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Related Concept Videos

Molecular Shapes01:18

Molecular Shapes

Molecules have characteristic shapes that are crucial for their function. The arrangement of various electron groups around the central atom dictates their molecular geometry. Electron pairs in the valence shell of a central atom will adopt an arrangement that minimizes repulsions between the electron pairs by maximizing the distance between them. The valence electrons form either bonding pairs, located primarily between bonded atoms, or lone pairs.Two regions of electron density in a diatomic...
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Two-Dimensional Microscopy in Microbiology

Two-dimensional (2D) microscopy encompasses a range of optical techniques that capture images within a single focal plane, offering detailed representations of microscopic structures. These techniques are essential in biological and medical research, enabling the visualization of cellular and subcellular structures with different levels of contrast and specificity.There are several major types of 2D microscopy, each with strengths and applications.Bright-Field MicroscopyBright-field microscopy...
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X-ray Diffraction of Biological Samples

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Spectroscopy of Carboxylic Acid Derivatives

Infrared spectroscopy is primarily used to determine the types of bonds and functional groups. In carboxylic acid derivatives, a typical carbonyl bond absorption is observed around 1650–1850 cm−1. For esters, the absorption is recorded at around 1740 cm−1, while acid halides show the absorption at about 1800 cm−1. Another acid derivative, the acid anhydrides, exhibit two carbonyl absorption around 1760 cm−1 and 1820 cm−1, arising from the symmetrical and unsymmetrical carbonyl vibration.
In the...
Molecules with Multiple Chiral Centers02:25

Molecules with Multiple Chiral Centers

Molecules that possess multiple chiral centers can afford a large number of stereoisomers. For instance, while some molecules like 2-butanol have one chiral center, defined as a tetrahedral carbon atom with four different substituents attached, several molecules like butane-2,3-diol have multiple chiral centers. A simple formula to predict the number of stereoisomers possible for a molecule with n chiral centers is 2n. However, there can be a lower number where some of the stereoisomers are...
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Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been developed.

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Updated: Jun 16, 2026

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SCALE: unsupervised multiscale domain identification in spatial omics data.

Behnam Yousefi1,2, Darius P Schaub1,3, Robin Khatri1,4

  • 1Institute of Medical Systems Bioinformatics, Center for Biomedical AI (bAIome), Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf, Hamburg 20251, Germany.

Nucleic Acids Research
|January 7, 2026
PubMed
Summary
This summary is machine-generated.

We developed SCALE, a new algorithm for identifying hierarchical functional domains in spatial transcriptomics data. This tool reveals multiscale tissue organization, advancing our understanding of biological systems in health and disease.

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Single-cell spatial transcriptomics maps cellular states within tissues.
  • Biological systems exhibit hierarchical organization with multiscale functional domains.
  • Identifying these domain hierarchies computationally is challenging.

Purpose of the Study:

  • Introduce SCALE, an unsupervised algorithm for multiscale domain identification in spatial transcriptomics.
  • Enable the discovery of hierarchical functional domains at various spatial scales.
  • Provide a robust and scalable tool for analyzing complex tissue architectures.

Main Methods:

  • SCALE employs deep learning-based graph representation learning.
  • An entropy-based search algorithm is integrated for scale detection.
  • The algorithm is validated on simulated and real-world spatial transcriptomics datasets.

Main Results:

  • SCALE effectively identifies multiscale functional domains across diverse tissues (murine brain, kidney).
  • Demonstrated robustness and scalability on Xenium and MERFISH data.
  • Outperformed state-of-the-art methods by up to 191.1 percentage points.

Conclusions:

  • SCALE is a user-friendly tool for uncovering hierarchical tissue organization.
  • Facilitates deeper insights into tissue function and cellular interactions.
  • Advances the study of biological systems in both health and disease contexts.