A novel R-package graphic user interface for the analysis of metabonomic profiles

Background Analysis of the plethora of metabolites found in the NMR spectra of biological fluids or tissues requires data complexity to be simplified. We present a graphical user interface (GUI) for NMR-based metabonomic analysis. The "Metabonomic Package" has been developed for metabonomics research as open-source software and uses the R statistical libraries. /Results The package offers the following options: Raw 1-dimensional spectra processing: phase, baseline correction and normalization. Importing processed spectra. Including/excluding spectral ranges, optional binning and bucketing, detection and alignment of peaks. Sorting of metabolites based on their ability to discriminate, metabolite selection, and outlier identification. Multivariate unsupervised analysis: principal components analysis (PCA). Multivariate supervised analysis: partial least squares (PLS), linear discriminant analysis (LDA), k-nearest neighbor classification. Neural networks. Visualization and overlapping of spectra. Plot values of the chemical shift position for different samples. Furthermore, the "Metabonomic" GUI includes a console to enable other kinds of analyses and to take advantage of all R statistical tools. /Conclusion We made complex multivariate analysis user-friendly for both experienced and novice users, which could help to expand the use of NMR-based metabonomics

Predictive values of early parental loss and psychopathological risk for physical problems in early adolescents

Background: Several studies have suggested that the early loss of parents is a potentially traumatic experience, exposing adolescents to a higher risk for the onset of psychopathological symptoms. Furthermore, research has shown an association between the loss of a parent in childhood and subsequent physical illnesses, but much less attention has been given to the predictive role of loss in the development of physical illness in adolescence. Methods: From a larger normative sample, we selected 418 early adolescents (and their surviving parents) each of whom had lost a parent in their first 3 years of life. We evaluate the offspring's and parents' psychopathological symptoms, dissociation, and physical problems over a 6-year period. Univariate and multivariate Cox proportional hazard regression analyses with time-dependent variables were used to examine the predictive values of the adolescents' and surviving parents' psychopathological symptoms, and youths' demographic characteristics (sex and age) for the occurrence of physical illness during a 6-year period of follow-up. Results: Independently of sex, the psychopathological risk of the surviving parents' and adolescents' affective problems and dissociation has been found to predict the occurrence of physical illnesses. Furthermore, dissociation was the most significant predictor of significant physical problems. Conclusion: These results may be relevant and an addition to the previous literature, opening up new possibilities for prevention and intervention that are oriented toward greater support for children who have experienced the loss of one parent and for surviving parents

A linear mixed model approach to gene expression-tumor aneuploidy association studies.

Aneuploidy, defined as abnormal chromosome number or somatic DNA copy number, is a characteristic of many aggressive tumors and is thought to drive tumorigenesis. Gene expression-aneuploidy association studies have previously been conducted to explore cellular mechanisms associated with aneuploidy. However, in an observational setting, gene expression is influenced by many factors that can act as confounders between gene expression and aneuploidy, leading to spurious correlations between the two variables. These factors include known confounders such as sample purity or batch effect, as well as gene co-regulation which induces correlations between the expression of causal genes and non-causal genes. We use a linear mixed-effects model (LMM) to account for confounding effects of tumor purity and gene co-regulation on gene expression-aneuploidy associations. When applied to patient tumor data across diverse tumor types, we observe that the LMM both accounts for the impact of purity on aneuploidy measurements and identifies a new association between histone gene expression and aneuploidy

Are the Baltic Countries Ready to Adopt the Euro? A Generalised Purchasing Power Parity Approach

This paper focuses on macroeconomic interdependencies between the Euro area and three transition economies (Estonia, Lithuania and Latvia), with the aim of establishing whether the latter are ready to adopt the Euro. The theoretical framework is based on the Generalised Purchasing Power Parity (GPPP) hypothesis, which is empirically tested within a Vector Error Correction (VEC) model. Using both monthly and quarterly data over the period 1993-2005, it is found that GPPP holds for the real exchange rate vis-à-vis the Euro of each Baltic country, reflecting a degree of real convergence consistent with Optimum Currency Area criteria. Further, the adopted joint modelling approach for the real exchange rates of the Baltic region outperforms a number of alternative models in terms of out-of-sample forecasts.transition economies, Euro area, (Generalised) Purchasing Power Parity, Vector Error Corrector models

Application of data mining techniques of batch profiles for process understanding and improvement

Batch processes are widely used in the chemical industry. Recently, much attention has been given to the monitoring and analysis of batch measurement data, or profiles, with an emphasis on the detection of problems. Similarly, methods to improve the final product quality in batch processes have multiplied in the literature. However, an area that is virtually unexplored is the utilization of the data mining techniques for monitoring and analysis of batch profiles for better understanding batch processes, rather than identifying problems in batches, in order to improve the process. The thrust of this work is to apply a systematic method to increase batch process understanding by sifting through the existing historical database of past batches, to discern directions for process improvement from the increased understanding, and to subsequently demonstrate better quality control through the use of online recipe adjustments. A database of past batches is generated from a simulated nylon-6, 6 process, with the main quality variable of interest being the number average molecular weight. The time and measurement variability in raw batch measurement profiles is characterized through scale parameters. These scale parameters are subjected to a standard principal component analysis (PCA) to understand the principal sources of variation present in a historical database of past batches. Directions for process improvement are discovered from the data mining study and appropriate manipulated variables to implement recipe adjustments are identified. Online predictions of the molecular weight are demonstrated which indicate off-target quality batches well before the end of the batch. A split-range linear molecular weight-based controller is developed that is able to reduce the variability in the quality around the target. Further process improvement is accomplished by reducing the cycle time in addition to tightly controlling the final quality. The approach for systematically analyzing batch process data is general and can be applied to any batch system, including non-reactive systems

Impaired aortic distensibility measured by computed tomography is associated with the severity of coronary artery disease.

Impaired aortic distensibility index (ADI) is associated with cardiovascular risk factors. This study evaluates the relation of ADI measured by computed tomographic angiography (CTA) with the severity of coronary atherosclerosis in subjects with suspected coronary artery disease (CAD). Two hundred and twenty-nine subjects,age 63 ± 9 years, 42% female, underwent coronary artery calcium (CAC) scanning and CTA, and their ADI and Framingham risk score (FRS) were measured. End-systolic and end-diastolic (ED) cross-sectional-area(CSA) of ascending-aorta (AAo) was measured 15-mm above the left-main coronary ostium. ADI was defined as: [(Δlumen-CSA)/(lumen-CSA in ED × systemic-pulse-pressure) × 10(3)]. ADI measured by 2D-trans-thoracic echocardiography (TTE) was compared with CTA-measured ADI in 26 subjects without CAC. CAC was defined as 0, 1-100, 101-400 and 400+. CAD was defined as luminal stenosis 0, 1-49% and 50%+. There was an excellent correlation between CTA- and TTE-measured ADI (r(2)=0.94, P=0.0001). ADI decreased from CAC 0 to CAC 400+; similarly from FRS 1-9% to FRS 20% + (P<0.05). After adjustment for risk factors, the relative risk for each standard deviation decrease in ADI was 1.66 for CAC 1-100, 2.26 for CAC 101-400 and 2.32 for CAC 400+ as compared to CAC 0; similarly, 2.36 for non-obstructive CAD and 2.67 for obstructive CAD as compared to normal coronaries. The area under the ROC-curve to predict significant CAD was 0.68 for FRS, 0.75 for ADI, 0.81 for CAC and 0.86 for the combination (P<0.05). Impaired aortic distensibility strongly correlates with the severity of coronary atherosclerosis. Addition of ADI to CAC and traditional risk factors provides incremental value to predict at-risk individuals