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Record W1489619084 · doi:10.1155/2015/941357

Development of Microsatellite Markers Derived from Expressed Sequence Tags of Polyporales for Genetic Diversity Analysis of Endangered<i>Polyporus umbellatus</i>

2015· article· en· W1489619084 on OpenAlexaff
Yuejin Zhang, Yuanyuan Chen, Rui-Hong Wang, Ailin Zeng, Michael K. Deyholos, Jia Shu, Hongbo Guo

Bibliographic record

VenueBioMed Research International · 2015
Typearticle
Languageen
FieldMedicine
TopicFungal Biology and Applications
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersMinistry of Education of the People's Republic of China
KeywordsUPGMABiologyGenetic diversityMicrosatelliteLoss of heterozygosityGeneticsBotanyGenetic variationGeneAllelePopulation

Abstract

fetched live from OpenAlex

A large scale of EST sequences of Polyporales was screened in this investigation in order to identify EST-SSR markers for various applications. The distribution of EST sequences and SSRs in five families of Polyporales was analyzed, respectively. Mononucleotide was the most abundant type, followed by trinucleotide. Among five families, Ganodermataceae occupied the most SSR markers, followed by Coriolaceae. Functional prediction of SSR marker-containing EST sequences in Ganoderma lucidum obtained three main groups, namely, cellular component, biological process, and molecular function. Thirty EST-SSR primers were designed to evaluate the genetic diversity of 13 natural Polyporus umbellatus accessions. Twenty one EST-SSRs were polymorphic with average PIC value of 0.33 and transferability rate of 71%. These 13 P. umbellatus accessions showed relatively high genetic diversity. The expected heterozygosity, Nei's gene diversity, and Shannon information index were 0.41, 0.39, and 0.57, respectively. Both UPGMA dendrogram and principal coordinate analysis (PCA) showed the same cluster result that divided the 13 accessions into three or four groups.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.279

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.177
GPT teacher head0.396
Teacher spread0.220 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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