Results 61 to 70 of about 5,220,012 (288)
Microbiome‐blood–brain barrier interactions in aging — mechanisms and therapeutic potential
FEBS Letters, EarlyView.Aging reshapes the gut microbiome (↓SCFA‐producing commensals; ↑pro‐inflammatory outputs), shifting circulating metabolites (↓SCFAs; ↑LPS, ↑TMAO, ↑PAA) that act at the BBB to increase nonspecific transcytosis, alter transport, and promote astrocyte reactivity, heightening brain vulnerability.
Daniel Cuervo‐Zanatta +3 more
wiley +1 more source
Topological Signal Processing from Stereo Visual SLAM
SensorsTopological signal processing is emerging alongside Graph Signal Processing (GSP) in various applications, incorporating higher-order connectivity structures—such as faces—in addition to nodes and edges, for enriched connectivity modeling.
Eleonora Di Salvo +4 more
doaj +1 more source
A Preconditioned Graph Diffusion LMS for Adaptive Graph Signal Processing
, 2018 Graph filters, defined as polynomial functions of a graph-shift operator (GSO), play a key role in signal processing over graphs. In this work, we are interested in the adaptive and distributed estimation of graph filter coefficients from streaming graph
Sayed, Ali +10 more
core +1 more source
FEBS Letters, EarlyView.Obesity raises blood levels of PAI‐1, a protein linked to metabolic dysfunction‐associated steatotic liver disease in people with obesity. In female mice fed a high‐fat diet, partially lowering PAI‐1 led to smaller subcutaneous fat cells and lower liver cholesterol, without changing body weight or insulin sensitivity.
Claudia E. Ramirez Bustamante +10 more
wiley +1 more source
pygrank: A Python package for graph node ranking
SoftwareX, 2022 We introduce pygrank, an open source Python package to define, run and evaluate node ranking algorithms. We provide object-oriented and extensively unit-tested algorithmic components, such as graph filters, post-processors, measures, benchmarks, and ...
Emmanouil Krasanakis +3 more
doaj +1 more source
A Hilbert space theory of generalized graph signal processing
, 2019 Graph signal processing (GSP) has become an important tool in many areas such as image processing, networking learning and analysis of social network data.
Ji, Feng, Tay, Wee Peng
core +1 more source
Emerging experimental and computational methods for studying redox‐regulated structural transitions
FEBS Letters, EarlyView.Redox reactions can reshape proteins and alter how they behave in cells, with important consequences for health and disease. This review explores emerging experimental and computational approaches for discovering these redox‐sensitive protein switches, revealing their structural effects, and predicting their behavior, opening new opportunities to ...
Tasneem Rass +2 more
wiley +1 more source
Verifying the Smoothness of Graph Signals: A Graph Signal Processing Approach
IEEE Transactions on Signal ProcessingGraph signal processing (GSP) deals with the representation, analysis, and processing of structured data, i.e. graph signals that are defined on the vertex set of a generic graph. A crucial prerequisite for applying various GSP and graph neural network (GNN) approaches is that the examined signals are smooth graph signals with respect to the underlying
Lital Dabush, Tirza Routtenberg
openaire +2 more sources
A Windowed Graph Fourier Transform [PDF]
, 2012 The prevalence of signals on weighted graphs is increasing; however, because of the irregular structure of weighted graphs, classical signal processing techniques cannot be directly applied to signals on graphs.
Ricaud, Benjamin +5 more
core +1 more source