Results 91 to 100 of about 6,131,436 (285)

Reveal flocking of birds flying in fog by machine learning

, 2020
We study the first-order flocking transition of birds flying in low-visibility conditions by employing three different representative types of neural network (NN) based machine learning architectures that are trained via either an unsupervised learning ...
Ai, Bao-quan, Guo, Wei-chen, He, Liang
core  

Conversion of Transplanted Mature Hepatocytes into Afp+ Reprogrammed Cells for Liver Regeneration After Injury

Advanced Science, EarlyView.
Donor‐derived tdTomato+ mature hepatocytes were FACS‐isolated and transplanted into Fah−/− host mice. During regeneration, these cells convert into proliferative, unipotent Afp+ rHeps. Their plasticity is governed by a PPARγ/AFP‐dependent metabolic switch, segregating into pro‐proliferative Afplow and pro‐survival Afphigh subpopulations.
Ting Fang, Chao Yang, Hua Qiu, Yuan Du, Xicheng Wang, Yuting Li, Mingyang Xu, Changcheng Liu, Xiuhua Li, Na Guo, Jun Shi, Wencheng Zhang, Zhiying He   +12 more
wiley   +1 more source

Machine Learning‐Enhanced Analysis of Exosomal Surface Sialic Acid Using Surface‐Enhanced Raman Spectroscopy for Ovarian Cancer Diagnosis and Therapeutic Monitoring

Advanced Science, EarlyView.
Machine learning‐assisted surface‐enhanced Raman spectroscopy analysis of exosomal sialic acid for ovarian cancer diagnosis, as well as independent monitoring of exosomal sialic acid expression levels across different treatment periods, reveals a potential correlation with treatment response.
Lili Cong, Jiaqi Wang, Sijun Huang, Xiaxia Man, Yi Guo, Shuping Xu, Songling Zhang   +6 more
wiley   +1 more source

An online deep extreme learning machine based on forgetting mechanism

Dianzi Jishu Yingyong, 2018
The development of deep learning promotes the development of deep online learning, and online learning tends to have strong effectiveness. Based on the principle of online extreme learning machine and the principle of autoencoder of deep extreme learning
Liu Buzhong
doaj   +1 more source

Microglial GPR35 Ameliorates Epileptogenesis and Neuroinflammation via PDGFA Domain 2 Signaling

Advanced Science, EarlyView.
Activation of microglial G protein–coupled receptor 35 (GPR35) by L‐kynurenic acid (L‐Kyna) initiates a platelet‐derived growth factor A (PDGFA)–dependent phosphoinositide 3‐kinase–protein kinase B (PI3K–AKT) signaling cascade that dampens hippocampal neuroinflammation, thereby restraining epileptogenesis, lowering seizure susceptibility, and ...
Qi Wang, Tingting Qu, Qibing Sun, Ran Li, Junfei Dong, Yuming Du, Ziyin Xuan, Lei Wang, Hanli Li, Jianyun Sun, Fangliang Chen, Jinshuai Liu, Zifan Yang, Jianxiang Lei, Qian Yang, Bin Wang, Zhiming Zhou, Yu Wang   +17 more
wiley   +1 more source

Integrating Spatial Proteogenomics in Cancer Research

Advanced Science, EarlyView.
Xx xx. ABSTRACT Background: Spatial proteogenomics marks a paradigm shift in oncology by integrating molecular analysis with spatial information from both spatial proteomics and other data modalities (e.g., spatial transcriptomics), thereby unveiling tumor heterogeneity and dynamic changes in the microenvironment.
Yida Wang, Yang Wu, Feng Zhang, Parthiban Periasamy, Haiyue You, Denise Goh, Rachel Elizabeth Ann Fincham, Xin Ning, Danping Wu, Lu Liu, Ying Jiang, Zhiwen Qian, Joe Yeong, Yan Zhang   +13 more
wiley   +1 more source

SpatialESD: Spatial Ensemble Domain Detection in Spatial Transcriptomics

Advanced Science, EarlyView.
ABSTRACT Spatial transcriptomics (ST) measures gene expression while preserving spatial context within tissues. One of the key tasks in ST analysis is spatial domain detection, which remains challenging due to the complex structure of ST data and the varying performance of individual clustering methods. To address this, we propose SpatialESD, a Spatial
Hongyan Cao, Gaiqin Liu, Jingyi Xia, Runle Chen, Tong Wang, Xiaoling Yang, Ruiling Fang, Yanhong Luo, Ping Zeng, Hongmei Yu, Yanbo Zhang, Yuehua Cui   +11 more
wiley   +1 more source

GBS-Assisted Quantum Unsupervised Machine Learning on a Universal Programmable Integrated Quantum Chip

Research
Quantum machine learning stands poised as a forefront application for near-term quantum devices, addressing scalability challenges posed by classical computers in handling large datasets.
Huihui Zhu, Wei Luo, Rudai Yan, Chao Ren, Jia Guo, Zichao Zhao, Haoran Ma, Tian Chen, Feng Gao, Leong Chuan Kwek, Hong Cai, Yuehai Wang, Jianyi Yang, Ai-Qun Liu   +13 more
doaj   +1 more source

Multi‐Scale Mapping of Gene Expression from Whole‐slide Images for Identifying Phenotype‐Associated Subpopulations

Advanced Science, EarlyView.
BiSCALE: A pathology‐driven deep learning framework for multi‐scale gene expression prediction from whole‐slide images. It accurately infers bulk and near‐cellular spot‐level expression, links predictions to clinical phenotypes, identifies disease‐associated niches, and enables applications in risk stratification and cell‐identity annotation, providing
Hailong Zheng, Jiajing Xie, Luqi Wang, Hailong Xie, Tong Zhi, Yujia Guo, Bofeng Zhu, Dong Wang, Yu Chen   +8 more
wiley   +1 more source

Unsupervised end-to-end training with a self-defined target

Neuromorphic Computing and Engineering
Designing algorithms for versatile AI hardware that can learn on the edge using both labeled and unlabeled data is challenging. Deep end-to-end training methods incorporating phases of self-supervised and supervised learning are accurate and adaptable to
Dongshu Liu, Jérémie Laydevant, Adrien Pontlevy, Damien Querlioz, Julie Grollier   +4 more
doaj   +1 more source