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Record W2741381084 · doi:10.1007/s12192-017-0832-7

Identification and expression analysis of multiple small heat shock protein genes in spruce budworm, Choristoneura fumiferana (L.)

2017· article· en· W2741381084 on OpenAlexafffund
Guoxing Quan, Jun Duan, Tim R. Ladd, Peter J. Krell

Bibliographic record

VenueCell Stress and Chaperones · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsUniversity of GuelphNatural Resources CanadaCanadian Forest Service
FundersOntario Ministry of Natural Resources and Forestry
KeywordsSpruce budwormChoristoneura fumiferanaMalpighian tubule systemBiologyHeat shock proteinPEST analysisLarvaGeneGene expressionBotanyMolecular biologyGeneticsTortricidae

Abstract

fetched live from OpenAlex

Fifteen small heat shock protein (sHSP) genes were identified from spruce budworm, Choristoneura fumiferana (L.), an important native forest pest in North America. The transcript levels of each CfHSP were measured under non-stress conditions in all life stages from egg to adult and in five different larval tissues. CfHSP transcript levels showed variation during development, with highest levels in adults and lowest in eggs. Most CfHSP transcripts are highly expressed in larval fat body and Malpighian tubules; two CfHSPs display extremely high expression in the head and epidermis. Upon heat stress, nine CfHSP genes are significantly upregulated, increasing by 50- to 2500-fold depending on developmental stage and tissue type. Upon starvation, eight CfHSPs are upregulated or downregulated, whereas six others retain constant expression. These results suggest that CfHSPs have important and multiple roles in spruce budworm development and in response to heat stress and starvation.

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 score0.004
Threshold uncertainty score0.008

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)0.0000.000

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 head0.018
GPT teacher head0.271
Teacher spread0.253 · 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

Citations24
Published2017
Admission routes2
Has abstractno

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