Iterative algorithm for spatial and intensity normalization of MEMRI images. Application to tract-tracing of rat olfactory pathways

Manganese (Mn)-enhanced magnetic resonance imaging (MEMRI) is an emerging technique for visualizing neuronal pathways and mapping brain activity modulation in animal models. Spatial and intensity normalizations of MEMRI images acquired from different subjects are crucial steps as they can influence the results of groupwise analysis. However, no commonly accepted procedure has yet emerged. Here, a normalization method is proposed that performs both spatial and intensity normalizations in a single iterative process without the arbitrary choice of a reference image. Spatial and intensity normalizations benefit from this iterative process. On one hand, spatial normalization increases the accuracy of region of interest (ROI) positioning for intensity normalization. On the other hand, improving the intensity normalization of the different MEMRI images leads to a better-averaged target on which the images are spatially registered. After automatic fast brain segmentation and optimization of the normalization process, this algorithm revealed the presence of Mn up to the posterior entorhinal cortex in a tract-tracing experiment on rat olfactory pathways. Quantitative comparison of registration algorithms showed that a rigid model with anisotropic scaling is the best deformation model for intersubject registration of three-dimensional MEMRI images. Furthermore, intensity normalization errors may occur if the ROI chosen for intensity normalization intersects regions where Mn concentration differs between experimental groups. Our study suggests that cross-comparing Mn-injected animals against a Mn-free group may provide a control to avoid bias introduced by intensity normalization quality. It is essential to optimize spatial and intensity normalization as the detectability of local between-group variations in Mn concentration is directly tied to normalization quality.     

SVD‐based method for intensity normalization,background correction and solvent subtraction in Raman spectroscopy exploiting the properties of water stretching vibrations

A semiautomated method combining intensity normalization with effective elimination of the solvent signal and non‐Raman background is presented for Raman spectra of biochemical and biological analytes in aqueous solutions. The method is particularly suitable for rapid and effortless preprocessing of extensive datasets taken as a function of gradually varied physicochemical parameters, e.g. analyte and/or ligand concentration, temperature, pH, pressure, ionic strength, time, etc. For intensity normalization, the strong Raman OH stretching band of water in the range of 2700–3900 cm⁻¹ recorded together with the analyte spectrum in the fingerprint region below 1800 cm⁻¹ is employed as internal intensity standard. Concomitant dependences of the solvent Raman spectra are taken into account and, in some cases, turned into advantage. Once the Raman spectra of the solvent are acquired for a particular range of the parameter varied, solvent contribution can be subtracted correctly from any analyte spectrum taken within this range. The procedure presented can be efficiently applied only for the analytes having their own Raman signal in the range of OH stretching vibrations much weaker than that of the solvent. However, this is the case for a great number of biochemical and biological samples. Accuracy, reliability and robustness of the method were tested under the conditions of spontaneous Raman, resonance Raman and surface‐enhanced Raman scattering. Serviceability of the method is demonstrated by several real‐world examples. Copyright © 2011 John Wiley & Sons, Ltd.     

简易高斯灰度扩散模型的误差分析及适用性研究

为了验证简易高斯灰度扩散模型的适用性,与传统高斯灰度扩散模型进行了对比分析。将两种高斯模型做归一化处理,设定检验像素,分析检验像元灰度的归一化值的相对误差;进行星图模拟,得到4个不同高斯半径(σ)下灰度赋值相对误差与像点映射位置偏离值的关系曲线,整体上误差随σ的增大而减小;对星图模拟得到的系列星像点采用灰度重心法提取质心,质心误差随σ增大而减小,传统模型模拟像点的质心提取精度比简易模型高约2个数量级。无噪声条件下σ=0.671时,简易模型模拟像点最大质心误差仅为0.033pixel。仿真结果表明:单就像点外形仿真而言,当σ较小时,简易模型不再适用;但针对像点的质心定位及后续算法,简易模型带来质心误差量级可以忽略,运算量更小,适于应用。     

3D numerical study of tumor blood perfusion and oxygen transport during vascular normalization

The changes of blood perfusion and oxygen transport in tumors during tumor vascular normalization are studied with 3-dimensional mathematical modeling and numerical simulation. The models of tumor angiogenesis and vascular-disrupting are used to simulate "un-normalized" and "normalized" vasculatures. A new model combining tumor hemodynamics and oxygen transport is developed. In this model, the intravasculartransvascular-interstitial flow with red blood cell(RBC) delivery is tightly coupled, and the oxygen resource is produced by heterogeneous distribution of hematocrit from the flow simulation. The results show that both tumor blood perfusion and hematocrit in the vessels increase, and the hypoxia microenvironment in the tumor center is greatly improved during vascular normalization. The total oxygen content inside the tumor tissue increases by about 67%, 51%, and 95% for the three approaches of vascular normalization,respectively. The elevation of oxygen concentration in tumors can improve its metabolic environment, and consequently reduce malignancy of tumor cells. It can also enhance radiation and chemotherapeutics to tumors.     

V-cluster algorithm: A new algorithm for clustering molecules based upon numeric data

Summary Clustering molecules based on numeric data such as, gene-expression data, physiochemical properties, or theoretical data is very important in drug discovery and other life sciences. Most approaches use hierarchical clustering algorithms, non-hierarchical algorithms (for examples, K-mean and K-nearest neighbor), and other similar methods (for examples, the Self-Organization Mapping (SOM) and the Support Vector Machine (SVM)). These approaches are non-robust (results are not consistent) and, computationally expensive. This paper will report a new, non-hierarchical algorithm called the V-Cluster (V stands for vector) Algorithm. This algorithm produces rational, robust results while reducing computing complexity. Similarity measurement and data normalization rules are also discussed along with case studies. When molecules are represented in a set of numeric vectors, the V-Cluster Algorithm clusters the molecules in three steps: (1) ranking the vectors based upon their overall intensity levels, (2) computing cluster centers based upon neighboring density, and (3) assigning molecules to their nearest cluster center. The program is written in C/C++ language, and runs on Window95/NT and UNIX platforms. With the V-Cluster program, the user can quickly complete the clustering process and, easily examine the results by use of thumbnail graphs, superimposed intensity curves of vectors, and spreadsheets. Multi-functional query tools have also been implemented.     

恒星巡天光谱中天光残留成分的自动识别与检测

天光作为一种主要的噪声,叠加在目标天体光谱之中,降低了光谱的信噪比。经过减天光处理后,若光谱中仍含有大量强度高的天光残差将不利于对目标光谱的后续分析。自动识别减天光异常恒星光谱的研究较少,目前只能通过人工检测的方法去寻找减天光异常的光谱,效率较低。首先对影响减天光结果的因素进行分析,找出减天光异常光谱的特征,然后提出一种简单有效的方法能够自动识别LAMOST巡天经过Pipeline处理之后仍然存在减天光异常的恒星光谱并检测其位置。该方法先对光谱进行归一化处理,然后通过检测天光线附近是否有一定强度的类似发射线或吸收线的残留来判定该天光线位置是否出现减天光异常,最后得出光谱中所有的减天光异常的天光位置。通过对LAMOST光谱数据的实验表明,这种方法可以有效识别出减天光异常的光谱和发现不同残留强度的天光线异常位置,并且该方法简单易懂,识别效率高,可以应用于大量的减天光异常光谱的识别与检测问题。     

迁移时间归一化法改善毛细管电泳表征中药大黄的重现性

探讨了迁移时间归一化法改善中药毛细管电泳分析迁移时间重现性的原理,并将其应用于实际样品的分析。迁移时间归一化法认为,在相同的操作电压、缓冲液组成和温度条件下,多次电泳实验中迁移时间产生差别的主要原因是多次电泳实验中电渗流产生了差异。迁移时间归一化法就是通过选择电泳谱图中的一个或两个峰作为标记峰,将各次电泳实验的迁移时间都归一到第一次电泳实验中的迁移时间。比较多次电泳实验中迁移时间(t)的相对标准偏差(RSD)、经单峰归一化处理的迁移时间(t′)的RSD、经双峰归一化处理的迁移时间(t″)的RSD、迁移时间比(t/tistd,istd代表所选择的标记物)的RSD,发现RSD(t″)相似文献   

R-基团相似性的比较

介绍了对化学取代基(R-基团)进行相似性比较的工作．每个R-基团主要依赖一些对反应影响较为明显,同时又便于计算的结构描述符来进行描述,例如电负性、氢键受体、氢键给体等参数．由于与反应核心相距过远的环境对反应的影响较小,R-基团相似性比较对象,就由每个R-基团截取从它与母体连接的位点出发向外扩展6层的子结构组成．在确立了对R-基团描述和距离限制的基础上,提出了用一个45维向量表示一个R-基团,并由化学结构的比较,转化为向量比较来实现R-基团相似性比较的方法．采用这一方法,成功地对大量的R-基团进行了相似性的区分,并实现了对未知化合物进行相似性预测的目标．     

基于微观本义复原的人脸图像光照正规化

为解决变光照下人脸识别的识别率低,光照正规化算法复杂.不易实现的问题,提出一个新的光照正规化方法一微观本义复原,即通过微观邻域上像素本义特征的定义,将整体图像上变光照下的非线性灰度变化转化为微观邻域内的线性变化,一定程度上避免了包括图像自身结构在内的不确定因素对图像复原的不利影响;并以邻域内的本义特征为光照不敏感特征,对本义特征进行结构编码,并用最小二乘法拟合编码值与光照方向之间的关系,最后根据得到的光照正规化参数复原图像.实验结果表明,该方法算法简单,易于实现,能适应实时的人脸识别系统,在光照变化90.以内的Yale B库的平均识别率可达94.1%.     

数据库规范化理论研究

数据库规范化理论是数据库领域研究的一个重要课题.本文综合论述了数据库规范化理论研究的现状,指出了从关系数据库、面向对象数据库到XML数据库等各发展阶段中数据库规范化问题的特点、主要思想和关键方法,描述了数据库规范化理论的重要研究成果以及存在的问题,指出将形式概念分析方法引入数据库规范化中,作为新环境下数据库规范化理论研究的新的发展方向.