadian institutionsnot available
Fundersnot available
KeywordsTannaseBiologyBotanyBiochemistry
DOInot available

Abstract

fetched live from OpenAlex

Tannase enzymes hydrolyze tannins, a class of plant polyphenolics that defend against herbivory. Prior to the 1980’s, most studies were focused on the tannase gene in fungi owing to the interest surrounding their industrial value. Since then a large number of bacterial tannase genes have also been discovered. I performed phylogenetic analysis on 110 fungal and bacterial tannase reference sequences in an effort to observe the relationships between fungal and bacterial tannase. The generated maximum likelihood tree shows eight strongly supported tannase clades, with a rift among fungal tannases, which either align with proteobacterial tannase or actinobacterial tannase. Metagenomes were used to assess the biogeographical distribution of the tannase clades, revealing that they may have environmental specificity.
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\nAn unpublished observation made by the Despland lab of Concordia University suggested that forest tent caterpillars (Malacosoma disstria) from western Canada are unable to survive on tannin-rich sugar maple foliage (Acer saccharum), whereas populations in eastern Canada develop on sugar maple. In Chapter 2 I examine this observation, and explore the possibility of tannase-expressing secondary symbionts in eastern M. disstria. For the first time, the microbiome of M. disstria has been described using 16S rRNA gene sequencing and is shown to contain several genera known for expressing the tannase gene.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.249
Teacher spread0.236 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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