2019新型冠状病毒 (Novel coronavirus),为新兴传染病“严重特殊传染性肺炎”病原,由世界卫生组织命名为2019-nCoV,又名武汉冠状病毒(Wuhan coronavirus)、武汉肺炎(Wuhan pneumonia)等,是一种具有包膜的正链单股RNA冠状病毒。2019-2020年新型冠状病毒肺炎事件爆发期间,研究人员在对肺炎阳性患者样本进行核酸检测以及基因组测序后发现了这一病毒。 https://zh.wikipedia.org/wiki/2019%E6%96%B0%E5%9E%8B%E5%86%A0%E7%8B%80%E7%97%85%E6%AF%92

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2020 年年初, 新型冠状病毒感染的肺炎 (COVID-19) 爆发, 中国采取了全面严格的防控举措 全力抗击疫情. 地方疫情指挥部门及时通报疫情感染数据, 有助公众了解疫情的发展, 及时做好防护 措施. 各地患者病例详情数据主要以文本形式记录, 信息描述复杂, 且各省市汇报的格式各异, 处理难 度较大. 我们面向全国湖北省外近二分之一匿名的患者病例详情数据, 提出了应用自然语言处理技术, 辅助病例数据结构化的方法. 该方法可以在标记样本较少的情况下, 借助预训练模型, 准确有效地提 取出病例文本中的关键信息. 通过对较大规模患者结构化病例数据的挖掘, 本文详细分析了新型冠状 肺炎总体发病性别和年龄分布特点、主要感染原因、潜伏期特点及疫情趋势等特征. 由于潜伏期等时 间延迟的存在, 确诊人数往往不能反映一个地区的真实感染情况, 结合出行大数据, 本文提出了一个 合理推断武汉市等城市实际感染人数的方法. 该方法有助于人们提前估计地区疫情发展情况, 及早采 取防护措施. 也可以辅助地方相关部门科学决策, 尽早调度医务人员和分配医疗资源。

http://scis.scichina.com/cn/2020/SSI-2020-0029.pdf

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With the enhancement of algorithms, cryo-EM has become the most efficient technique to solve structures of molecules. Take a recent event for example, after the outbreak of COVID-19 in January, the first structure of 2019-nCoV Spike trimer was published in March using cryo-EM, which has provided crucial medical insight for developing vaccines. The enabler behind this efficiency is the GPU-accelerated computation which shortens the whole analysis process to 12 days. However, the data characteristics include strong noise, huge dimension, large sample size and high heterogeneity with unknown orientations have made analysis very challenging. Though, the popular GPU-accelerated Bayesian approach has been shown to be successful in 3D refinement. It is noted that the traditional method based on multireference alignment may better differentiate subtle structure differences under low signal to noise ratio (SNR). However, a modular GPU-acceleration package for multireference alignment is still lacking in the field. In this work, a modular GPU-accelerated alignment library called Cryo-RALib is proposed. The library contains both reference-free alignment and multireference alignment that can be widely used to accelerate state-of-the-art classification algorithms. In addition, we connect the cryo-EM image analysis with the python data science stack which enables users to perform data analysis, visualization and inference more easily. Benchmark on the TaiWan Computing Cloud container, our implementation can accelerate the computation by one order of magnitude. The library has been made publicly available at https://github.com/phonchi/Cryo-RAlib

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