Identification of Host Cellular Protein Substrates of SARS-COV-2 Main Protease

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease-19 (COVID-19) being associated with severe pneumonia. Like with other viruses, the interaction of SARS-CoV-2 with host cell proteins is necessary for successful replication, and cleavage of cellular targets by the viral protease also may contribute to the pathogenesis, but knowledge about the human proteins that are processed by the main protease (3CLpro) of SARS-CoV-2 is still limited. We tested the prediction potentials of two different in silico methods for the identification of SARS-CoV-2 3CLpro cleavage sites in human proteins. Short stretches of homologous host-pathogen protein sequences (SSHHPS) that are present in SARS-CoV-2 polyprotein and human proteins were identified using BLAST analysis, and the NetCorona 1.0 webserver was used to successfully predict cleavage sites, although this method was primarily developed for SARS-CoV. Human C-terminal-binding protein 1 (CTBP1) was found to be cleaved in vitro by SARS-CoV-2 3CLpro, the existence of the cleavage site was proved experimentally by using a His₆-MBP-mEYFP recombinant substrate containing the predicted target sequence. Our results highlight both potentials and limitations of the tested algorithms. The identification of candidate host substrates of 3CLpro may help better develop an understanding of the molecular mechanisms behind the replication and pathogenesis of SARS-CoV-2.     

基于数据拼接的金刚石刀具刃口三维形貌表征技术

原子力显微镜(AFM)是获取金刚石刀具刃口纳米级三维形貌的重要手段,但其存在测量范围小,难以表征参与切削区域整段刃口形貌特征的问题。采用基于迭代最近点法的刃口AFM测量数据拼接算法,获得了由多组数据拼接得到的刃口纳米级三维形貌,并进行了切削实验验证。实验结果表明,采用的数据拼接方法可较好地用于表征刀具刃口三维形貌。     

Application of the novel Droplet digital PCR technology for identification of meat species

The authenticity and traceability of meat products are issues of primary importance to ensure food safety. Unfortunately, food adulteration (e.g. the addition of inexpensive cuts to minced meat products) and mislabelling (e.g. the inclusion of meat from species other than those declared) happens frequently worldwide. The aim of this study was to apply a droplet digital PCR assay for the detection and quantification (copies μL⁻¹) of the beef, pork, horse, sheep, chicken and turkey in meat products. The analysis conducted on commercial meat showed the presence of traces of DNA from other animal species than those declared. We show that the method is highly sensitive, specific and accurate (accuracy = 100%). This method could be adopted by competent food safety authorities to verify compliance with the labelling of meat products and to ensure quality and safety throughout the meat supply chain, from primary production to consumption.     

风速时间序列混沌判定方法比较研究

混沌识别是对非线性时间序列进行混沌预测的前提。针对时间序列风速确定性与随机性相结合的复杂非线性特征,研究了不同的混沌识别方法,并对风速时间序列进行混沌特征识别。应用随机噪声、周期运动及经典混沌系统的时间序列对所选方法进行可靠性验证。对美国国家风能研究中心M2测风塔实测时间序列风速数据进行非线性混沌特征识别。结果表明:风速时间序列具有明显的混沌特征;各风速时间序列表现出不同程度的混沌特征;各混沌识别方法对风速时间序列混沌特征的表达形式不同,互为补充,相互验证。     

A Unified Analysis of Structured Sonar-Terrain Data Using Bayesian Functional Mixed Models

Sonar emits pulses of sound and uses the reflected echoes to gain information about target objects. It offers a low cost, complementary sensing modality for small robotic platforms. Although existing analytical approaches often assume independence across echoes, real sonar data can have more complicated structures due to device setup or experimental design. In this article, we consider sonar echo data collected from multiple terrain substrates with a dual-channel sonar head. Our goals are to identify the differential sonar responses to terrains and study the effectiveness of this dual-channel design in discriminating targets. We describe a unified analytical framework that achieves these goals rigorously, simultaneously, and automatically. The analysis was done by treating the echo envelope signals as functional responses and the terrain/channel information as covariates in a functional regression setting. We adopt functional mixed models that facilitate the estimation of terrain and channel effects while capturing the complex hierarchical structure in data. This unified analytical framework incorporates both Gaussian models and robust models. We fit the models using a full Bayesian approach, which enables us to perform multiple inferential tasks under the same modeling framework, including selecting models, estimating the effects of interest, identifying significant local regions, discriminating terrain types, and describing the discriminatory power of local regions. Our analysis of the sonar-terrain data identifies time regions that reflect differential sonar responses to terrains. The discriminant analysis suggests that a multi- or dual-channel design achieves target identification performance comparable with or better than a single-channel design. Supplementary materials for this article are available online.     

手机信令数据在地质灾害预警中的应用

本文是在地质灾害监测预警项目的实践基础上,结合移动运营商对手机信令数据的侦测应用,提出了基于手机信令数据的地质灾害预警发布的解决方案。该方案可实现地质灾害隐患点威胁区域的人员实时统计、预警信息发布、预警效果判断等功能,弥补了现有基于短信和大喇叭预警发布解决方案的不足。它能有效降低地质灾害发生时造成的人员伤亡,并且适用于我国所有的地质灾害影响区域。     

船体结构冰载荷反演方法及试验验证

冰载荷是影响船舶冰区航行期间结构安全的重要环境载荷。船舶的冰压监测通常采用应变传感器，合理地布放传感器是识别冰载荷的基础。通过对比船体外板结构试验中的冲击载荷和不同测试方案下的应变信号，确定了最佳应变传感器布放方案；采用Green核函数方法建立了船体外板结构应变冲击载荷间的响应关系，并对采集信号在噪声影响下反演的不适定性进行了分析；采用Tikhonov正则化方法克服了载荷反演过程中出现的数值不稳定问题；最后将试验中的响应用到载荷识别分析中，反演的载荷可以较为准确地反映冲击载荷的时域特征并且载荷识别精度良好。     

基于街景地图数据的北京市东城区 街道绿色空间正义评估研究

绿色空间正义是城市空间正义的典型代表，意为在人群中公平分配维护绿色空间的责任和享有绿色空间的权益。根据其定义，以北京市东城区为例，从责任和权益2个角度着眼，一方面基于百度街景地图数据提炼街道空间绿视率的空间分布特点，表征街道绿色空间的客观可感知水平；另一方面将街道绿视率和实时人口热力叠加，探讨街道绿色空间的实际被感知程度，从而完成对北京市东城区街道绿色空间维护和享有情况的评估。发现邻近使用者维护街道绿色空间的责任对绿化客观可感知水平的重要性，以及合理的道路断面设计对同时提升街道空间活力和绿视率即绿化实际被感知程度的有效性。由此总结出2点街道绿色空间规划设计建议，即明晰权责边界并保证公共权益优先级，以及加强落地性设计导则中对绿色空间正义问题的关注，并对未来研究提出展望。     

双馈风机风电场联络线出口故障方向判别

由于双馈风机风电场故障特性与传统同步机存在较大差别,因此传统方向元件应用于双馈风机风电场时存在适应性问题。针对双馈风机风电场存在的频率偏移、等值内阻抗不稳定和弱馈等特点,提出一种基于时域波形相似度的故障方向元件。根据推算的联络线对侧电压与保护安装处记忆电压波形的余弦相似度判断故障方向,能够有效解决双馈风机风电场出口故障方向判别存在的问题。在PSCAD/EMTDC中搭建典型的双馈风机接入系统,仿真结果表明,所提故障方向元件针对不同的故障类型均能有效判断故障方向。     

因素空间理论下基点分类算法研究

目前，基于因素空间理论的背景基提取算法计算过程复杂，初始化必须依赖各因素极值，基点数量提取冗余等原因，未能在应用中取得很好效果。为此，结合内点判别法和知识可继承、可扩展的思想，提出一种计算简单、初始化独立、基点数量小的改进的背景基提取算法。然后，利用改进的背景基提取算法构造出一种全新的数据分类算法-基点分类算法，基点分类算法以提取每一类样本的背景基为预测模型，再通过新定义的λ-背景基，优化预测模型。数值实验表明:基点分类算法原理简单、构造难度小、分类模型泛化能力强，预测能力准确率高，同时严格的模型限定区域又能为识别新类别提供新方法。