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Record W4401642841 · doi:10.1590/1984-3143-ar2024-0030

Haploid embryos and embryonic stem cells to produce offspring with predetermined parental genomes in cattle

2024· article· en· W4401642841 on OpenAlexafffund
Lawrence C. Smith, Luis Águila, Rafael Vilar Sampaio, Ricardo Perecin Nociti, Jacinthe Therrien, Flávio Vieira Meirelles

Bibliographic record

VenueAnimal Reproduction · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Genetics and Reproduction
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of CanadaAgencia Nacional de Investigación y DesarrolloFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsBiologyOffspringEmbryonic stem cellEmbryoPloidyGenomeGeneticsStem cellAndrologyEmbryogenesisCell biologyGenePregnancy

Abstract

fetched live from OpenAlex

Selection strategies are performed post-fertilization when the random combination of paternal and maternal genomes has already occurred. It would be greatly advantageous to eliminate meiotic uncertainty by selecting genetically superior gametes before fertilization. To achieve this goal, haploid embryonic cells and embryonic stem cell lineages could be derived, genotyped, and used to substitute gametes. On the paternal side, androgenetic development can be achieved by removing the maternal chromosomes from the oocyte before or after fertilization. We have shown that once developed into an embryo, haploid cells can be removed for genotyping and, if carrying the selected genome, be used to replace sperm at fertilization. A similar strategy can be used on the maternal side by activating the oocyte parthenogenetically and using some embryonic cells for genotyping while the remaining are used to produce diploid embryos by fertilization. Placed together, both androgenetic and parthenogenetic haploid cells that have been genotyped to identify optimal genomes can be used to produce offspring with predetermined genomes. Successes and problems in developing such a breeding platform to achieve this goal are described and discussed below.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.633

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 head0.010
GPT teacher head0.234
Teacher spread0.223 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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