Results 211 to 220 of about 68,766 (311)

Model-based iterative reconstruction with adaptive regularization for artifact reduction in electron tomography. [PDF]

Sci Rep
Venkatakrishnan S, Rahman O, Amichi L, Arregui-Mena JD, Yu H, Cullen DA, Ziabari A.   +6 more
europepmc   +1 more source

Data‐Driven Modeling of Composition–Processing–Microstructure Relations for Recycled Aluminum Cast Alloys

Advanced Science, EarlyView.
Interpretable machine learning reveals how composition and processing govern the formation and microstructural burden of Fe‐rich intermetallic compounds in recycled Al–Si–Fe–Mn alloys. By separating morphology selection from morphology‐conditioned burden partitioning, this framework shows that identical Fe contents can yield different intermetallic ...
Jaemin Wang, Waleed Mohammed, Dierk Raabe   +2 more
wiley   +1 more source

Comparison of image quality in carotid dual-energy computed tomography angiography at 55 keV virtual monoenergetic imaging using deep learning and adaptive iterative reconstruction algorithm. [PDF]

J Clin Imaging Sci
Liu X, Wang C, Long J, Wu Y, Liu Z, Yu M, Wang C, Xu W, Zhang H, Sun A, Zhang S, Xu K, Meng Y.   +12 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

Feasibility study of "double-low" scanning protocol combined with artificial intelligence iterative reconstruction algorithm for abdominal computed tomography enhancement in patients with obesity. [PDF]

BMC Med Imaging
Ji MT, Wang RR, Wang Q, Li HS, Zhao YX.
europepmc   +1 more source

Eco‐Geography Reverses Dominant AMR Reservoirs in Klebsiella pneumoniae: Integron‐Rich Mobilomes and Cross‐Niche Connectivity

Advanced Science, EarlyView.
Dominant antimicrobial resistance reservoirs in Klebsiella pneumoniae vary across eco‐geographic settings rather than following a universal pattern. Integrated One Health and global genomic analyses show that lineage structure, integron load, and cross‐niche connectivity shape whether AMR burden accumulates primarily in human or nonhuman compartments ...
Hui Lin, Biao Tang, Hao Xu, Jiajie Qian, Feifan Shi, Junhui Zhao, Xiaorui Mao, Xiaohe Hu, Ruishan Liu, Wenhong Liu, Xiawei Jiang, Beiwen Zheng, Guoping Zhao   +12 more
wiley   +1 more source

Deep learning image reconstruction and adaptive statistical iterative reconstruction on coronary artery calcium scoring in high risk population for coronary heart disease. [PDF]

BMC Med Inform Decis Mak
Zhu L, Shi X, Tang L, Machida H, Yang L, Ma M, Ha R, Shen Y, Wang F, Chen D.   +9 more
europepmc   +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

Comparison of image quality in 40 keV virtual monoenergetic images of dual-energy CT pulmonary angiography using deep learning and iterative reconstruction algorithms under optimized low dose scanning protocols. [PDF]

Quant Imaging Med Surg
Zhang D, Zhang L, Long J, Wu Y, Zhang H, Wang C, Sun B, Wang C, Zhang H, Sun X, Sun A, Meng Y, Hu C, Xu K.   +13 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