Results 141 to 150 of about 36,871 (305)

Signal denoising through topographic modularity of neural circuits. [PDF]

Elife, 2023
Zajzon B, Dahmen D, Morrison A, Duarte R.   +3 more
europepmc   +1 more source

Combining Spatial Multi‐Omics Data to Decipher Spatial Domains and Elucidate Cell Heterogeneity Based on Self‐Supervised Graph Learning

Advanced Science, EarlyView.
A self‐supervised multi‐view graph fusion framework integrates spatial multi‐omics, excelling in domain identification and denoising. It reconstructs spatial pseudo‐expression, jointly analyzes multi‐omics data, infers RNA velocity, predicts spatial omics features from single‐cell multi‐omics, and detects spatially dark genes and transcription factors,
Yuejing Lu, Rui Qiao, Ying Li, Junhong Li, Nuoya Yue, Jing Ge, Jiao Wang, Luonan Chen, Peiluan Li   +8 more
wiley   +1 more source

Underwater Acoustic Signal LOFAR Spectrogram Denoising Based on Enhanced Simulation

Applied Sciences
In complex marine environments, extracting target features from acoustic signal is very difficult, making the targets hard to be recognized. Therefore, it is necessary to perform denoising method on the acoustic signal to highlight the target features ...
Tianxiang He, Sheng Feng, Jie Yang, Kun Yu, Junlin Zhou, Duanbing Chen   +5 more
doaj   +1 more source

Deep learning-based protoacoustic signal denoising for proton range verification. [PDF]

Biomed Phys Eng Express, 2023
Wang J, Sohn JJ, Lei Y, Nie W, Zhou J, Avery S, Liu T, Yang X.   +7 more
europepmc   +1 more source

STAID: A Self‐Refining Deep Learning Framework for Spatial Cell‐Type Deconvolution with Biologically Informed Modeling

Advanced Science, EarlyView.
STAID is a unified deep learning framework that couples iterative pseudo‐spot refinement with neural network training through a feedback loop and exploits gene co‐expression information to model higher‐order interactions, achieving accurate and robust cell‐type deconvolution in spatial transcriptomics.
Jixin Liu, Shuli Sun, Zhengliang Lv, Xinyu Liu, Yihua Wang, Bingqiang Liu   +5 more
wiley   +1 more source

Porous Media Characterization by Micro-Tomographic Image Processing

, 2011
In this thesis, we have focused our attention on the characterization of porous media through micro-tomographic image processing. A porous medium can be simply seen as a solid material with "holes" in it, which, connected or isolated, may or may not ...
Matrecano, Marcella
core  

Sparse Overcomplete Denoising: Aggregation Versus Global Optimization

, 2017
Denoising is often addressed via sparse coding with respect to an overcomplete dictionary. There are two main approaches when the dictionary is composed of translates of an orthonormal basis.
Alessandro Foi, Giacomo Boracchi, Brendt Wohlberg, Carrera, Diego, Diego Carrera, Boracchi, Giacomo, Foi, Alessandro, Wohlberg, Brendt   +7 more
core   +1 more source

Radiation-induced acoustic signal denoising using a supervised deep learning framework for imaging and therapy monitoring. [PDF]

Phys Med Biol, 2023
Jiang Z, Wang S, Xu Y, Sun L, Gonzalez G, Chen Y, Wu QJ, Xiang L, Ren L.   +8 more
europepmc   +1 more source

Macrophage‐to‐Myofibroblast Transdifferentiation Contributes to Pulmonary Fibrosis via the MERTK‐SPP1‐SRC‐TKS5 Signaling Axis

Advanced Science, EarlyView.
MERTK is upregulated in fibrotic macrophages and regulates the expression and activity of SRC and TKS5 through SPP1, mediating transdifferentiation of macrophages‐to‐myofibroblasts (MMT) and promoting pulmonary fibrosis. The figure was created with BioRender.com.
Yungeng Wei, Hua Guo, Xiangsheng Yang, Xiao Xiao Tang   +3 more
wiley   +1 more source

EMD-based filtering (EMDF) of low-frequency noise for speech enhancement

, 2012
An Empirical Mode Decomposition based filtering (EMDF) approach is presented as a post-processing stage for speech enhancement. This method is particularly effective in low frequency noise environments.
Navin Chatlani, Soraghan, John J., John J. Soraghan, Soraghan, J.J., Chatlani, Navin   +4 more
core   +1 more source