Results 101 to 110 of about 4,990,305 (257)
In macrophages, senkyunolide I (SEI) directly targets the K12 residue of VDAC1 to inhibit its stress‐induced oligomerization, a critical upstream event that effectively prevents mitochondrial DNA release and subsequent cGAS‐STING pathway activation.
Zhiming Ye +9 more
wiley +1 more source
Robust Stitching Interface and Deep Learning Empowered Hydrogel Human‐Machine Interface
A molecular design strategy is presented that exploits synergistic carboxylate anion–quaternary ammonium interactions to simultaneously strengthen the hydrogel–PET interface and the bulk hydrogel network. Integrated with deep learning signal processing, the stable hydrogel–PET interface enables consistent signal acquisition and reliable human‐machine ...
Hao Dong +11 more
wiley +1 more source
Neuromorphic Devices and Computing for Sensing, Memory, and Control
This review introduces neuromorphic devices made from diverse materials. These devices mimic neuronal functions and architectures and, when integrated with artificial or biological computing, can form closed loops with neurons for pressure, optical, acoustic, and biochemical sensing and modulation.
Zhengguang Zhu +2 more
wiley +1 more source
Single‐cell, spatial, molecular, and pathology analyses identify a CDH3‐associated malignant epithelial state in thymic epithelial tumors. This state links stem‐like and EMT programs to M2 macrophage–rich immunosuppressive niches, genomic instability, poor survival, and drug vulnerability.
Yuntao Feng +13 more
wiley +1 more source
Learning Work Function via Implicit Reasoning on Electrostatic Potential Landscapes
StructPot‐CLR establishes a cross‐modal contrastive learning framework that aligns the crystal structures of 2D materials with plane‐averaged electrostatic potential landscapes for physically informed work‐function prediction. The model achieves an MAE of 0.265 eV and an R2 of 0.902 on the held‐out test set while accurately preserving key morphological
Haoyu Wan, Yue Wu, Tianhao Su, Deng Pan
wiley +1 more source
DDGCN: A Dynamic Directed Graph Convolutional Network for Action Recognition
We propose a Dynamic Directed Graph Convolutional Network (DDGCN) to model spatial and temporal features of human actions from their skeletal representations.
Xin Li +3 more
core +1 more source
MolDBG is a site‐aware, sequence‐only framework that unifies drug‐target affinity prediction, binding‐site identification, and affinity‐conditioned molecular generation for structured proteins. Guided by multi‐task binding‐site supervision, it aligns interaction‐critical residues before learning drug‐target representations and simultaneously infers ...
Gang Luo +6 more
wiley +1 more source
STWave transforms massive microscopic‐resolution spatial transcriptomics into interpretable fine‐scale tissue maps through patch‐wise inference, wavelet‐based multi‐scale encoding, and dual‐domain reconstruction. It reduces noise while preserving weak spatial signals, enabling efficient analysis of 6 40 000 spots of 2.47 GB GPU memory and revealing ...
Tao Jiang +9 more
wiley +1 more source
A multimodal fusion framework integrating sequence, atomic, and fragment representations captures drug–target interactions across multiple scales. The model delivers strong predictive performance and enables efficient virtual screening. Applied to hematopoietic progenitor kinase 1 (HPK1), it identifies structurally diverse inhibitors with nanomolar ...
Shuo Liu +7 more
wiley +1 more source
Fusing graph transformer with multi-aggregate GCN for enhanced drug–disease associations prediction
Background Identification of potential drug–disease associations is important for both the discovery of new indications for drugs and for the reduction of unknown adverse drug reactions. Exploring the potential links between drugs and diseases is crucial
Shihui He, Lijun Yun, Haicheng Yi
doaj +1 more source

