ArticleMolecular biology of the cell2025
Automated segmentation of soft X-ray tomography: Native cellular structure with submicron resolution at high-throughput for whole-cell quantitative imaging in yeast.
Article in Molecular biology of the cell, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Nuclear size and physical properties of the nucleoplasm are determined by colloid osmotic pressure at the nuclear envelope.bioRxiv : the preprint server for biology · 2026Article
- A scoping study of the whole-cell imaging literature as a foundation for the emerging field of cell anatomy.BMC biology · 2026Article
- Optimal disk packing of chloroplasts in plant cells.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A scoping study of the whole-cell imaging literature: a foundational corpus, potential for mesoscale data synthesis, and implications for standardization of an emerging field.bioRxiv : the preprint server for biology · 2025Article
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7 authors.
Funding
Abstract
Soft X-ray tomography (SXT) is an invaluable tool for quantitatively analyzing cellular structures at suboptical isotropic resolution. However, it has traditionally depended on manual segmentation, limiting its scalability for large datasets. Here, we leverage a deep learning-based autosegmentation pipeline to segment and label cellular structures in hundreds of cells across three
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.