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Record W4318710351 · doi:10.2478/jofnem-2022-0058

Genome Characterization and Development of Real-Time PCR Assays for <i>Ditylenchus dipsaci</i> and <i>D. weischeri</i>

2022· article· en· W4318710351 on OpenAlexafffund
Екатерина Пономарева, Ahmed Badiss, Tahera Sultana, Qing Yu, Hai D. T. Nguyen

Bibliographic record

VenueJournal of Nematology · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicNematode management and characterization studies
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsBiologyNematodeGenomeWeedGeneGeneticsDNA sequencingGenomicsBotanyComputational biologyEcology

Abstract

fetched live from OpenAlex

Abstract The stem and bulb nematode Ditylenchus dipsaci is a destructive nematode pest on many crops and is internationally quarantined in many countries, whereas Ditylenchus weischeri , only known to infect a weed plant ( Cirsium arvense ), is an unregulated nematode species with no known economic importance. In this study, we used comparative genomics to identify multiple gene regions and developed novel real-time PCR assays for the detection of D. dipsaci and D. weischeri . We sequenced the genomes of two mixed-stage nematode populations of D. dipsaci and two mixed-stage nematode populations of D. weischeri . The assembled genomes of D. dipsaci were 228.2 Mb and 239.5 Mb, and the genomes of D. weischeri were 177.0 Mb and 196.3 Mb. Depending on the species, 21,403–27,365 gene models were predicted. Using orthologous group analysis, single-copy and species-specific genes were identified. Primers and probes were designed targeting two species-specific genes in each species. The assays detected as low as 12 pg of DNA from the target species, or as few as five nematodes, with a C q of 31 cycles or less. Our study provides genome data for two additional D. dipsaci isolates and two D. weischeri isolates, and four new and validated molecular assays to be used for rapid detection and identification of the two species.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.980
Threshold uncertainty score0.182

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 head0.020
GPT teacher head0.218
Teacher spread0.198 · 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

Citations9
Published2022
Admission routes2
Has abstractyes

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