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Record W2097813014 · doi:10.1177/104063870001200405

Representational Differential Analysis Detects Amplification of Satellite Sequences in Postweaning Multisystemic Wasting Syndrome of Pigs

2000· article· en· W2097813014 on OpenAlexaff
Ana Bratanich, John A. Ellis, A. Blanchetot

Bibliographic record

VenueJournal of Veterinary Diagnostic Investigation · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Virus Infections Studies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsBiologyEthidium bromideHomology (biology)Agarose gel electrophoresisSuppression subtractive hybridizationVirologygenomic DNAclone (Java method)Sequence analysisDNA sequencingMolecular biologyGeneticsGeneDNAPeptide sequence

Abstract

fetched live from OpenAlex

Representational difference analysis (RDA) was used as a molecular approach to identify unique sequences associated with postweaning multisystemic wasting syndrome (PMWS) in pigs. Three rounds of subtractive hybridization and amplification between driver DNA extracted from normal pigs and tester DNA from PMWS-affected animals were performed. The final product corresponding to sequences associated with PMWS in pigs was analyzed using agarose gel electrophoresis, and 9 fragments were visualized after staining with ethidium bromide. Eight recombinants were successively cloned and sequenced, and the results were then compared with existing databases. Most of the PMWS clones isolated were satellite sequences from pig centrometric regions and 1 was a microsatellite sequence. One clone represented a microsatellite sequence, and 2 clones showed no homology with any gene found in the databases. The sequence comparison data did not reveal any homology with an infectious agent such as a virus or a bacterium. In the present experimental setting, it was concluded that PMWS in pigs triggers molecular changes such as an amplification of genomic regions containing repeated sequences.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.468
Threshold uncertainty score0.215

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.044
GPT teacher head0.268
Teacher spread0.224 · 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

Citations3
Published2000
Admission routes1
Has abstractyes

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