地下强爆炸诱发地表塌陷的试验模拟与应用

地下核爆炸后会在地表产生下陷弹坑、塌陷带等不可逆变形爆炸后效应,利用地表形变信息对地下核试验进行有效监控和评估具有十分重要的意义。基于考虑重力影响的地下强爆炸塌陷成坑相似理论,利用陆军工程大学自主研制的地下爆炸效应真空室模拟试验装置,对2017年9月3日朝鲜地下核试验诱发的地表不可逆变形进行了模型试验。试验结果表明,地表塌陷带半径为257 m,下陷弹坑的半径为154 m,与美国、前苏联等国家已有的地下核试验经验公式的数据结果基本相当,并且符合天基雷达TS-InSar卫星监测数据的估算结果,验证了地下爆炸真空室模型试验在地下强爆炸诱发地表不可逆变形区域模拟和评估的可行性,成为有效补充地震波和卫星监测地下强爆炸的一种研究手段。     

Determination of Brain Tissue Samples Storage Conditions for Reproducible Intraoperative Lipid Profiling

Ex-vivo molecular profiling has recently emerged as a promising method for intraoperative tissue identification, especially in neurosurgery. The short-term storage of resected samples at room temperature is proposed to have negligible influence on the lipid molecular profiles. However, a detailed investigation of short-term molecular profile stability is required to implement molecular profiling in a clinic. This study evaluates the effect of storage media, temperature, and washing solution to determine conditions that provide stable and reproducible molecular profiles, with the help of ambient ionization mass spectrometry using rat cerebral cortex as model brain tissue samples. Utilizing normal saline for sample storage and washing media shows a positive effect on the reproducibility of the spectra; however, the refrigeration shows a negligible effect on the spectral similarity. Thus, it was demonstrated that up to hour-long storage in normal saline, even at room temperature, ensures the acquisition of representative molecular profiles using ambient ionization mass spectrometry.     

金银花的毛细管电泳指纹图谱研究

采用毛细管区带电泳法,以50 mmol/L硼砂(含20 mmol/L β-环糊精(CD),用磷酸调pH 8.0)为背景电解质,运行电压12 kV,紫外检测波长254 nm,重力进样15 s（高度8.5 cm）,建立了金银花药材水提取液的毛细管电泳指纹图谱(CEFP)。将13个不同产地的金银花药材供试液的CEFP进行比较,以电泳峰出现率100%计,确定金银花的共有指纹峰为18个。该CEFP具有较好的精密度和重现性,分离效能高且成本低廉。提出了指纹图谱宏观含量相似度R、投影含量相似度C和定量相似度P的概念,可从总体上评价药材化学组分的整体含量情况。从两个方面评价各产地药材与对照CEFP间的总体相似性,合格药材应具备以下两个条件：(1)代表化学成分分布相似性的定性相似度(S)≥0.90;(2)描述药材整体化学成分含量的定量相似度(R,C,P,Q)应在80%～120%。以此二类相似度指标控制金银花的质量,建立了指纹图谱评价的又一新方法。     

基于样本库的人脸图像处理方法

本文给出了一种基于样本库的人脸图像超分辨率方法,这一方法结合了人脸图像全局相似性和局部特征相似性的约束.局部结构的相似性将超分辨率解约束到人脸局部结构空间,不同尺度上的局部相似性指导搜索最优解,然后将局部结构重建的人脸图像投影到由人脸图像确定的特征人脸空间.实验结果表明该算法得到的人脸具有很好的准确性和视觉效果.     

QSAR models of cytochrome P450 enzyme 1A2 inhibitors using CoMFA,CoMSIA and HQSAR

Quantitative structure–activity relationship (QSAR) studies were conducted on an in-house database of cytochrome P450 enzyme 1A2 inhibitors using the comparative molecular field analysis (CoMFA), comparative molecular similarity analysis (CoMSIA) and hologram QSAR (HQSAR) approaches. The database consisted of 36 active molecules featuring varied core structures. The model based on the naphthalene substructure alignment incorporating 19 molecules yielded the best model with a CoMFA cross validation value q² of 0.667 and a Pearson correlation coefficient r² of 0.976; a CoMSIA q² value of 0.616 and r² value of 0.985; and a HQSAR q² value of 0.652 and r² value of 0.917. A second model incorporating 34 molecules aligned using the benzene substructure yielded an acceptable CoMFA model with q² value of 0.5 and r² value of 0.991. Depending on the core structure of the molecule under consideration, new CYP1A2 inhibitors will be designed based on the results from these models.     

含氮杂环类润滑油添加剂CoMFA-QSTR及CoMSIA-QSTR抗磨损性能模型的构建

依据摩擦学定量构效关系理论(QSTR),采用比较分子力场分析(CoMFA)和比较分子相似性指数分析(CoMSIA)这两种方法研究了含氮杂环类润滑油添加剂的抗磨损性能的摩擦学三维定量构效关系(3D-QSTR),并建立了相应的3D-QSTR模型.结果表明:仅利用静电场构建CoMFA或CoMSIA模型时,模型预测能力最好,r~2,q~2均大于0.5.根据CoMFA或CoMSIA模型等高线图分析得出:分子静电场对含氮杂环类润滑油添加剂的抗磨损性能影响最大,在特定区域的引入带负电荷或带正电荷的基团将有助于抗磨性能的提高.     

一个基于MPEG-7颜色与形状的图像检索系统

结合MPEG-7的颜色与形状两种描述符设计出了一个新的图像检索系统,利用MPEG-7提供的主颜色描述符及基于区域的形状描述算法--角放射变换(ART)对图像进行特征提取、描述.实验用MPEG-7的评价准则进行评判,由包括花卉、自然风景及商标组成的图像库进行测试,结果表明,综合这两种特征进行检索比使用单一特征取得的检索效果更佳.     

Computational Drug Repurposing Based on a Recommendation System and Drug–Drug Functional Pathway Similarity

Drug repurposing identifies new clinical indications for existing drugs. It can be used to overcome common problems associated with cancers, such as heterogeneity and resistance to established therapies, by rapidly adapting known drugs for new treatment. In this study, we utilized a recommendation system learning model to prioritize candidate cancer drugs. We designed a drug–drug pathway functional similarity by integrating multiple genetic and epigenetic alterations such as gene expression, copy number variation (CNV), and DNA methylation. When compared with other similarities, such as SMILES chemical structures and drug targets based on the protein–protein interaction network, our approach provided better interpretable models capturing drug response mechanisms. Furthermore, our approach can achieve comparable accuracy when evaluated with other learning models based on large public datasets (CCLE and GDSC). A case study about the Erlotinib and OSI-906 (Linsitinib) indicated that they have a synergistic effect to reduce the growth rate of tumors, which is an alternative targeted therapy option for patients. Taken together, our computational method characterized drug response from the viewpoint of a multi-omics pathway and systematically predicted candidate cancer drugs with similar therapeutic effects.