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Record W2620754563 · doi:10.1038/s41598-017-03570-7

Comparison of Different Drying Methods for Recovery of Mushroom DNA

2017· article· en· W2620754563 on OpenAlexafffund
Shouxian Wang, Yu Liu, Jianping Xu

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldMedicine
TopicFungal Biology and Applications
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMushroomAgaricus bisporusPolymerase chain reactiongenomic DNABiologyDNADNA extractionEdible mushroomPopulationGeneFood scienceBotanyBiochemistryMedicine

Abstract

fetched live from OpenAlex

Several methods have been reported for drying mushroom specimens for population genetic, taxonomic, and phylogenetic studies. However, most methods have not been directly compared for their effectiveness in preserving mushroom DNA. In this study, we compared silica gel drying at ambient temperature and oven drying at seven different temperatures. Two mushroom species representing two types of fruiting bodies were examined: the fleshy button mushroom Agaricus bisporus and the leathery shelf fungus Trametes versicolor. For each species dried with the eight methods, we assessed the mushroom water loss rate, the quality and quantity of extracted DNA, and the effectiveness of using the extracted DNA as a template for PCR amplification of two DNA fragments (ITS and a single copy gene). Dried specimens from all tested methods yielded sufficient DNA for PCR amplification of the two genes in both species. However, differences among the methods for the two species were found in: (i) the time required by different drying methods for the fresh mushroom tissue to reach a stable weight; and (ii) the relative quality and quantity of the extracted genomic DNA. Among these methods, oven drying at 70 °C for 3-4 h seemed the most efficient for preserving field mushroom samples for subsequent molecular work.

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)0.001
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 score0.025
Threshold uncertainty score0.217

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.089
GPT teacher head0.454
Teacher spread0.365 · 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

Citations16
Published2017
Admission routes2
Has abstractyes

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