Chemometric techniques in multivariate statistical modelling of process plant

The techniques of principal component analysis (PCA) and partial least squares (PLS) are introduced from the point of view of providing a multivariate statistical method for modelling process plants. The advantages and limitations of PCA and PLS are discussed from the perspective of the type of data and problems that might be encountered in this application area. These concepts are exemplified by two case studies dealing first with data from a continuous stirred tank reactor (CSTR) simulation and second a literature source describing a low-density polyethylene (LDPE) reactor simulation.

Analysis of epistasis in linked regions in the Irish study of high-density schizophrenia families

Epistasis may be important in the etiology of schizophrenia. Analysis of epistasis has been important in the positional cloning of a gene involved in the etiology of type II diabetes mellitus. We investigated the importance of epistasis among six linked regions in 268 multiplex pedigrees in the Irish Study of High-Density Schizophrenia Families (ISHDSF) by computing pairwise correlations between nonparametric linkage scores for narrow, intermediate, and broad diagnostic definitions. The linked regions were on chromosomes 2, 4, 5, 6, 8, and 10. No correlation reached our a priori level of statistical significance. Using this statistical approach, we did not find evidence of important epistatic effects among these six regions in the ISHDSF.

A statistical process control approach for automatic anti-islanding detection using synchrophasors

Anti-islanding protection is becoming increasingly important due to the rapid installation of distributed generation from renewable resources like wind, tidal and wave, solar PV, bio-fuels, as well as from other resources like diesel. Unintentional islanding presents a potential risk for damaging utility plants and equipment connected from the demand side, as well as to public and personnel in utility plants. This paper investigates automatic islanding detection. This is achieved by deploying a statistical process control approach for fault detection with the real-time data acquired through a wide area measurement system, which is based on Phasor Measurement Unit (PMU) technology. In particular, the principal component analysis (PCA) is used to project the data into principal component subspace and residual space, and two statistics are used to detect the occurrence of fault. Then a fault reconstruction method is used to identify the fault and its development over time. The proposed scheme has been used in a real system and the results have confirmed that the proposed method can correctly identify the fault and islanding site.

Depression and mortality: Artifact of measurement and analysis?

Background: Previous research demonstrates various associations between depression, cardiovascular disease (CVD) incidence and mortality, possibly as a result of the different methodologies used to measure depression and analyse relationships. This analysis investigated the association between depression, CVD incidence (CVDI) and mortality from CVD (MCVD), smoking related conditions (MSRC), and all causes (MALL), in a sample data set, where depression was measured using items from a validated questionnaire and using items derived from the factor analysis of a larger questionnaire, and analyses were conducted based on continuous data and grouped data.

Methods: Data from the PRIME Study (N=9798 men) on depression and 10-year CVD incidence and mortality were analysed using Cox proportional hazards models.

Results: Using continuous data, both measures of depression resulted in the emergence of positive associations between depression and mortality (MCVD, MSRC, MALL). Using grouped data, however, associations between a validated measure of depression and MCVD, and between a measure of depression derived from factor analysis and all measures of mortality were lost.

Limitations: Low levels of depression, low numbers of individuals with high depression and low numbers of outcome events may limit these analyses, but levels are usual for the population studied.

Conclusions: These data demonstrate a possible association between depression and mortality but detecting this association is dependent on the measurement used and method of analysis. Different findings based on methodology present clear problems for the elucidation and determination of relationships. The differences here argue for the use of validated scales where possible and suggest against over-reduction via factor analysis and grouping.

A computational intelligent approach to multi-factor analysis of violent crime information systems

Various scientific studies have explored the causes of violent behaviour from different perspectives, with psychological tests, in particular, applied to the analysis of crime factors. The relationship between bi-factors has also been extensively studied including the link between age and crime. In reality, many factors interact to contribute to criminal behaviour and as such there is a need to have a greater level of insight into its complex nature. In this article we analyse violent crime information systems containing data on psychological, environmental and genetic factors. Our approach combines elements of rough set theory with fuzzy logic and particle swarm optimisation to yield an algorithm and methodology that can effectively extract multi-knowledge from information systems. The experimental results show that our approach outperforms alternative genetic algorithm and dynamic reduct-based techniques for reduct identification and has the added advantage of identifying multiple reducts and hence multi-knowledge (rules). Identified rules are consistent with classical statistical analysis of violent crime data and also reveal new insights into the interaction between several factors. As such, the results are helpful in improving our understanding of the factors contributing to violent crime and in highlighting the existence of hidden and intangible relationships between crime factors.

The use of handheld near-infrared reflectance spectroscopy (NIRS) for the proximate analysis of poultry feed and to detect melamine adulteration of soya bean meal

The use of handheld near infrared (NIR) instrumentation, as a tool for rapid analysis, has the potential to be used widely in the animal feed sector. A comparison was made between handheld NIR and benchtop instruments in terms of proximate analysis of poultry feed using off-the-shelf calibration models and including statistical analysis. Additionally, melamine adulterated soya bean products were used to develop qualitative and quantitative calibration models from the NIRS spectral data with excellent calibration models and prediction statistics obtained. With regards to the quantitative approach, the coefficients of determination (R2) were found to be 0.94-0.99 with the corresponding values for the root mean square error of calibration and prediction were found to be 0.081-0.215 % and 0.095-0.288 % respectively. In addition, cross validation was used to further validate the models with the root mean square error of cross validation found to be 0.101-0.212 %. Furthermore, by adopting a qualitative approach with the spectral data and applying Principal Component Analysis, it was possible to discriminate between adulterated and pure samples.

Effective grain size distribution analysis for interpretation of tidal-deltaic facies: West Bengal Sundarbans

Research over the past two decades on the Holocene sediments from the tide dominated west side of the lower Ganges delta has focussed on constraining the sedimentary environment through grain size distributions (GSD). GSD has traditionally been assessed through the use of probability density function (PDF) methods (e.g. log-normal, log skew-Laplace functions), but these approaches do not acknowledge the compositional nature of the data, which may compromise outcomes in lithofacies interpretations. The use of PDF approaches in GSD analysis poses a series of challenges for the development of lithofacies models, such as equifinal distribution coefficients and obscuring the empirical data variability. In this study a methodological framework for characterising GSD is presented through compositional data analysis (CODA) plus a multivariate statistical framework. This provides a statistically robust analysis of the fine tidal estuary sediments from the West Bengal Sundarbans, relative to alternative PDF approaches.

Evaluating the use of testate amoebae for palaeohydrological reconstruction in permafrost peatlands

The melting of high-latitude permafrost peatlands is a major concern due to a potential positive feedback on global climate change. We examine the ecology of testate amoebae in permafrost peatlands, based on sites in Sweden (~ 200 km north of the Arctic Circle). Multivariate statistical analysis confirms that water-table depth and moisture content are the dominant controls on the distribution of testate amoebae, corroborating the results from studies in mid-latitude peatlands. We present a new testate amoeba-based water table transfer function and thoroughly test it for the effects of spatial autocorrelation, clustered sampling design and uneven sampling gradients. We find that the transfer function has good predictive power; the best-performing model is based on tolerance-downweighted weighted averaging with inverse deshrinking (performance statistics with leave-one-out cross validation: R2 = 0.87, RMSEP = 5.25 cm). The new transfer function was applied to a short core from Stordalen mire, and reveals a major shift in peatland ecohydrology coincident with the onset of the Little Ice Age (c. AD 1400). We also applied the model to an independent contemporary dataset from Stordalen and find that it outperforms predictions based on other published transfer functions. The new transfer function will enable palaeohydrological reconstruction from permafrost peatlands in Northern Europe, thereby permitting greatly improved understanding of the long-term ecohydrological dynamics of these important carbon stores as well as their responses to recent climate change.

Untargeted Metabolomic Analysis of Human Plasma Indicates Differentially Affected Polyamine and L-Arginine Metabolism in Mild Cognitive Impairment Subjects Converting to Alzheimer’s Disease

This study combined high resolution mass spectrometry (HRMS), advanced chemometrics and pathway enrichment analysis to analyse the blood metabolome of patients attending the memory clinic: cases of mild cognitive impairment (MCI; n = 16), cases of MCI who upon subsequent follow-up developed Alzheimer's disease (MCI_AD; n = 19), and healthy age-matched controls (Ctrl; n = 37). Plasma was extracted in acetonitrile and applied to an Acquity UPLC HILIC (1.7μm x 2.1 x 100 mm) column coupled to a Xevo G2 QTof mass spectrometer using a previously optimised method. Data comprising 6751 spectral features were used to build an OPLS-DA statistical model capable of accurately distinguishing Ctrl, MCI and MCI_AD. The model accurately distinguished (R2 = 99.1%; Q2 = 97%) those MCI patients who later went on to develop AD. S-plots were used to shortlist ions of interest which were responsible for explaining the maximum amount of variation between patient groups. Metabolite database searching and pathway enrichment analysis indicated disturbances in 22 biochemical pathways, and excitingly it discovered two interlinked areas of metabolism (polyamine metabolism and L-Arginine metabolism) were differentially disrupted in this well-defined clinical cohort. The optimised untargeted HRMS methods described herein not only demonstrate that it is possible to distinguish these pathologies in human blood but also that MCI patients 'at risk' from AD could be predicted up to 2 years earlier than conventional clinical diagnosis. Blood-based metabolite profiling of plasma from memory clinic patients is a novel and feasible approach in improving MCI and AD diagnosis and, refining clinical trials through better patient stratification.

Analysis of single nucleotide polymorphisms implicate mTOR signalling in the development of new-onset diabetes after transplantation

Introduction
Despite excellent first year outcomes in kidney transplantation, there remain significant long-term complications related to new-onset diabetes after transplantation (NODAT). The purpose of this study was to validate the findings of previous investigations of candidate gene variants in patients undergoing a protocolised, contemporary immunosuppression regimen, using detailed serial biochemical testing to identify NODAT development.

Methods
One hundred twelve live and deceased donor renal transplant recipients were prospectively followed-up for NODAT onset, biochemical testing at days 7, 90, and 365 after transplantation. Sixty-eight patients were included after exclusion for non-white ethnicity and pre-transplant diabetes. Literature review to identify candidate gene variants was undertaken as described previously.

Results
Over 25% of patients developed NODAT. In an adjusted model for age, sex, BMI, and BMI change over 12 months, five out of the studied 37 single nucleotide polymorphisms (SNPs) were significantly associated with NODAT: rs16936667:PRDM14 OR 10.57;95% CI 1.8–63.0;p = 0.01, rs1801282:PPARG OR 8.5; 95% CI 1.4–52.7; p = 0.02, rs8192678:PPARGC1A OR 0.26; 95% CI 0.08–0.91; p = 0.03, rs2144908:HNF4A OR 7.0; 95% CI 1.1–45.0;p = 0.04 and rs2340721:ATF6 OR 0.21; 95%CI 0.04–1.0; p = 0.05.

Conclusion
This study represents a replication study of candidate SNPs associated with developing NODAT and implicates mTOR as the central regulator via altered insulin sensitivity, pancreatic β cell, and mitochondrial survival and dysfunction as evidenced by the five SNPs.

General significance
1) Highlights the importance of careful biochemical phenotyping with oral glucose tolerance tests to diagnose NODAT in reducing time to diagnosis and missed cases.
2)This alters potential genotype:phenotype association.
3)The replication study generates the hypothesis that mTOR signalling pathway may be involved in NODAT development.

Systematic Uncertainties Associated with the Cosmological Analysis of the First PAN-STARRS1 Type Ia Supernova Sample

We probe the systematic uncertainties from the 113 Type Ia supernovae (SN Ia) in the Pan-STARRS1 (PS1) sample along with 197 SN Ia from a combination of low-redshift surveys. The companion paper by Rest et al. describes the photometric measurements and cosmological inferences from the PS1 sample. The largest systematic uncertainty stems from the photometric calibration of the PS1 and low-z samples. We increase the sample of observed Calspec standards from 7 to 10 used to define the PS1 calibration system. The PS1 and SDSS-II calibration systems are compared and discrepancies up to ∼0.02 mag are recovered. We find uncertainties in the proper way to treat intrinsic colors and reddening produce differences in the recovered value of w up to 3%. We estimate masses of host galaxies of PS1 supernovae and detect an insignificant difference in distance residuals of the full sample of 0.037 ± 0.031 mag for host galaxies with high and low masses. Assuming flatness and including systematic uncertainties in our analysis of only SNe measurements, we find w = -1.120^+0.360_-0.206(Stat)^+0.269_-0.291(Sys). With additional constraints from Baryon acoustic oscillation, cosmic microwave background (CMB) (Planck) and H₀ measurements, we find w = -1.166^+0.072_-0.069 and Ω_m = 0.280^+0.013_-0.012 (statistical and systematic errors added in quadrature). The significance of the inconsistency with w = -1 depends on whether we use Planck or Wilkinson Microwave Anisotropy Probe measurements of the CMB: w_{BAO+H0+SN+WMAP} = -1.124^+0.083_-0.065.

Performance and Fault Tolerance of Preconditioned Iterative Solvers on Low-Power ARM Architectures

As the complexity of computing systems grows, reliability and energy are two crucial challenges asking for holistic solutions. In this paper, we investigate the interplay among concurrency, power dissipation, energy consumption and voltage-frequency scaling for a key numerical kernel for the solution of sparse linear systems. Concretely, we leverage a task-parallel implementation of the Conjugate Gradient method, equipped with an state-of-the-art pre-conditioner embedded in the ILUPACK software, and target a low-power multi core processor from ARM.In addition, we perform a theoretical analysis on the impact of a technique like Near Threshold Voltage Computing (NTVC) from the points of view of increased hardware concurrency and error rate.

A kinetic analysis methodology to elucidate the roles of metal, support and solvent for the hydrogenation of 4-phenyl-2-butanone over Pt/TiO2

The rate and, more importantly, selectivity (ketone vs aromatic ring) of the hydrogenation of 4-phenyl-2-butanone over a Pt/TiO2 catalyst have been shown to vary with solvent. In this study, a fundamental kinetic model for this multi-phase reaction has been developed incorporating statistical analysis methods to strengthen the foundations of mechanistically sound kinetic models. A 2-site model was determined to be most appropriate, describing aromatic hydrogenation (postulated to be over a platinum site) and ketone hydrogenation (postulated to be at the platinum–titania interface). Solvent choice has little impact on the ketone hydrogenation rate constant but strongly impacts aromatic hydrogenation due to solvent-catalyst interaction. Reaction selectivity is also correlated to a fitted product adsorption constant parameter. The kinetic analysis method shown has demonstrated the role of solvents in influencing reactant adsorption and reaction selectivity.

Numerical analysis of impact events in a centrifugal impact pin mill

Milling is an important operation in many industries, such as mining and pharmaceutical. Although the comminution process during milling has been extensively studied, the material fragmentation mechanisms in a mill are still not well understood partly because of the lack of an understanding on the local stressing and dynamic information under operational conditions in mills. This paper presents a DEM simulation of particle dynamics and impact events in a centrifugal impact pin mill. The main focus is the statistical characteristics of the dominant stressing modes during the milling process. The frequency, velocity and force of the different impact events between particles and mill components, or between particles, are analysed. © 2013 AIP Publishing LLC.

A systematic review and meta-analysis of the survival of non-feldspathic porcelain veneers over 5 and 10 years

PURPOSE: This systematic review aimed to report and explore the survival of dental veneers constructed from non-feldspathic porcelain over 5 and 10 years.

MATERIALS AND METHODS: A total of 4,294 articles were identified through a systematic search involving all databases in the Cochrane Library, MEDLINE (OVID), EMBASE, Web of Knowledge, specific journals (hand-search), conference proceedings, clinical trials registers, and collegiate contacts. Articles, abstracts, and gray literature were sought by two independent researchers. There were no language limitations. One hundred sixteen studies were identified for full-text assessment, with 10 included in the analysis (5 qualitative, 5 quantitative). Study characteristics and survival (Kaplan-Meier estimated cumulative survival and 95% confidence interval [CI]) were extracted or recalculated. A failed veneer was one which required an intervention that disrupted the original marginal integrity, had been partially or completely lost, or had lost retention more than twice. A meta-analysis and sensitivity analysis of Empress veneers was completed, with an assessment of statistical heterogeneity and publication bias. Clinical heterogeneity was explored for results of all veneering materials from included studies.

RESULTS: Within the 10 studies, veneers were fabricated with IPS Empress, IPS Empress 2, Cerinate, and Cerec computer-aided design/computer-assisted manufacture (CAD/CAM) materials VITA Mark I, VITA Mark II, Ivoclar ProCad. The meta-analysis showed the pooled estimate for Empress veneers to be 92.4% (95% CI: 89.8% to 95.0%) for 5-year survival and 66% to 94% (95% CI: 55% to 99%) for 10 years. Data regarding other non-feldspathic porcelain materials were lacking, with only a single study each reporting outcomes for Empress 2, Cerinate, and various Cerec porcelains over 5 years. The sensitivity analysis showed data from one study had an influencing and stabilizing effect on the 5-year pooled estimate.

CONCLUSION: The long-term outcome (> 5 years) of non-feldspathic porcelain veneers is sparsely reported in the literature. This systematic review indicates that the 5-year cumulative estimated survival for etchable non-feldspathic porcelain veneers is over 90%. Outcomes may prove clinically acceptable with time, but evidence remains lacking and the use of these materials for veneers remains experimental.