Xiaojun Yu

TL;DR: A swellable MN patch that can rapidly extract ISF is developed and can be efficiently recovered from MN patch by centrifugation for the subsequent offline analysis of metabolites such as glucose and cholesterol.

TL;DR: This study proposes, for the first time, a novel data adaptive empirical wavelet transform (EWT) based signal decomposition method for improving the classification accuracy of MI based EEG signals and shows the effectiveness and great potential of EWT for BCI system applications.

TL;DR: A robust and simple automated multivariate empirical wavelet transform (MEWT) algorithm for the decoding of different MI tasks and a robust correlation-based feature selection strategy is applied to largely reduce the system complexity and computational load.

TL;DR: Empirical wavelet transform (EWT) helped to explore the hidden patterns of MI tasks by decomposing EEG data into different modes and regularization parameter tuning of NCA guaranteed to improve classification performance with significant features for each subject.

TL;DR: A nanoparticle‐embedding MN system that contains a dissolvable hyaluronic acid (HA) matrix and mesoporous silica‐coated upconversion nanoparticles (UCNPs@mSiO2) shell is introduced that is used to deliver molecular beacons and siRNA targeting transforming growth factor‐beta type I receptor that is potentially used for abnormal scar treatment.