Inorganic Elements in Tuber of Chinese Herbal Medicine Radix Pseudostellariae from Guizhou Province

By analyzing the contents of inorganic elements in Radix Pseudostellariae from Guizhou Province, the aim was to assess the present quality of Radix Pseu- dostellariae, set limited standards of heavy metals, establish element fingerprints, and find out the characteristic elements. ICP-MS was used to measure the content of inorganic elements and map the element fingerprints. Additionally, WM/T2-2004 was applied to evaluate the quality of heavy metal elements, and the characteristic elements were determined by principal component analysis. The results showed that the contents of inorganic elements in Radix Pseudostellariae were between 0.057 and 959 mg/kg with the coefficients of variation ranging from 0.134 to 1.478, and the contents of Cd, As, Pb, and Hg were below the Standard of WM/T2-2004 in 90% of Radix Pseudostellariae. The standard limits of heavy metals in Radix Pseu- dostellariae were Cr≤6.5 mg/kg, Cu≤10 mg/kg, As≤2.0 mg /kg, Cd≤0.3 mg/kg, Hg≤0.15 mg/kg, and Pb≤4.0 mg/kg. The features of the inorganic eJements finger- prints could provide theoretical basis of identifying the quality of Radix Pseudostel- lariae and distinguishing Radix Pseudostellariae from other Chinese herbal medicines. The characteristic inorganic elements of Radix Pseudostellariae were found to be Cd, Cu, Co, Zn, Fe, Ca, Mg, and AI. Radix Pseudostellariae from Guizhou Province contained abundant inorganic elements, and the contents of heavy metals were below the evaluation criterion. The study provided a reference for the future development of the limiting values of heaw metals in Radix Pseudostellariae.

Fault Isolation by Partial Dynamic Principal Component Analysis in Dynamic Process

Principal component analysis （PCA） is a useful tool in process fault detection, but offers little support on fault isolation. In this article, structured residual with strong isolation property is introduced. Although it is easy to get the residual by transformation matrix in static process, unfortunately, it becomes hard in dynamic process under control loop. Therefore, partial dynamic PCA（PDPCA） is proposed to obtain structured residual and enhance the isolation ability of dynamic process monitoring, and a compound statistic is introduced to resolve the problem resulting from independent variables in every variable subset. Simulations on continuous stirred tank reactor （CSTR） show the effectiveness of the proposed method.

A Robust Statistical Batch Process Monitoring Framework and Its Application

In order to reduce the variations of the product quality in batch processes, multivariate statistical process control methods according to multi-way principal component analysis (MPCA) or multi-way projection to latent structure (MPLS) were proposed for on-line batch process monitoring. However, they are based on the decomposition of relative covariance matrix and strongly affected by outlying observations. In this paper, in view of an efficient projection pursuit algorithm, a robust statistical batch process monitoring (RSBPM) framework,which is resistant to outliers, is proposed to reduce the high demand for modeling data. The construction of robust normal operating condition model and robust control limits are discussed in detail. It is evaluated on monitoring an industrial streptomycin fermentation process and compared with the conventional MPCA. The results show that the RSBPM framework is resistant to possible outliers and the robustness is confirmed.

Morphometric studies of genus Placocheilus （Teleostei： Cypriniformes） from Red River, China

It is practically difficult to differentiate Placocheilus robustus and Placocheilus caudofasciatus from Red River drainage of China. Without stated reasons, P. robustus has been assumed as the synonyms of P. caudofasciatus. The present study aims to decipher the morphological differences between two species so as to provide reliable clues for their classification by multivariate morphometry. A total of 72 specimens of two species in genus Placocheilus were examined. Besides morphological character comparisons, 10 anatomic landmarks were utilized and 23 frame structures and 15 general characters measured. The scatter plot results of principal component analysis showed that all specimens were clustered together and could not be defined as two distinct species. No significant morphological differences existed in four diagnostic characters between P. robustus and P. caudofasciatus. Thus the results of the present study fail to support P. robustus as a valid and independent species.

Application of fuzzy clustering method to determining sub-fault planes of earthquake from aftershocks sequence

Earthquake rupture process generally involves several faults activities instead of a single fault A new method using both fuzzy clustering and principal component analysis makes it possible to reconstruct three dimensional structure of involved faults in earthquake if the aftershocks around the active fault planes distribute uniformly. When seismic events are given, the optimal faults structures can be determined by our new method. Each of sub-fault planes is fully characterized by its central location, length, width, strike and dip. The resolution determines the number of fault segments needed to describe the earthquake catalog. The higher the resolution, the finer the structure of the reconstructed fault segments. The new method successfully reconstructs the fault segments using synthetic earthquake catalogs. By taking the 28 June 1992 Landers earthquake oceured in southern California as an example, the reconstructed fault segments are consistent with the faults already known on geological maps or blind faults that appeared quite frequently in longer-term catalogs.

OPTIMIZATION STUDY IN MANUFACTURING PROCESS FOR PC/ABS BLENDS

The optimization of injection molding process for polycarbonate/acrylonitrile-butadiene-styrene (PC/ABS) blends is studied using Taguchi method and principalcomponent analysis (PCA). Four controllable process factors are studied at three levels each in themanufacturing process. The L_9 orthogonal array is conducted to determine the optimum processfactor/level combination for single quality of mechanical properties. In addition, the principalcomponent analysis is employed to trans-form the correlated mechanical properties to a set ofuncorrelated components and to evaluate a comprehensive index for multi-response cases. Then theoptimum process factor/level combination for multiple qualities can be determined. Finally, theanalysis of variance is used to find out the most influential injection molding parameter for singleand multiple qualities problems.

Parametric shape prior model used in image segmentation

Due to the frequency of occlusion, cluttering and lowcontrast edges, gray intensity based active contour models oftenfail to segment meaningful objects. Prior shape information is usuallyutilized to segment desirable objects. A parametric shape priormodel is proposed. Firstly, principal component analysis is employedto train object shape and transformation is added to shaperepresentation. Then the energy function is constructed througha combination of shape prior energy, gray intensity energy andshape constraint energy of the kernel density function. The objectboundary extraction process is converted into the parameters solvingprocess of object shape. Besides, two new shape prior energyfunctions are defined when desirable objects are occluded by otherobjects or some parts of them are missing. Finally, an alternatingdecent iteration solving scheme is proposed for numerical implementation.Experiments on synthetic and real images demonstratethe robustness and accuracy of the proposed method.

A study on the application of ICA to GMAW

A novel method, independent component analysis （ ICA ） , is introduced to gas metal arc welding （GMAW） process monitoring. ICA was applied to arc signals, i. e. welding current and arc voltage, to remove the correlation between them and extract an independent component IC. Two series of robotic GMA W experiments were carried out to study the feasibility of ICA for online monitoring. It was found that IC put up an abnormity when there was a step disturbance in the welding process. Experimental results showed that the IC could be used as a state variable representing the process variation. By applying statistical process control （SPC） for the obtained IC, a burning-through defect was isolated from the normal operation. The comparison between ICA and principal component analysis （PCA） was also made for the processes, which led to an interesting result and was in need for further study.

Discriminating Important Agronomic and Industrial Parameters of White Oat Cultivars Treated with Fungicide Based on SIMCA Algorithm

Selection of effective agronomic and industrial parameters of oat cultivars is a decisive step in oat breeding programs fordevelopment of new oat and elite cultivars. In this study, a new approach was utilized to distinguish the most informative agronomicand industrial parameters that are most affected with fungicide application in oat cultivars. Four subsequent field experiments from2007 to 2010 were conducted in completely randomized block design （CRBD） with split plots. Total nine oat cultivars with orwithout fungicide application were evaluated for plant height, sieve yield, grain yield, lodging index, weight of hectoliter andde-hulling index. Soft independent modeling of class analogy （SIMCA） was conducted as one-class and multi-classes models toidentify important variables that can be used to discriminate samples. Results showed that SIMCA was effective, and lodging index,de-hulling index, sieve yield, plant height and grain yield were most affected oat parameters. Therefore, SIMCA algorithm can beused to easily discriminate some agronomic and quality parameters of oats.

Active Shape Model of Combining Pca and Ica: Application to Facial Feature Extraction

Active Shape Model （ASM） is a powerful statistical tool to extract the facial features of a face image under frontal view. It mainly relies on Principle Component Analysis （PCA） to statistically model the variability in the training set of example shapes. Independent Component Analysis （ICA） has been proven to be more efficient to extract face features than PCA. In this paper, we combine the PCA and ICA by the consecutive strategy to form a novel ASM. Firstly, an initial model, which shows the global shape variability in the training set, is generated by the PCA-based ASM. And then, the final shape model, which contains more local characters, is established by the ICA-based ASM. Experimental results verify that the accuracy of facial feature extraction is statistically significantly improved by applying the ICA modes after the PCA modes.

Fabric Defect Detection Using Independent Component Analysis and Phase Congruency

A novel method based on independent component analysis and phase congruency is proposed for detecting defects in textile fabric images. By independent component, we can obtain textile structural features of fabric-free images. By phase congru- ency, structure information is reduced, which can distinguish the defect region from the defect-free regions. Finally, we have the detecting result from binary image which is obtained by a thresh- old step, Compared with other algorithms, the proposed method not only has robustness with high detection rate, but also detects various types of defects quite well.

Neuroprotective effect of Naomaitong extract following focal cerebral ischemia induced by middle cerebral artery occlusion in rats

OBJECTIVE: To examine the neuroprotective effect of extract from Naomaitong following focal cerebral ischemia reperfusion induced by occlusion of middle cerebral artery(MCA), and to determine the biochemical alterations in urine using proton nuclear magnetic resonance spectroscopy and principal component analysis.METHODS: Wistar rats were randomly assigned tothree groups: sham-operated group, MCA focal cerebral ischemia reperfusion model group, and active extract of Naomaitong treatment group. The model was established by an improved MCA occlusion(MCAO) method. Sham-operated rats received the same surgical procedure, but without occlusion. The Naomaitong treatment group were treated with active extract from Naomaitong at a dose of3.0 g·kg-·1d-1. Brain tissues and urine samples were collected from all groups for histopathological assessment and proton nuclear magnetic resonance spectroscopy-based metabonomics, respectively.RESULTS: Hematoxylin-eosin and triphenyl tetrazolium chloride staining of brain tissues showed a significant decrease in cerebral infarction area in the Naomaitong group. In model rats, metabonomic analyses showed increased urinary levels of glutamate, taurine, trimetlylamine oxide, betaine, and glycine, and reduced levels of creatinine and creatine.Naomaitong regulated the metabolic changes by acting on multiple metabolic pathways, including glycine metabolism, glutaminolysis, transmethylation metabolism and creatinine metabolism.CONCLUSION: These data demonstrate that extract from Naomaitong is neuroprotective against focal cerebral ischemia induced by MCAO, and can alleviate biochemical changes in urinary metabolism. Metabonomics may be a useful approach for assessing the biochemical mechanisms underlying the neuroprotective actions of extract from Naomaitong.

Spatial variations of river water quality in Pearl River Delta, China

In Pearl River Delta （PRD）, river water quality has deteriorated gradually due to population increase and ongoing industrialization and urbanization. In this study, multivariate statistic methods were used to assess water quality spatial pattern and to identify characteristics of water quality variation in the PRD. Water quality monitoring of the PRD during the year 2005 and 2008 was conducted at 25 different stations. Seventeen water quality parameters were analyzed for further studying. Results of one-way analysis of variance （ANOVA） indicated that all the parameters except air temperature, water temperature and zinc showed significant difference among monitoring stations in both dry and wet season. Monitoring stations in the PRD were separately classified into three statistically significant clusters at （Olink/Omax） （ 2 in dry and wet season, respectively. The three clusters indicated the similarity and dissimilarity of river water quality among 25 monitoring stations, corresponding to heavy pollution, moderate pollution and slight pollution. Thus, the results of this study are useful to evaluate water quality and manage water resources in the PRD.

A statistical model to predict total column ozone in Peninsular Malaysia

This study aims to predict monthly columnar ozone in Peninsular Malaysia based on concentrations of several atmospheric gases. Data pertaining to five atmo- spheric gases （CO2, 03, CH4, NO2, and H2O vapor） were retrieved by satellite scanning imaging absorption spectro- metry for atmospheric chartography from 2003 to 2008 and used to develop a model to predict columnar ozone in Peninsular Malaysia. Analyses of the northeast monsoon （NEM） and the southwest monsoon （SWM） seasons were conducted separately. Based on the Pearson correlation matrices, columnar ozone was negatively correlated with HzO vapor but positively correlated with COz and NO2 during both the NEM and SWM seasons from 2003 to 2008. This result was expected because NO/is a precursor of ozone. Therefore, an increase in columnar ozone concentration is associated with an increase in NO2 but a decrease in H/O vapor. In the NEM season, columnar ozone was negatively correlated with H20 （-0.847）, NO2 （0.754）, and CO2 （0.477）; columnar ozone was also negatively but weakly correlated with CH4 （-0.035）. In the SWM season, columnar ozone was highly positively correlated with NO2 （0.855）, CO2 （0.572）, and CH4（0.321） and also highly negatively correlated with H2O（-0.832）. Both multiple regression and principal component analyses were used to predict the columnar ozone value in Peninsular Malaysia. We obtained the best-fitting regression equations for the columnar ozone data using four independent variables. Our results show approxi- mately the same R value （≈0.83） for both the NEM and SWM seasons.

A pilot study for distinguishing basal cell carcinoma from normal human skin tissues using visible resonance Raman spectroscopy

Aim: The aim of the study is to test visible resonance Raman (VRR) spectroscopy for rapid skin cancer diagnosis,and evaluate its effectiveness as a new optical biopsy method to distinguish basal cell carcinoma (BCC) from normal skin tissues.Methods: The VRR spectroscopic technique was undertaken using 532 nm excitation. Normal and BCC human skin tissue samples were measured in seconds. The molecular fingerprints of various native biomolecules as biomarkers were analyzed. A principal component analysis - support vector machine (PCA-SVM) statistical analysis method based on the molecular fingerprints was developed for differentiating BCC from normal skin tissues.Results: VRR provides a rapid method and enhanced Raman signals from biomolecules with resonant and nearresonant absorption bands as compared with using a near-infrared excitation light source. The VRR technique revealed chemical composition changes of native biomarkers such as tryptophan, carotenoids, lipids and proteins.The VRR spectra from BCC samples showed a strong enhancement in proteins including collagen type I combined with amide I and amino acids, and a decrease in carotenoids and lipids. The PCA-SVM statistical analysis based on the molecular fingerprints of the biomarkers yielded a 93.0% diagnostic sensitivity, 100% specificity, and 94.5%accuracy compared with histopathology reports.Conclusion: VRR can enhance molecular vibrational modes of various native biomarkers to allow for very fast display of Raman modes in seconds. It may be used as a label-free molecular pathology method for diagnosis of skin cancer and other diseases and be used for combined treatment with Mohs surgery for BCC.