Results 61 to 70 of about 150,011 (287)
Generating Dynamic Structures Through Physics‐Based Sampling of Predicted Inter‐Residue Geometries
Advanced Science, EarlyView.While static structure prediction has been revolutionized, modeling protein dynamics remains elusive. trRosettaX2‐Dynamics is presented to address this challenge. This framework leverages a Transformer‐based network to predict inter‐residue geometric constraints, guiding conformation generation via physics‐based iterative sampling. The resulting method
Chenxiao Xiang +3 more
wiley +1 more source
On Use of Independent Component Analysis for Ocular Artifacts Reduction of Electroencephalogram and While Using Kurtosis as the Threshold [PDF]
International Journal Bioautomation, 2017 Brain electrical activity commonly represented by the Electroencephalogram (EEG), due to its miniscule amplitude (on the order of a hundred microvolts), is often contaminated with various artifacts.
Kazi Aminul Islam, Gleb Tcheslavski
doaj
Comparison of IVA and GIG-ICA in Brain Functional Network Estimation Using fMRI Data
Frontiers in Neuroscience, 2017 Spatial group independent component analysis (GICA) methods decompose multiple-subject functional magnetic resonance imaging (fMRI) data into a linear mixture of spatially independent components (ICs), some of which are subsequently characterized as ...
Yuhui Du +10 more
doaj +1 more source
Advanced Science, EarlyView.Soybean production is severely threatened by salt stress. This study reveals that the GmSIN1‐GmRNF1a‐GmCSN5a module enhances salt tolerance by stabilizing the GmSIN1 protein. GmRNF1a acts as an E3 ligase to ubiquitinate GmSIN1 for degradation, a process inhibited by GmCSN5a.
Jinlong Xu +15 more
wiley +1 more source
A Tutorial on Independent Component Analysis [PDF]
, 2014 Independent component analysis (ICA) has become a standard data analysis technique applied to an array of problems in signal processing and machine learning.
Shlens, Jonathon
core
Complex Independent Component Analysis of Frequency-Domain Electroencephalographic Data
, 2003 Independent component analysis (ICA) has proven useful for modeling brain and electroencephalographic (EEG) data. Here, we present a new, generalized method to better capture the dynamics of brain signals than previous ICA algorithms.
Amari +24 more
core +5 more sources
Deep Learning‐Assisted Coherent Raman Scattering Microscopy
Advanced Intelligent Discovery, EarlyView.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
Frontiers in Human Neuroscience, 2020 Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) can potentially enable people to non-invasively and directly communicate with others using brain activities.
Suguru Kanoga +3 more
doaj +1 more source
Image encoding by independent principal components [PDF]
, 2010 The encoding of images by semantic entities is still an unresolved task. This paper proposes the encoding of images by only a few important components or image primitives. Classically, this can be done by the Principal Component Analysis (PCA). Recently,
Arlt, Björn, Brause, Rüdiger W.
core
Microrobotic Catheterization of the Ophthalmic Artery for Targeted Treatment of Retinoblastoma
Advanced Intelligent Systems, EarlyView.A microrobotic platform is presented that allows teleoperated and autonomous navigation of flow‐driven magnetic microcatheters, MagFlow, into the ophthalmic artery for superselective intra‐arterial infusion of chemotherapy to treat retinoblastoma. Extensive benchtop validations with patient‐derived biomimetic phantoms under optical and fluoroscopic ...
Artur Banach +5 more
wiley +1 more source