Xiang Zhang

TL;DR: It is demonstrated that a senescence-like state enables lung cancer cells to survive dual inhibition of EGFR and MEK mediated by the YAP/TEAD pathway, which drives epigenomic reprogramming and EMT to counteract apoptosis.

TL;DR: This work proposes a class of spectral singularities emerging from the coincidence of two independent singularities with highly directional responses that result from resonance trapping induced by the interplay between parity-time symmetry and Fano resonances.

TL;DR: In this article, a resonance plasmon mode is formed between a gold nanorod and an infinite slab in infrared range, with local electric field enhancement factor over 40.0 nN/(mW/μm2).

TL;DR: In this paper, the authors proposed an end-to-end deep learning based framework to predict demand imbalance in multi-modal data, which comprises three parts: a cascade graph convolutional recurrent neural network to extract spatial-temporal correlations within citywide historical vehicle demand data; two multi-layer LSTM networks to represent the external meteorological data and time meta separately; and an encoder-decoder module to fuse the above two parts and decode the representation to achieve prediction over a longer time period into the future.