ToF-SIMS studies of Bacillus using multivariate analysis with possible identification and taxonomic applications

In this paper we discuss the application of ToF-SIMS with an Au₃⁺ primary ion beam, combined with principal components analysis (PCA) and discriminant function analysis (DFA) for the identification of individual strains of two Bacillus species. The ToF-SIMS PC-DFA methodology is capable of distinguishing bacteria at the strain level based on analysis of surface chemical species. By classifying the data using hierarchical cluster analysis (HCA) we are able to show quantitative separation of species and of these strains. This has taxonomic implications in the areas of rapid identification of pathogenic microbes isolated from the clinic, food and environment.     

Ablation of subsurface tumors using an ultra-short pulse laser

This paper demonstrates the novel technique of ablating subsurface tumors with minimal thermal damage to surrounding healthy tissue using a focused laser beam from an ultra-short pulse diode laser source. Experiments were performed on anesthetized healthy mice as well as mice with mammary tumors in order to demonstrate the fundamental advantages of using a focused-beam, ultra-short pulse laser to ablate subcutaneous tissues. The technique was demonstrated through histological analysis of tissue samples after irradiation of anesthetized mice with or without mammary tumors. To demonstrate the efficacy of subsurface focusing, temperature was monitored at the subsurface tumor location and at the surface in an untranslated sample while irradiating with a focused ultra-short pulsed 1552 nm laser. Results show that temperature rise was dramatically greater at the focal depth than at the surface. Irradiation at the subsurface tumor location while translated over time across the tumor location resulted in precise ablation of the tumor. This work shows that a focused-beam ultra-short pulse 1552 nm laser results in precise ablation at the desired location with high efficacy and a minimal zone of collateral thermal and/or mechanical damage.     

等离子体X射线激光介质均匀性测量

通过合理而有效地按排多种探测仪器，观察到等离子体Ｘ射线激光介质的空间非均匀现象。在保证入射激光经最佳组合透镜后焦线均匀性前提下，由实验显示等离子体介质的空间不均匀性主要来自靶材质量和调焦精度。此外，入射激光脉宽的大小变化将会引起等离子体整个辐射空间和时间范围的明显变化。     

基于相似性分类的高光谱主成分融合方法比较

为了提高复杂场景弱小目标高光谱融合图像的质量,提出了基于相似性分类的主成分融合方法。光谱数据像素向量的相似性测度分类产生类矩阵,通过由类矩阵主成分变换的降维投影矩阵来投影变换原有光谱数据,获得降维数据矩阵。对比了传统PCA与基于欧式距离分类的PCA(ED_PCA)、基于光谱角分类的PCA(SA_PCA)、基于光谱信息散度分类的PCA(SID_PCA)和基于正交投影散度分类的PCA(OPD_PCA)四种改进方法的融合性能。实验结果表明:SA_PCA和SID_PCA方法兼具了ED_PCA和OPD_PCA的优点,对比度提升较好,阈值参数不敏感,运行时间较短。     

~(12)C~(6 )离子辐照小鼠Lewis肿瘤的研究

研究了HIRFL提供的12C6 离子辐照C57BL/6J小鼠右后腿移植的Lewis肿瘤的生长延迟和碳离子辐照对Lewis肿瘤小鼠寿命的延长.结果表明,碳离子照射的小鼠肿瘤生长体积明显地小于对照的体积.在剂量相同、分次数辐照越多时,肿瘤生长就越缓慢,肿瘤抑制就越高.在辐照分次数相同时,不同剂量对受照肿瘤生长体积未产生差异.在4Gy×3,8Gy×3和12Gy×2组3种辐照剂量下,小鼠寿命的P值小于0.05;可知小鼠的寿命延长具有统计学意义.     

用特征光谱荧光标记技术分析水中溶解有机物特性

讨论了一种自然水体中有机物的快速诊断分析方法。介绍了总荧光光谱(TLS)和特征光谱荧光标记(SFS)技术;以激光诱导荧光(LIF)方法测量了水体的总荧光光谱,利用特征光谱荧光标记技术对水体中溶解有机物(DOM)及叶绿素a(Chla)进行了分析,并给出了不同浓度腐殖酸的归一化荧光强度与浓度的关系曲线．结果表明,利用特征光谱荧光标记技术对水体总荧光光谱的分析,可以进行水体污染物的快速、实时和在线监测。     

球面弯晶在X射线诊断中的应用

为了诊断等离子体X射线,利用X射线布拉格衍射原理研制了球面弯晶谱仪。实验采用α-石英作为其晶体分析器色散元件,晶体弯曲半径为250 mm,布拉格角为30°～67.5°;采用接收面积10 mm×50 mm的X射线胶片作为摄谱器件,接收等离子体X射线谱线信息。通过在“阳”加速器装置上进行实验,得到了钛等离子体X射线K壳层激发谱线信息,其光谱分辨力可达到1 000以上,光谱带宽约为0.43 eV。     

基于可见-近红外光谱的可乐品牌鉴别方法研究

提出了一种采用可见-近红外光谱分析技术快速鉴别可乐品牌的新方法。采用美国ASD公司的便携式光谱仪对三种不同品牌的可乐进行光谱分析,各获取55个样本数据。将样本随机分成150个建模样本和15个预测样本,采用平均平滑法和标准归一化方法对样本数据进行预处理,再用主成分分析法对光谱数据进行聚类分析并获得各主成分数据。将建模样本的主成分数据作为BP网络的输入变量,可乐品牌作为输出变量,建立三层人工神经网络鉴别模型,并用模型对15个预测样本进行预测。结果表明,预测准确率为100%,实现了可乐品牌快速、准确的鉴别。     

Quantum chaos and fluctuation properties of regular and irregular spectra

Fluctuation properties of the regular and irregular energy levels for the Hénon-Heiles Hamiltonian are examined. The spacing distributions and the calculated values of the Δ₃-statistic show that there is no difference in the short range correlation properties of these spectra. Remarkably, the Δ₃ values agree with the results of random matrix theory. on leave of absence from P.R.L., Ahmedabad     

基于主成分分析和神经网络的近红外光谱苹果品种鉴别方法研究

提出了一种用近红外光谱技术快速鉴别苹果品种的新方法,首先用主成分分析法对苹果品种进行聚类分析并获取苹果的近红外指纹图谱,再结合人工神经网络技术进行品种鉴别。主成分分析表明,主成分1和主成分2的累积可信度已达98％,以主成分1和2对所有建模样本的得分值做出的得分图,对不同种类苹果具有很好的聚类作用。利用主成分分析得到的载荷图可以得到对于苹果品种敏感的特征波段,用特征波段图谱作为神经网络的输入建立三层BP人工神经网络模型。每个品种各25个苹果共75个用来建立神经网络模型,余下的共15个用于预测。对未知的15个样本进行预测,品种识别准确率达到100%。说明文章提出的方法具有很好的分类和鉴别作用,为苹果的品种鉴别提供了一种新方法。     

Rapid Identification of Bacterial Species by Fluorescence Spectroscopy and Classification Through Principal Components Analysis

This work presents the development of a method for rapid bacterial identification based on the autofluorescence spectrum. It was demonstrated differences in the autofluorescence spectrum in three bacterial species and the subsequent separation, through the Principal Components Analysis (PCA) technique, in groups with high likeness, that could identify the bacteria in less than 10 min. Fluorescence spectra of 60 samples of 3 different bacterial species (Escherichia coli, EC, Enterococcus faecalis, EF and Staphylococcus aureus, SA), previously identified by automated equipment Mini API, were collected in 10 excitation wavelengths from 330 to 510 nm. The PCA technique applied to the fluorescence spectra showed that bacteria species could be identified with sensitivity and specificity higher than 90% according to differences that occur within the spectra with excitation of 410 nm and 430 nm. This work presented a method of bacterial identification of three more frequent and more clinically significant species based on the autofluorescence spectra in the excitation wavelengths of 410 and 430 nm and the classification of the spectra in three groups using PCA. The results demonstrated that the bacterial identification is very efficient with such methodology. The proposed method is rapid, ease to perform and low cost compared to standard methods.     

应用近红外光谱快速鉴别酸奶品种的研究

提出了一种用近红外光谱技术快速鉴别酸奶品种的新方法。首先应用光谱仪获得5种典型酸奶品种的光谱曲线,用主成分分析法对5种酸奶品种进行聚类分析,再结合人工神经网络技术建立模型进行品种鉴别。主成分分析表明,主成分1和主成分2的累积可信度已达98.96%,前7个主成分的累积可信度达到99.97%。以每一个样品的前7个主成分作为神经网络的输入,品种类型作为神经网络的输出,建立三层BP人工神经网络模型。每个品种各27个样本,5个品种共135个样本用来建立神经网络模型,余下每个品种各5个共25个用于预测。建模品种的拟合率和预测品种的识别率均为100%。说明该方法能快速无损的检测酸奶品种,为酸奶的品种鉴别提供了一种新方法。     

胃癌患者血红蛋白的表面增强拉曼光谱分析

胃癌患者能够得到早期诊断对其治疗具有十分重要的意义,疾病状态下的血红蛋白拉曼光谱检测在血液代用品的高铁血红蛋白含量检测中以及血氧饱和定量测定中占很大优势。本实验采用微波加热法制备银胶体粒子,依次对20例胃癌患者和11例健康人的血红蛋白进行表面增强拉曼光谱分析。采用SERS谱峰归属分析结果显示胃癌患者血红蛋白中的酪氨酸、苯丙氨酸和吡咯环的含量均低于正常人。本文还讨论了血红素的分子结构,在血红蛋白和氧气结合的前后,Fe2+分别处于高低自旋态,离子半径也随着与氧结合缩小了0.075 nm从而滑落入卟啉环平面中央的孔隙之中。这种空间的拉伸变化会牵动与铁相连的F8His,使得珠蛋白中两股螺旋之间空隙缩小,导致将HC2酪氨酸排挤出空隙。利用这一机理,对血红蛋白1 560 cm-1的吸收峰进行研究,证实了胃癌患者酪氨酸含量确实低于正常人。为了能得到更加明显的诊断区分,利用降维的思想,采用主成分分析(PCA)的方法对所有的拉曼光谱进行多元统计分析,得出三维的诊断散点图。为了更加精确的得出诊断准确率,用判别分析得出诊断的灵敏度和特异性分别为90.0%和90.9%,总判别正确率为90.3%。此项研究表明：对氧合血红蛋白的表面增强拉曼光谱诊断分析有希望成为一项新型的胃癌诊断技术应用于医学领域。     

Spectral unmixing combined with Raman imaging,a preferable analytic technique for molecule visualization

This article reviews spectral unmixing used as a new method for Raman imaging. Raman spectroscopy explores the vibrational information about specific chemical bonds in molecules, which can be used for label-free molecular visualization. However, chemical bonds are usually shared among different molecules, which results in closed or mixed Raman peaks of many molecules. Therefore, the acquired spectra cannot be directly used to reconstruct the Raman images, as pure component spectra are hidden under the acquired spectra. Spectral unmixing is an effective method to provide a meaningful spectrum of each component with no priori spectral information and to also reconstruct the compositional distribution images. This article summarizes some representative spectral unmixing approaches used for Raman imaging and many related researches. This review strives to introduce the combination of spectral unmixing and Raman imaging as an efficient analytic technique to characterize various constituents and make subtle understanding of those complex structures.     

纳秒激光烧蚀固体靶产生的等离子体在外加横向磁场中膨胀时的温度和密度参数演化

利用等离子体光学波段自发光成像、光学光谱和光学探针干涉等诊断手段, 观察了纳秒脉冲激光烧蚀固体靶产生的等离子体在外加横向磁场中的膨胀过程. 根据实验参数特征建立了简化的磁流体物理模型, 结合自发光强度的时间演化, 理论计算了等离子体温度和密度参数的时间演化, 理论计算结果与实验测量结果基本符合, 证实了碰撞磁扩散过程在等离子体演化中发挥了关键作用.     

Analysis of Spectral Reflectance Using Normalization Method from Endoscopic Spectroscopy System

Objective assessment of gastrointestinal mucosal color is extremely important in the endoscopic diagnosis of digestive tract disease. In this paper, we propose a method to clarify the spectral characteristics of gastric and colon cancer. A large number of spectral reflectance data of mucous membrane are measured by the endoscopic spectroscopy system (ESS) in the National Cancer Center Hospital East, Kashiwa, Japan and the Department of Internal Medicine, Self-Defense Force Center Hospital, Tokyo, Japan. We assume that early cancer appears primarily in the spectral data of short wavelength, because it is usually present in a superficial cell where short wavelength light is scattered more strongly than long wavelength light. To identify the features in the short wavelength components, the spectral reflectance was divided by the reflectance of a long wavelength. We investigated the possibility of distinguishing early cancer from normal spectral data through statistical analysis, employing the projection axis as the mean difference between them. Early cancer and normal spectral data were projected on the projection axis, and the Student’s T-test was applied to evaluate the mean of the distribution between these data.