Results 141 to 150 of about 1,256,985 (257)

The Cuttlebone Blueprint for Multifunctional Metamaterials: Design Taxonomy, Functional Decoupling, and Future Horizons

Advanced Functional Materials, EarlyView.
Cuttlebone‐inspired metamaterials exploit a septum‐wall architecture to achieve excellent mechanical and functional properties. This review classifies existing designs into direct biomimetic, honeycomb‐type, and strut‐type architectures, summarizes governing design principles, and presents a decoupled design framework for interpreting multiphysical ...
Xinwei Li, Zhendong Li
wiley   +1 more source

Sensorized Engineered Tissues with Built‐in Thermoregulation and Nutrient Supply

Advanced Functional Materials, EarlyView.
This work introduces a granular hydrogel‐based tissue engineering platform that includes a closed‐loop temperature control to maintain 37°C and sustainably releases nutrients, thereby enabling cells to retain a high viability even if stored at room temperature for up to 24 h.
Antonia Georgopoulou, Jakob Schreiner, Miriam Filippi, Aiste Balciunaite, Robert K. Katzschmann, Esther Amstad   +5 more
wiley   +1 more source

Multi-instance learning of graph neural networks for aqueous pKa prediction. [PDF]

Bioinformatics, 2022
Xiong J, Li Z, Wang G, Fu Z, Zhong F, Xu T, Liu X, Huang Z, Liu X, Chen K, Jiang H, Zheng M.   +11 more
europepmc   +1 more source

Optoelectronic Synaptic Devices Using Molecular Telluride Phase‐Change Inks for Three‐Factor Learning

Advanced Functional Materials, EarlyView.
Optoelectronic synaptic devices based on solution‐processed molecular telluride GST‐225 phase‐change inks are demonstrated for three‐factor learning. A global optical signal broadcast through a silicon waveguide induces non‐volatile conductance updates exclusively in locally electrically flagged memristors.
Kevin Portner, Dhananjeya Kumaar, Giacomo Torti, Killian Keller, Till Zellweger, Florian M. Schenk, Christoph Weilenmann, Daniel Chelladurai, Vara Prasad Jonnalagadda, Ghazi Sarwat Syed, Vanessa Wood, Jürg Leuthold, Alexandros Emboras, Maksym Yarema, Mathieu Luisier   +14 more
wiley   +1 more source

Pathological diagnosis and prognosis of Gastric cancer through a multi-instance learning method. [PDF]

EBioMedicine, 2021
Cao R, Gong L, Dong D.
europepmc   +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

Breast Cancer Molecular Subtype Prediction on Pathological Images with Discriminative Patch Selection and Multi-Instance Learning. [PDF]

Front Oncol, 2022
Liu H, Xu WD, Shang ZH, Wang XD, Zhou HY, Ma KW, Zhou H, Qi JL, Jiang JR, Tan LL, Zeng HM, Cai HJ, Wang KS, Qian YL.   +13 more
europepmc   +1 more source

Metal–Organic Frameworks for Gaseous Pollutant Management: From Capture to Neutralization and Reutilization

Advanced Functional Materials, EarlyView.
This review maps how MOFs can manage hazardous gases by combining adsorption, neutralization, and reutilization, enabling sustainable air‐pollution control. Covering chemical warfare agent simulants, SO2, NOx, NH3, H2S, and volatile organic compounds, it highlights structure‐guided strategies that boost selectivity, water tolerance, and cycling ...
Yuanmeng Tian, Zeyu Tao, Kaikai Ma, Xiaoliang Wang, Bo Yu, Yiu Wan Yip, Zhijie Chen, Jin Shang, Bin Fei   +8 more
wiley   +1 more source

DeepLION: Deep Multi-Instance Learning Improves the Prediction of Cancer-Associated T Cell Receptors for Accurate Cancer Detection. [PDF]

Front Genet, 2022
Xu Y, Qian X, Zhang X, Lai X, Liu Y, Wang J.   +5 more
europepmc   +1 more source

BACH, a Bayesian Optimization Protocol for Accurate Coarse‐Grained Parameterization of Organic Liquids

Advanced Functional Materials, EarlyView.
We present a fully automated Bayesian optimization (BO) protocol for the parameterization of nonbonded interactions in coarse‐grain CG force fields (BACH). Using experimental thermophysical data, we apply the protocol to a broad range of liquids, spanning linear, branched, and unsaturated hydrocarbons, esters, triglycerides, and water.
Janak Prabhu, Akhil Pratap Singh, Pablo Campomanes, Stefano Vanni   +3 more
wiley   +1 more source