Intelligent wireless power transfer via a 2-bit compact reconfigurable transmissive-metasurface-based router.

With the rapid development of the Internet of Things, numerous devices have been deployed in complex environments for environmental monitoring and information transmission, which brings new power supply challenges. Wireless power transfer is a promising solution since it enables power delivery without cables, providing well-behaved flexibility for power supplies. Here we propose a compact wireless power transfer framework. The core components of the proposed framework include a plane-wave feeder and a transmissive 2-bit reconfigurable metasurface-based beam generator, which constitute a reconfigurable power router. The combined profile of the feeder and the beam generator is 0.8 wavelengths. In collaboration with a deep-learning-driven environment sensor, the router enables object detection and localization, and intelligent wireless power transfer to power-consuming targets, especially in dynamic multitarget environments. Experiments also show that the router is capable of simultaneous wireless power and information transfer. Due to the merits of low cost and compact size, the proposed framework may boost the commercialization of metasurface-based wireless power transfer routers.

A Novel Method for Observing Tumor Margin in Hepatoblastoma Based on Microstructure 3D Reconstruction.

Objective: We investigated three-dimensional (3 D) reconstruction for the assessment of the tumor margin microstructure of hepatoblastoma (HB). Methods: Eleven surgical resections of childhood hepatoblastomas obtained between September 2018 and December 2019 were formalin-fixed, paraffin-embedded, serially sectioned at 4 µm, stained with hematoxylin and eosin (every 19th and 20th section stained with alpha-fetoprotein and glypican 3), and the digital images of all sections were acquired at 100× followed by image registration using the B-spline based method with modified residual complexity. Reconstruction was performed using 3 D Slicer software. Results: The reconstructed orthogonal 3 D images clearly presented the internal microstructure of the tumor margin. The rendered 3 D image could be rotated at any angle. Conclusions: Microstructure 3 D reconstruction is feasible for observing the pathological structure of the HB tumor margin.

The novel HLA-A*31:191 allele was identified in a Chinese platelet donor.

HLA-A*31:191 differs from HLA-A*31:01:02:01 by one single nucleotide substitution at nucleotide 74.

Identification of Metastatic Lymph Nodes in MR Imaging with Faster Region-Based Convolutional Neural Networks.

MRI is the gold standard for confirming a pelvic lymph node metastasis diagnosis. Traditionally, medical radiologists have analyzed MRI image features of regional lymph nodes to make diagnostic decisions based on their subjective experience; this diagnosis lacks objectivity and accuracy. This study trained a faster region-based convolutional neural network (Faster R-CNN) with 28,080 MRI images of lymph node metastasis, allowing the Faster R-CNN to read those images and to make diagnoses. For clinical verification, 414 cases of rectal cancer at various medical centers were collected, and Faster R-CNN-based diagnoses were compared with radiologist diagnoses using receiver operating characteristic curves (ROC). The area under the Faster R-CNN ROC was 0.912, indicating a more effective and objective diagnosis. The Faster R-CNN diagnosis time was 20 s/case, which was much shorter than the average time (600 s/case) of the radiologist diagnoses.Significance: Faster R-CNN enables accurate and efficient diagnosis of lymph node metastases. Cancer Res; 78(17); 5135-43. ©2018 AACR.