Results 141 to 150 of about 11,230,345 (378)

Event History Analysis of Dynamic Communication Networks

open access: yes, 2018
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  

Escape from TGF‐β‐induced senescence promotes aggressive hallmarks in epithelial hepatocellular carcinoma cells

open access: yesMolecular Oncology, EarlyView.
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

open access: yesSensors, 2016
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

open access: yesIEEE Transactions on Knowledge and Data Engineering, 2017
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

Targeting the AKT/mTOR pathway attenuates the metastatic potential of colorectal carcinoma circulating tumor cells in a murine xenotransplantation model

open access: yesMolecular Oncology, EarlyView.
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

Dynamics of boolean networks

open access: yesDiscrete & Continuous Dynamical Systems - S, 2011
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

Time, the final frontier

open access: yesMolecular Oncology, EarlyView.
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

Chemoresistome mapping in individual breast cancer patients unravels diversity in dynamic transcriptional adaptation

open access: yesMolecular Oncology, EarlyView.
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

open access: yesCAAI Transactions on Intelligence Technology
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

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