Asymptotic robustness in multi-sample analysis of multivariate linear relations

Standard methods for the analysis of linear latent variable models oftenrely on the assumption that the vector of observed variables is normallydistributed. This normality assumption (NA) plays a crucial role inassessingoptimality of estimates, in computing standard errors, and in designinganasymptotic chi-square goodness-of-fit test. The asymptotic validity of NAinferences when the data deviates from normality has been calledasymptoticrobustness. In the present paper we extend previous work on asymptoticrobustnessto a general context of multi-sample analysis of linear latent variablemodels,with a latent component of the model allowed to be fixed across(hypothetical)sample replications, and with the asymptotic covariance matrix of thesamplemoments not necessarily finite. We will show that, under certainconditions,the matrix $\Gamma$ of asymptotic variances of the analyzed samplemomentscan be substituted by a matrix $\Omega$ that is a function only of thecross-product moments of the observed variables. The main advantage of thisis thatinferences based on $\Omega$ are readily available in standard softwareforcovariance structure analysis, and do not require to compute samplefourth-order moments. An illustration with simulated data in the context ofregressionwith errors in variables will be presented.

Multi-sample analysis of moment-structures: Asymptotic validity of inferences based on second-order moments

In moment structure analysis with nonnormal data, asymptotic valid inferences require the computation of a consistent (under general distributional assumptions) estimate of the matrix $\Gamma$ of asymptotic variances of sample second--order moments. Such a consistent estimate involves the fourth--order sample moments of the data. In practice, the use of fourth--order moments leads to computational burden and lack of robustness against small samples. In this paper we show that, under certain assumptions, correct asymptotic inferences can be attained when $\Gamma$ is replaced by a matrix $\Omega$ that involves only the second--order moments of the data. The present paper extends to the context of multi--sample analysis of second--order moment structures, results derived in the context of (simple--sample) covariance structure analysis (Satorra and Bentler, 1990). The results apply to a variety of estimation methods and general type of statistics. An example involving a test of equality of means under covariance restrictions illustrates theoretical aspects of the paper.

Scaled and adjusted restricted tests in multi-sample analysis of moment structures

We extend to score, Wald and difference test statistics the scaled and adjusted corrections to goodness-of-fit test statistics developed in Satorra and Bentler (1988a,b). The theory is framed in the general context of multisample analysis of moment structures, under general conditions on the distribution of observable variables. Computational issues, as well as the relation of the scaled and corrected statistics to the asymptotic robust ones, is discussed. A Monte Carlo study illustrates thecomparative performance in finite samples of corrected score test statistics.

Informatics for cross-sample analysis with comprehensive two-dimensional gas chromatography and high-resolution mass spectrometry (GCxGC-HRMS)

This paper describes informatics for cross-sample analysis with comprehensive two-dimensional gas chromatography (GCxGC) and high-resolution mass spectrometry (HRMS). GCxGC-HRMS analysis produces large data sets that are rich with information, but highly complex. The size of the data and volume of information requires automated processing for comprehensive cross-sample analysis, but the complexity poses a challenge for developing robust methods. The approach developed here analyzes GCxGC-HRMS data from multiple samples to extract a feature template that comprehensively captures the pattern of peaks detected in the retention-times plane. Then, for each sample chromatogram, the template is geometrically transformed to align with the detected peak pattern and generate a set of feature measurements for cross-sample analyses such as sample classification and biomarker discovery. The approach avoids the intractable problem of comprehensive peak matching by using a few reliable peaks for alignment and peak-based retention-plane windows to define comprehensive features that can be reliably matched for cross-sample analysis. The informatics are demonstrated with a set of 18 samples from breast-cancer tumors, each from different individuals, six each for Grades 1-3. The features allow classification that matches grading by a cancer pathologist with 78% success in leave-one-out cross-validation experiments. The HRMS signatures of the features of interest can be examined for determining elemental compositions and identifying compounds.

Household health assessment and associated water sample analysis in the Miches watershed of the Dominican Republic

Background. Diarrhea and malnutrition are the leading causes of mortality for children age one to four in the Dominican Republic. Communities within the Miches watershed lack sanitation infrastructure and water purification systems, which increases the risk of exposure to water-borne pathogens. The purpose of this cross-sectional study was to analyze health information gathered through household interviews and to test water samples for the presence of diarrheagenic pathogens and antibiotic-resistant bacteria within the Miches watershed. Methods. Frequency counts and thematic analysis were used to investigate Human Health Survey responses and Fisher's exact test was used to determine correlation between water source and reported illness. Bacteria cultured from water samples were analyzed by Gram stain, real-time PCR, API® 20E biochemical identification, and for antibiotic resistance. Results. Community members reported concerns about water sources with respect to water quality, availability, and environmental contamination. Pathogenic strains of E. coli were present in the water samples. Drinking aquifer water was positively-correlated with reported stomach aches (p=0.04) while drinking from rivers or creeks was associated with the reported absence of “gripe” (cold or flu) (p=0.01). The lack of association between reported illnesses and water source for the majority of variables suggested that there were multiple vehicles of disease transmission. Antibiotic resistant bacteria were isolated from the water samples tested. Conclusions. The presence of pathogenic E. coli in water samples suggested that water is at least one route of transmission for diarrheagenic pathogens in the Miches watershed. The presence of antibiotic-resistant bacteria in the water samples may indicate the proliferation of resistance plasmids in the environment as a result of antibiotic overuse in human and animal populations and a lack of sanitation infrastructure. An intervention that targets areas of hygiene, sanitation, and water purification is recommended to limit human exposure to diarrheagenic pathogens and antibiotic-resistant organisms. ^

Asbestos-containing materials in school buildings : bulk sample analysis quality assurance program : bulk sample rounds 16, 17 and 18 /

Mode of access: Internet.

Hubble parameter reconstruction from a principal component analysis: minimizing the bias

Aims. A model-independent reconstruction of the cosmic expansion rate is essential to a robust analysis of cosmological observations. Our goal is to demonstrate that current data are able to provide reasonable constraints on the behavior of the Hubble parameter with redshift, independently of any cosmological model or underlying gravity theory. Methods. Using type Ia supernova data, we show that it is possible to analytically calculate the Fisher matrix components in a Hubble parameter analysis without assumptions about the energy content of the Universe. We used a principal component analysis to reconstruct the Hubble parameter as a linear combination of the Fisher matrix eigenvectors (principal components). To suppress the bias introduced by the high redshift behavior of the components, we considered the value of the Hubble parameter at high redshift as a free parameter. We first tested our procedure using a mock sample of type Ia supernova observations, we then applied it to the real data compiled by the Sloan Digital Sky Survey (SDSS) group. Results. In the mock sample analysis, we demonstrate that it is possible to drastically suppress the bias introduced by the high redshift behavior of the principal components. Applying our procedure to the real data, we show that it allows us to determine the behavior of the Hubble parameter with reasonable uncertainty, without introducing any ad-hoc parameterizations. Beyond that, our reconstruction agrees with completely independent measurements of the Hubble parameter obtained from red-envelope galaxies.

Online titrimetric and off-gas analysis for examining nitrification processes in wastewater treatment

The two steps of nitrification, namely the oxidation of ammonia to nitrite and nitrite to nitrate, often need to be considered separately in process studies. For a detailed examination, it is desirable to monitor the two-step sequence using online measurements. In this paper, the use of online titrimetric and off-gas analysis (TOGA) methods for the examination of the process is presented. Using the known reaction stoichiometry, combination of the measured signals (rates of hydrogen ion production, oxygen uptake and carbon dioxide transfer) allows the determination of the three key process rates, namely the ammonia consumption rate, the nitrite accumulation rate and the nitrate production rate. Individual reaction rates determined with the TOGA sensor under a number of operation conditions are presented. The rates calculated directly from the measured signals are compared with those obtained from offline liquid sample analysis. Statistical analysis confirms that the results from the two approaches match well. This result could not have been guaranteed using alternative online methods. As a case study, the influences of pH and dissolved oxygen (DO) on nitrite accumulation are tested using the proposed method. It is shown that nitrite accumulation decreased with increasing DO and pH. Possible reasons for these observations are discussed. (C) 2003 Elsevier Science Ltd. All rights reserved.

New microfluidic-based sampling procedure for overcoming the hematocrit problem associated with dried blood spot analysis.

Hematocrit (Hct) is one of the most critical issues associated with the bioanalytical methods used for dried blood spot (DBS) sample analysis. Because Hct determines the viscosity of blood, it may affect the spreading of blood onto the filter paper. Hence, accurate quantitative data can only be obtained if the size of the paper filter extracted contains a fixed blood volume. We describe for the first time a microfluidic-based sampling procedure to enable accurate blood volume collection on commercially available DBS cards. The system allows the collection of a controlled volume of blood (e.g., 5 or 10 μL) within several seconds. Reproducibility of the sampling volume was examined in vivo on capillary blood by quantifying caffeine and paraxanthine on 5 different extracted DBS spots at two different time points and in vitro with a test compound, Mavoglurant, on 10 different spots at two Hct levels. Entire spots were extracted. In addition, the accuracy and precision (n = 3) data for the Mavoglurant quantitation in blood with Hct levels between 26% and 62% were evaluated. The interspot precision data were below 9.0%, which was equivalent to that of a manually spotted volume with a pipet. No Hct effect was observed in the quantitative results obtained for Hct levels from 26% to 62%. These data indicate that our microfluidic-based sampling procedure is accurate and precise and that the analysis of Mavoglurant is not affected by the Hct values. This provides a simple procedure for DBS sampling with a fixed volume of capillary blood, which could eliminate the recurrent Hct issue linked to DBS sample analysis.

Analysis of Ant Communities Comparing Two Methods for Sampling Ants in an Urban Park in the City of São Paulo, Brazil

This study aimed to analyze the species composition and functional groups of the ant community and to assess the efficiency of two sampling methods, pitfall and leaf litter sampling, in an urban park. A total of 1,401 ants were collected, which belonged to six subfamilies and 36 species. The predominant species was Wasmannia auropunctata (present in 45.36% of the samples), while the functional group of opportunistic ants were the most frequent (present in 83.75% of the samples) and abundant (95.29% of the total collected specimens) functional group. The Jaccard Similarity Index showed a low similarity between the two sampling methods, as the difference of the number of individuals for each species between these two methods was not significant in only one case (Linepithema sp. 1, p = 0.4561). The fungus-growing and cryptic ants were more collected in leaflitter samples (p<0.0001; p = 0.0348 respectively). Although there was no significant difference (p = 0.6397) between the two sampling methods for the total individuals of opportunistic ants, more species of this group were collected in pitfall traps. This difference was not significant because of the high presence of W. auropunctata, an opportunistic ant, in samples of leaf litter. Due to the predominance of tramp ants in the studied area, this article illustrates the importance of green urban areas in ant control strategies, since these sites could be used as a source of new colonization for these ants. Furthermore, the combination of the two sampling methods seems to be complementary for obtaining a more complete picture of the ant community.

Analysis of Steroids using Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry-Mass Spectrometry (SPME-GC-MS-MS)

Direct immersion SPME-GC-MS-MS was used for the analysis of steroids in water at part-per-trillion(ppt) and lower concentrations. The method was validated and extended to real sample analysis. The method were linear from 0.01 to 5 ng/ml with precision less than 10% relative standard deviation for a steroid mixture at 1 ng/ml. Limit of quantitation and limit of detection was found to be 200- 1200 pg/L and 30-200 pg/L respectively and recoveries ranged from 88-103 %. To understand the extraction efficiency of the fiber, a depletion study was performed. The fiber/ sample partition coefficients for the steroids were determined to be 1.0 x 104 to 1.5 x 104 . The extraction was performed without derivatization or the use of an internal standard. SPMEGC-MS-MS effectively demonstrated ultra-trace level detection of steroids in water.

Ragu: a free tool for the analysis of EEG and MEG event-related scalp field data using global randomization statistics

We present a program (Ragu; Randomization Graphical User interface) for statistical analyses of multichannel event-related EEG and MEG experiments. Based on measures of scalp field differences including all sensors, and using powerful, assumption-free randomization statistics, the program yields robust, physiologically meaningful conclusions based on the entire, untransformed, and unbiased set of measurements. Ragu accommodates up to two within-subject factors and one between-subject factor with multiple levels each. Significance is computed as function of time and can be controlled for type II errors with overall analyses. Results are displayed in an intuitive visual interface that allows further exploration of the findings. A sample analysis of an ERP experiment illustrates the different possibilities offered by Ragu. The aim of Ragu is to maximize statistical power while minimizing the need for a-priori choices of models and parameters (like inverse models or sensors of interest) that interact with and bias statistics.