Chromosome organization and chromatin modification: influence on genome function and evolution
Bibliographic record
Abstract
Histone modifications of nucleosomes distinguish euchromatic from heterochromatic chromatin states, distinguish gene regulation in eukaryotes from that of prokaryotes, and appear to allow eukaryotes to focus recombination events on regions of highest gene concentrations. Four additional epigenetic mechanisms that regulate commitment of cell lineages to their differentiated states are involved in the inheritance of differentiated states, e.g., DNA methylation, RNA interference, gene repositioning between interphase compartments, and gene replication time. The number of additional mechanisms used increases with the taxon's somatic complexity. The ability of siRNA transcribed from one locus to target, in trans, RNAi-associated nucleation of heterochromatin in distal, but complementary, loci seems central to orchestration of chromatin states along chromosomes. Most genes are inactive when heterochromatic. However, genes within beta-heterochromatin actually require the heterochromatic state for their activity, a property that uniquely positions such genes as sources of siRNA to target heterochromatinization of both the source locus and distal loci. Vertebrate chromosomes are organized into permanent structures that, during S-phase, regulate simultaneous firing of replicon clusters. The late replicating clusters, seen as G-bands during metaphase and as meiotic chromomeres during meiosis, epitomize an ontological utilization of all five self-reinforcing epigenetic mechanisms to regulate the reversible chromatin state called facultative (conditional) heterochromatin. Alternating euchromatin/heterochromatin domains separated by band boundaries, and interphase repositioning of G-band genes during ontological commitment can impose constraints on both meiotic interactions and mammalian karyotype evolution.
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 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 | 0.000 | 0.001 |
| 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) | 0.003 | 0.001 |
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".