Nano‐Dissection and Sequencing of DNA at Single Sub‐Nuclear Structures
Bibliographic record
Abstract
The relative positioning of gene loci within a mammalian nucleus is non-random and plays a role in gene regulation. Some sub-nuclear structures may represent "hubs" that bring specific genetic loci into close proximity where co-regulatory mechanisms can operate. The identification of loci in proximity to a shared sub-nuclear structure can provide insights into the function of the associated structure, and reveal relationships between the loci sharing a common association. A technique is introduced based on the nano-dissection of DNA from thin sections of cells by high-precision nano-tools operated inside a scanning electron microscope. The ability to dissect and identify gene loci occupying a shared site at a single sub-nuclear structure is demonstrated here for the first time. The technique is applied to the nano-dissection of DNA in vicinity of a single promyelocytic leukemia nuclear body (PML NB), and reveals novel loci from several chromosomes that are confirmed to associate at PML NBs with statistical significance in a cell population. Furthermore, it is demonstrated that pairs of loci from different chromosomes congregate at the same nuclear body. It is proposed that this technique is the first that allows the de novo determination of gene loci associations with single nuclear sub-structures.
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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 | 0.000 | 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) | 0.001 | 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".