Diverse higher-order structures, foundational for supporting a network's "meta-functions", play a vital role in structure, functionality, and the emergence of complex dynamics. Nevertheless, the problem of dismantling them has been consistently overlooked. In this paper, we introduce the concept of dismantling higher-order structures, with the objective of disrupting not only network connectivity but also eradicating all higher-order structures in each branch, thereby ensuring thorough functional paralysis. Given the diversity and unknown specifics of higher-order structures, identifying and targeting them individually is not practical or even feasible. Fortunately, their close association with -cores arises from their internal high connectivity. Thus, we transform higher-order structure measurement into measurements on -cores with corresponding orders. Furthermore, we propose the Belief Propagation-guided Higher-order Dismantling (BPHD) algorithm, minimizing dismantling costs while achieving maximal disruption to connectivity and higher-order structures, ultimately converting the network into a forest. BPHD exhibits the explosive vulnerability of network higher-order structures, counterintuitively showcasing decreasing dismantling costs with increasing structural complexity. Our findings offer a novel approach for dismantling malignant networks, emphasizing the substantial challenges inherent in safeguarding against such malicious attacks.
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http://dx.doi.org/10.3390/e26030248 | DOI Listing |
Sci Rep
January 2025
University of São Paulo, ICMC, São Carlos, 13566-590, Brazil.
Identifying driver genes is crucial for understanding oncogenesis and developing targeted cancer therapies. Driver discovery methods using protein or pathway networks rely on traditional network science measures, focusing on nodes, edges, or community metrics. These methods can overlook the high-dimensional interactions that cancer genes have within cancer networks.
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January 2025
School of Computer Science, Qufu Normal University, Rizhao 276826, China.
Brain imaging genetics aims to explore the association between genetic factors such as single nucleotide polymorphisms (SNPs) and brain imaging quantitative traits (QTs). However, most existing methods do not consider the nonlinear correlations between genotypic and phenotypic data, as well as potential higher-order relationships among subjects when identifying bi-multivariate associations. In this paper, a novel method called deep hyper-Laplacian regularized self-representation learning based structured association analysis (DHRSAA) is proposed which can learn genotype-phenotype associations and obtain relevant biomarkers.
View Article and Find Full Text PDFAuditory processing in the cerebral cortex is considered to begin with thalamocortical inputs to layer 4 (L4) of the primary auditory cortex (A1). In this canonical model, A1 L4 inputs initiate a hierarchical cascade, with higher-order cortices receiving pre-processed information for the slower integration of complex sounds. Here, we identify alternative ascending pathways in mice that bypass A1 and directly reach multiple layers of the secondary auditory cortex (A2), indicating parallel activation of these areas alongside sequential information processing.
View Article and Find Full Text PDFEur J Obstet Gynecol Reprod Biol
January 2025
Baylor College of Medicine Houston TX, United States.
Objective: Monochorionic diamniotic (MCDA) twins with amniotic fluid abnormalities that do not meet criteria for twin-twin transfusion syndrome (TTTS) concern physicians and families. This study aimed to describe the natural history of amniotic fluid abnormalities.
Methods: In this retrospective case-control study, TTTS screening ultrasounds and clinical records throughout all MCDA twin gestations were reviewed between 2018 and 2022 at a tertiary fetal care center.
Hum Mol Genet
January 2025
Department of Human Genetics, McGill University, 3666 McTavish Street, Montreal, QC H3A 1Y2, Canada.
Many genes in the human genome encode proteins that are dosage sensitive, meaning they require protein levels within a narrow range to properly execute function. To investigate if clinically relevant variation in protein levels impacts the same downstream pathways in human disease, we generated cell models of two SETBP1 syndromes: Schinzel-Giedion Syndrome (SGS) and SETBP1 haploinsufficiency disease (SHD), where SGS is caused by too much protein, and SHD is caused by not enough SETBP1. Using patient and sex-matched healthy first-degree relatives from both SGS and SHD SETBP1 cases, we assessed how SETBP1 protein dosage affects downstream pathways in human forebrain progenitor cells.
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