Comparative analyses of anthropometry associated with overweight and obesity: PCA and ICA approaches

This study undertakes to explore the co-varying structure in anthropometric variables that might be affected by the recent surge of overweight and obesity. The increase of overweight and obesity makes the distribution of body dimensions asymmetric by the long tail in distribution (skewness, kurtosis). Principal component analysis (PCA) has been well applied to understand the co-varying body dimensions. However, because PCA decomposes covariance/correlation matrix, the effects of overweight and obesity may not be well captured. Independent component analysis (ICA) is a variant of PCA with the additional assumptions of components being non-Gaussian and independent, in which kurtosis is decomposed. PCA and ICA are applied on the anthropometric data from the North American portion of the Civilian American and European Surface Anthropometry Resource (CAESAR) project. ICA yields more interpretable results by visual inspection than corresponding PCA results. The first independent component (IC 1) is associated with hip/thigh circumferences and chest/waist circumferences and has the largest correlation coefficients with body mass index (BMI). Only the second IC shows the overall size factor that reveals gender difference while principal components 1, 2 and 3 show gender difference. The ICs 3 (torso length) and 4 (arm and leg lengths) are associated with individual differences in body dimensions. The ranges of 38 body dimensions are identified in order to satisfactorily meet the anthropometric variations for both males and females. The ICA gives promise of becoming a valuable tool in the field of ergonomics.     

中国男性飞行员头面部尺寸

根据数据用户的实际使用需求，选定实际测量的人体尺寸项目，并明确每个测量项目的测点、定义和测量方法。划分基础项目的推荐项目。编制人体测量数据库，拟定数据质量控制措施。对主测人员进行岗前专业培训。针对30个人体头面部基础项目实测空军现役男性飞行员1739人，针对23个头面部推荐项目实测空军现役男性飞行员904人。给出了各测量项目的平均值、标准差、最小值、最大值、第1、第2、第3、第5、第50、第95、第97、第98、第99百分位数数据。总体看来，数据质量稳定、可靠，为研制飞行员头面部防护装备提供了新的科学依据。     

Compressed sensing with MCT and I(2D)2PCA processing for efficient face recognition

This article describes an effective human face recognition algorithm. Even though the principle component analysis (PCA) is one of the most common feature extraction methods, it is not suitable to implement a real‐time embedded system for face recognition because large amount of computational load and memory capacity are necessary. To overcome this problem, we employ the incremental two‐directional two‐dimensional PCA (I(2D)²PCA) which is a combination of the (2D)²PCA to demand much less computational complexity than the conventional PCA and the incremental PCA (IPCA) to adapt the eigenspace only by using a new incoming sample datum without reusing of all the previous trained data. Furthermore, the modified census transform (MCT), a local normalization method useful for real‐world application and implementation in an embedded system, is adopted to address robustness to illumination variations. To achieve better recognition accuracy with less computational load, the processed features are classified by the compressive sensing approach using ?²–minimization. Experimental results on the Yale Face Database B show that the described system using the ?²–minimization‐based classification method for input data processed by the I(2D)²PCA and the MCT provided efficient and robust face recognition. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 133–139, 2013     

Concise representation of mass spectrometry images by probabilistic latent semantic analysis

Imaging mass spectrometry (IMS) is a promising technology which allows for detailed analysis of spatial distributions of (bio)molecules in organic samples. In many current applications, IMS relies heavily on (semi)automated exploratory data analysis procedures to decompose the data into characteristic component spectra and corresponding abundance maps, visualizing spectral and spatial structure. The most commonly used techniques are principal component analysis (PCA) and independent component analysis (ICA). Both methods operate in an unsupervised manner. However, their decomposition estimates usually feature negative counts and are not amenable to direct physical interpretation. We propose probabilistic latent semantic analysis (pLSA) for non-negative decomposition and the elucidation of interpretable component spectra and abundance maps. We compare this algorithm to PCA, ICA, and non-negative PARAFAC (parallel factors analysis) and show on simulated and real-world data that pLSA and non-negative PARAFAC are superior to PCA or ICA in terms of complementarity of the resulting components and reconstruction accuracy. We further combine pLSA decomposition with a statistical complexity estimation scheme based on the Akaike information criterion (AIC) to automatically estimate the number of components present in a tissue sample data set and show that this results in sensible complexity estimates.     

Combining PARAFAC analysis of HPLC-PDA profiles and structural characterization using HPLC-PDA-SPE-NMR-MS experiments: commercial preparations of St. John's Wort

Herbal preparations represent very complex mixtures, potentially containing multiple pharmacologically active entities. Methods for global characterization of the composition of such mixtures are therefore of pertinent interest. In this work, chemometric analysis of high-performance liquid chromatography with photodiode-array detection (HPLC-PDA) data from extracts of commercial preparations of Hypericum perforatum (St. John's wort) that originate from several continents is described. The spectral HPLC profiles were aligned in the elution mode using correlation optimized warping in order to remove peak misalignment caused by retention time shifts due to matrix effects. Furthermore, the warping was assisted by HPLC-PDA-SPE-NMR-MS (SPE = solid-phase extraction) experiments that yielded 1H NMR and 13C NMR data (from 1H-detected heteronuclear correlations), as well as ESI-MS and HRMS data, which enabled the identification of all major mixture constituents. The preprocessed HPLC-PDA data were subjected to parallel factor analysis (PARAFAC), a chemometric method that is a generalization of principal component analysis (PCA) to multi-way data arrays. PCA of the peak areas obtained from the PARAFAC analysis was used to facilitate sample comparison and allowed straightforward interpretation of constituents responsible for the differences in composition between individual preparations. In addition, loadings from the PARAFAC analysis provided pure elution profiles and pure UV spectra even for coeluting peaks, thus enabling the identification of chromatographically unresolved components. In conclusion, PARAFAC analysis of the readily accessible HPLC-PDA data provides the means for unsupervised and unbiased assessment of the composition of herbal preparations, of interest for assessment of their pharmacological activity and clinical efficacy.     

Comprehensive three-dimensional gas chromatography with parallel factor analysis

Development of a comprehensive, three-dimensional gas chromatograph (GC3) instrument is described. The instrument utilizes two six-port diaphragm valves as the interfaces between three, in-series capillary columns housed in a standard Agilent 6890 gas chromatograph fitted with a high data acquisition rate flame ionization detector. The modulation periods for sampling column one by column two and column two by column three are set so that a minimum of three slices (more commonly four or five) are acquired by the subsequent dimension resulting in both comprehensive and quantitative data. A 26-component test mixture and quantitative standards are analyzed using the GC3 instrument. A useful methodology for three-dimensional (3D) data analysis is evaluated, based on the chemometric technique parallel factor analysis (PARAFAC). Since the GC3 instrument produces trilinear data, we are able to use this powerful chemometric technique, which is better known for the analysis of two-dimensional (2D) separations with multichannel detection (e.g., GC x GC-TOFMS) or multiple samples (or replicates) of 2D data. Using PARAFAC, we mathematically separate (deconvolute) the 3D data "volume" for overlapped analytes (i.e., ellipsoids), provided there is sufficient chromatographic resolution in each of the three separation dimensions. Additionally, PARAFAC is applied to quantify analyte standards. For the quantitative analysis, it is demonstrated that PARAFAC may provide a 10-fold improvement in the signal-to-noise ratio relative to a traditional integration method applied to the raw, baseline-corrected data. The GC3 instrument obtains a 3D peak capacity of 3500 at a chromatographic resolution of one in each separation dimension. Furthermore, PARAFAC deconvolution provides a considerable enhancement in the effective 3D peak capacity.     

Regional Cultural Differences Within European Countries: Evidence from Multi-Country Surveys

The purpose of this study is to investigate the patterns of regional cultural differences within European countries. This study extends our previous work (Kaasa et al. 2013) that used data from the European Social Survey (ESS), by using more recent data and combining the ESS and the European Values Study (EVS) as data sources. We aim to study how much attention should be paid to within-country differences and how they differ across countries. The indicators of cultural dimensions were created using confirmatory factor analysis based on the initial indicators selected relying on Hofstede’s cultural dimensions. The extent and character of within-country cultural differences were examined. Our results reveal a wide diversity of regional cultural variability among the observed countries. We identified countries where within-country cultural variability is larger (Spain, Portugal, France) and smaller (Finland, Sweden, Norway) than cross-national variability. Also, in many countries a more detailed regional division (more and smaller regions) brought out larger differences, although this cannot be taken as a rule. Patterns along regions differ across cultural dimensions, making grouping regions difficult. Studying regional cultural differences within countries is important. The extent of the optimal depth of regional division applied depends on the particular country, its regional cultural variability, and the needs and aims of the researcher.     

Comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry analysis of metabolites in fermenting and respiring yeast cells

Comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry coupled with rapid chemometric analysis were used to identify chemical differences in metabolite extracts isolated from yeast cells either metabolizing glucose (repressed (R) cells) via fermentation or metabolizing ethanol by respiration (derepressed (DR) cells). Principal component analysis (PCA) followed by parallel factor analysis (PARAFAC) in concert with the LECO ChromaTOF software located and identified the differences in composition between the two types of cell extracts and provided a reliable ratio of the metabolite concentrations. In this report, we demonstrate the analytical method developed to provide relatively rapid analysis of three selective mass channels (m/z 73, 205, 387), although in principle all collected mass channels could be analyzed. Twenty-six metabolites that differentiate repressed cells from derepressed cells were identified. The DR/R ratio of metabolite concentrations ranged from 0.02 for glucose to 67 for trehalose. The average biological variation of the sample extracts was 31%. This analysis demonstrates the utility and benefit of using PCA combined with PARAFAC and ChromaTOF software on extremely complex samples to derive useful information from complex three-dimensional chromatographic data objectively and relatively rapidly.     

Incidence, time trends and regional variation of childhood leukaemia in Germany and Europe

This paper presents data on the German and Europe-wide incidence, time trends and regional variations of childhood leukaemia. Data were provided by the German Childhood Cancer Registry (GCCR), a population-based cancer registry recording all cases of malignant diseases in children under 15 y of age residing in Germany and by the Automated Childhood Cancer Information System (ACCIS) co-ordinated at International Agency for Research on Cancer, Lyon, that combines and evaluates data from several European population-based cancer registries. The incidence of leukaemia (44.0 per million) has increased in Europe as well as in Germany in the last decades (0.6% annually on average). Germany shows no systematic aggregation of regions with low or high cancer incidence in terms of regional clustering. Incidence rates differ between European regions with the highest rates in Northern Europe (48.0 per million) and the lowest rates in Eastern Europe (39.1). Altogether, the results from ACCIS and the GCCR show good agreement.     

Fluorescence spectral analysis for the discrimination of complex, similar mixtures with the aid of chemometrics

An analytical method for the classification of complex real-world samples was researched and developed with the use of excitation-emission fluorescence matrix (EEFM) spectroscopy, using the medicinal herbs, Rhizoma corydalis decumbentis (RCD) and Rhizoma corydalis (RC) as example samples. The data set was obtained from various authentic RCD-A and RC-A, adulterated AD, and commercial RCD-C and RC-C samples. The spectra (range: λ(ex) = 215～395 nm and λ(em) = 290～560 nm), arranged in two- and three-way data matrix formats, were processed using principal component analysis (PCA) and parallel factor analysis (PARAFAC) to produce two-dimensional component-by-component plots for qualitative data classification. The RCD-A and RC-A object groups were clearly discriminated, but the AD and the RCD-C as well as RC-C samples were less well separated. PARAFAC analysis produced somewhat better discrimination, and loadings plots revealed the presence of the marker compound Protopine-a strongly fluorescing substance-as well as at least two other unidentified fluorescent components. Classification performance of the common K-nearest neighbors (KNN) and linear discrimination analysis (LDA) methods was relatively poor when compared with that of the back propagation- and radial basis function-artificial neural networks (BP-ANN and RBF-ANN) models on the basis of two- and three-way formatted data. The best results were obtained with the three-way fingerprints and the RBF-ANN model. Subsequently, the quality of the commercial samples (RCD-C and RC-C) was classified on the best optimized RBF-ANN model. Thus, EEFM spectroscopy, which provides three-way measured data, is potentially a powerful analytical technique for the analysis of complex real-world substances provided the classification is performed by the RBF-ANN or similar ANN methods.     

A new indicator for international visibility: exploring Brazilian scientific community

Brazilian science has increased fast during the last decades. An example is the increasing in the country’s share in the world’s scientific publication within the main international databases. But what is the actual weight of international publications to the whole Brazilian productivity? In order to respond this question, we have elaborated a new indicator, the International Publication Ratio (IPR). The data source was Lattes Database, a database organized by one of the main Brazilian S&;T funding agency, which encompasses publication data from 1997 to 2004 of about 51,000 Brazilian researchers. Influences of distinct parameters, such as sectors, fields, career age and gender, are analyzed. We hope the data presented may help S&;T managers and other S&;T interests to better understand the complexity under the concept scientific productivity, especially in peripheral countries in science, such as Brazil.     

The Shape of the Normal Work Area in the Horizontal Plane

The dimensions of the normal work area originally proposed by Barnes and modified by Squires were determined for a sample representative of the current United States population. One standard work area is not satisfactory for everyone. More emphasis should be given to movements straight ahead of the worker and less emphasis should be given to movements to the side. Work areas are given for the 5th, 50th, and 95th percentiles of the male and female United States population.     

PARAFAC2 and MCR-ALS quantification of Diltiazem antihypertensor based on a kinetic spectrophotometric methodology

A Diltiazem kinetic spectrophotometric UV–Vis method, based on a reaction of the Diltiazem with hidroxylamine and a ferric salt, was used for the quantification of Diltiazem in different pharmaceutical formulations. This method is based on the acquisition of three-way data structures [wavelength (nm) × time (s) × concentration (mg/L)] followed by chemometric analysis by an appropriate PARAFAC2 or MCR-ALS second-order calibration model. The results obtained are compared with those obtained by direct determination, at maximum wavelength, and by the United States Pharmacopeia (USP) standard chromatographic method. For all the pharmaceutical formulations analysed good quantification results were found with PARAFAC2 and MCR-ALS second-order calibration models. For bulk drug analysis, detection limits of 6 and 2 mg/L, and for pharmaceutical formulations analysis, an average detection limit of 41 and 39 mg/L were found, respectively with PARAFAC2 and MCR-ALS.     

Unfolded partial least-squares with residual quadrilinearization: A new multivariate algorithm for processing five-way data achieving the second-order advantage. Application to fourth-order excitation-emission-kinetic-pH fluorescence analytical data

Unfolded partial least-squares in combination with residual quadrilinearization (U-PLS/RQL), is developed as a new latent structured algorithm for the processing of fourth-order instrumental data. In order to check its analytical predictive ability, fluorescence excitation-emission-kinetic-pH data were measured and processed. The concentration of the fluorescent pesticide carbaryl was determined in the presence of the pesticides fuberidazole and thiabendazole as uncalibrated interferents, in the first example of fourth-order multivariate calibration. The hydrolysis of the analyte was followed at different pH values using a fast-scanning spectrofluorimeter, recording the excitation-emission fluorescence matrices during its evolution to produce 1-naphthol, which does also emit fluorescence. A set of test samples containing the above mentioned fluorescent contaminants was analyzed with the new model, comparing the results with those from parallel factor analysis (PARAFAC). The newly developed U-PLS/RQL model provides better figures of merit for analyte quantitation (average prediction error, 7 μg L⁻¹, relative prediction error, 5%, calibration range, 50-250 μg L⁻¹), and is considerably simpler than PARAFAC in its implementation. The latter, however, furnishes important physicochemical information regarding the chemical process under study, although this requires the data to be unfolded into an array of lower dimensions, due to the lack of quadrilinearity of the experimental data.     

Voxel-based models representing the male and female ICRP reference adult--the skeleton

For the forthcoming update of organ dose conversion coefficients, the International Commission on Radiological Protection (ICRP) will use voxel-based computational phantoms due to their improved anatomical realism compared with the class of mathematical or stylized phantoms used previously. According to the ICRP philosophy, these phantoms should be representative of the male and female reference adults with respect to their external dimensions, their organ topology and their organ masses. To meet these requirements, reference models of an adult male and adult female have been constructed at the GSF, based on existing voxel models segmented from tomographic images of two individuals whose body height and weight closely resemble the ICRP Publication 89 reference values. The skeleton is a highly complex structure of the body, composed of cortical bone, trabecular bone, red and yellow bone marrow and endosteum ('bone surfaces' in their older terminology). The skeleton of the reference phantoms consists of 19 individually segmented bones and bone groups. Sub-division of these bones into the above-mentioned constituents would be necessary in order to allow a direct calculation of dose to red bone marrow and endosteum. However, the dimensions of the trabeculae, the cavities containing bone marrow and the endosteum layer lining these cavities are clearly smaller than the resolution of a normal CT scan and, thus, these volumes could not be segmented in the tomographic images. As an attempt to represent the gross spatial distribution of these regions as realistically as possible at the given voxel resolution, 48 individual organ identification numbers were assigned to various parts of the skeleton: every segmented bone was subdivided into an outer shell of cortical bone and a spongious core; in the shafts of the long bones, a medullary cavity was additionally segmented. Using the data from ICRP Publication 89 on elemental tissue composition, from ICRU Report 46 on material mass densities, and from ICRP Publication 70 on the distribution of the red bone marrow among and marrow cellularity in individual bones, individual elemental compositions for these segmented bone regions were derived. Thus, most of the relevant source and target regions of the skeleton were provided. Dose calculations using these regions will be based on fluence-to-dose response functions that are multiplied with the particle fluence inside specific bone regions to give the dose quantities of interest to the target tissues.     

绝对重力仪国际比对新动态

全球绝对重力仪国际比对活动从1981年起每隔4年在法国国际计量局举办。近两年来,该比对活动发生了一些变化,同时,区域性绝对重力仪国际比对活动日益兴起。中国计量科学研究院代表我国参与了这方面的工作,并介绍了中国计量科学研究自主研制的NIM-II型绝对重力仪参加2009年第8届全球绝对重力仪国际比对的情况和结果     

Dynamic processes monitoring using predictive PCA

Abstract

Principal component analysis (PCA) has been extensively used for the monitoring of industrial systems when the measurements are highly correlated or corrupted with noise. The basic assumptions for monitoring using PCA are that the considered processes are stationary and their operating regions are unchanged. In an actual plant, the operation conditions might sometimes vary with time because of its dynamic behavior or the effects of disturbance. In this paper, a new predictive monitoring method is proposed that is composed of three parts: (1) the training data is divided into several parts, and each part stands for the operating region at that stage (2) PCA is applied to the first part of the divided raw data sets, and the other parts will be projected by the same PCA model (3) time series models are built to interpret the operating centers obtained in step 2, and the operating region can be estimated for future monitoring. From these, a more reasonable monitoring region and future process deviations can be built. Based on this monitoring scheme, false alarms will be reduced. Moreover, a measure of the difference of the principal component directions between the training data set and the monitored data set is used to check whether a process fault occurs in processes. The effectiveness of this proposed method is demonstrated with simulation results.     

A study of the differences and characteristics between the nations,in the measured values of the human body and the working area

This paper deals with: (1) measuring 61 body features of Koreans; (2) comparing the dimensions of body features among the nations; (3) constructing the working area of a Korean from the measured data; (4) comparing working areas among the nations. Some physical characteristics and variations in body features and in working areas are discussed.