关键词： 黄芩   HPLC指纹图谱   化学模式识别  

摘要： 目的建立黄芩野生和栽培药材的HPLC指纹图谱,并以指纹图谱共有峰数据构建化学模式识别模型,评价不同来源黄芩药材的差异性机制。方法采用高效液相色谱法建立20批不同来源黄芩药材的指纹图谱,构建HPLC-指纹图谱共有模式,整合相似度评价、主成分分析(PCA)和最小偏二乘法-判别分析(PLS-DA)对不同来源黄芩药材进行评价。结果建立了黄芩药材的HPLC指纹图谱,标定了19个共有峰,20批黄芩样品的指纹图谱与对照指纹图谱相似度>0.9。最小偏二乘法-判别分析能对野生与栽培黄芩进行明确的区分,表明导致差异的主要化学标记物为黄芩苷、野黄芩苷、黄芩素、汉黄芩素等6个成分。结论建立的黄芩HPLC指纹图谱,与模式识别方法相结合可为黄芩药材质量评价提供参考。

关键词： 电子战   机器学习   智能控制   人工智能   数据挖掘   模式识别  

摘要： 人工智能是人类进入信息产业革命时代,认识和改造客观世界能力的新高峰。以人工智能、大数据为代表的颠覆性技术,正加速推进战争形态向智能化战争演变。围绕电子战装备作战运用面临的挑战,分析了电子战智能化装备的表征模型,提出分三个阶段、三个维度协同发展装备智能化能力的框架,从面向数据的处理智能、面向人员的操作智能、面向装备的控制智能的视角,探讨了电子战装备智能化发展路径,并给出了思考与建议,指出"人+智能"是弱人工智能向强人工智能跨域的关键路径,混合智能将是电子战装备智能化发展的重点方向。

关键词： 模式识别   煤岩界面   三维建模   图像处理  

摘要： 由于矿井的环境复杂、煤岩赋存条件不同,使得煤岩界面识别成为国际上的一大难题。文章从煤炭开采对煤岩界面识别的需求出发,简述了基于图像的煤岩界面识别技术的研究现状,并对各类方法的适应条件进行分析。提出将基于图像的三维建模方法和关注煤与岩石不同介电特性的方法相结合,对煤岩界面进行纵向可视化,从而更加高效地控制采煤机头的运动。最后,探讨了煤岩界面识别技术的潜在应用,并指出摄像头安装位置的重要性。

关键词： 厚朴   凹叶厚朴   UPLC指纹图谱   化学模式识别   聚类分析   主成分分析   厚朴酚   和厚朴酚  

摘要： 目的建立厚朴Magnolia officinalis、凹叶厚朴M. officinalis var. biloba 2种基原厚朴药材的超高效液相色谱(UPLC)指纹图谱,为厚朴药材的质量控制提供参考。方法采用UPLC法,色谱柱为Waters Acquity UPLC■BEH C_(18)(100 mm×2.1 mm,1.7μm);流动相为乙腈-0.4%磷酸水溶液,梯度洗脱;体积流量为0.4 mL/min;波长294 nm;柱温30℃;进样量为1μL。通过相似度评价、聚类分析(cluster analysis,HCA)、主成分分析(principal component analysis,PCA)、正交偏最小二乘法-判别式分析(orthogonal partial least squares method-discriminant analysis,OPLS-DA)和t检验对2种基原厚朴的指纹图谱进行评价。结果厚朴、凹叶厚朴指纹图谱均具有9个共有峰,通过与对照品比对,指认出其中2个共有峰,分别为厚朴酚、和厚朴酚。13批厚朴指纹图谱与对照指纹图谱的相似度为0.907~0.993,6批凹叶厚朴与厚朴对照指纹图谱的相似度为0.770~0.895;CA将19批厚朴归为3类,并结合PCA和OPLS-DA发现4个差异性标志物,经t检验,2种基原厚朴4种差异性标志物的含量具有显著差异(P<0.05);凹叶厚朴中峰2、4、7代表的化学成分含量高于厚朴,而厚朴中和厚朴酚含量要高于凹叶厚朴。结论该方法操作简单,专属性强,为2种基原厚朴药材的区分和质量控制提供参考。

关键词： Immunohistochemistry scoring   computer-aided learning   tissue models  

摘要： This study aimed to clarify whether the pattern recognition involved in scoring proliferation fractions can be trained by abstract computerized images of virtual tissues. Twenty computer-generated images with randomly distributed blue or red dots were scored by 12 probands (all co-workers or collaborators of the Institute of Pathology, University of Bonn). Afterward, the probands underwent a training phase during which they received an immediate feedback on the actual rate of positivity after each image. Finally, the initial testing series was rescored. In a second round with 15 different probands, 20 Ki-67 immunohistochemistry images of tonsil tissue were scored, followed by the same training phase with computer-generated images, before the immunohistochemistry slides were scored again. Paired t-tests were used to compare the differences in mean rates pre- and post-training. Concerning computerized images, untrained probands scored the percentages of positive dots with a mean deviation from the true rates of 8.2%. Following training, the same testing series was scored significantly better with a mean deviation of 4.9% (mean improvement 3.3%, p < 0.001). Scoring real immunohistochemistry slides, the training with computerized images also improved correct estimations, albeit to a lesser degree (mean improvement 1%, p = 0.03). Abstract computerized images of virtual tissues may be a useful tool to train and improve the accuracy of pattern recognition involved in semiquantitative scoring of immunohistochemistry slides. As a side results, this study highlights the value of computer-generated images to verify the performance of image-analysis software.

摘要： In this thematic issue, several mechanisms of tumor dormancy and relapse are discussed. The reviews suggest mutual interactions and communications between malignant cells and other cells in their niche during tumor dormancy. Nevertheless, a complete understanding of tumor dormancy remains elusive. This is because we are getting lost in details of cell-intrinsic and cell-extrinsic molecular pathways without being able to discover the pattern of tumor dormancy. Here, we discuss some conceptual frameworks and methodological approaches that facilitate pattern recognition of tumor dormancy, and propose that settling on certain biological scale such as mitochondria would be the key to discover the pattern of tumor dormancy and relapse.

关键词： Time series analysis   Histograms   Control charts   Market research   Support vector machines   Pattern matching   Process control   Control chart pattern recognition (CCPR)   multiscale control chart pattern (MS-CCP)   time series subsequence matching  

摘要： In the research of control chart pattern recognition (CCPR), most previous methods used a classifier to label abnormal CCPs. However, long-term control chart data often contains a large number of small abnormal patterns, with characteristics unlike those seen from a global view of the entire chart. There is also a high probability that local abnormal patterns are worthy of analysis. This article presents a novel multiscale control chart pattern recognition scheme, MS-CCPR, which does not focus on the classification of data from a single chart. Rather, the proposed scheme uses a proposed histogram-based data representation in conjunction with time series subsequence matching to identify abnormal patterns on various scales from a long series of control charts. Experimental results demonstrate the efficacy of the proposed framework in the efficient detection of chart patterns at various scales, outperforming the state-of-the-art time series subsequence matching algorithms.

关键词： Signal smoothing   Signal denoising   Pattern recognition   Zero-phase filters   Convex optimization   Electroencephalography (EEG)   K-complexes   Sleep   Polysomnography  

摘要： We address the problem of signal denoising and pattern recognition in processing batch-mode time-series data by combining linear time-invariant filters, orthogonal multiresolution representations, and sparsity-based methods. We propose a novel approach to designing higher-order zero-phase low-pass, high-pass, and band-pass infinite impulse response filters as matrices, using spectral transformation of the state-space representation of digital filters. We also propose a proximal gradient-based technique to factorize a special class of zero-phase high-pass and band-pass digital filters so that the factorization product preserves the zero-phase property of the filter and incorporates a sparse-derivative component of the input in the signal model. To demonstrate applications of our novel filter designs, we validate and propose new signal models to simultaneously denoise and identify patterns of interest. To denoise or detect a pattern of interest in the signal, our proposed signal models simultaneously combine linear time-invariant (LTI) filters and sparsity-based methods with orthogonal multiresolution representations, such as wavelets and short-time Fourier transform. We illustrate the capabilities of our proposed signal models using sleep-electroencephalography (EEG) data to detect K-complexes and sleep spindles. Reproducible research is available at https://***/prateekgv/sasdpr.