With the development of information technology, the interactions between nodes are no longer restricted to two nodes. Recently, researchers have proposed a higher-order network, which is more suitable to describe the multidimensional interaction relationships in systems. A higher-order network with good robustness can effectively resist natural disasters and deliberate attacks. How to improve the robustness of the higher-order network is worth studying. In this paper, we construct two higher-order networks based on the simplex structure. In addition, we propose a capacity load model that can describe the robustness of higher-order networks. The simulation results show that the robustness of the higher-order network is positively correlated with the size of the high-order network, the larger the size of the higher-order network, the more robust the higher-order network is in two attack strategies. In addition, the robustness of higher-order is related to the number of 2-simplexes in the network. Furthermore, the robustness is affected by the weight coefficients of 1-simplex and 2-simplex interactions. Therefore, we can improve robustness of higher-order networks by controlling the weight coefficients of the 1- and 2-simplex in higher-order networks. We verified the conclusions by two synthetic higher-order networks and a constructed higher-order network based on real data.
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http://dx.doi.org/10.1038/s41598-025-91842-y | DOI Listing |
Proc Jpn Acad Ser B Phys Biol Sci
March 2025
Laboratory for Sleeping-Brain Dynamics, Research Center for Idling Brain Science, University of Toyama, Toyama, Japan.
Over the past decades, the understanding of sleep has evolved to be a fundamental physiological mechanism integral to the processing of different types of memory rather than just being a passive brain state. The cyclic sleep substates, namely, rapid eye movement (REM) sleep and non-REM (NREM) sleep, exhibit distinct yet complementary oscillatory patterns that form inter-regional networks between different brain regions crucial to learning, memory consolidation, and memory retrieval. Technical advancements in imaging and manipulation approaches have provided deeper understanding of memory formation processes on multi-scales including brain-wide, synaptic, and molecular levels.
View Article and Find Full Text PDFComput Struct Biotechnol J
February 2025
Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas, 4505 S Maryland Pkwy, Las Vegas, NV 89154, USA.
Protein sequences primarily determine their stability and functions. Mutations may occur at one, two, or three positions at the same time (low-order variants) or at multiple positions simultaneously (high-order variants), which affect protein functions. So far, low-order variants, such as single variants, double variants, and triple variants, have been well-studied through high-throughput experimental scanning techniques and computational prediction methods.
View Article and Find Full Text PDFSci Rep
March 2025
Department of Computer Science and Engineering, Galgotias University, Greater Noida, UP, India.
In this paper, the author introduces the Neural-ODE Hybrid Block Method, which serves as a direct solution for solving higher-order ODEs. Many single and multi-step methods employed in numerical approximations lose their stability when applied in the solution of higher-order ODEs with oscillatory and/or exponential features, as in this case. A new hybrid approach is formulated and implemented, which incorporates both the approximate power of neural networks and the stability and robustness of block numerical methods.
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Interdisciplinary Nanoscience Center (iNANO), Aarhus University, Gustav Wieds Vej 14, Aarhus C 8000, Denmark. Electronic address:
Hydrogel biomaterials have been extensively explored for applications in medicine, materials science, and the development of functionalized materials. Traditionally, hydrogels were produced using simple polymers, but advancements over recent decades have enabled the use of biological materials such as proteins, peptides, polysaccharides, and even amyloid fibrils. Among these, amyloid-based hydrogels have demonstrated unique advantages, including enhanced cell adhesion and differentiation.
View Article and Find Full Text PDFBrief Bioinform
March 2025
Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore.
Even with the significant advances of AlphaFold-Multimer (AF-Multimer) and AlphaFold3 (AF3) in protein complex structure prediction, their accuracy is still not comparable with monomer structure prediction. Efficient and effective quality assessment (QA) or estimation of model accuracy models that can evaluate the quality of the predicted protein-complexes without knowing their native structures are of key importance for protein structure generation and model selection. In this paper, we leverage persistent homology (PH) to capture the atomic-level topological information around residues and design a topological deep learning-based QA method, TopoQA, to assess the accuracy of protein complex interfaces.
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