Results 71 to 80 of about 575,581 (334)

Rapid Arbitrary‐Shape Microscopy of Unsectioned Tissues for Precise Intraoperative Tumor Margin Assessment

Advanced Science, EarlyView.
This study presents a novel microscopic imaging system capable of rapid, section‐free scanning of irregular tissue surfaces, delivering high sensitivity for detecting cancer cell clusters during intraoperative tumor margin assessment. Abstract Rapid and accurate intraoperative examination of tumor margins is crucial for precise surgical treatment, yet ...
Zhicheng Shao, Xiangwei Meng, Peifen Fu, Keer Huang, Xibei Chen, Yue Ying, Jun Li, Yuxin Zheng, Xiaolong Li, Hyeon Jeong Lee, Jingyi Feng, Yuexin Lu, Runping Cai, Xiongling Jiang, Qing Chen, Hong Zhang, Xiaoyong Man, Li Lin   +17 more
wiley   +1 more source

Explicit Compression Degradation Estimations for Low‐Sampling Single‐Pixel Imaging using Hadamard Basis

Advanced Science, EarlyView.
This study proposes a degradation estimation technique to explicitly describe compressive sampling for low‐sampling Hadamard single‐pixel imaging. Blur kernels in explicit degradation models are estimated by the self‐supervised learning method without labeled data and implicit priors.
Haoyu Zhang, Jie Cao, Chang Zhou, Haifeng Yao, Qun Hao   +4 more
wiley   +1 more source

Hump‐Shaped Relationship Between Microbial Carbon Use‐Efficiency and Soil Organic Carbon in Alpine Grasslands

Advanced Science, EarlyView.
On the Qinghai–Tibetan Plateau, microbial carbon use efficiency (CUE) peaks at intermediate soil organic carbon levels and declines thereafter. In carbon‐rich soils, the formation of stable mineral‐associated organic carbon is decoupled from microbial CUE.
Yuting Wang, Yongneng Wei, Gangsheng Wang, Yang Ruan, Ling Li, Xiang Liu, Yunfeng Yang, Qirong Shen, Ning Ling   +8 more
wiley   +1 more source

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

Structural Variation and 3D Genome‐Driven DNA/RNA Methylation Divergence Contributing to Cotton Fiber Domestication

Advanced Science, EarlyView.
These findings elucidated the crucial role of the “SVs‐3D genome remodeling‐epigenetic modifications‐gene expression” cascade regulatory network in cotton fiber domestication, offering both a theoretical foundation and genetic resources for molecular design breeding in fiber crops.
Lei Shao, Shangkun Jin, Haojie Jiang, Tianyu Pan, Zesheng Rui, Xiaowen Shi, Ting Zhao, Zhanfeng Si, Xueying Guan, Yan Hu, Tianzhen Zhang, Lei Fang   +11 more
wiley   +1 more source

scPER: A Rigorous Computational Approach to Determine Cellular Subtypes in Tumors Aligned With Cancer Phenotypes From Total RNA Sequencing

Advanced Science, EarlyView.
scPER presents an adversarial‐autoencoder framework that deconvolves bulk total RNA‐seq to quantify tumor‐microenvironment cell types and uncover phenotype‐linked subclusters. Across diverse benchmarks, scPER improves accuracy over existing tools.
Bingrui Li, Xiaobo Zhou, Raghu Kalluri
wiley   +1 more source

S3RL: Enhancing Spatial Single‐Cell Transcriptomics With Separable Representation Learning

Advanced Science, EarlyView.
Separable Spatial Representation Learning (S3RL) is introduced to enhance the reconstruction of spatial transcriptomic landscapes by disentangling spatial structure and gene expression semantics. By integrating multimodal inputs with graph‐based representation learning and hyperspherical prototype modeling, S3RL enables high‐fidelity spatial domain ...
Laiyi Fu, Penglei Wang, Gaoyuan Xu, Jitao Lu, Qinke Peng, Danyang Wu, Hequan Sun   +6 more
wiley   +1 more source

Convergence to equilibrium for finite Markov processes, with application to the Random Energy Model

, 2003
We estimate the distance in total variation between the law of a finite state Markov process at time t, starting from a given initial measure, and its unique invariant measure. We derive upper bounds for the time to reach the equilibrium.
MATHIEU, Pierre, PICCO, Pierre
core   +1 more source

Mechanism‐Driven Screening of Membrane‐Targeting and Pore‐Forming Antimicrobial Peptides

Advanced Science, EarlyView.
To combat antibiotic resistance, this study employs mechanism‐driven screening with machine learning to identify pore‐forming antimicrobial peptides from amphibian and human metaproteomes. Seven peptides are validated, showing minimal toxicity and membrane disruption.
Jiaxuan Li, Chenguang Yang, Ruihan Dong, Juan Francisco Bada Juarez, Lei Wang, Maximilian Emanuel Wettstein, Dali Wang, Chan Cao, Ying Lu, Chen Song   +9 more
wiley   +1 more source

Lie algebraic discussion for affinity based information diffusion in social networks

Open Physics, 2017
In this paper we develop a dynamical information diffusion model which features the affinity of people with information disseminated in social networks.
Shang Yilun
doaj   +1 more source