Some patients with positive chest CT findings may present with negative results of real time reverse-transcription-polymerase chain-reaction (RT-PCR) for 2019 novel coronavirus (2019-nCoV). In this report, we present chest CT findings from five patients with 2019-nCoV infection who had initial negative RT-PCR results. All five patients had typical imaging findings, including ground-glass opacity (GGO) (5 patients) and/or mixed GGO and mixed consolidation (2 patients). After isolation for presumed 2019-nCoV pneumonia, all patients were eventually confirmed with 2019-nCoV infection by repeated swab tests. A combination of repeated swab tests and CT scanning may be helpful when for individuals with high clinical suspicion of nCoV infection but negative RT-PCR screening Abbreviations PT-PCR=reverse-transcription-polymerase chain-reaction GGO=ground glass opacity

Metallic zinc (Zn) has been regarded as an ideal anode material for aqueous batteries because of its high theoretical capacity (820 mA h g), low potential (-0.762 V versus the standard hydrogen electrode), high abundance, low toxicity and intrinsic safety. However, aqueous Zn chemistry persistently suffers from irreversibility issues, as exemplified by its low coulombic efficiency (CE) and dendrite growth during plating/ stripping, and sustained water consumption. In this work, we demonstrate that an aqueous electrolyte based on Zn and lithium salts at high concentrations is a very effective way to address these issues. This unique electrolyte not only enables dendrite-free Zn plating/stripping at nearly 100% CE, but also retains water in the open atmosphere, which makes hermetic cell configurations optional. These merits bring unprecedented flexibility and reversibility to Zn batteries using either LiMnO or O cathodes-the former deliver 180 W h kg while retaining 80% capacity for >4,000 cycles, and the latter deliver 300 W h kg (1,000 W h kg based on the cathode) for >200 cycles.

In this work, we propose "Residual Attention Network", a convolutional neural network using attention mechanism which can incorporate with state-of-art feed forward network architecture in an end-to-end training fashion. Our Residual Attention Network is built by stacking Attention Modules which generate attention-aware features. The attention-aware features from different modules change adaptively as layers going deeper. Inside each Attention Module, bottom-up top-down feedforward structure is used to unfold the feedforward and feedback attention process into a single feedforward process. Importantly, we propose attention residual learning to train very deep Residual Attention Networks which can be easily scaled up to hundreds of layers.Extensive analyses are conducted on CIFAR-10 and CIFAR-100 datasets to verify the effectiveness of every module mentioned above. Our Residual Attention Network achieves state-of-the-art object recognition performance on three benchmark datasets including CIFAR-10 (3.90% error), CIFAR-100 (20.45% error) and ImageNet (4.8% single model and single crop, top-5 error). Note that, our method achieves 0.6% top-1 accuracy improvement with 46% trunk depth and 69% forward FLOPs comparing to ResNet-200. The experiment also demonstrates that our network is robust against noisy labels.

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