Results 121 to 130 of about 3,278,052 (298)

Senkyunolide I Inhibits mtDNA‐cGAS‐STING Signaling in Macrophages via Targeting VDAC1 Oligomerization to Attenuate Ulcerative Colitis

Advanced Science, EarlyView.
In macrophages, senkyunolide I (SEI) directly targets the K12 residue of VDAC1 to inhibit its stress‐induced oligomerization, a critical upstream event that effectively prevents mitochondrial DNA release and subsequent cGAS‐STING pathway activation.
Zhiming Ye, Yihang Huang, Bohao Han, Lei Zhang, Can Yu, Yang Yang, Bing Liu, Yongping Jian, Zhixiang Xu, Cheng Zeng   +9 more
wiley   +1 more source

Robust Stitching Interface and Deep Learning Empowered Hydrogel Human‐Machine Interface

Advanced Science, EarlyView.
A molecular design strategy is presented that exploits synergistic carboxylate anion–quaternary ammonium interactions to simultaneously strengthen the hydrogel–PET interface and the bulk hydrogel network. Integrated with deep learning signal processing, the stable hydrogel–PET interface enables consistent signal acquisition and reliable human‐machine ...
Hao Dong, Yiming Luo, Chuanliu Liu, Mahamood Modupe Rasheedat, Wenjiayi Tan, Fangyu Zhao, Yixue Zhang, Chris Connor, Hua Hou, Hamdy Khamees Thabet, Ben Bin Xu, Huige Wei   +11 more
wiley   +1 more source

Rotation Invariance in Graph Convolutional Networks [PDF]

Annals of computer science and information systems, 2021
Nguyen Anh Mac, Hung Son Nguyen
doaj   +1 more source

End-to-End Stroke Imaging Analysis Using Effective Connectivity and Interpretable Artificial Intelligence

IEEE Access
In this paper, we propose a reservoir computing-based and directed graph analysis pipeline. The goal of this pipeline is to define an efficient brain representation for connectivity in stroke data derived from magnetic resonance imaging. Ultimately, this
Wojciech Ciezobka, Joan Falco-Roget, Cemal Koba, Alessandro Crimi   +3 more
doaj   +1 more source

Neuromorphic Devices and Computing for Sensing, Memory, and Control

Advanced Science, EarlyView.
This review introduces neuromorphic devices made from diverse materials. These devices mimic neuronal functions and architectures and, when integrated with artificial or biological computing, can form closed loops with neurons for pressure, optical, acoustic, and biochemical sensing and modulation.
Zhengguang Zhu, Nicholas Schaffer, Xiao Yang   +2 more
wiley   +1 more source

Graph Convolutional Neural Network [PDF]

Procedings of the British Machine Vision Conference 2016, 2016
Michael Edwards, Xianghua Xie
openaire   +2 more sources

Multi‐Omics Integration Identifies a CDH3‐Associated Malignant Epithelial State and Immunosuppressive Niche to Predict Prognosis in Thymic Epithelial Tumors

Advanced Science, EarlyView.
Single‐cell, spatial, molecular, and pathology analyses identify a CDH3‐associated malignant epithelial state in thymic epithelial tumors. This state links stem‐like and EMT programs to M2 macrophage–rich immunosuppressive niches, genomic instability, poor survival, and drug vulnerability.
Yuntao Feng, Jingyu Chen, Lang Xia, Tao Wang, Yuzhou Wang, Lei Zhang, Guofang Zhao, Long Xu, Juemin Yu, Yunlang She, Junqi Wu, Yue Zhao, Chang Chen, Deping Zhao   +13 more
wiley   +1 more source

Learning Work Function via Implicit Reasoning on Electrostatic Potential Landscapes

Advanced Science, EarlyView.
StructPot‐CLR establishes a cross‐modal contrastive learning framework that aligns the crystal structures of 2D materials with plane‐averaged electrostatic potential landscapes for physically informed work‐function prediction. The model achieves an MAE of 0.265 eV and an R2 of 0.902 on the held‐out test set while accurately preserving key morphological
Haoyu Wan, Yue Wu, Tianhao Su, Deng Pan
wiley   +1 more source

Graph-based vision transformer with sparsity for training on small datasets from scratch

Scientific Reports
Vision Transformers (ViTs) have achieved impressive results in large-scale image classification. However, when training from scratch on small datasets, there is still a significant performance gap between ViTs and Convolutional Neural Networks (CNNs ...
Peng Li, Lu Huang, Jin Li, Haiyan Yan, Dongjing Shan   +4 more
doaj   +1 more source

Non-convolutional graph neural networks.

Advances in Neural Information Processing Systems 37
Rethink convolution-based graph neural networks (GNN) -- they characteristically suffer from limited expressiveness, over-smoothing, and over-squashing, and require specialized sparse kernels for efficient computation. Here, we design a simple graph learning module entirely free of convolution operators, coined random walk with unifying memory (RUM ...
Yuanqing Wang, Kyunghyun Cho
openaire   +3 more sources