White matter hyperintensities (WMH) are commonly found in the brains of healthy elderly individuals and have been associated with various neurological and geriatric disorders. In this paper, we present a study using deep fully convolutional network and ensemble models to automatically detect such WMH using fluid attenuation inversion recovery (FLAIR) and T1 magnetic resonance (MR) scans. The algorithm was evaluated and ranked 1st in the WMH Segmentation Challenge at MICCAI 2017. In the evaluation stage, the implementation of the algorithm was submitted to the challenge organizers, who then independently tested it on a hidden set of 110 cases from 5 scanners. Averaged dice score, precision and robust Hausdorff distance obtained on held-out test datasets were 80%, 84% and 6.30 mm respectively. These were the highest achieved in the challenge, suggesting the proposed method is the state-of-the-art. Detailed descriptions and quantitative analysis on key components of the system were provided. Furthermore, a study of cross-scanner evaluation is presented to discuss how the combination of modalities affect the generalization capability of the system. The adaptability of the system to different scanners and protocols is also investigated. A quantitative study is further presented to show the effect of ensemble size and the effectiveness of the ensemble model. Additionally, software and models of our method are made publicly available. The effectiveness and generalization capability of the proposed system show its potential for real-world clinical practice.

Few layer COFs was incorporated with rGO to provide composite cathode materials with improved energy storage capability.

As the world moves toward electromobility, our daily lives are flooded with variety of lithium ion batteries (LIBs), and the concerns of cost, safety and environmental friendliness of LIBs spring up in the minds of scientists. Although organic electrodes have been considered as promising alternatives to their inorganic counterparts, some intrinsic weaknesses still plague scientists, such as high solubility, low conductivity and sluggish ion diffusion. The emergence of covalent organic frameworks (COFs) attracts our attention because of their robust networks and open pores that could facilitate the infiltration of electrolyte ions when used as electrodes for metal-ion batteries (MIBs). In this review, we summarized the recent progress of COFs as electrode materials, and the strategies toward enhancing electrochemical performance of COFbased electrode in MIBs are discussed. Hopefully, this review will provide a fundamental guidance for future development of COF-based electrodes.

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Ruthenium-based catalyst is one of the most active catalysts for oxygen evolution reaction (OER) in acid media. However, the strong bonding between the Ru sites and oxygen intermediates leads to high overpotential to trigger the OER process. Hence, pyrochlore rare-earth ruthenate (RE 2-Ru 2 O 7) structures with a series of rare-earth elements (Nd, Sm, Gd, Er, and Yb) were constructed to tune the electronic structure of the Ru sites. Surface structure analysis indicated that the increase of the radius of the rare-earth cations resulted in higher content of defective oxygen (the percentage of the defective oxygen increased from 29.5% to 49.7%) in the RE 2 Ru 2 O 7 structure due to the weakened hybridization of the Ru-O bond. This reduced the valence states of the Ru sites and enlarged the gap between the 4d band center and the Fermi level (E F) of Ru, resulting in the weakened adsorption of oxygen intermediates and the improved OER performance in acid media. Among the as-prepared ruthenium pyrochlores, Nd 2 Ru 2 O 7 displayed the lowest OER onset overpotential (210 mV) and Tafel slope (58.48 mV dec −1), as well as 30 times higher intrinsic activity and much higher durability than the state-of-art RuO 2 catalyst.

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