Results 131 to 140 of about 23,580,719 (284)

TSTScope Unifies Single‐Cell Multi‐Omics to Identify Functional T Cell States Predictive of Immunotherapy Response

Advanced Science, EarlyView.
TSTScope is an interpretable AI framework that integrates single‐cell transcriptomes with TCR information through curated gene‐program constraints. By linking receptor context to functional T cell states, it reveals response‐associated tumor‐specific T cell programs in lung cancer immunotherapy cohorts and defines an MPR score associated with ...
Shiwei Cao, Jinyu Cheng, Feng‐ao Wang, Chenxin Yi, Jiajun Chen, Keyue Wang, Lulu Liu, Junwei Liu, Yixue Li   +8 more
wiley   +1 more source

Adhesives: Ecological and technological developments and their influence on the european wood-based panel industries

, 2022
Marutzky, R.
core   +1 more source

Multi‐Scale Bionic Structure Constructs Biomass Flame‐Retardant Thermal Insulation Foam Material

Advanced Science, EarlyView.
Inspired by mussel adhesion and hierarchical brick‐and‐mortar architectures, a cellulose‐based biomimetic foam is constructed by anchoring bentonite nanosheets onto a polydopamine‐decorated cellulose nanofiber network. The resulting foam combines low thermal conductivity with intrinsic flame retardancy and full biodegradability, presenting a ...
Jianming Liao, Lijun Fan, Yunyuan Dong, Xuyu Bao, Xiaobin Chen, Qifu Zheng, Guanqing Zhang, Shen Wang, Wei Chen, Min Zhang, Shuaiming He   +10 more
wiley   +1 more source

Trustworthy Multimodal Attention Framework for Creep Rupture Life Prediction Under Data‐Scarce Conditions: A Case Study on IN718

Advanced Science, EarlyView.
An attention‐based multimodal deep learning framework is developed to predict the creep life of Ni‐based superalloys by fusing processing parameters with microstructural micrographs. The model achieves high accuracy (R2 = 0.92), aligns with metallurgical principles by capturing δ‐phase influence, and incorporates uncertainty quantification, offering a ...
Haopeng Lv, Dayong Wu, Ziyuan Rao, Qian Wang, Haikun Ma, Jie Kang, Wang Li, Huicong Dong, Yandong Wang, Zhinan Yang, Ru Su   +10 more
wiley   +1 more source

Effects of Nitrogen Ion Implantation on Wettability and Surface Roughness of WC-Co Tools Used for Wood-Based Panel Machining. [PDF]

Materials (Basel)
Barlak M, Wilkowski J, Auriga R, Zagórski J, Boruszewski P, Borysiuk P.   +5 more
europepmc   +1 more source

Biochemically Constrained Multi‐Omics Integration Reveals Protein–Metabolite Dependencies Across Diseases

Advanced Science, EarlyView.
ProMetNet introduces a biologically constrained deep learning framework for proteo‐metabolomic integration by embedding Reactome‐derived pathway topology into neural networks. It captures non‐linear molecular dependencies and pathway‐level metabolic reorganization, enabling interpretable discrimination.
Minghui Zhao, Na Zhou, Ruotong Liu, Xiaofei Li, Jian Li, Fuzhong Xue, Qingzhen Hou   +6 more
wiley   +1 more source

Data on the effect of wood-based panel-emitted volatile organic compounds on mice. [PDF]

Data Brief
Hwang I, Kim K, Ryu SH, Ahn C, Cui XS, Jeung EB.   +5 more
europepmc   +1 more source

National Education Goals Panel

, 1998
The National Education Goals Panel (NEGP) is a unique bipartisan and intergovernmental body of federal and state officials created in July 1990 to assess and report state and national progress toward achieving the National Education Goals.
National Education Goals Panel
core  

Neuromorphic Optical Tracking and Imaging of Randomly Moving Targets Through Dynamic Dense Scattering Media

Advanced Science, EarlyView.
Vision begins not with images but with change: the retina fires only when something moves, handing the brain a stream of spikes. A neuromorphic imaging system adopts the same strategy—an event sensor that sees like the retina, a spiking network that thinks like the brain—to track and reconstruct fully randomly moving targets through tissue (phantom ...
Ning Zhang, Arto Nurmikko
wiley   +1 more source

Sustainability, Circularity, and Innovation in Wood-based Panel Manufacturing in the 2020s: Opportunities and Challenges. [PDF]

Curr For Rep
Garcia R, Calvez I, Koubaa A, Landry V, Cloutier A.   +4 more
europepmc   +1 more source