Results 101 to 110 of about 82,326 (277)

Multi-Source Unsupervised Domain Adaptation with Prototype Aggregation

Mathematics
Multi-source domain adaptation (MSDA) plays an important role in industrial model generalization. Recent efforts regarding MSDA focus on enhancing multi-domain distributional alignment while omitting three issues, e.g., the class-level discrepancy ...
Min Huang, Zifeng Xie, Bo Sun, Ning Wang
doaj   +1 more source

Spatial Profiling Reveals Distinct Molecular and Immune Evolution of Mouse Lung Adenocarcinoma Precancers with or Without Carcinogen Exposure

Advanced Science, EarlyView.
Tumor evolution in lung adenocarcinoma is shaped by genetic alterations and spatial immune dynamics. By integrating whole‐exome sequencing, imaging mass cytometry, and spatial transcriptomics across two mouse models, this study reveals how mutational burden, immune infiltration, and cell–state interactions evolve during early and late carcinogenesis ...
Bo Zhu, Muhammad Aminu, Pingjun Chen, Jian‐Rong Li, Chuanpeng Dong, Chenyang Li, Yanhua Tian, Shao‐Wei Lu, Hong Chen, Chenxi Ma, Xin Hu, Jie Ye, Andrew Y. Liu, Beibei Huang, Frank R. Rojas, Parra Cuentas. Edwin Roger, Ou Shi, Monique B. Nilsson, Alissa Poteete, Khaja B. Khan, Wei Lu, Luisa M. Solis Soto, Junya Fujimoto, Cara Haymaker, Ignacio I. Wistuba, Zhubo Wei, Linghua Wang, Don L. Gibbons, Ken Chen, Alexandre Reuben, Jason M. Schenkel, John V. Heymach, Chao Cheng, Jia Wu, Jianjun Zhang   +34 more
wiley   +1 more source

The Gut Microbiota Regulates Motor Deficits via Butyrate in a Gnal+/− Mouse Model of DYT25 Dystonia

Advanced Science, EarlyView.
The present study provides compelling evidence for a modulatory role of the gut microbiota in the pathology of DYT25 dystonia, and butyrate supplementation alleviates the motor deficits of dystonia in Gnal+/− mice. Abstract Dystonia is the third most common movement disorder, following essential tremor and Parkinson's disease. The underlying mechanisms
Jingya Guo, Seong‐Gook Kang, Kunlun Huang, Tao Tong   +3 more
wiley   +1 more source

Online Unsupervised Domain Adaptation

, 2022
Deep Learning models have seen great application in demanding tasks such as machine translation and autonomous driving. However, building such models has proved challenging, both from a computational perspective and due to the requirement of a plethora of annotated data. Moreover, when challenged on new situations or data distributions (target domain),
openaire   +1 more source

Developing a Single‐Cell Spatial Transcriptomics Workflow for In Vivo Evaluation of Implanted Biomaterials

Advanced Science, EarlyView.
This work establishes a standardized, high‐resolution spatial transcriptomics workflow for characterizing host responses to biomaterial implants. Using the 10× Xenium platform, the pipeline integrates spatial mapping, subclustering, and functional enrichment to resolve immune and stromal cell organization at single‐cell resolution, enabling ...
Alex H.P. Chan, Yunfei Hu, Billie Pardavi, Xueying Xu, Angus J. Grant, Steven G. Wise, Lipin Loo, Richard P. Tan   +7 more
wiley   +1 more source

Domain-guided conditional diffusion model for unsupervised domain adaptation

Neural Networks
Limited transferability hinders the performance of deep learning models when applied to new application scenarios. Recently, Unsupervised Domain Adaptation (UDA) has achieved significant progress in addressing this issue via learning domain-invariant features. However, the performance of existing UDA methods is constrained by the large domain shift and
Yulong Zhang, Shuhao Chen, Weisen Jiang, Yu Zhang, Jiangang Lu, James T. Kwok   +5 more
openaire   +3 more sources

HIDF: Integrating Tree‐Structured scRNA‐seq Heterogeneity for Hierarchical Deconvolution of Spatial Transcriptomics

Advanced Science, EarlyView.
The prevailing neglect of cellular hierarchies in current spatial transcriptomics deconvolution often obscures cellular heterogeneity and impedes the identification of fine‐grained subtypes. To address this issue, HIDF employs a cluster‐tree and dual regularization to systematically model cellular hierarchical structures.
Zhiyi Zou, Yuting Bai, Bo Wang, Wanwan Shi, Xiao Liang, Jiawei Luo   +5 more
wiley   +1 more source

Unsupervised Domain Adaptation Via Style-Aware Self-Intermediate Domain

Pattern Recognition
13 pages, 7 ...
Lianyu Wang, Meng Wang, Daoqiang Zhang, Huazhu Fu   +3 more
openaire   +2 more sources

HiST: Histological Images Reconstruct Tumor Spatial Transcriptomics via MultiScale Fusion Deep Learning

Advanced Science, EarlyView.
HiST, a multiscale deep learning framework, reconstructs spatially resolved gene expression profiles directly from histological images. It accurately identifies tumor regions, captures intratumoral heterogeneity, and predicts patient prognosis and immunotherapy response.
Wei Li, Dong Zhang, Eryu Peng, Shijun Shen, Hamid Alinejad‐Rokny, Yao Liu, Junke Zheng, Cizhong Jiang, Youqiong Ye   +8 more
wiley   +1 more source

Exploring the Role of Depth Information From DAM in Diverse Remote Sensing Semantic Segmentation Tasks

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Existing semantic segmentation methods for remote sensing images focus mainly on planar features to boost performance but inadequately consider the potential advantages of incorporating depth features.
Dehao Zhou, Fukun Bi, Xinghai Hou, Xianping Ma, Zhiliu Yang, Binbin Yang   +5 more
doaj   +1 more source