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Record W2058436850 · doi:10.5539/jas.v5n7p82

Comparative Analysis of Six DNA Extraction Methods in Cowpea (Vigna unguiculata L.Walp)

2013· article· en· W2058436850 on OpenAlexvenueno aff
Huaqiang Tan, Haitao Huang, Mamman Tie, Jianyao Ma, Huanxiu Li

Bibliographic record

VenueJournal of Agricultural Science · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural pest management studies
Canadian institutionsnot available
FundersSichuan Agricultural University
KeywordsVignaDNA extractionRestriction enzymePolymerase chain reactionDNABiologyExtraction (chemistry)BiotechnologyGenetic analysisHorticultureChromatographyChemistryGeneGenetics

Abstract

fetched live from OpenAlex

High quality DNA extractions are a prerequisite for genetic studies of a variety of plants including cowpea (Vigna unguiculata). Nowadays, there are a great number of plant DNA extraction methods, and commercially available extraction kits are also becoming more and more popular. It appears that different procedures work best for different plant groups. Thus in the genetic studies of cowpea, which DNA extraction method to choose becomes a concern. To solve this problem, five classic plant DNA isolation methods, including three CTAB methods and two SDS methods, were compared and evaluated while isolation using a commercial kit was also undertaken. The DNA extracted by these six methods from two-week-old cowpea seedlings were analyzed according to their cost and time, yield, purity, integrity, and functionality in restriction endonuclease digestion and PCR (polymerase chain reaction) based downstream analysis. After the evaluation, one most suitable method, described by Dellaporta et al. (1983) was selected and chosen for isolating DNA from young leaves of cowpea seedlings. The cost and time required in this method was relatively low. In addition, the quantity and the quality of the DNA extracted by this method were high enough to perform hundreds of PCR-based reactions.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.965
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.007
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.045
GPT teacher head0.341
Teacher spread0.296 · 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

Citations10
Published2013
Admission routes1
Has abstractyes

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