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

EPSRC, the European Community IST FP6 Integrated, etc

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.

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.

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。

Spatial distribution of benthic algae in the Gangqu River, Shangrila, China

This study consisted of sampling benthic algae at 32 sites in the Gangqu River, an important upstream tributary of the Yangtze River. Our aims were to characterize the benthic algae communities and relationships with environmental variables. Among the 162 taxa observed, Achnanthes linearis and Achnanthes lanceolata var. elliptica were the dominant species (17.10% and 14.30% of the total relative abundance, respectively). Major gradients and principal patterns of variation within the environmental variables were detected by principal component analysis (PCA). Then non-metric multidimensional scaling (NMS) divided all the sites into three groups, which were validated by multi-response permutation procedures (MRPP). Canonical correspondence analysis (CCA) indicated that three environmental variables (TN, TDS, and TP) significantly affected the distribution of benthic algae. Weighted averaging regression and cross-calibration produced strong models for predicting TN and TDS concentration, which enabled selection of algae taxa as potentially sensitive indicators of certain TN and TDS levels: for TN, Achnanthes lanceolata, Achnanthes lanceolata var. elliptica, and Cymbella ventricosa var. semicircularis; for TDS, Cocconeis placentula, Cymbella alpina var. minuta, and Fragilaria virescens. The present study represents an early step in establishing baseline conditions. Further monitoring is suggested to gain a better understanding of this region.

Reduced swimming abilities in fast-growing transgenic common carp Cyprinus carpio associated with their morphological variations

Critical swimming speeds (U-crit) and morphological characters were compared between the F-4 generation of GH-transgenic common carp Cyprinus carpio and the non-transgenic controls. Transgenic fish displayed a mean absolute U-crit value 22.3% lower than the controls. Principal component analysis identified variations in body shape, with transgenic fish having significantly deeper head, longer caudal length of the dorsal region, longer standard length (L-S) and shallower body and caudal region, and shorter caudal length of the ventral region. Swimming speeds were related to the combination of deeper body and caudal region, longer caudal length of the ventral region, shallower head depth, shorter caudal length of dorsal region and L-S. These findings suggest that morphological variations which are poorly suited to produce maximum thrust and minimum drag in GH-transgenic C. carpio may be responsible for their lower swimming abilities in comparison with non-transgenic controls.

Effects of seasonal succession and water pollution on the protozoan community structure in an eutrophic lake

The purpose of the research is to study the seasonal succession of protozoa community and the effect of water quality on the protozoa community to characterize biochemical processes occurring at a eutrophic Lake Donghu, a large shallow lake in Wuhan City, China. Samples of protozoa communities were obtained monthly at three stations by PFU (polyurethane foam unit) method over a year. Synchronously, water samples also were taken from the stations for the water chemical quality analysis. Six major variables were examined in a principal component analysis (PCA), which indicate the fast changes of water quality in this station I and less within-year variation and a comparatively stable water quality in stations II and III. The community data were analyzed using multivariate techniques, and we show that clusters are rather mixed and poorly separated, suggesting that the community structure is changing gradually, giving a slight merging of clusters form the summer to the autumn and the autumn to the winter. Canonical correspondence analysis (CCA) was used to infer the relationship between water quality variables and phytoplankton community structure, which changed substantially over the survey period. From the analysis of cluster and CCA, coupled by community pollution value (CPV), it is concluded that the key factors driving the change in protozoa community composition in Lake Donghu was water qualities rather than seasons. (c) 2006 Elsevier Ltd. All rights reserved.

Population genetic structure of Carchesium polypinum (Ciliophora : Peritrichia) in four chinese lakes inferred from ISSR fingerprinting: High diversity but low differentiation

Although the peritrichous ciliate Carchesium polypinum is common in freshwater, its population genetic structure is largely unknown. We used inter-simple sequence repeat (ISSR) fingerprinting to analyze the genetic structure of 48 different isolates of the species from four lakes in Wuhan, central China. Using eight polymorphic primers, 81 discernible DNA fragments were detected, among which 76 (93.83%) were polymorphic, indicating high genetic diversity at the isolate level. Further, Nei's gene diversity (h) and Shannon's Information index (I) between the different isolates both revealed a remarkable genetic diversity, higher than previously indicated by their morphology. At the same time, substantial gene flow was found. So the main factors responsible for the high level of diversity within populations are probably due to conjugation (sexual reproduction) and wide distribution of swarmers. Analysis of molecular variance (AMOVA) showed that there was low genetic differentiation among the four populations probably due to common ancestry and flooding events. The cluster analysis and principal component analysis (PCA) suggested that genotypes isolated from the same lake displayed a higher genetic similarity than those from different lakes. Both analyses separated C. polypinum isolates into subgroups according to the geographical locations. However, there is only a weak positive correlation between the genetic distance and geographical distance, suggesting a minor effect of geographical distance on the distribution of genetic diversity between populations of C. polypinum at the local level. In conclusion, our studies clearly demonstrated that a single morphospecies may harbor high levels of genetic diversity, and that the degree of resolution offered by morphology as a marker for measuring distribution patterns of genetically distinct entities is too low.

Contamination and distribution of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/F) in agriculture fields in Ya-Er Lake area, China

The contamination and distribution of polychlorinated dibeinizo-p-dioxins and dibenzofurans (PCDD/Fs) from two agricultural fields of a heavily polluted lake area in China (Ya-Er Lake) are presented. The vertical distribution pattern of total PCDD/Fs in soil cores reveals that the maximum concentration was in the layer of 20-30 cm. The concentrations in the top layer of soil at the two sites were similar (17.48 ng/kg at Site 1 and 18.10 ng/kg at Site 2), but the maximum concentration of Site 1 (120.8 ng/kg) was two times higher than that of Site 2 (64.39 ng/kg). The maximum concentration of PCDD/Fs in mud cores in rice fields (0-50 cm) at Sites 1 and 2 was in the layer of 0-10 cm. The maximum PCDD/F concentration in the top layer in mud at Site 1 (203.1 ng/kg) was higher than that at Site 2: (143.3 ng/kg). Significant correlations were found between the mind PCDD/Fs and the organic carbon content (R = 0.9743, P< 0,05 at Site 1; R = 0.9821, P< 0.05 at Site 2), the two variables being highly correlated (R = 0.9049, P< 0.05, at Site 1; R = 0.9916, P< 0.05 at Site 2). All correlation coefficients were significant at the 95% level. Concentrations were highly correlated with organic carbon, indicating that sorption to organic carbon was the dominant mechanism. Using principal component analysis, the homologue profiles of soil, mud, and plants (rice and radish) were compared. The PCDD/F patterns in plants were found not to be correlated to those in soil and mud. This suggests that atmospheric deposition may be the main source of PCDD/Fs in rice grain. However, mixed exposure involving uptake mechanisms and atmospheric deposition is considered main the source of PCDD/F pollution in radishes. (C) 2002 Elsevier Science (USA).

Mobility and profiles of polychlorinated dibenzo-p-dioxins and dibenzofurans in sediment of Ya-Er Lake, China

The sediment of Ya-Er Lake had been heavily polluted by polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) from the former chloralkali industry. The total amounts of PCDD/Fs and I-TEQ decreased along the water flow direction and also decreased from top to bottom layers of sediment cores. Sediment of Pond 1 was dominated by PCDF, especially TCDF. In contrast, in the other four ponds, PCDD dominated in all layers and octachlorinated dibenzo-p-dioxin (OCDD) predominated in all of the homologues. When homologue profiles from sediments and water samples were compared using principal component analysis (PCA), the first two principal components represented 95.2% of the variance in the data. The first component explained 75.9% of the variance and the second one 19.3%. Two clusters were most distinct, presenting a shift in PCDD/Fs composition from PCDF to heptachlorinated dibenzo-p-dioxin (HpCDD) and OCDD in sediments and water from Pond I to Ponds 2-5. The pattern variation between Pond 1 and Ponds 2-5 in Ya-Er Lake was most likely due to the change of process in the chemical plant after the dams between the ponds were built. The results of the present study also showed that log K-oc of PCDD/Fs calculated from data of sediment and water in the field were comparable with theoretical log K-oc. The results also implied that the concentrations of PCDD/Fs in water and sediments could be predicted from each other by log K-oc. (C) 2001 Elsevier Science Ltd. All rights reserved.

Fast Discrimination of Commerical Corn Varieties Based on Near Infrared Spectra

The existing methods for the discrimination of varieties of commodity corn seed are unable to process batch data and speed up identification, and very time consuming and costly. The present paper developed a new approach to the fast discrimination of varieties of commodity corn by means of near infrared spectral data. Firstly, the experiment obtained spectral data of 37 varieties of commodity corn seed with the Fourier transform near infrared spectrometer in the wavenurnber range from 4 000 to 12 000 cm (1). Secondly, the original data were pretreated using statistics method of normalization in order to eliminate noise and improve the efficiency of models. Thirdly, a new way based on sample standard deviation was used to select the characteristic spectral regions, and it can search very different wavenumbers among all wavenumbers and reduce the amount of data in part. Fourthly, principal component analysis (PCA) was used to compress spectral data into several variables, and the cumulate reliabilities of the first ten components were more than 99.98%. Finally, according to the first ten components, recognition models were established based on BPR. For every 25 samples in each variety, 15 samples were randomly selected as the training set. The remaining 10 samples of the same variety were used as the first testing set, and all the 900 samples of the other varieties were used as the second testing set. Calculation results showed that the average correctness recognition rate of the 37 varieties of corn seed was 94.3%. Testing results indicate that the discrimination method had higher precision than the discrimination of various kinds of commodity corn seed. In short, it is feasible to discriminate various varieties of commodity corn seed based on near infrared spectroscopy and BPR.