Results 81 to 90 of about 537,310 (265)

Bio‐Orthogonally Crosslinked Supramolecular Polymer Bottlebrush Hydrogels for Long‐Term 3D Cell Culture

Advanced Functional Materials, EarlyView.
Fibrous benzenetrispeptide (BTP) hydrogels, fabricated via strain‐promoted azide‐alkyne cycloaddition (SPAAC) crosslinking, form robust, bioinert networks. These hydrogels can support 3D cell culture, where cell viability and colony growth depend on the fiber content.
Ceren C. Pihlamagi, David Pretzel, Peer Nölte, Kourosh Rezaei, Ulrich S. Schubert, Johannes C. Brendel   +5 more
wiley   +1 more source

Incorporating GAN for Negative Sampling in Knowledge Representation Learning

, 2018
Knowledge representation learning aims at modeling knowledge graph by encoding entities and relations into a low dimensional space. Most of the traditional works for knowledge embedding need negative sampling to minimize a margin-based ranking loss ...
Li, Shuangyin, pan, Rong, Wang, Peifeng
core   +1 more source

Graph Representation Learning in Biomedicine

, 2021
Biomedical networks (or graphs) are universal descriptors for systems of interacting elements, from molecular interactions and disease co-morbidity to healthcare systems and scientific knowledge. Advances in artificial intelligence, specifically deep learning, have enabled us to model, analyze, and learn with such networked data. In this review, we put
Li, Michelle M., Huang, Kexin, Zitnik, Marinka   +2 more
openaire   +2 more sources

Predicting Atomic Charges in MOFs by Topological Charge Equilibration

Advanced Functional Materials, EarlyView.
An atomic charge prediction method is presented that is able to accurately reproduce ab‐initio‐derived reference charges for a large number of metal–organic frameworks. Based on a topological charge equilibration scheme, static charges that fulfill overall neutrality are quickly generated.
Babak Farhadi Jahromi, Sumukh Shankar Sharadaprasad, Rochus Schmid   +2 more
wiley   +1 more source

Sequence-to-sequence modeling for graph representation learning

Applied Network Science, 2019
We propose sequence-to-sequence architectures for graph representation learning in both supervised and unsupervised regimes. Our methods use recurrent neural networks to encode and decode information from graph-structured data.
Aynaz Taheri, Kevin Gimpel, Tanya Berger-Wolf   +2 more
doaj   +1 more source

In Situ 3D Bioprinting: Impact of Cross‐Linking on the Adhesive Properties of Hydrogels

Advanced Functional Materials, EarlyView.
In situ 3D bioprinting enables the direct deposition of cell‐laden, adhesive biomaterials for on‐site tissue regeneration. This review provides a comprehensive overview of how cross‐linking influences the bioadhesive properties of hydrogels used in 3D bioprinting, highlighting cross‐linking triggers, bioadhesion mechanisms, polymer interpenetration ...
Odile Romero Fernandez, Mulazim Hussain Asim, Shumaila Arshad, Gergely Kali, Andreas Bernkop‐Schnürch   +4 more
wiley   +1 more source

Artifact‐Minimizing Ultrathin Transparent Electrodes Fabricated via iCVD for In Vivo Optogenetic Stimulation and Neural Signal Monitoring of Primary Visual Cortex

Advanced Functional Materials, EarlyView.
We present ultrathin flexible transparent electrodes through iCVD‐enabled molecular control of 10 nm gold films on poly(dimethylaminomethylstyrene). In vivo validation demonstrated photoelectric artifact reduction vs. opaque electrodes and preservation of natural neural dynamics.
Tae Jin Mun, Minju Kim, Chaesung Kim, Yunyoung Choi, In‐Yeop Baek, Younghak Cho, Gyeongyong Lee, Yerim Jang, Min‐Ho Nam, Wonryung Lee, Maesoon Im, Hyejeong Seong   +11 more
wiley   +1 more source

Text summarization based on semantic graphs: an abstract meaning representation graph-to-text deep learning approach

Journal of Big Data
Nowadays, due to the constantly growing amount of textual information, automatic text summarization constitutes an important research area in natural language processing.
Panagiotis Kouris, Georgios Alexandridis, Andreas Stafylopatis   +2 more
doaj   +1 more source

Distribution Preserving Graph Representation Learning

, 2022
Graph neural network (GNN) is effective to model graphs for distributed representations of nodes and an entire graph. Recently, research on the expressive power of GNN attracted growing attention. A highly-expressive GNN has the ability to generate discriminative graph representations.
Mao, Chengsheng, Luo, Yuan
openaire   +2 more sources

Meniscus Pixel Printing for Contact‐Lens Vision Sensing and Robotic Control

Advanced Functional Materials, EarlyView.
A visual‐sensing contact lens is enabled by meniscus pixel printing (MPP), which rapidly patterns a 200 µm perovskite photodetector pixel in 1 s without masks, vacuum processing, or bulky equipment. A deep‐learning‐based super‐resolution reconstructs sparse on‐lens signals into 80 × 80 high‐resolution visual information, while AI‐driven eye‐tracking ...
Byung‐Hoon Gong, Dohyean Kim, Jiyun Jeong, Taekyeong Kim, Soon‐sung Kwon, Junyoung Seo, Il Jeon, Im Doo Jung   +7 more
wiley   +1 more source