Results 81 to 90 of about 5,220,012 (288)

Frames for Signal Processing on Cayley Graphs

Journal of Fourier Analysis and Applications
The spectral decomposition of graph adjacency matrices is an essential ingredient in the design of graph signal processing (GSP) techniques. When the adjacency matrix has multi-dimensional eigenspaces, it is desirable to base GSP constructions on a particular eigenbasis that better reflects the graph's symmetries.
Kathryn Beck, Mahya Ghandehari, Skyler Hudson, Jenna Paltenstein   +3 more
openaire   +3 more sources

Prospecting the protein design landscape

FEBS Letters, EarlyView.
This review outlines the current state of various protein design approaches. We discuss the current possibilities enabled by recently released tools, highlight future avenues to pursue in protein design, and underscore the crucial role of key databases and resources for successful protein design workflows.
Jakob R. Riccabona, Katharina T. Stonig, Jens Meiler, Clara T. Schoeder, Monica L. Fernández‐Quintero   +4 more
wiley   +1 more source

A context‐dependent modulatory role for eIF6 in acquired resistance to vemurafenib in melanoma

FEBS Letters, EarlyView.
Acquired resistance to vemurafenib upregulates the translation factor eIF6 in melanoma cells. Silencing eIF6 in resistant cells reduces proliferation and partially restores drug sensitivity, whereas its overexpression increases sensitivity across melanoma lines regardless of BRAF status, via modulation of mTOR, S6K, and MAPK signaling.
George Kyriakopoulos, Antonia Petropoulou, Alexandra Anastogianni, Angelina Bania, Vassilis Stamatakis, Argyris Alexiou, Chi‐Ping Day, Glenn Merlino, Katerina Grafanaki, Constantinos Stathopoulos   +9 more
wiley   +1 more source

Grasp: A matlab toolbox for graph signal processing [PDF]

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017
The GraSP toolbox aims at processing and visualizing graphs and graphs signal with ease. In the demo, we show those capabilities using several examples from the literature and from our own experiments.
Girault, Benjamin, Narayanan, Shrikanth, Ortega, Antonio, Gonçalves, Paulo, Fleury, Eric   +4 more
openaire   +2 more sources

Abstract message passing and distributed graph signal processing [PDF]

, 2022
Graph signal processing is a framework to handle graph structured data. The fundamental concept is graph shift operator, giving rise to the graph Fourier transform.
Lu, Yiqi, Ji, Feng, Tay, Wee Peng, Chong, Edwin   +3 more
core   +1 more source

Extended Adjacency and Scale-dependent Graph Fourier Transform via Diffusion Distances [PDF]

, 2020
This article proposes the augmentation of the adjacency model of networks for graph signal processing. It is assumed that no information about the network is available, apart from the initial adjacency matrix.
Elias, Vitor, Elias, Vitor R.M., ALVES MARTINS, Wallace, Martins, Wallace Alves, Elias, Vitor Rosa Meireles, Martin, Wallace, Werner, Stefan   +6 more
core   +1 more source

Neutralization of the PTX3/CD44 axis suppresses TGF‐β1 signaling in kidney fibrosis

FEBS Letters, EarlyView.
This study investigates how the PTX3/CD44 axis promotes kidney fibrosis by activating TGF‐β1 signaling. We demonstrate that the neutralizing antibody WHC‐001 effectively inhibits this pathway, suppressing myofibroblast activation and partial epithelial–mesenchymal transition (EMT).
Chao‐Chun Cheng, Shen‐Shin Chang, Feng‐Wei Chen, Ying‐Chieh Su, Thanh Phuong Nguyen, Yu‐Wei Hsiao, Jhih‐Ying Chi, Hsin‐Yin Liang, Ju‐Ming Wang   +8 more
wiley   +1 more source

Recovery of traffic information through graph signal processing

EURASIP Journal on Advances in Signal Processing
The generation of data sets from traffic variables within the roads of a city is increasing due to the implementation of sensors, monitoring stations, or more elaborate systems, such as synchronised drones to record the dynamics of a city.
Rafael Alejandro Martínez Márquez, Giuseppe Patanè   +1 more
doaj   +1 more source

Graph Learning Based on Spatiotemporal Smoothness for Time-Varying Graph Signal

IEEE Access, 2019
Graph learning often boils down to uncovering the hidden structure of data, which has been applied in various fields such as biology, sociology, and environmental studies.
Yueliang Liu, Lishan Yang, Kangyong You, Wenbin Guo, Wenbo Wang   +4 more
doaj   +1 more source

Statistical Graph Signal Processing: Stationarity and Spectral Estimation

, 2018
Stationarity is a cornerstone property that facilitates the analysis and processing of random signals in the time domain. Although time-varying signals are abundant in nature, in many contemporary applications the information of interest resides in more ...
Segarra, Santiago (author), Marques, Antonio G. (author), Chepuri, S.P. (author), Leus, G.J.T. (author)   +3 more
core   +1 more source