Artificial neural network classification based on high-performance liquid chromatography of urinary and serum nucleosides for the clinical diagnosis of cancer

Nucleosides in human urine and serum have frequently been studied as a possible biomedical marker for cancer, acquired immune deficiency syndrome (AIDS) and the whole-body turnover of RNAs. Fifteen normal and modified nucleosides were determined in 69 urine and 42 serum samples using high-performance liquid chromatography (HPLC). Artificial neural networks have been used as a powerful pattern recognition tool to distinguish cancer patients from healthy persons. The recognition rate for the training set reached 100%. In the validating set, 95.8 and 92.9% of people were correctly classified into cancer patients and healthy persons when urine and serum were used as the sample for measuring the nucleosides. The results show that the artificial neural network technique is better than principal component analysis for the classification of healthy persons and cancer patients based on nucleoside data. (C) 2002 Elsevier Science B.V. All rights reserved.

A QUANTITATIVE ASSESSMENT OF SOURCE CONTRIBUTIONS TO INHALABLE PARTICULATE MATTER POLLUTION IN METROPOLITAN BOSTON

In this paper, source apportionment techniques are employed to identify and quantify the major particle pollution source classes affecting a monitoring site in metropolitan Boston, MA. A Principal Component Analysis (PCA) of paniculate elemental data allows the estimation of mass contributions for five fine mass panicle source classes (soil, motor vehicle, coal related, oil and salt aerosols), and six coarse panicle source classes (soil, motor vehicle, refuse incineration, residual oil, salt and sulfate aerosols). Also derived are the elemental characteristics of those source aerosols and their contributions to the total recorded elemental concentrations (i.e. an elemental mass balance). These are estimated by applying a new approach to apportioning mass among various PCA source components: the calculation of Absolute Principal Component Scores, and the subsequent regression of daily mass and elemental concentrations on these scores.

Spatial variability in water quality and surface sediment diatom assemblages in a complex lake basin,lake of the woods,ontario,canada

Lake of the Woods (LOW) is an international waterbody spanning the Canadian provinces of Ontario and Manitoba, and the U.S. state of Minnesota. In recent years, there has been a perception that water quality has deteriorated in northern regions of the lake, with all increase in the frequency and intensity of toxin-producing cyanobacterial blooms. However, given the lack of long-term data these trends are difficult to verify. As a first step, we examine spatial and seasonal patterns in water quality in this highly complex lake on the Canadian Shield. Further, we examine surface sediment diatom assemblages across multiple sites to determine if they track within-take differences in environmental conditions. Our results show that there are significant spatial patterns in water quality in LOW. Principal Component Analysis divides the lake into three geographic zones based primarily on algal nutrients (i.e., total phosphorus, TP), with the highest concentrations at sites proximal to Rainy River. This variation is closely tracked by sedimentary diatom assemblages, with [TP] explaining 43% of the variation in diatom assemblages across sites. The close correlation between water quality and the surface sediment diatom record indicate that paleoecological models could be used to provide data on the relative importance of natural and anthropogenic sources of nutrients to the lake.

小麦抗旱生态分类中适合性聚类方法的研究

探索了适合于小麦品种抗旱生态分类的聚类方法 .选用 2 1个农艺性状和 15个冬小麦品种 (系 ) ,在聚类分析的各环节上 ,通过采用不同的策略 ,大规模进行了各种分类结果的比较 .结果表明 ,在与专家经验分类接近程度上 ,数据转换方法中 ,原始数据法依次大于普通相关阵基础上的方差极大正交旋转法、Promax斜交旋转法、主成份法 ;相似性度量上 ,欧氏距离大于马氏距离 ;聚类方式上 ,对应分析法和模糊聚类法大于最短距离法、最长距离法、类平均法 ;所有可组合的方法中 ,以对应分析法和直接用原始数据的模糊聚类法的分类结果最接近专家经验分类 .结合各方法理论上优缺点的分析与检验 ,认为这两种方法也是较理想的方法 .

Fast Haar Transform Based Feature Extraction for Face Representation and Recognition

Subspace learning is the process of finding a proper feature subspace and then projecting high-dimensional data onto the learned low-dimensional subspace. The projection operation requires many floating-point multiplications and additions, which makes the projection process computationally expensive. To tackle this problem, this paper proposes two simple-but-effective fast subspace learning and image projection methods, fast Haar transform (FHT) based principal component analysis and FHT based spectral regression discriminant analysis. The advantages of these two methods result from employing both the FHT for subspace learning and the integral vector for feature extraction. Experimental results on three face databases demonstrated their effectiveness and efficiency.

L1-Norm-Based 2DPCA

In this paper, we first present a simple but effective L1-norm-based two-dimensional principal component analysis (2DPCA). Traditional L2-norm-based least squares criterion is sensitive to outliers, while the newly proposed L1-norm 2DPCA is robust. Experimental results demonstrate its advantages.

Supervised Gaussian Process Latent Variable Model for Dimensionality Reduction

The Gaussian process latent variable model (GP-LVM) has been identified to be an effective probabilistic approach for dimensionality reduction because it can obtain a low-dimensional manifold of a data set in an unsupervised fashion. Consequently, the GP-LVM is insufficient for supervised learning tasks (e. g., classification and regression) because it ignores the class label information for dimensionality reduction. In this paper, a supervised GP-LVM is developed for supervised learning tasks, and the maximum a posteriori algorithm is introduced to estimate positions of all samples in the latent variable space. We present experimental evidences suggesting that the supervised GP-LVM is able to use the class label information effectively, and thus, it outperforms the GP-LVM and the discriminative extension of the GP-LVM consistently. The comparison with some supervised classification methods, such as Gaussian process classification and support vector machines, is also given to illustrate the advantage of the proposed method.

NMR and pattern recognition studies on the acute biochemical effects of Lu(NO3)(3)

Pattern recognition methods were applied to the analysis of 600 MHz H-1 NMR spectra of urine from rats dosed with compounds that induced organ-specific damage in the liver and kidney. Male Wistar rats were separated into groups (n=4) and each was treated with one of following compounds: HgCl2, CCl4, Lu(NO3)(3) and Changle (a kind of rare earth complex mixed with La, Ce, Pr and Nd). Urine samples from the rats dosed with HgCl2, CCl4 and Lu(NO3)(3) were collected over a 24 h time course and the samples from the rats administrated with Changle were gained after 3 months. These samples were measured by 600 MHz NMR spectroscopy. Each spectrum was data-processed to provide 223 intensity-related descriptors of spectra. Urine spectral data corresponding to the time intervals, 0-8 h (HgCl2 and CCl4), 4-8 (Lu(NO3)(3)) h and 90 d (Changle) were analyzed using principal component analysis (PCA). Successful classification of the toxicity and biochemical effects of Lu(NO3)(3) was achieved.

Investigation on acute biochemical effects of Ce(NO3)(3) on liver and kidney tissues by MAS H-1 NMR spectroscopic-based metabonontic approach

High resolution magic angle spinning (MAS)-H-1 nuclear magnetic resonance (NMR) spectroscopic-based metabonomic approach was applied to the investigation on the acute biochemical effects of Ce(No-3)(3). Male Wistar rats were administrated with various doses of Ce (NO3)(3)(2, 10, and 50 mg(.)kg(-1) body weight), and MAS H-1 NMR spectra of intact liver and kidney tissues were analyzed using principal component analysis to extract toxicity information. The biochemical effects of Ce (NO3)(3) were characterized by the increase of triglycerides and lactate and the decrease of glycogen in rat liver tissue, together with an elevation of the triglyceride level and a depletion of glycerophosphocholine and betaine in kidney tissues. The target lesions of Ce (NO3)(3) on liver and kidney were found by MAS NMR-based metabonomic method. This study demonstrates that the combination of MAS H-1 NMR and pattern recognition analysis can be an effective method for studies of biochemical effects of rare earths.

FRACTIONATED CRYSTALLIZATION IN POLYOLEFINS-NYLON-6 BLENDS

The crystallization behavior of polyolefins-nylon 6 polymer blends was studied by differential scanning calorimetry (DSC) measurements. In these blends, the crystallization of the minor component often starts with distinctly deeper supercooling than that

Comparison of nonlinear and linear PCA on surface wind, surface height, and SST in the South China Sea

We compared nonlinear principal component analysis (NLPCA) with linear principal component analysis (LPCA) with the data of sea surface wind anomalies (SWA), surface height anomalies (SSHA), and sea surface temperature anomalies (SSTA), taken in the South China Sea (SCS) between 1993 and 2003. The SCS monthly data for SWA, SSHA and SSTA (i.e., the anomalies with climatological seasonal cycle removed) were pre-filtered by LPCA, with only three leading modes retained. The first three modes of SWA, SSHA, and SSTA of LPCA explained 86%, 71%, and 94% of the total variance in the original data, respectively. Thus, the three associated time coefficient functions (TCFs) were used as the input data for NLPCA network. The NLPCA was made based on feed-forward neural network models. Compared with classical linear PCA, the first NLPCA mode could explain more variance than linear PCA for the above data. The nonlinearity of SWA and SSHA were stronger in most areas of the SCS. The first mode of the NLPCA on the SWA and SSHA accounted for 67.26% of the variance versus 54.7%, and 60.24% versus 50.43%, respectively for the first LPCA mode. Conversely, the nonlinear SSTA, localized in the northern SCS and southern continental shelf region, resulted in little improvement in the explanation of the variance for the first NLPCA.

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.

One century record of contamination by polycyclic aromatic hydrocarbons and polychlorinated biphenyls in core sediments from the southern Yellow Sea

Sixteen polycyclic aromatic hydrocarbons (PAHs) and 28 polychlorinated biphenyls (PCBs) were measured at a 2-cm interval in a core sample from the middle of the southern Yellow Sea for elucidating their historical variations in inflow and sources. The chronology was obtained using the Pb-210 method. PAHs concentrations decreased generally with depth and two climax values occurred in 14-16 cm and 20-22 cm layers, demonstrating that the production and usage of PAHs might reach peaks in the periods of 1956-1962 and 1938-1944. The booming economy and the navy battles of the Second World War might explain why the higher levels were detected in the two layers. The result of principal component analysis (PCA) revealed that PAHs were primarily owing to the combustion product. Down-cored variation of PCB concentrations was complex. Higher concentrations besides the two peaks being the same as PAHs were detected from 4 to 8 cm, depositing from 1980 to 1992, which probably resulted from the disposal of the out-dated PCB-containing equipment. The average Cl percentage of PCBs detected was similar to that of the mixture of Aroclor 1254 and 1242, suggesting they might origin from the dielectrical and heat-transfer fluid. The total organic carbon (TOC) content played a prevalent role in the adsorption of high molecular weight PAHs (>= 4-ring), while no obvious relationship among total PCBs, the concentration of congeners, and TOC was found.

Phenotypic plasticity of gut structure and function during periods of inactivity in Apostichopus japonicus

Apostichopus japonicus is a common sea cucumber that undergoes seasonal inactivity phases and ceases feeding during the summer months. We used this sea cucumber species as a model in which to examine phenotypic plasticity of the digestive tract in response to food deprivation. We measured the body mass, gross gut morphology and digestive enzyme activities of A. japonicus before, during, and after the period of inactivity to examine the effects of food deprivation on the gut structure and function of this animal. Individuals were sampled semi-monthly from June to November (10 sampling intervals over 178 days) across temperature changes of more than 18 degrees C. On 5 September, which represented the peak of inactivity and lack of feeding, A. japonicus decreased its body mass, gut mass and gut length by 50%, 85%, and 70%, respectively, in comparison to values for these parameters preceding the inactive period. The activities of amylase, cellulase and lipase decreased by 77%, 98%, and 35% respectively, in comparison to mean values for these enzymes in June, whereas pepsin activity increased two-fold (luring the inactive phase. Alginase and trypsin activities were variable and did not change significantly across the 178-day experiment. With the exception of amylase and cellulase, all body size indices and digestive enzyme activities recovered and even surpassed the mean values preceding the inactive phase during the latter part of the experiment (October-November). Principal Component Analysis (PCA) utilizing the digestive enzyme activity and body size index data divided the physiological state of this cucumber into four phases: an active stage, prophase of inactivity peak inactivity, and a reversion phase. These phases are all consistent with previously suggested life stages for this species, but our data provide more defined characteristics of each phase. A. japonicus clearly exhibits phenotypic plasticity (or life-cycle staging) of the digestive tract during its annual inactive period. (C) 2008 Elsevier Inc. All rights reserved.

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.