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Fineness of Cement01:15

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The fineness of cement directly influences the rate of hydration, as the hydration begins at the surface of the cement particles. In addition to hydration, the fineness of cement is vital for various properties of concrete including workability, gypsum requirement, and long-term behavior. The fineness of cement is represented in terms of the specific surface of cement which is typically measured in square meters per kilogram, with several methods available for this determination.
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The fineness modulus (FM) of aggregate is a numerical index that measures the coarseness or fineness of the particles. It is calculated by adding the cumulative percentages of aggregate retained on each of a specified series of sieves and dividing the sum by 100.
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Polymers are classified as linear or branched on the basis of their chain architecture. The polymer chains in linear polymers have a long chain-like structure with minimal to no branching at all. Even if a polymer features large substituent groups on the monomer, which appear as branches to the skeleton, it is not considered a branched polymer. A branched polymer contains secondary polymer chains that arise from the main polymer chain. The branching occurs when the polymer growth shifts from...
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Hist2Cell: Deciphering fine-grained cellular architectures from histology images.

Weiqin Zhao1, Zhuo Liang1, Xianjie Huang2

  • 1School of Computing and Data Science, the University of Hong Kong, Hong Kong SAR, China.

Cell Genomics
|January 27, 2026
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Summary

Hist2Cell accurately identifies fine-grained cell types from histology images, improving spatial transcriptomics analysis and cancer prognosis. This method enhances cost-efficient studies in large-scale spatial biology.

Keywords:
cell-type predictioncomputational pathologygraph learningspatial transcriptomics

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Area of Science:

  • Computational biology
  • Genomics
  • Pathology

Background:

  • Histology images are cost-effective for predicting cellular phenotypes via spatial transcriptomics.
  • Current methods lack accuracy in individual gene expression and fine-grained cell type prediction.

Purpose of the Study:

  • To introduce Hist2Cell, a novel framework for resolving fine-grained transcriptional cell types directly from histology images.
  • To enhance the accuracy and applicability of spatial transcriptomics analysis in cancer research.

Main Methods:

  • Developed Hist2Cell, a vision graph-transformer framework.
  • Trained the model on human lung and breast cancer datasets.
  • Validated generalization on The Cancer Genome Atlas (TCGA) cohorts.

Main Results:

  • Hist2Cell accurately predicts cell-type abundance (Pearson correlation > 0.80).
  • The framework captures cellular colocalization and generalizes across large datasets without re-training.
  • Identified relationships between tissue microenvironments, cell types, and patient mortality.

Conclusions:

  • Hist2Cell enables cost-efficient, large-scale spatial biology studies.
  • The framework facilitates precise cancer prognosis by analyzing tissue microenvironments.
  • Hist2Cell advances the integration of histology imaging and transcriptomics for biomedical research.