A high performance liquid chromatography and electrospray ionization mass spectrometry method for the analysis of the natural medicine, Forsythia suspensa

Samples of Forsythia suspensa from raw (Laoqiao) and ripe (Qingqiao) fruit were analyzed with the use of HPLC-DAD and the EIS-MS techniques. Seventeen peaks were detected, and of these, twelve were identified. Most were related to the glucopyranoside molecular fragment. Samples collected from three geographical areas (Shanxi, Henan and Shandong Provinces), were discriminated with the use of hierarchical clustering analysis (HCA), discriminant analysis (DA), and principal component analysis (PCA) models, but only PCA was able to provide further information about the relationships between objects and loadings; eight peaks were related to the provinces of sample origin. The supervised classification models-K-nearest neighbor (KNN), least squares support vector machines (LS-SVM), and counter propagation artificial neural network (CP-ANN) methods, indicated successful classification but KNN produced 100% classification rate. Thus, the fruit were discriminated on the basis of their places of origin.

Information-theoretic analysis of brain white matter fiber orientation distribution functions

We propose a new information-theoretic metric, the symmetric Kullback-Leibler divergence (sKL-divergence), to measure the difference between two water diffusivity profiles in high angular resolution diffusion imaging (HARDI). Water diffusivity profiles are modeled as probability density functions on the unit sphere, and the sKL-divergence is computed from a spherical harmonic series, which greatly reduces computational complexity. Adjustment of the orientation of diffusivity functions is essential when the image is being warped, so we propose a fast algorithm to determine the principal direction of diffusivity functions using principal component analysis (PCA). We compare sKL-divergence with other inner-product based cost functions using synthetic samples and real HARDI data, and show that the sKL-divergence is highly sensitive in detecting small differences between two diffusivity profiles and therefore shows promise for applications in the nonlinear registration and multisubject statistical analysis of HARDI data.

Combining HPLC-DAD and ICP-MS data for improved analysis of complex samples: Classification of the root samples from Cortex moutan

A combined data matrix consisting of high performance liquid chromatography–diode array detector (HPLC–DAD) and inductively coupled plasma-mass spectrometry (ICP-MS) measurements of samples from the plant roots of the Cortex moutan (CM), produced much better classification and prediction results in comparison with those obtained from either of the individual data sets. The HPLC peaks (organic components) of the CM samples, and the ICP-MS measurements (trace metal elements) were investigated with the use of principal component analysis (PCA) and the linear discriminant analysis (LDA) methods of data analysis; essentially, qualitative results suggested that discrimination of the CM samples from three different provinces was possible with the combined matrix producing best results. Another three methods, K-nearest neighbor (KNN), back-propagation artificial neural network (BP-ANN) and least squares support vector machines (LS-SVM) were applied for the classification and prediction of the samples. Again, the combined data matrix analyzed by the KNN method produced best results (100% correct; prediction set data). Additionally, multiple linear regression (MLR) was utilized to explore any relationship between the organic constituents and the metal elements of the CM samples; the extracted linear regression equations showed that the essential metals as well as some metallic pollutants were related to the organic compounds on the basis of their concentrations

Combined NIR/MIR analysis: A novel method for the classification of complex substances such as Illicium verum Hook. F. and its adulterants

A novel combined near- and mid-infrared (NIR and MIR) spectroscopic method has been researched and developed for the analysis of complex substances such as the Traditional Chinese Medicine (TCM), Illicium verum Hook. F. (IVHF), and its noxious adulterant, Iuicium lanceolatum A.C. Smith (ILACS). Three types of spectral matrix were submitted for classification with the use of the linear discriminant analysis (LDA) method. The data were pretreated with either the successive projections algorithm (SPA) or the discrete wavelet transform (DWT) method. The SPA method performed somewhat better, principally because it required less spectral features for its pretreatment model. Thus, NIR or MIR matrix as well as the combined NIR/MIR one, were pretreated by the SPA method, and then analysed by LDA. This approach enabled the prediction and classification of the IVHF, ILACS and mixed samples. The MIR spectral data produced somewhat better classification rates than the NIR data. However, the best results were obtained from the combined NIR/MIR data matrix with 95–100% correct classifications for calibration, validation and prediction. Principal component analysis (PCA) of the three types of spectral data supported the results obtained with the LDA classification method.

A novel near-infrared spectroscopy and chemometrics method for rapid analysis of several chemical components and antioxidant activity of mint (mentha haplocalyx briq.) samples

A novel near-infrared spectroscopy (NIRS) method has been researched and developed for the simultaneous analyses of the chemical components and associated properties of mint (Mentha haplocalyx Briq.) tea samples. The common analytes were: total polysaccharide content, total flavonoid content, total phenolic content, and total antioxidant activity. To resolve the NIRS data matrix for such analyses, least squares support vector machines was found to be the best chemometrics method for prediction, although it was closely followed by the radial basis function/partial least squares model. Interestingly, the commonly used partial least squares was unsatisfactory in this case. Additionally, principal component analysis and hierarchical cluster analysis were able to distinguish the mint samples according to their four geographical provinces of origin, and this was further facilitated with the use of the chemometrics classification methods-K-nearest neighbors, linear discriminant analysis, and partial least squares discriminant analysis. In general, given the potential savings with sampling and analysis time as well as with the costs of special analytical reagents required for the standard individual methods, NIRS offered a very attractive alternative for the simultaneous analysis of mint samples.

Image processing and classification procedure for the analysis of Australian frog vocalisations

Frog species have been declining worldwide at unprecedented rates in the past decades. There are many reasons for this decline including pollution, habitat loss, and invasive species [1]. To preserve, protect, and restore frog biodiversity, it is important to monitor and assess frog species. In this paper, a novel method using image processing techniques for analyzing Australian frog vocalisations is proposed. An FFT is applied to audio data to produce a spectrogram. Then, acoustic events are detected and isolated into corresponding segments through image processing techniques applied to the spectrogram. For each segment, spectral peak tracks are extracted with selected seeds and a region growing technique is utilised to obtain the contour of each frog vocalisation. Based on spectral peak tracks and the contour of each frog vocalisation, six feature sets are extracted. Principal component analysis reduces each feature set down to six principal components which are tested for classification performance with a k-nearest neighbor classifier. This experiment tests the proposed method of classification on fourteen frog species which are geographically well distributed throughout Queensland, Australia. The experimental results show that the best average classification accuracy for the fourteen frog species can be up to 87%.

An analysis of theories of search and search behavior

Theories of search and search behavior can be used to glean insights and generate hypotheses about how people interact with retrieval systems. This paper examines three such theories, the long standing Information Foraging Theory, along with the more recently proposed Search Economic Theory and the Interactive Probability Ranking Principle. Our goal is to develop a model for ad-hoc topic retrieval using each approach, all within a common framework, in order to (1) determine what predictions each approach makes about search behavior, and (2) show the relationships, equivalences and differences between the approaches. While each approach takes a different perspective on modeling searcher interactions, we show that under certain assumptions, they lead to similar hypotheses regarding search behavior. Moreover, we show that the models are complementary to each other, but operate at different levels (i.e., sessions, patches and situations). We further show how the differences between the approaches lead to new insights into the theories and new models. This contribution will not only lead to further theoretical developments, but also enables practitioners to employ one of the three equivalent models depending on the data available.

Genome-wide association study of major depressive disorder: New results, meta-analysis, and lessons learned

Major depressive disorder (MDD) is a common complex disorder with a partly genetic etiology. We conducted a genome-wide association study of the MDD2000+ sample (2431 cases, 3673 screened controls and >1 M imputed single-nucleotide polymorphisms (SNPs)). No SNPs achieved genome-wide significance either in the MDD2000+ study, or in meta-analysis with two other studies totaling 5763 cases and 6901 controls. These results imply that common variants of intermediate or large effect do not have main effects in the genetic architecture of MDD. Suggestive but notable results were: (a) gene-based tests suggesting roles for adenylate cyclase 3 (ADCY3, 2p23.3) and galanin (GAL, 11q13.3); published functional evidence relates both of these to MDD and serotonergic signaling; (b) support for the bipolar disorder risk variant SNP rs1006737 in CACNA1C (P=0.020, odds ratio=1.10), and; (c) lack of support for rs2251219, a SNP identified in a meta-analysis of affective disorder studies (P=0.51). We estimate that sample sizes 1.8- to 2.4-fold greater are needed for association studies of MDD compared with those for schizophrenia to detect variants that explain the same proportion of total variance in liability. Larger study cohorts characterized for genetic and environmental risk factors accumulated prospectively are likely to be needed to dissect more fully the etiology of MDD.

The Behavior of Hydroxyl Units of Synthetic Goethite and its Dehydroxylated Product Hematite

The behavior of the hydroxyl units of synthetic goethite and its dehydroxylated product hematite was characterized using a combination of Fourier transform infrared (FTIR) spectroscopy and X-ray diffraction (XRD) during the thermal transformation over a temperature range of 180-270 degrees C. Hematite was detected at temperatures above 200 degrees C by XRD while goethite was not observed above 230 degrees C. Five intense OH vibrations at 3212-3194, 1687-1674, 1643-1640, 888-884 and 800-798 cm(-1), and a H2O vibration at 3450-3445 cm(-1) were observed for goethite. The intensity of hydroxyl stretching and bending vibrations decreased with the extent of dehydroxylation of goethite. Infrared absorption bands clearly show the phase transformation between goethite and hematite: in particular. the migration of excess hydroxyl units from goethite to hematite. Two bands at 536-533 and 454-452 cm(-1) are the low wavenumber vibrations of Fe-O in the hematite structure. Band component analysis data of FTIR spectra support the fact that the hydroxyl units mainly affect the a plane in goethite and the equivalent c plane in hematite.

Development of self-efficacy and expectancy measures for benzodiazepines

This study aimed to develop and assess the reliability and validity of a pair of self-report questionnaires to measure self-efficacy and expectancy associated with benzodiazepine use, the Benzodiazepine Refusal Self- Efficacy Questionnaire (BRSEQ) and the Benzodiazepine Expectancy Questionnaire (BEQ). Internal structure of the questionnaireswas established by principal component analysis (PCA) in a sample of 155 respondents, and verified by confirmatory factor analyses (CFA) in a second independent sample (n=139) using structural equation modeling. The PCA of the BRSEQ resulted in a 16-item, 4-factor scale, and the BEQ formed an 18-item, 2-factor scale. Both scales were internally reliable. CFA confirmed these internal structures and reduced the questionnaires to a 14-item self-efficacy scale and a 12-item expectancy scale. Lower self-efficacy and higher expectancy were moderately associated with higher scores on the SDS-B. The scales provide reliable measures for assessing benzodiazepine self-efficacy and expectancies. Future research will examine the utility of the scales in prospective prediction of benzodiazepine cessation.

Diffuse reflectance near infrared spectroscopy can distinguish normal from enzymatically digested cartilage

A non-destructive, diffuse reflectance near infrared spectroscopy (DR-NIRS)approach is considered as a potential tool for determining the component-level structural properties of articular cartilage. To this end, DR-NIRS was applied in vitro to detect structural changes, using principal component analysis as the statistical basis for characterization. The results show that this technique, particularly with first-derivative pretreatment, can distinguish normal, intact cartilage from enzymatically digested cartilage. Further, this paper establishes that the use of DR-NIRS enables the probing of the full depth of the uncalcified cartilage matrix, potentially allowing the assessment of degenerative changes in joint tissue, independent of the site of initiation of the osteoarthritic process.

Characterisation of soluble components and PAH in PM10 atmospheric particulate matter in Brisbane

Fours sets of PM10 samples were collected in three sites in SEQ from December 2002 to August 2004. Three of these sets of samples were collected by QLD EPA as a part of their regular air monitoring program at Woolloongabba, Rocklea and Eagle Farm. Half of the samples were used in this study for the analysis of water-soluble ions, which are Na+, K+, Mg2+, Ca2+, NH4 +, Cl-, NO3 -, SO4 2-, F-, Br-, NO2 -, PO4 -3 and the other half was retained by QLD EPA. The fourth set of samples was collected at Rocklea, specifically for this study. A quarter of the samples obtained from this set of samples were used to analyse water-soluble ions; a quarter of the sample was used to analyse Pb, Cu, Al, Fe, Mn and Zn; and the rests were used to analyse US EPA 16 priority PAHs. The water-soluble ions were extracted ultrasonically with water and the major watersoluble anions as well as NH4 + were analysed using IC. Na+, K+, Mg2+, Ca2+ Pb, Cu, Al, Fe, Mn and Zn were analysed using ICP-AES while PAHs were extracted by acetonitrile and analysed using HPLC. Of the analysed water-soluble ions, Cl-, NO3 -, SO4 2-, Na+, K+, Mg2+ and Ca2+ were high in concentration and determined in all the samples. F-, Br-, NO2 -, PO4 -3 and NH4 + ions were lower in concentration and determined only in some samples. Na+ and Cl- were high in all samples indicating the importance of a marine source. Principal Component Analysis (PCA) was used to examine the temporal variations of the water-soluble ions at the three sites. The results indicated that there was no major difference between the three sites. However, comparing the average concentrations of ions and Cl-/Na+ it was concluded that Woolloongabba had more marine influence than the other sites. Al, Fe and Zn were detected in all samples. Al and Fe were high in all samples indicating the significance of a source of crustal matter. Cu, Mn and Pb were in low concentrations and were determined only in some samples. The lower Pb concentrations observed in the study than in previous studies indicate that the phasing-out of leaded petrol had an appreciable impact on Pb levels in SEQ. This study reports for the first time, simultaneous data on the water-soluble, metal ion and PAH levels of PM10 aerosols in Brisbane, and provides information on the most likely sources of these chemical species. Such information can be used alongside those that already exist to formulate PM10 pollution reduction strategies for SEQ in order to protect the community from the adverse effects of PM pollution.

Multivariate methods to identify cancer-related symptom clusters

Multivariate methods are required to assess the interrelationships among multiple, concurrent symptoms. We examined the conceptual and contextual appropriateness of commonly used multivariate methods for cancer symptom cluster identification. From 178 publications identified in an online database search of Medline, CINAHL, and PsycINFO, limited to articles published in English, 10 years prior to March 2007, 13 cross-sectional studies met the inclusion criteria. Conceptually, common factor analysis (FA) and hierarchical cluster analysis (HCA) are appropriate for symptom cluster identification, not principal component analysis. As a basis for new directions in symptom management, FA methods are more appropriate than HCA. Principal axis factoring or maximum likelihood factoring, the scree plot, oblique rotation, and clinical interpretation are recommended approaches to symptom cluster identification.

Characterisation of soils with the use of instrumental techniques : a multivariate forensic study

The value of soil evidence in the forensic discipline is well known. However, it would be advantageous if an in-situ method was available that could record responses from tyre or shoe impressions in ground soil at the crime scene. The development of optical fibres and emerging portable NIR instruments has unveiled a potential methodology which could permit such a proposal. The NIR spectral region contains rich chemical information in the form of overtone and combination bands of the fundamental infrared absorptions and low-energy electronic transitions. This region has in the past, been perceived as being too complex for interpretation and consequently was scarcely utilized. The application of NIR in the forensic discipline is virtually non-existent creating a vacancy for research in this area. NIR spectroscopy has great potential in the forensic discipline as it is simple, nondestructive and capable of rapidly providing information relating to chemical composition. The objective of this study is to investigate the ability of NIR spectroscopy combined with Chemometrics to discriminate between individual soils. A further objective is to apply the NIR process to a simulated forensic scenario where soil transfer occurs. NIR spectra were recorded from twenty-seven soils sampled from the Logan region in South-East Queensland, Australia. A series of three high quartz soils were mixed with three different kaolinites in varying ratios and NIR spectra collected. Spectra were also collected from six soils as the temperature of the soils was ramped from room temperature up to 6000C. Finally, a forensic scenario was simulated where the transferral of ground soil to shoe soles was investigated. Chemometrics methods such as the commonly known Principal Component Analysis (PCA), the less well known fuzzy clustering (FC) and ranking by means of multicriteria decision making (MCDM) methodology were employed to interpret the spectral results. All soils were characterised using Inductively Coupled Plasma Optical Emission Spectroscopy and X-Ray Diffractometry. Results were promising revealing NIR combined with Chemometrics is capable of discriminating between the various soils. Peak assignments were established by comparing the spectra of known minerals with the spectra collected from the soil samples. The temperature dependent NIR analysis confirmed the assignments of the absorptions due to adsorbed and molecular bound water. The relative intensities of the identified NIR absorptions reflected the quantitative XRD and ICP characterisation results. PCA and FC analysis of the raw soils in the initial NIR investigation revealed that the soils were primarily distinguished on the basis of their relative quartz and kaolinte contents, and to a lesser extent on the horizon from which they originated. Furthermore, PCA could distinguish between the three kaolinites used in the study, suggesting that the NIR spectral region was sensitive enough to contain information describing variation within kaolinite itself. The forensic scenario simulation PCA successfully discriminated between the ‘Backyard Soil’ and ‘Melcann® Sand’, as well as the two sampling methods employed. Further PCA exploration revealed that it was possible to distinguish between the various shoes used in the simulation. In addition, it was possible to establish association between specific sampling sites on the shoe with the corresponding site remaining in the impression. The forensic application revealed some limitations of the process relating to moisture content and homogeneity of the soil. These limitations can both be overcome by simple sampling practices and maintaining the original integrity of the soil. The results from the forensic scenario simulation proved that the concept shows great promise in the forensic discipline.