Results 51 to 60 of about 1,497 (170)

Cross‐Organ Multi‐Omics Profiling of Systemic Alterations in 5xFAD Mice and Changes Associated with Huanglian Jiedu Decoction Treatment

Advanced Science, EarlyView.
Cross‐organ multi‐omics reveals coordinated inflammatory and metabolic disturbances in 5xFAD mice. Huanglian Jiedu Decoction improves cognition and alleviates brain and liver abnormalities. Complementary hepatocyte–microglia conditioned‐medium experiments suggest PPAR‐γ involvement in treatment‐associated changes in microglial inflammation, providing ...
Wenya Gao, Jing Gao, Xiaohui Su, Xiangying Kong, Xiaolu Wei, Tunan Ding, Tiantian Guo, Shuangrong Gao, Yanyan Zhou, Junyi Zhou, Haiyu Zhao   +10 more
wiley   +1 more source

A CT Lesion Foundation Model for Non‐Invasive Assessment of Hepatocellular Carcinoma Aggressiveness and Prediction of Recurrence Risk

Advanced Science, EarlyView.
A computed tomography lesion foundation model, built by sequential self‐supervised pretraining on generic and hepatocellular carcinoma‐specific lesion images, non‐invasively assesses microvascular invasion and pathological grade from contrast‐enhanced CT and stratifies postoperative recurrence risk.
Wei Xia, Zhehan Shen, Mengsi Li, Jianxin Lu, Ji Xia, Yang Cheng, Wenzheng Li, Shunjie Dong, Lin Deng, Ruokun Li   +9 more
wiley   +1 more source

Al1−xHfxN$\text{Al$_{1-x}$Hf$_{x}$N}$ Thin Films with Enhanced Piezoelectric Responses for GHz Surface Acoustic Wave Devices

Advanced Electronic Materials, EarlyView.
Reactively sputtered wurtzite Al1‐xHfxN thin films, exhibiting strong c‐axis texture and enhanced piezoelectric response, are demonstrated as a cost‐effective alternative to Al1‐xScxN. X‐ray absorption spectroscopy reveals that cross‐gap N 2p–Hf 5d hybridization increases the Born effective charge and enables a multifold increase in piezoelectric ...
Laura I. Wagner, Verena Streibel, Esperanza Luna, Katarina S. Flashar, Valentin Walbrunn, Walid Anders, Nicole Volkmer, Doreen Steffen, Katrin Morgenroth, Frans Munnik, Tsedenia A. Zewdie, Saswati Santra, Jutta Schwarzkopf, Ian D. Sharp, Mingyun Yuan   +14 more
wiley   +1 more source

A Compact Resistively Loaded Metamaterial Absorber for 2.4 GHz RF Energy Harvesting With Impedance‐Matched Rectifier Analysis

Advanced Electronic Materials, EarlyView.
A compact resistively loaded metamaterial absorber captures RF energy at 2.4 GHz and directs most of the accepted power to modeled loads. The resonant response is confirmed experimentally for the fabricated absorber, while an impedance‐matched rectifier is evaluated through simulation. The stage‐wise results distinguish absorber, rectifier, and cascade
Md. Murad Kabir Nipun, Md. Jahedul Islam
wiley   +1 more source

Broadband Near‐Field Computational Microwave Imaging Enabled by Nonperiodic Dispersive Unit‐Cell Loading in a Passive Metasurface

Advanced Electronic Materials, EarlyView.
A compact passive engineered surface converts changes in microwave frequency into many distinct illumination patterns, allowing nearby objects to be imaged without mechanical scanning, active tuning, or a large antenna array. Operating from 18 to 28 GHz, the single‐port system uses measured field patterns to reconstruct metallic targets and distinguish
Mohammed H. Arif, Firas Slewa Dawod, Henrik Jansen, Caner Kadıoğlu, Sayan Roy, Dirk Heberling, Renato Negra   +6 more
wiley   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Large‐Scale Machine Learning to Screen for Small‐Molecule Senolytics

Advanced Intelligent Discovery, EarlyView.
A consistent workflow underpins all experiments in this study. A dedicated model‐selection dataset first identifies optimal hyperparameters for each algorithm. Models are then trained and rigorously evaluated on independent sets of molecules using the senolytic ratio SR. Comprehensive hyperparameter exploration across SMILES representations, task types,
Alexis Dougha, Jesus Gil, Pedro J. Ballester   +2 more
wiley   +1 more source

Structure and Function of a Multi‐Megadalton Virus‐Like Proteolytic Dodecahedron

Angewandte Chemie, EarlyView.
Zuzalysin, a virulence metallopeptidase of the periodontal pathogen Porphyromonas gingivalis, is secreted as a latent, flexible ≈95‐kDa monomer. Calcium triggers cysteine‐switch activation and hierarchical assembly into pentamers, bipentamers, tripentamers, and a ≈5.6‐MDa, ≈355‐Å dodecahedron. Structures at 1.8–3.6 Å reveal a virus‐sized catalytic cage
Mariusz Madej, Arturo Rodríguez‐Banqueri, Danuta Mizgalska, Borys Szmigielski, Zuzanna Nowakowska, Małgorzata Benedyk‐Machaczka, Monika Bzowska, Katarzyna Mikruta, Juan Sebastián Ramírez‐Larrota, Chinanu Agunanne, Olivier Julien, Anthony J. O'Donoghue, Carsten Scavenius, Mario López‐Martín, Enrique Marcos, Łukasz Koziej, Sebastian Glatt, Pablo Guerra, Ulrich Eckhard, Jan Potempa, F. Xavier Gomis‐Rüth   +20 more
wiley   +2 more sources

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

Data‐Efficient Cycle‐Level Capacity Prediction Using 1D Deep Convolutional Network

Advanced Intelligent Discovery, EarlyView.
We introduce DeepBat, a deep learning framework featuring a 1D convolutional backbone designed to extract latent degradation patterns from a microstructurally diverse electrode dataset. By learning complex formulation–performance relationships, the model accurately predicts long‐term specific discharge capacity using limited early‐cycle data, providing
Tao Huang, Bing Sun, Zhigang Chen, Chenyu Zhao, Eugene Bakker, Yaojie Lei, Lingfei Zhao, Shijian Wang, Chao Liu, Virginia Xiaojun He, Bahram Shirzadi, Hao Liu, Xin Xi, Ruiliu Liu, Dongqing Wu, Guodong Long, Guoxiu Wang   +16 more
wiley   +1 more source