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Extraction and detection of baculoviral DNA from lake water, detritus and forest litter

2001· article· en· W2073993634 on OpenAlexafffund
Laura S. England, J. T. Trevors, Stephen B. Holmes

Bibliographic record

VenueJournal of Applied Microbiology · 2001
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicViral Infectious Diseases and Gene Expression in Insects
Canadian institutionsUniversity of GuelphCanadian Forest Service
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDNA extractionBiologyExtraction (chemistry)DNAChromatographyDetection limitPolymerase chain reactionMolecular biologyChemistryBiochemistryGene

Abstract

fetched live from OpenAlex

AIMS: This paper describes a quick, reproducible, sensitive method for baculoviral DNA extraction, purification and detection from freshwater and forest litter environments. METHODS AND RESULTS: The extraction protocol utilizes enzymatic and chemical lysis and physical disruption. To assess the efficiency of the extraction and purification protocol, PCR was used to detect a 530 bp DNA fragment from the genome of a genetically-modified baculovirus, Choristoneura fumiferana NPVegt-/lacZ+. The detection limit of PCR amplification was routinely about 4.1 x 102 occlusion bodies (OBs) 450 microl-1 lake water. Template DNA from the detritus and forest litter samples required 100-fold dilutions before use in PCR reactions. The detection limits for detritus and forest litter samples were routinely about 7.41 x 103 and 2.08 x 104 OBs 0.5 g-1 dry weight, respectively. CONCLUSION: The DNA extraction and purification methodology is reproducible, sensitive and can be used in lieu of, or in conjunction with, insect bioassays. SIGNIFICANCE AND IMPACT OF THE STUDY: The DNA extraction and purification protocol described in this paper will facilitate risk assessment and ecological studies of both wild-type and genetically-modified baculoviruses.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.302

Codex and Gemma teacher scores by category

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 head0.004
GPT teacher head0.216
Teacher spread0.212 · 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

Citations25
Published2001
Admission routes2
Has abstractyes

Explore more

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