Broadband terahertz time-domain spectroscopy of drugs-of-abuse and the use of principal component analysis

EPSRC, the European Community IST FP6 Integrated, etc

Principle and analysis of the moving-optical-wedge interferometer

A new type of interferometer, the moving-optical-wedge interferometer, is presented, and its principle and properties are studied. The novel interferometer consists of one beam splitter, two flat fixed mirrors, two fixed compensating plates, one fixed optical wedge, and one moving optical wedge. The optical path difference (OPD) as a function of the displacement of the moving optical wedge from the zero path difference position is accomplished by the straight reciprocating motion of the moving optical wedge. A large physical shift of the moving optical wedge corresponds to a very short OPD value of the new interferometer if the values of the wedge angle and the refractive index of the two optical wedges are given properly. The new interferometer is not so sensitive to the velocity variation of the moving optical wedge and the mechanical disturbances compared with the Michelson interferometer, and it is very applicable to low-spectral-resolution application for any wavenumber region from the far infrared down to the ultraviolet. (C) 2008 Optical Society of America.

On Combining Morphological Component Analysis and Concentric Morphology Model for Mammographic Mass Detection

Mammographic mass detection is an important task for the early diagnosis of breast cancer. However, it is difficult to distinguish masses from normal regions because of their abundant morphological characteristics and ambiguous margins. To improve the mass detection performance, it is essential to effectively preprocess mammogram to preserve both the intensity distribution and morphological characteristics of regions. In this paper, morphological component analysis is first introduced to decompose a mammogram into a piecewise-smooth component and a texture component. The former is utilized in our detection scheme as it effectively suppresses both structural noises and effects of blood vessels. Then, we propose two novel concentric layer criteria to detect different types of suspicious regions in a mammogram. The combination is evaluated based on the Digital Database for Screening Mammography, where 100 malignant cases and 50 benign cases are utilized. The sensitivity of the proposed scheme is 99% in malignant, 88% in benign, and 95.3% in all types of cases. The results show that the proposed detection scheme achieves satisfactory detection performance and preferable compromises between sensitivity and false positive rates.

Evaluation of a combined wavelet and a combined principal component analysis classification system for BCG diagnostic problem

Heart disease is one of the main factor causing death in the developed countries. Over several decades, variety of electronic and computer technology have been developed to assist clinical practices for cardiac performance monitoring and heart disease diagnosis. Among these methods, Ballistocardiography (BCG) has an interesting feature that no electrodes are needed to be attached to the body during the measurement. Thus, it is provides a potential application to asses the patients heart condition in the home. In this paper, a comparison is made for two neural networks based BCG signal classification models. One system uses a principal component analysis (PCA) method, and the other a discrete wavelet transform, to reduce the input dimensionality. It is indicated that the combined wavelet transform and neural network has a more reliable performance than the combined PCA and neural network system. Moreover, the wavelet transform requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced.

滇池金线鲃形态度量学分析(Cypriniformes:Cyprinidae)

运用多变量形态度量学和框架结构形态学的方法,测量了采自滇池流域6个不同地点的149尾滇池金线鲃(Sinocyclocheilus grahami)的标本.应用多变量形态度量学分析,选择了20个框架结构性状和19个常规性状,进行主成成分分析.主成成分分析结果的散布图显示,6个不同地点的标本聚在一起没有分开,表明滇池金线鲃的各居群之间在可数性状和可量性状上均未表现出明显的差异,说明滇池金线鲃在形态上还未发生明显的种下分化.

主成分分析和聚类分析在软件重构中的应用

重构是软件系统不断演化的关键之一,也是一项复杂而又困难的活动.传统的定位重构代码方法依赖开发者的观察和主观意识,耗时耗力,尤其在重构代码较多时.因此,提出了一套自动化定位重构的方法.该方法利用基于面向对象软件度量指标获取代码特征信息,使用相关性检验查验特征信息数据,应用主成分分析压缩和解释特征信息,应用聚类分析分类相似代码段,迅速准确定位重构.一个简单的实例表明该方法是简单有效的,并且优于传统方法.

Effect of water temperature on digestive enzyme activity and gut mass in sea cucumber Apostichopus japonicus (Selenka), with special reference to aestivation

The effect of water temperature on gut mass and digestive enzyme activity in sea cucumber Apostichopus japonicus, including relative gut mass (RGM), amylase, lipase, pepsin and trypsin activities were studied at temperatures of 7, 14, 21, and 28A degrees C over a period of 40 days. Results show that RGM significantly decreased after 40 days at 21A degrees C and markedly decreased over the whole experiment period at 28A degrees C; however, no significant effect of duration was observed at 7 or 14A degrees C. At 14A degrees C, trypsin activity significantly decreased over 10 and 20 days, then increased; amylase and trypsin activity significantly decreased after 40 days at 28A degrees C. However, no significant effect of duration was found on amylase, pepsin or trypsin activities in the other temperature treatment groups. At 28A degrees C, lipase activity peaked in 20 days and then markedly decreased to a minimum at the end of the experiment. On the other hand, pepsin activity at 28A degrees C continuously increased over the whole experimental period. Principle component analysis showed that sea cucumbers on day 40 in the 21A degrees C group and in the previous 20 days in the 28A degrees C group were in the prophase of aestivation. At 28A degrees C, sea cucumbers aestivated at 30-40 days after the start of the experiment. It is concluded that the effect of temperature on the digestion of A. japonicus is comparatively weak within a specific range of water temperatures and aestivation behavior is accompanied by significant changes in RGM and digestive enzyme activities.

Genetic mapping of size-related quantitative trait loci (QTL) in the bay scallop (Argopecten irradians) using AFLP and microsatellite markers

We constructed genetic linkage maps for the bay scallop Argopecten irradians using AFLP and microsatellite markers and conducted composite interval mapping (CIM) of body-size-related traits. Three hundred seventeen AFLP and 10 microsatellite markers were used for map construction. The female parent map contained 120 markers in 15 linkage groups, spanning 479.6 cM with an average interval of 7.0 cM. The male parent map had 190 markers in 17 linkage groups, covering 883.8 cM at 7.2 cM per marker. The observed coverage was 70.4% for the female and 81.1% for the male map. Markers that were distorted toward the same direction were closely linked to each other on the genetic maps, suggesting the presence of genes important for survival. Six size-related traits, shell length, shell height, shell width, total weight, soft tissue weight, and shell weight, were measured for QTL mapping. The size data were significantly correlated with each other. We subjected the data, log transformed firstly, to a principle component analysis and use the first principle component for QTL mapping. CIM analysis revealed one significant QTL (LOD=2.69, 1000 permutation, P<0.05) in linkage group 3 on the female parent map. The mapping of size-related QTL in this study raises the possibility of improving the growth of bay scallops through marker-assisted selection. (c) 2007 Published by Elsevier B.V.

基于独立分量分析的地震属性分析方法研究

Conventional seismic attribute analysis is not only time consuming, but also has several possible results. Therefore, seismic attribute optimization and multi-attribute analysis are needed. In this paper, Fuyu oil layer in Daqing oil field is our main studying object. And there is much difference between seismic attributes and well logs. So under this condition, Independent Component Analysis (ICA) and Kohonen neural net are introduced to seismic attribute optimization and multi-attribute analysis. The main contents are as follows: (1) Now the method of seismic attribute compression is mainly principal component analysis (PCA). In this article, independent component analysis (ICA), which is superficially related to PCA, but much more powerful, is used to seismic reservoir characterizeation. The fundamental, algorithms and applications of ICA are surveyed. And comparation of ICA with PCA is stydied. On basis of the ne-entropy measurement of independence, the FastICA algorithm is implemented. (2) Two parts of ICA application are included in this article: First, ICA is used directly to identify sedimentary characters. Combined with geology and well data, ICA results can be used to predict sedimentary characters. Second, ICA treats many attributes as multi-dimension random vectors. Through ICA transform, a few good new attributes can be got from a lot of seismic attributes. Attributes got from ICA optimization are independent. (3) In this paper, Kohonen self-organizing neural network is studied. First, the characteristics of neural network’s structure and algorithm is analyzed in detail, and the traditional algorithm is achieved which has been used in seism. From experimental results, we know that the Kohonen self-organizing neural network converges fast and classifies accurately. Second, the self-organizing feature map algorithm needs to be improved because the result of classification is not very exact, the boundary is not quite clear and the velocity is not fast enough, and so on. Here frequency sensitive principle is introduced. Combine it with the self-organizing feature map algorithm, then get frequency sensitive self-organizing feature map algorithm. Experimental results show that it is really better. (4) Kohonen self-organizing neural network is used to classify seismic attributes. And it can be avoided drawing confusing conclusions because the algorithm’s characteristics integrate many kinds of seismic features. The result can be used in the division of sand group’s seismic faces, and so on. And when attributes are extracted from seismic data, some useful information is lost because of difference and deriveative. But multiattributes can make this lost information compensated in a certain degree.

从第四轮“关于科研单位的组织与业绩的国际比较研究”（ICSOPRU）看满意度对科研单位业绩的影响

What role satisfaction plays in the factors contributing to performance continues to be a major area of interest in the study of industrial and organization psychology, but there is a lack of quantative study dealing with this question in research units. The author has a try in this paper to answer this question using the data from China, Ghana, Hungary and Mexico of the Fourth Round International Comparative Study on the Organization and Performance of Research Units (ICSOPRU). The data-analysis include the principle component factor analysis of the performance and the satisfaction items in the Fourth Round ICSOPRU Questionnaires, and the multiple classification analysis, the multivariate nominal analysis of the performance and the satisfaction factors. The main findings show that a certain facet of the satisfaction explains the largest proportion of variances of a certain dimention of the performance and has a higher relative importance in contributing to the understanding of the performance. There also a comparison between the results from the four countries and that from China.

A new dynamic model for study of dislocation pattern formation

Based on the principle given in nonlinear diffusion-reaction dynamics, a new dynamic model for dislocation patterning is proposed by introducing a relaxation time to the relation between dislocation density and dislocation flux. The so-called chemical potential like quantities, which appear in the model can be derived from variation principle for free energy functional of dislocated media, where the free energy density function is expressed in terms of not only the dislocation density itself but also their spatial gradients. The Linear stability analysis on the governing equations of a simple dislocation density shows that there exists an intrinsic wave number leading to bifurcation of space structure of dislocation density. At the same time, the numerical results also demonstrate the coexistence and transition between different dislocation patterns.

葡萄果皮花色苷构成特点、遗传规律及其在果实成熟过程中的变化

通过利用高效液相色谱-质谱联用技术，研究110 个不同基因型（包括3 个种和5 个种间杂种）葡萄品种的花色苷含量和成分特点。在所有品种中，最多鉴定出29 种花色苷。对葡萄的花色苷总量来说，一般情况下，欧亚种和欧美杂交种的花色苷含量较低，而野生种和砧木品种显著高于其它的种间杂种；在同一个种内，酿酒品种高于鲜食品种；在大多数高花色苷含量的种质中，二甲基花翠素类花色苷是主要的花色苷，而在低总花色苷量的品种，花青素类和花翠素类花色苷是主要的成分。此外，在欧亚种葡萄中，仅检测到单糖苷类花色苷，而在其它葡萄种质中，既有单糖苷花色苷又有双糖苷花色苷。在欧亚鲜食葡萄中，Pn-3-glucoside 是主要的花色苷，而在欧亚酿酒葡萄中，Mv-3-glucoside 是主要的花色苷。通过主成分分析，最终根据花色苷总量的不同和单、双糖苷含量的不同，110 个品种在散点图中被明显的分成3 部分。 通过连续两年调查3 个欧亚鲜食葡萄杂交组合的亲本和后代的花色苷含量来分析花色苷的遗传特点。共鉴定出16 种花色苷，且均为单糖苷类。母本中各花色苷的比例决定了后代中花色苷含量的比例，但是后代中花色苷的绝对含量不受亲本影响。不论亲本还是后代中，Peonidin 3-O-glucoside 和Malvidin3-O-glucoside 都是含量最高的花色苷。花色苷的有或无是寡基因控制的质量性状，而含量的多少是多基因控制的数量性状。通过主成分分析可以得知：在杂交后代中， peonidin 3-O-glucoside, malvidin 3-O-glucoside, delphinidin3-O-glucoside, cyanidin 3-O-glucoside, petunidin 3-O-glucoside, peonidin3-O-(6-O-coumaryl)-glucoside 和malvidin 3-O-(6-O-coumaryl)-glucoside 是影响果皮中花色苷总量的主要种类。花色苷的含量是一种高广义遗传力的性状，而且这种性状在两年间是稳定的（0.65-0.98）。 5 个不同基因型葡萄品种在成熟过程中果实品质的变化也被研究。始熟期开始后，果粒重量继续增加，果粒较大的鲜食品种增长很慢，而果粒较小的制汁和酿酒品种增长幅度很大；果实内两种主要的糖（葡萄糖和果糖）开始快速上升，且在整个成熟过程中保持1：1；有机酸的含量开始快速下降，苹果酸下降的幅度大于酒石酸。多酚物质在果实始熟期也发生巨大变化，花色苷快速积累。 ‘北紫’和‘梅鹿辄’中的花色苷在成熟前1-2 周达到最大值，‘黑奥林’、‘康可’和‘北醇’在整个成熟过程中花色苷一直增加；对非花色苷类多酚来说，‘黑奥林’和‘梅鹿辄’在果实成熟过程中一直增加，而在另3 个品种中是下降的；花色苷之间以及与黄酮醇之间成正相关，花色苷和酚酸成负相关关系，酚酸和黄酮醇也成负相关关系，黄烷醇物质之间以及与其它类黄酮物质之间成负相关关系。