Results 211 to 220 of about 112,162 (267)

Mesenchymal Stromal Cell‐Mediated Intercellular Communication: Mapping the Interactome for Skeletal Muscle Homeostasis and Regeneration

Advanced Science, EarlyView.
Liu et al. define a systems‐level interactome of fibroadipogenic progenitor (FAP)‐mediated signaling in skeletal muscle by integrating single‐cell transcriptomics with FAP depletion‐based perturbation analysis. Functional interrogation using a conditioned media bioassay links predicted signaling to multicellular outcomes, establishing a framework to ...
Xingyu Liu, Edgar E. Perez Carbajal, Yih‐Chii Hwang, Sahil A. Mapkar, Benjamin W. Gilman, Sarah A. Bliss, Lam B. Tran, Kalgi T. Mehta, Jacob O. Banyasz, Ming Yu, Reynold R. Liu, Matthew N. Ly, Christapher S. Morrissey, Michael N. Wosczyna   +13 more
wiley   +1 more source

A deep reinforcement learning approach to dance movement analysis. [PDF]

Sci Rep
Yin P, Li X.
europepmc   +1 more source

Deep Contrastive Learning for High‐Throughput Prediction of Drug Resistance Mutations from Sequences

Advanced Science, EarlyView.
This study presents DeepMutDTA, a deep learning framework aimed at predicting mutation‐induced changes in protein‐drug interactions and prioritizing variants potentially linked to drug resistance. Trained on large‐scale data, it incorporates SimSiam‐MuTF, a label‐aware contrastive fine‐tuning strategy that encourages separation between WT and MT ...
Xiaowen Hu, Pan Zhang, Shangqian Wu, Hao Sun, Minwei Li, Sophia Tsoka, Zizhang Sheng, Lei Deng   +7 more
wiley   +1 more source

Strategies for updating rules driven by reinforcement learning to solve social dilemmas. [PDF]

PLoS One
Wang Y, Yu X, Lu S.
europepmc   +1 more source

In Situ X‐Ray Tomography and Acoustic Emission Monitoring of Damage Evolution in C/C‐SiC Composites Fabricated by Liquid Silicon Infiltration

Advanced Science, EarlyView.
This study investigates how the internal structure of fiber‐reinforced ceramic composites affects their resistance to damage. By combining 3D X‐ray imaging with acoustic emission monitoring during mechanical testing, it reveals how silicon distribution influences crack formation.
Yang Chen, Renato S. M. Almeida, Stefan Flauder, Guillaume Couégnat, Kamen Tushtev, Jürgen Horvath, Peter Wriggers, Kurosch Rezwan   +7 more
wiley   +1 more source

Signatures of reinforcement learning in natural behavior. [PDF]

Curr Dir Psychol Sci
Hartley CA, Benear SL, Heller AS.
europepmc   +1 more source

Machine Learning for Green Solvents: Assessment, Selection and Substitution

Advanced Science, EarlyView.
Environmental regulations have intensified demand for green solvents, but discovery is limited by Solvent Selection Guides (SSGs) that quantify solvent sustainability. Training a machine learning model on GlaxoSmithKline SSG, a database of sustainability metrics for 10,189 solvents, GreenSolventDB is developed. Integrated with Hansen solubility metrics,
Rohan Datta, Janhavi Nistane, Abhishek Sose, Harikrishna Sahu, Rampi Ramprasad   +4 more
wiley   +1 more source

Information-Theoretic Intrinsic Motivation for Reinforcement Learning in Combinatorial Routing. [PDF]

Entropy (Basel)
Xi R, Ni Y, Wu W.
europepmc   +1 more source

Safety-Constrained Reinforcement Learning for Energy-Aware Transmission Scheduling in Seismic Wireless Sensor Networks. [PDF]

Sensors (Basel)
Nazamdin I, Reid A.
europepmc   +1 more source

Portfolio management based on value distribution reinforcement learning algorithm. [PDF]

Front Artif Intell
Yang Y, Wang T, Fu Y, Huang J, Zhou D.
europepmc   +1 more source