Characterizing the three‐dimensional organization of telomeres
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Quantitative analysis can be used in combination with fluorescence microscopy. Although the human eye is able to obtain good qualitative results, when analyzing the spatial organization of telomeres in interphase nuclei, there is a need for quantitative results based on image analysis. METHODS: We developed a tool for analyzing three-dimensional images of telomeres stained by fluorescence in situ hybridization in interphase nuclei with DNA counterstained with 4',6-diamidino-2-phenylindole. After deconvolution of the image, we segmented individual telomeres. From the location of the telomeres we derived a distribution parameter rhoT, which indicated whether the telomeres were in a disk (rhoT >> 1) or not (rhoT approximately 1). We sorted mouse lymphocyte nuclei and measured rhoT. We also performed a bromodeoxyuridine synchronous cell sorting experiment on live cells and measured rhoT at several instances. RESULTS: Measuring rhoT for nuclei in G0/G1, S, and G2 produced 1.4 +/- 0.1, 1.5 +/- 0.2, and 14 +/- 2, respectively, showing a significant difference between G2 and G0/G1 or S. For the bromodeoxyuridine synchronous cell sorting experiment, we found a cell cycle dependency of rhoT and a correlation between rhoT and an observer. CONCLUSIONS: In this study we present a quantitative method to characterize the organization of telomeres using three-dimensional imaging, image processing, and image analysis.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 it