Results 101 to 110 of about 65,281 (258)
DL-GPRd: Deep Learning for Ground Penetrating Radar Deconvolution
Abstract Ground-penetrating radar (GPR) is a vital tool for subsurface investigation, but traditional deconvolution methods have limitations. This study explores the integration of Deep Learning (DL) techniques to enhance GPR data processing.
G. Roncoroni, E. Forte, M. Pipan
openaire +1 more source
This study develops a multi‐dimensional vision Transformer‐based model, GAVR, to accurately distinguish gastric cancer T4a/b stages preoperatively. Validated across multi‐center cohorts, it achieves excellent performance and significantly improves radiologists’ diagnostic accuracy, offering a promising tool for clinical decision‐making.
Guoliang Zheng +20 more
wiley +1 more source
A Generative Neuro‐Symbolic AI for Protein Sequence Design
We introduce EffieDes, a neuro‐symbolic framework coupling deep learning‐based fitness landscape parameterization with exact automated reasoning. Unlike greedy sampling, EffieDes identifies sequences that globally optimize fitness while satisfying intricate design constraints.
Marianne Defresne +12 more
wiley +1 more source
A novel exercise‐inducible myokine acidic ribosomal protein P2 (RPLP2), initially identified from human trials, is presented here, whose circulating levels negatively correlate with clinical anxiety severity. Muscle‐derived RPLP2 enhances hippocampal ribosomal assembly and adult neurogenesis to rescue stress‐induced anxiety deficits.
Peiyu Luo +18 more
wiley +1 more source
StackingNet: Collective Inference Across Independent AI Foundation Models
ABSTRACT Artificial intelligence (AI) built on large foundation models has transformed language understanding, computer vision, and reasoning, yet these systems remain isolated and cannot readily share their capabilities. Coordinating the complementary strengths of independently developed, black‐box foundation models is essential for trustworthy ...
Siyang Li +4 more
wiley +1 more source
Using grilled lamb skewers as a model system, this work builds a multiscale coupling framework from oral processing to retronasal aroma perception, reveals dual‐kinetic release patterns and Electroencephalogram‐characterized central encoding features, and proposes an interpretable physics‐guided deep learning model validated by multiphysics simulation,
Che Shen +12 more
wiley +1 more source
A Review on Medical Image Analysis Using Deep Learning
The objective of the medical image analysis is to increase the effectiveness of the diagnosis options. The Coevolution Neural Network (CNN) is the predominant neural network architecture used in Deep Learning (DL) for medical image analysis.
Raju Egala, M. V. S. Sairam
doaj +1 more source
Automation and Active Learning for the Multi‐Objective Optimization of Antibody Formulations
Successful antibody formulation necessitates balancing factors such as thermal stability, colloidal stability, and viscosity across a vast excipient design space. This work integrates robotic liquid handling, high‐throughput biophysical characterization, and multi‐objective Bayesian optimization in an iterative closed‐loop Design‐Build‐Test‐Learn cycle.
D. Christopher Radford +3 more
wiley +1 more source
ABSTRACT Atrial fibrillation (AF) increases the risk of stroke and heart failure, yet accurate quantification of AF burden in daily life remains difficult. Although smartwatch photoplethysmography (PPG) supports continuous monitoring, complex rhythms and signal noise can impair burden estimation.
Song Zuo +27 more
wiley +1 more source
In NIID, expanded NOTCH2NLC repeats give rise to nuclear polyG inclusions. Tracer‐guided in situ cryo‐electron tomography enables cross‐scale structural analysis from mouse brain to native neuronal nuclei, revealing dense‐core/peripheral‐halo inclusions built from compact polyG ribbons.
Hui Dong +13 more
wiley +1 more source

