Results 241 to 250 of about 823,230 (286)

GloPath: An Entity‐Centric Foundation Model for Glomerular Lesion Assessment and Clinicopathological Insights

Advanced Science, EarlyView.
An entity‐centric foundation model, GloPath, is introduced for comprehensive glomerular lesion assessment from routine renal biopsy images. Trained on over one million glomeruli, the framework enables robust lesion recognition, grading, and cross modality diag nosis, while uncovering large‐scale clinicopathological associations.
Qiming He, Jing Li, Tian Guan, Yifei Ma, Zimo Zhao, Yanxia Wang, Hongjing Chen, Yingming Xu, Shuang Ge, Yexing Zhang, Yizhi Wang, Xinrui Chen, Lianghui Zhu, Yiqing Liu, Qingxia Hou, Shuyan Zhao, Xiaoqin Wang, Lili Ma, Peizhen Hu, Qiang Huang, Zihan Wang, Zhiyuan Shen, Junru Cheng, Siqi Zeng, Jiurun Chen, Zhen Song, Chao He, Zhe Wang, Yonghong He   +28 more
wiley   +1 more source

An Implantable Scaffold Sequentially Releasing STING Agonist and B7‐H3 Antibody for Bone Metastasis Immunotherapy

Advanced Science, EarlyView.
We developed an implantable dual‐drug depot using GelMA for bone metastasis treatment, co‐delivering MSA‐2 and αB7‐H3‐loaded CaCO3 microparticles. Sustained release from GelMA scaffold enables MSA‐2 to activate STING signaling and enhance T‐cell infiltration and activation, while sequentially released αB7‐H3 blocks MSA‐2‐induced B7‐H3 upregulation ...
Qijun Lin, Hong Xiao, Shuai Fan, Kaimin Cai, Yanteng Xu, Xinwen Wang, Guanhong Chen, Chuandong Lang, Xinsheng Peng, Mingqiang Li, Yuhu Dai   +10 more
wiley   +1 more source

Spintronic Bayesian Hardware Driven by Stochastic Magnetic Domain Wall Dynamics

Advanced Science, EarlyView.
Magnetic Probabilistic Computing (MPC) utilizes intrinsic stochastic dynamics in domain walls to establish a hardware foundation for uncertainty‐aware artificial intelligence. Thermally driven domain‐wall fluctuations, voltage‐controlled magnetic anisotropy, and TMR readout enable fully electrical, tunable probabilistic inference.
Tianyi Wang, Bingqian Dai, Shijie Xu, Kin Wong, Yaochen Li, Yang Cheng, Qingyuan Shu, Haoran He, Puyang Huang, Hanshen Huang, Xixiang Zhang, Kang L. Wang   +11 more
wiley   +1 more source

A Guide for Spatial Omics Technologies: Innovation, Evaluation, and Application

Advanced Science, EarlyView.
This review presents a strategy‐centric framework for spatial omics technologies, organizing methods by how spatial information is experimentally encoded. It compares key performance trade‐offs across sequencing‐ and imaging‐based approaches, examines computational and practical limitations, and highlights biomedical applications. The analysis provides
Xiaofeng Wu, Weize Xu, Da Lin, Leqiang Sun, Jinxia Dai, Gang Cao   +5 more
wiley   +1 more source

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Relativistic Stochastic Processes

Journal of Mathematical Physics, 1968
Relativistic stochastic processes in μ-space are defined and studied in a completely (and manifestly) covariant manner, without particularizing the time variable. It is shown that a number of usual definitions such as ``Gaussian process,'' etc., cannot be given a fully invariant meaning. Markovian processes are also studied.
openaire   +2 more sources

Rescaling Stochastic Processes: Asymptotics

2008
In this chapter the authors investigate the links among different scales, from a probabilistic point of view. Particular attention is being paid to the mathematical modelling of the social behavior of interacting individuals in a biological population, on one hand because there is an intrinsic interest in dynamics of population herding, on the other ...
V. Capasso, D. Morale
openaire   +3 more sources

Stochastic Processes

2010
A stochastic process is any process describing the evolution in time of a random phenomenon. From a mathematical point of view, the theory of stochastic processes was settled around 1950. Since then, stochastic processes have become a common tool for mathematicians, physicists, engineers, and the field of application of this theory ranges from the ...
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Continuous-Time Stochastic Processes

1987
Applications of continuous-time stochastic processes to economic modelling are largely focused on the areas of capital theory and financial markets: In these applications as in mathematics generally, the most widely studied continuous time process is a Brownian motion — so named for its early application as a model of the seemingly random movements of ...
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Stochastic Processes

2002
Existence of Diffusions with Continuous Coefficients On the Uniqueness of Markov Process Associated with the Boltzmann Equation of Maxwellian Molecules Stochastic Differential Equations with Reflecting Boundary Condition in Convex Regions Limit Distributions for One-Dimensional Diffusion Processes in Self-Similar Random Environments Recurrence of a ...
Makoto Maejima, Tokuzo Shiga
openaire   +1 more source

Multiple-scale stochastic processes: Decimation, averaging and beyond

Physics Reports, 2017
Stefano Bo, Antonio Celani
exaly