Results 231 to 240 of about 172,799 (305)
Entorhinal Silencing Reveals Energy Cascade Organization of Hippocampal Oscillations. [PDF]
Zhao B, Qin Y, Burke SN, Maurer AP.
europepmc +1 more source
A novel machine learning approach classifies macrophage phenotypes with up to 98% accuracy using only nuclear morphology from DAPI‐stained images. Bypassing traditional surface markers, the method proves robust even on complex textured biomaterial surfaces. It offers a simpler, faster alternative for studying macrophage behavior in various experimental
Oleh Mezhenskyi +5 more
wiley +1 more source
Unconventional high-harmonic generation in resonant membrane metasurfaces. [PDF]
Tonkaev P +7 more
europepmc +1 more source
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia +3 more
wiley +1 more source
Chip-Scale Optomechanical Frequency Comb with a 1-70 GHz Span. [PDF]
Gou X +5 more
europepmc +1 more source
Deep Learning‐Assisted Coherent Raman Scattering Microscopy
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu +4 more
wiley +1 more source
Rapid invisible frequency tagging (RIFT) does not evoke intermodulation components in the neural response. [PDF]
Zhigalov A, Jensen O.
europepmc +1 more source
A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons
We benchmark six large atomistic foundation models on 2429 crystalline materials for phonon transport properties. The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces.
Md Zaibul Anam +5 more
wiley +1 more source

