Results 131 to 140 of about 58,601 (260)
Advanced Science, EarlyView.Large‐scale UK Biobank analyses identify clinical and proteomic signatures for early prediction of valvular heart disease and its subtypes. Proteins add predictive value for VHD, AVS, and MVR, with outcome‐specific compact panels showing translational potential. Multi‐layer evidence highlights matrix remodeling, protease regulation, immune inflammation,
Zhihao Jiang +10 more
wiley +1 more source
Quantitative performance and optimal regularization parameter in block sequential regularized expectation maximization reconstructions in clinical 68Ga-PSMA PET/MR. [PDF]
EJNMMI Res, 2018 Ter Voert EEGW +6 more
europepmc +1 more source
Advanced Science, EarlyView.S100A8/A9‐high macrophages are markedly enriched in the stenotic intestinal tissue of patients with Crohn's disease. These profibrotic macrophages secrete mCCL6 in a STAT3‐dependent manner. mCCL6 and its human ortholog hCCL15 activate fibroblasts via the CCR1 receptor, thereby driving excessive collagen deposition.
Shu Wang +12 more
wiley +1 more source
Advanced Science, EarlyView.This study demonstrates that thermally evaporated lithium anodes combined with LiF passivation provide a high‐performance thin anode design for next‐generation ASSBs. This approach increases the critical current density from 1.6 to 2.6 mA cm−2, enabling full cells to achieve over 500 and 300 cycles at 1.5 and 3 mA cm−2, respectively, outperforming even
Jinsong Zhang +4 more
wiley +1 more source
On optimal regularization parameters via bilevel learning
Variational regularization is commonly used to solve linear inverse problems, and involves augmenting a data fidelity by a regularizer. The regularizer is used to promote a priori information and is weighted by a regularization parameter. Selection of an appropriate regularization parameter is critical, with various choices leading to very different ...
Ehrhardt, Matthias J. +2 more
openaire +3 more sources
A Generative Neuro‐Symbolic AI for Protein Sequence Design
Advanced Science, EarlyView.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
StackingNet: Collective Inference Across Independent AI Foundation Models
Advanced Science, EarlyView.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
Refining penalized ridge regression: a novel method for optimizing the regularization parameter in genomic prediction. [PDF]
G3 (Bethesda)Montesinos-López A +5 more
europepmc +1 more source
ON THE A POSTERIORI PARAMETER CHOICE IN REGULARIZATION METHODS
Proceedings of the Estonian Academy of Sciences. Physics. Mathematics, 1999 Hämarik, Uno, Raus, Toomas
openaire +2 more sources
Advanced Science, EarlyView.To accelerate the inverse design of heterostructured metal matrix composites, a closed‐loop scientific machine learning framework integrates continual learning prediction with NSGA‐II‐PMCP optimization. The framework maps microstructural descriptors to strength, toughness, and modulus, expands high‐quality Pareto solutions, and guides experimentally ...
Zhiyan Zhong +11 more
wiley +1 more source