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Related Concept Videos

Cohesion01:07

Cohesion

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Cohesion is the attraction between molecules of the same type, such as water molecules. Water molecules have an overall neutral charge but are polar molecule. An oxygen atom in one water molecule has a partial negative charge that can bind to a hydrogen atom with a partial positive charge in a second water molecule, forming a hydrogen bond. Each water molecule can form up to four hydrogen bonds with other water molecules. Hydrogen bonds are responsible for water's cohesive nature.
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Depth Perception and Spatial Vision01:15

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Coordination Number and Geometry02:57

Coordination Number and Geometry

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For transition metal complexes, the coordination number determines the geometry around the central metal ion. Table 1 compares coordination numbers to molecular geometry. The most common structures of the complexes in coordination compounds are octahedral, tetrahedral, and square planar.
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VSEPR Theory for Determination of Electron Pair Geometries
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Molecular Geometry and Dipole Moments02:36

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The VSEPR theory can be used to determine the electron pair geometries and molecular structures as follows:
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Radicals: Electronic Structure and Geometry01:07

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This lesson delves into the geometry of a radical, which is influenced by the electronic structure of the molecule. The principle is similar to that of a lone pair, where the unpaired electron influences the geometry at the radical center.
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Updated: Jan 24, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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BioLACE: unifying spatial geometry and marker priors for cohesive cell-type clustering in spatial transcriptomics.

Haoran Qin, Yunfei Hu, Yuling Zhu

    Biorxiv : the Preprint Server for Biology
    |January 23, 2026
    PubMed
    Summary

    BioLACE is a new framework for spatial transcriptomics (ST) that integrates spatial data, gene expression, and marker genes. It improves cell type clustering and provides interpretable results for ST analysis.

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    Area of Science:

    • Genomics
    • Computational Biology
    • Bioinformatics

    Background:

    • Spatial transcriptomics (ST) offers high-resolution tissue architecture insights.
    • Current graph-based deep learning methods for ST lack interpretability and biological prior integration.
    • Marker gene information is crucial for accurate cell type identification in ST.

    Purpose of the Study:

    • To introduce BioLACE, a scalable framework for spatial transcriptomics analysis.
    • To unify spatial structure, transcriptomic variation, and marker gene profiles.
    • To enhance cell type clustering accuracy and biological interpretability in ST.

    Main Methods:

    • Developed BioLACE, a framework utilizing a shared Variational Autoencoder (VAE) latent space.
    • Implemented three joint optimization objectives: VAE reconstruction loss, graph Laplacian regularizer, and marker-informed contrastive loss.
    • Applied BioLACE to MERFISH hypothalamus, mouse spinal cord, and Slide-seq mouse cerebellum datasets.

    Main Results:

    • BioLACE achieved superior cell type clustering accuracy across diverse ST datasets.
    • Demonstrated well-defined, biologically consistent boundaries in reconstructed tissue architectures.
    • Generated interpretable latent representations, facilitating biological insights.

    Conclusions:

    • BioLACE offers a scalable and generalizable approach for modern spatial transcriptomics analysis.
    • The framework effectively integrates spatial, transcriptomic, and marker gene information.
    • BioLACE enhances the interpretability and accuracy of ST data analysis.