Variation in MLH1 distribution in recombination maps for individual chromosomes from human males
Bibliographic record
Abstract
Meiotic recombination is essential for the segregation of homologous chromosomes and the formation of normal haploid gametes. Little is known about patterns of meiotic recombination in human germ cells or the mechanisms that control these patterns. Documentation of the normal range of variability of recombination distribution over the genome among individuals is an essential prerequisite for understanding abnormal recombination patterns, which may be associated with non-disjunction and chromosome rearrangements. In this article, variation in recombination maps for individual chromosomes among 10 normal human males is examined for the first time. An immunocytogenetic approach allowed analysis of pachytene cells, using antibodies to detect the mature synaptonemal complex (SCP1/SCP3), the centromere (CREST) and sites of crossing over (MLH1). Individual bivalents were identified with centromere-specific multicolor fluorescence in situ hybridization. Significant heterogeneity in MLH1 focus frequency across donors was observed for larger chromosome arms (P<0.05, one-way ANOVA). Significant inter-donor variation in the overall crossover frequency per cell was also found (P<0.0001, one-way ANOVA). Furthermore, several chromosome arms showed significant differences in crossover distribution along the SCs among donors. Inter-individual variation in interference distances was observed for all chromosomes. The significance of altered recombination patterns among individuals and the role of interference are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".