We describe here the implementation of the statistical total correlation spectroscopy (STOCSY) analysis method for aiding the identification of potential biomarker molecules in metabonomic studies based on NMR spectroscopic data. STOCSY takes advantage of the multicollinearity of the intensity variables in a set of spectra (in this case 1H NMR spectra) to generate a pseudo-two-dimensional NMR spectrum that displays the correlation among the intensities of the various peaks across the whole sample. This method is not limited to the usual connectivities that are deducible from more standard two-dimensional NMR spectroscopic methods, such as TOCSY. Moreover, two or more molecules involved in the same pathway can also present high intermolecular correlations because of biological covariance or can even be anticorrelated. This combination of STOCSY with supervised pattern recognition and particularly orthogonal projection on latent structure−discriminant analysis (O-PLS-DA) offers a new powerful framework for analysis of metabonomic data. In a first step O-PLS-DA extracts the part of NMR spectra related to discrimination. This information is then cross-combined with the STOCSY results to help identify the molecules responsible for the metabolic variation. To illustrate the applicability of the method, it has been applied to 1H NMR spectra of urine from a metabonomic study of a model of insulin resistance based on the administration of a carbohydrate diet to three different mice strains (C57BL/6Oxjr, BALB/cOxjr, and 129S6/SvEvOxjr) in which a series of metabolites of biological importance can be conclusively assigned and identified by use of the STOCSY approach.
The development of a surface water monitoring network is a critical element in the assessment, restoration, and protection of stream water quality. This study applied principal component analysis (PCA) and principal factor analysis (PFA) techniques to evaluate the effectiveness of the surface water quality-monitoring network in a river where the evaluated variables are monitoring stations. The objective was to identify monitoring stations that are important in assessing annual variations of river water quality. Twenty-two stations used for monitoring physical, chemical, and biological parameters, located at the main stem of the lower St. Johns River in Florida, USA, were selected for the purpose of this study. Results show that 3 monitoring stations were identified as less important in explaining the annual variance of the data set, and therefore could be the non-principal stations. In addition, the PFA technique was also employed to identify important water quality parameters. Results reveal that total organic carbon, dissolved organic carbon, total nitrogen, dissolved nitrate and nitrite, orthophosphate, alkalinity, salinity, Mg, and Ca were the parameters that are most important in assessing variations of water quality in the river. This study suggests that PCA and PFA techniques are useful tools for identification of important surface water quality monitoring stations and parameters.
This paper provides a general overview of developments and progress in quantitative computer image analysis as applied to wear particle identification/classification technology, over the last two decades. Since many technical disciplines are involved in this ‘infant-stage’ technical area, an attempt is made to put into perspective mechanical failure prediction/diagnosis and prevention through quantitative wear particle morphological analysis. The problems experienced with applying conventional wear particle analysis methods in machinery condition monitoring, notably the employment of wear debris morphological diagnostic systems, revealed that it is not prudent to rely solely on human interpretation in the analysis of ‘filtergram’ slides. This has highlighted the need for improving the provision of ‘intelligent’ objective methods for performing this type of analysis. In this paper, some of the developments reported in the literature relating to progress made with wear particle image analysis are reported and examined as a basis for establishing improved methods of diagnostic analysis.
PowerMarker delivers a data-driven, integrated analysis environment (IAE) for genetic data. The IAE integrates data management, analysis and visualization in a user-friendly graphical user interface. It accelerates the analysis lifecycle and enables users to maintain data integrity throughout the process. An ever-growing list of more than 50 different statistical analyses for genetic markers has been implemented in PowerMarker.
Natural energy decomposition analysis (NEDA) is a method for partitioning molecular interaction energies into physically meaningful components, including electrical interaction, charge transfer, and core repulsions. The method is a numerically stable procedure that was originally developed for analyzing Hartree−Fock (HF) wave functions based on the localized orbital description of natural bond orbital analysis. In this work, we extend NEDA to treat charge densities from density functional theory (DFT) calculations, replacing the intermolecular exchange (EX) component of the HF analysis with an exchange-correlation (XC) component. DFT/NEDA is applied to hydrogen bonding interactions and cooperative effects in water clusters. Electrical interactions and charge transfer contribute importantly to hydrogen bonding. Comparison of HF and DFT results reveals that the exchange and correlation effects of DFT slightly enhance the extent of charge transfer and core repulsions in the water clusters. Cooperative stabilization of the cyclic water trimer and tetramer is considered by performing a many-body expansion of the interaction energy. Natural energy decomposition analysis of this expansion suggests that charge transfer is the leading source of cooperative stabilization. Polarization effects have only marginal influence on cooperativity.
Labyrinth seals are used in various kinds of turbo machines to reduce internal leakage flow. The working fluid, or the gas passing through the rotor shaft labyrinth seals, often generates driving force components that may increase the unstable vibration of the rotor. It is important to know the accurate rotordynamic force components for predicting the instability of the rotor-bearing-seal system. The major goals of this research were to calculate the rotordynamic force of a labyrinth seals utilizing a commercial CFD program and to further compare those results to an existing bulk flow computer program currently used by major US machinery manufacturers. The labyrinth seals of a steam turbine and a compressor eye seal are taken as objects of analysis. For each case, a 3D model with eccentric rotor was solved to obtain the rotordynamic force components. The leakage flow and rotor dynamics force predicted by CFX TASCFlow are compared with the results of the existing bulk flow analysis program DYNLAB. The results show that the bulk flow program gives a pessimistic prediction of the destabilizing forces for the conditions under investigation. Further research work will be required to fully understand the complex leakage flows in turbo machinery.
Background: There is ample evidence that short-term ozone exposure is associated with transient decrements in lung functions and increased respiratory symptoms, but the short-term mortality effect of such exposures has not been established. Methods: We conducted a review and meta-analysis of short-term ozone mortality studies, identified unresolved issues, and conducted an additional time-series analysis for 7 U.S. cities (Chicago, Detroit, Houston, MinneapolisSt. Paul, New York City, Philadelphia, and St. Louis). Results: Our review found a combined estimate of 0.39% (95% confidence interval = 0.26-0.51%) per 10-ppb increase in 1-hour daily maximum ozone for the all-age nonaccidental cause/single pollutant model (43 studies). Adjusting for the funnel plot asymmetry resulted in a slightly reduced estimate (0.35%; 0.23-0.47%). In a subset for which particulate matter (PM) data were available (15 studies), the corresponding estimates were 0.40% (0.27-0.53%) for ozone alone and 0.37% (0.20-0.54%) with PM in model. The estimates for warm seasons were generally larger than those for cold seasons. Our additional time-series analysis found that including PM in the model did not substantially reduce the ozone risk estimates. However, the difference in the weather adjustment model could result in a 2-fold difference in risk estimates (eg, 0.24% to 0.49% in multicity combined estimates across alternative weather models for the ozone-only all-year case). Conclusions: Overall, the results suggest short-term associations between ozone and daily mortality in the majority of the cities, although the estimates appear to be heterogeneous across cities.