Results 141 to 150 of about 11,230,345 (378)
Event History Analysis of Dynamic Communication Networks
Statistical analysis on networks has received growing attention due to demand from various emerging applications. In dynamic networks, one of the key interests is to model the event history of time-stamped interactions amongst nodes.
Sit, Tony, Ying, Zhiliang, Yu, Yi
core
Chronic TGF‐β exposure drives epithelial HCC cells from a senescent state to a TGF‐β resistant mesenchymal phenotype. This transition is characterized by the loss of Smad3‐mediated signaling, escape from senescence, enhanced invasiveness and metastatic potential, and upregulation of key resistance modulators such as MARK1 and GRM8, ultimately promoting
Minenur Kalyoncu+11 more
wiley +1 more source
Secure and Cost-Effective Distributed Aggregation for Mobile Sensor Networks
Secure data aggregation (SDA) schemes are widely used in distributed applications, such as mobile sensor networks, to reduce communication cost, prolong the network life cycle and provide security.
Kehua Guo, Ping Zhang, Jianhua Ma
doaj +1 more source
Tracking Influential Individuals in Dynamic Networks
In this paper, we tackle a challenging problem inherent in a series of applications: tracking the influential nodes in dynamic networks. Specifically, we model a dynamic network as a stream of edge weight updates.
Yu Yang+3 more
semanticscholar +1 more source
Dual targeting of AKT and mTOR using MK2206 and RAD001 reduces tumor burden in an intracardiac colon cancer circulating tumor cell xenotransplantation model. Analysis of AKT isoform‐specific knockdowns in CTC‐MCC‐41 reveals differentially regulated proteins and phospho‐proteins by liquid chromatography coupled mass spectrometry. Circulating tumor cells
Daniel J. Smit+19 more
wiley +1 more source
Boolean networks are special types of finite state time-discrete dynamical systems. A Boolean network can be described by a function from an n-dimensional vector space over the field of two elements to itself. A fundamental problem in studying these dynamical systems is to link their long term behaviors to the structures of the functions that define ...
openaire +3 more sources
This article advocates integrating temporal dynamics into cancer research. Rather than relying on static snapshots, researchers should increasingly consider adopting dynamic methods—such as live imaging, temporal omics, and liquid biopsies—to track how tumors evolve over time.
Gautier Follain+3 more
wiley +1 more source
Dynamical Effects in the EUV Chromospheric Network [PDF]
F. Chiuderi-Drago, G. Poletto
openalex +1 more source
This study used longitudinal transcriptomics and gene‐pattern classification to uncover patient‐specific mechanisms of chemotherapy resistance in breast cancer. Findings reveal preexisting drug‐tolerant states in primary tumors and diverse gene rewiring patterns across patients, converging on a few dysregulated functional modules. Despite receiving the
Maya Dadiani+14 more
wiley +1 more source
Optimisation of sparse deep autoencoders for dynamic network embedding
Network embedding (NE) tries to learn the potential properties of complex networks represented in a low‐dimensional feature space. However, the existing deep learning‐based NE methods are time‐consuming as they need to train a dense architecture for deep
Huimei Tang+6 more
doaj +1 more source