Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, Maryland 20742, USA.
Published: September 2006
AI Article Synopsis
Article Abstract
The largest eigenvalue of the adjacency matrix of networks is a key quantity determining several important dynamical processes on complex networks. Based on this fact, we present a quantitative, objective characterization of the dynamical importance of network nodes and links in terms of their effect on the largest eigenvalue. We show how our characterization of the dynamical importance of nodes can be affected by degree-degree correlations and network community structure. We discuss how our characterization can be used to optimize techniques for controlling certain network dynamical processes and apply our results to real networks.
Background: Duchenne muscular dystrophy (DMD) is a devastating disease characterized by progressive muscle wasting that leads to diminished lifespan. In addition to the inherent weakness of dystrophin-deficient muscle, the dysfunction of resident muscle stem cells (MuSC) significantly contributes to disease progression.
Methods: Using the mdx mouse model of DMD, we performed an in-depth characterization of disease progression and MuSC function in dystrophin-deficient skeletal muscle using immunohistology, isometric force measurements, transcriptomic analysis and transplantation assays.
Aim: Multiple sclerosis (MS) is a chronic autoimmune disease affecting the central nervous system (CNS). While extensively studied, its molecular subtypes and mechanisms remain poorly understood, hindering the identification of effective therapeutic targets.
Methods: We used ConsensusClusterPlus to analyze transcriptome data from 215 MS patient samples, identifying distinct molecular subtypes.
National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Introduction: The striped hamster, often parasitized by ectoparasites in nature, is an ideal model for studying host-ectoparasite molecular interactions. Investigating the response to ectoparasites under laboratory conditions helps elucidate the mechanism of host adaptations to ectoparasite pressure.
Methods: Using transcriptome sequencing, we analyzed gene expression in striped hamsters after short-term (3 days) and long-term (28 days) flea () parasitism.
Background: Major depressive disorder (MDD) poses a significant treatment challenge, with some patients unresponsive to conventional therapies. Electroconvulsive therapy (ECT) can be effective but its mechanisms are not fully understood. This study employs functional Near-Infrared Spectroscopy (fNIRS) to explore the neurobiological changes induced by ECT in A MDD patient, aiming to shed light on its therapeutic effects.
Introduction: Heart failure is a leading global cause of mortality, with ischemic heart failure (IHF) being a major contributor. IHF is primarily driven by coronary artery disease, and its underlying mechanisms are not fully understood, particularly the role of immune responses and inflammation in cardiac muscle remodeling. This study aims to elucidate the immune landscape of heart failure using multi-omics data to identify biomarkers for preventing cardiac fibrosis and disease progression.
