We review pro and contra of the hypothesis that generic polymer properties of topological constraints are behind many aspects of chromatin folding in eukaryotic cells. For that purpose, we review, first, recent theoretical and computational findings in polymer physics related to concentrated, topologically simple (unknotted and unlinked) chains or a system of chains. Second, we review recent experimental discoveries related to genome folding. Understanding in these fields is far from complete, but we show how looking at them in parallel sheds new light on both.
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http://dx.doi.org/10.1088/0034-4885/77/2/022601 | DOI Listing |
Recurrent neural networks (RNNs) have emerged as a prominent tool for modeling cortical function, and yet their conventional architecture is lacking in physiological and anatomical fidelity. In particular, these models often fail to incorporate two crucial biological constraints: i) Dale's law, i.e.
View Article and Find Full Text PDFEntropy (Basel)
December 2024
National Institute for Fusion Science, Oroshi, Toki 509-5292, Gifu, Japan.
A topological constraint, characterized by the Casimir invariant, imparts non-trivial structures in a complex system. We construct a kinetic theory in a constrained phase space (infinite-dimensional function space of macroscopic fields), and characterize a self-organized structure as a thermal equilibrium on a leaf of foliated phase space. By introducing a model of a grand canonical ensemble, the Casimir invariant is interpreted as the number of topological particles.
View Article and Find Full Text PDFPNAS Nexus
January 2025
School of Physical Science and Engineering, Tongji University, Shanghai 200092, P. R. China.
Wiring patterns of brain networks embody a trade-off between information transmission, geometric constraints, and metabolic cost, all of which must be balanced to meet functional needs. Geometry and wiring economy are crucial in the development of brains, but their impact on artificial neural networks (ANNs) remains little understood. Here, we adopt a wiring cost-controlled training framework that simultaneously optimizes wiring efficiency and task performance during structural evolution of sparse ANNs whose nodes are located at arbitrary but fixed positions.
View Article and Find Full Text PDFNeuroimage
January 2025
College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China. Electronic address:
Dynamic brain networks (DBNs) can capture the intricate connections and temporal evolution among brain regions, becoming increasingly crucial in the diagnosis of neurological disorders. However, most existing researches tend to focus on isolated brain network sequence segmented by sliding windows, and they are difficult to effectively uncover the higher-order spatio-temporal topological pattern in DBNs. Meantime, it remains a challenge to utilize the structure connectivity prior in the DBNs analysis.
View Article and Find Full Text PDFNat Nanotechnol
January 2025
State Key Laboratory for Mesoscopic Physics, School of Physics, Peking University, Beijing, China.
Interfacial ferroelectricity emerges in non-centrosymmetric heterostructures consisting of non-polar van der Waals (vdW) layers. Ferroelectricity with concomitant Coulomb screening can switch topological currents or superconductivity and simulate synaptic response. So far, it has only been realized in bilayer graphene moiré superlattices, posing stringent requirements to constituent materials and twist angles.
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