We formulate a hydrodynamic theory of confluent epithelia: i.e. monolayers of epithelial cells adhering to each other without gaps. Taking advantage of recent progresses toward establishing a general hydrodynamic theory of -atic liquid crystals, we demonstrate that collectively migrating epithelia feature both nematic (i.e. = 2) and hexatic (i.e. = 6) orders, with the former being dominant at large and the latter at small length scales. Such a remarkable multiscale liquid crystal order leaves a distinct signature in the system's structure factor, which exhibits two different power-law scaling regimes, reflecting both the hexagonal geometry of small cells clusters and the uniaxial structure of the global cellular flow. We support these analytical predictions with two different cell-resolved models of epithelia - i.e. the self-propelled Voronoi model and the multiphase field model - and highlight how momentum dissipation and noise influence the range of fluctuations at small length scales, thereby affecting the degree of cooperativity between cells. Our construction provides a theoretical framework to conceptualize the recent observation of multiscale order in layers of Madin-Darby canine kidney cells and pave the way for further theoretical developments.
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http://dx.doi.org/10.7554/eLife.86400 | DOI Listing |
Soft Matter
January 2025
Department of Physical Chemistry, Complutense University of Madrid, Av. Complutense s/n, 28040 Madrid, Spain.
Sci Rep
January 2025
School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China.
In order to solve the problem of weak single domain generalization ability in existing crowd counting methods, this study proposes a new crowd counting framework called Multi-scale Attention and Hierarchy level Enhancement (MAHE). Firstly, the model can focus on both the detailed features and the macro information of structural position changes through the fusion of channel attention and spatial attention. Secondly, the addition of multi-head attention feature module facilitates the model's capacity to effectively capture complex dependency relationships between sequence elements.
View Article and Find Full Text PDFNat Commun
January 2025
College of Materials Science and Technology; Key Laboratory of Material Preparation and Protection for Harsh Environment; Nanjing University of Aeronautics and Astronautics, Nanjing, 211100, China.
With the development of nanotechnology, nano-functional units of different dimensions, morphologies, and sizes exhibit the potential for efficient microwave absorption (MA) performance. However, the multi-unit coupling enhancement mechanism triggered by the alignment and orientation of nano-functional units has been neglected, hindering the further development of microwave absorbing materials (MAMs). In this paper, two typical ZIF-derived nanomaterials are self-assembled into two-dimensional ordered polyhedral superstructures by the simple ice template method.
View Article and Find Full Text PDFNature
January 2025
Department of Mechanical Engineering, Columbia University, New York, NY, USA.
Mechanical force is an essential feature for many physical and biological processes, and remote measurement of mechanical signals with high sensitivity and spatial resolution is needed for diverse applications, including robotics, biophysics, energy storage and medicine. Nanoscale luminescent force sensors excel at measuring piconewton forces, whereas larger sensors have proven powerful in probing micronewton forces. However, large gaps remain in the force magnitudes that can be probed remotely from subsurface or interfacial sites, and no individual, non-invasive sensor is capable of measuring over the large dynamic range needed to understand many systems.
View Article and Find Full Text PDFNeural Netw
December 2024
The school of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China. Electronic address:
Emotion recognition via electroencephalogram (EEG) signals holds significant promise across various domains, including the detection of emotions in patients with consciousness disorders, assisting in the diagnosis of depression, and assessing cognitive load. This process is critically important in the development and research of brain-computer interfaces, where precise and efficient recognition of emotions is paramount. In this work, we introduce a novel approach for emotion recognition employing multi-scale EEG features, denominated as the Dynamic Spatial-Spectral-Temporal Network (DSSTNet).
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