Results 211 to 220 of about 29,876 (304)

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

Evaluation of optimal ClearInfinity artificial intelligence deep learning reconstruction algorithm weights with a 70-kVp tube voltage combined with "dual-low" abdominal computed tomography angiography. [PDF]

Quant Imaging Med Surg
Xiang J, Wang S, Liu C, Liu H, Zhu P, Wang G, Chen W.   +6 more
europepmc   +1 more source

Integrated Ultrasonic Platform for Bioelectronic Control through Biological Barriers Based on Metasurface

Advanced Science, EarlyView.
A metasurface‐enabled ultrasonic platform achieves precise multi‐focal control through biological barriers, real‐time physiology‐driven modulation, and simultaneous wireless power and data transmission. By unifying wavefront engineering, closed‐loop feedback, and acoustic communication, this work establishes key enabling technologies toward scalable ...
Chuanxin Zhang, Hanjie Xiao, Xue Jiang, Dean Ta   +3 more
wiley   +1 more source

Decoding Spatial Heterogeneity and Multi‐Omics Regulation with Hierarchical Graph Learning

Advanced Science, EarlyView.
ABSTRACT Recent advances in spatial multi‐omics technologies have enabled the simultaneous profiling of multiple molecular layers within the same tissue slice, providing unprecedented opportunities to investigate tissue spatial organization. However, most existing computational methods identify spatial domains in a purely data‐driven manner, rarely ...
Jiazhou Chen, Jiahui Xie, Yi Liao, Junyu Li, Longqi Liu, Xin Gao, Hongmin Cai   +6 more
wiley   +1 more source

Two-Stage Sparse Recovery for Off-Grid Cascaded Channel Estimation in RIS-Assisted mmWave Systems. [PDF]

Sensors (Basel)
Han Z, Liu Q, Cao Y, Wang Y, Lin Y.
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

A Comparative Study of Regression Methods for Solving the Timepix Calibration Task. [PDF]

Sensors (Basel)
Broulím J, Prokop M, Nouzák L, Smrčka P.   +3 more
europepmc   +1 more source

DMS‐MaPseq and DREEM Analyses Implicate the Critical Role of RNA Structural Dynamics in Turnip Yellow Mosaic Virus Pathogenicity

Advanced Science, EarlyView.
RNA structural profiling of Turnip Yellow Mosaic Virus by DMS‐MaPseq and DREEM analyses uncover that viral genome‐wide RSS is highly complicated and heterogeneous, with alternative RSSs widely distributed across the genome. Notably, the viral 3’ tRNA‐like structure adopts alternative conformations in vivo.
Jiaying Zhu, Changhao Li, Chisimbily Onyia, Xindi Li, Xingxing Yan, Songxiao Zhong, Taerin Oh, Xiuren Zhang   +7 more
wiley   +1 more source

IFA-ICP: A Low-Complexity and Image Feature-Assisted Iterative Closest Point (ICP) Scheme for Odometry Estimation in SLAM, and Its FPGA-Based Hardware Accelerator Design. [PDF]

Sensors (Basel)
Li JE, Hwang YT.
europepmc   +1 more source

Mechanism‐Informed Machine Learning Enables Discovery of Oncolytic Peptides for Cancer Immunotherapy

Advanced Science, EarlyView.
MISPOP integrates ensemble learning with membrane‐active physicochemical priors to identify Dermaseptin‐S9, a natural oncolytic peptide that disrupts tumor membranes, triggers immunogenic cell death, and shows strong antitumor activity. The study illustrates a mechanism‐informed route from peptide sequence data to cancer immunotherapy leads.
Wen Zhang, Shengxin Lu, Guangyong Zheng, Shensuo Li, Hongyu Chen, Mei Hong, Xiangru Zhou, Ruotian Tang, Ye Wu, Weidong Zhang, Dong Lu, Xin Luan   +11 more
wiley   +1 more source