Improved principal component monitoring using the local approach

This paper shows that current multivariate statistical monitoring technology may not detect incipient changes in the variable covariance structure nor changes in the geometry of the underlying variable decomposition. To overcome these deficiencies, the local approach is incorporated into the multivariate statistical monitoring framework to define two new univariate statistics for fault detection. Fault isolation is achieved by constructing a fault diagnosis chart which reveals changes in the covariance structure resulting from the presence of a fault. A theoretical analysis is presented and the proposed monitoring approach is exemplified using application studies involving recorded data from two complex industrial processes. © 2007 Elsevier Ltd. All rights reserved.

Harnessing the complexity of gene expression data from cancer: from single gene to structural pathway methods

High-dimensional gene expression data provide a rich source of information because they capture the expression level of genes in dynamic states that reflect the biological functioning of a cell. For this reason, such data are suitable to reveal systems related properties inside a cell, e.g., in order to elucidate molecular mechanisms of complex diseases like breast or prostate cancer. However, this is not only strongly dependent on the sample size and the correlation structure of a data set, but also on the statistical hypotheses tested. Many different approaches have been developed over the years to analyze gene expression data to (I) identify changes in single genes, (II) identify changes in gene sets or pathways, and (III) identify changes in the correlation structure in pathways. In this paper, we review statistical methods for all three types of approaches, including subtypes, in the context of cancer data and provide links to software implementations and tools and address also the general problem of multiple hypotheses testing. Further, we provide recommendations for the selection of such analysis methods.

The prospective effects of workplace violence on physicians' job satisfaction and turnover intentions: the buffering effect of job control

Background: Health care professionals, including physicians, are at high risk of encountering workplace violence. At the same time physician turnover is an increasing problem that threatens the functioning of the health care sector worldwide. The present study examined the prospective associations of work-related physical violence and bullying with physicians’ turnover intentions and job satisfaction. In addition, we tested whether job control would modify these associations.

Methods: The present study was a 4-year longitudinal survey study, with data gathered in 2006 and 2010.The present sample included 1515 (61% women) Finnish physicians aged 25–63 years at baseline. Analyses of covariance (ANCOVA) were conducted while adjusting for gender, age, baseline levels, specialisation status, and employment sector.

Results: The results of covariance analyses showed that physical violence led to increased physician turnover intentions and that both bullying and physical violence led to reduced physician job satisfaction even after adjustments. We also found that opportunities for job control were able to alleviate the increase in turnover intentions resulting from bullying.

Conclusions: Our results suggest that workplace violence is an extensive problem in the health care sector and may lead to increased turnover and job dissatisfaction. Thus, health care organisations should approach this problem through different means, for example, by giving health care employees more opportunities to control their own work.

Relative Effects of Gamete Compatibility and Hydrodynamics on Fertilization in the Green Sea Urchin Strongylocentrotus droebachiensis

Intraspecific variation in gamete compatibility among male/female pairs causes variation in the concentration of sperm required to achieve equivalent fertilization levels. Gamete compatibility is therefore potentially an important factor controlling mating success. Many broadcast-spawning marine invertebrates, however, also live in a dynamic environment where hydrodynamic conditions can affect the concentration of sperm reaching eggs during spawning. Thus flow conditions may moderate the effects of gamete compatibility on fertilization. Using the green sea urchin Strongylocentrotus droebachiensis as a model system, we assessed the relative effects of gamete compatibility (the concentration of sperm required to fertilize 50% of the eggs in specific male/female pairs; F50) and the root-mean-square of total velocity (urms; 0.01-0.11 m s(-1)) on fertilization in four locations near a spawning female (water column, wake eddy, substratum, and aboral surface) in both unidirectional and oscillatory flows. Percent fertilization decreased significantly with increasing urms at all locations and both flow regimes. However, although gamete compatibility varied by almost 1.5 orders of magnitude, it was not a significant predictor of fertilization for most combinations of position and flow. The notable exception was a significant effect of gamete compatibility on fertilization on the aboral surface under unidirectional flow. Our results suggest that selection on variation in gamete compatibility may be strongest in eggs fertilized on the aboral surface of sea urchins and that hydrodynamic conditions may add environmental noise to selection outcomes.

Using geostatistical Bayesian Updating to integrate airborne radiometrics and soil geochemistry to improve mapping for mineral exploration

Mineral exploration programmes around the world use data from remote sensing, geophysics and direct sampling. On a regional scale, the combination of airborne geophysics and ground-based geochemical sampling can aid geological mapping and economic minerals exploration. The fact that airborne geophysical and traditional soil-sampling data are generated at different spatial resolutions means that they are not immediately comparable due to their different sampling density. Several geostatistical techniques, including indicator cokriging and collocated cokriging, can be used to integrate different types of data into a geostatistical model. With increasing numbers of variables the inference of the cross-covariance model required for cokriging can be demanding in terms of effort and computational time. In this paper a Gaussian-based Bayesian updating approach is applied to integrate airborne radiometric data and ground-sampled geochemical soil data to maximise information generated from the soil survey, to enable more accurate geological interpretation for the exploration and development of natural resources. The Bayesian updating technique decomposes the collocated estimate into a production of two models: prior and likelihood models. The prior model is built from primary information and the likelihood model is built from secondary information. The prior model is then updated with the likelihood model to build the final model. The approach allows multiple secondary variables to be simultaneously integrated into the mapping of the primary variable. The Bayesian updating approach is demonstrated using a case study from Northern Ireland where the history of mineral prospecting for precious and base metals dates from the 18th century. Vein-hosted, strata-bound and volcanogenic occurrences of mineralisation are found. The geostatistical technique was used to improve the resolution of soil geochemistry, collected one sample per 2 km2, by integrating more closely measured airborne geophysical data from the GSNI Tellus Survey, measured over a footprint of 65 x 200 m. The directly measured geochemistry data were considered as primary data in the Bayesian approach and the airborne radiometric data were used as secondary data. The approach produced more detailed updated maps and in particular maximized information on mapped estimates of zinc, copper and lead. Greater delineation of an elongated northwest/southeast trending zone in the updated maps strengthened the potential to investigate stratabound base metal deposits.

Validation and application of a reporter gene assay for the determination of estrogenic endocrine disruptor activity in milk

Endocrine disruptors (EDs) are compounds known to interfere with the endocrine system by disturbing the action or pathways of natural hormones which may lead to infertility or cancer.Our diet is considered to be one of the main exposure routes to EDs. Since milk and dairy products are major components of our diet they should be monitored for ED contamination. Most assays developed to date utilise targeted, chromatography based methods which lack information on the biological activity and mixture effects of the monitored compounds.A biological reporter gene assay (RGA) was developed to assess the total estrogen hormonal load in milk. It has been validated according to EU decision 2002/657/EC. Analytes were extracted by liquid-liquid extraction with acetonitrile followed by clean up on a HLB column which yielded good recovery and small matrix effects. The method has been shown to be estrogen specific, repeatable and reproducible, with covariance values below 20%. In conclusion, this method enables the detection of low levels of estrogen hormonal activity in milk with a detection capability of 36pgg EEQ and has been successfully applied in testing a range of milk samples. © 2014 Elsevier Ltd.

Physical activity, sedentary behaviour and fetal macrosomia in uncomplicated pregnancies: A prospective cohort study

Objective: to explore maternal energy balance, incorporating free living physical activity and sedentary behaviour, in uncomplicated pregnancies at risk of macrosomia.

Methods: a parallel-group cross-sectional analysis was conducted in healthy pregnant women predicted to deliver infants weighing Z4000 g (study group) or o4000 g (control group). Women were recruited in a 1:1 ratio from antenatal clinics in Northern Ireland. Women wore a SenseWears Body Media Pro3 physical activity armband and completed a food diary for four consecutive days in the third trimester. Physical activity was measured in Metabolic Equivalent of Tasks (METs) where 1 MET¼1 kcal per kilogram of body weight per hour. Analysis of covariance (ANCOVA) was employed using the General Linear Model to adjust for potential confounders.

Findings: of the 112 women recruited, 100 complete datasets were available for analysis. There was no significant difference in energy balance between the two groups. Intensity of free living physical activity (average METs) of women predicted to deliver macrosomic infants (n¼50) was significantly lower than that of women in the control group (n¼50) (1.3 (0.2) METs (mean, standard deviation) versus 1.2 (0.2) METs; difference in means 0.1 METs (95% confidence interval: 0.19, 0.01); p¼0.021). Women predicted to deliver macrosomic infants also spent significantly more time in sedentary behaviour (r1 MET) than the control group (16.1 (2.8) hours versus 13.8 (4.3) hours; 2.0 hours (0.3, 3.7), p¼0.020).

Key conclusions and implications for practice: although there was no association between predicted fetal macrosomia and energy balance, those women predicted to deliver a macrosomic infant exhibited increased sedentary behaviour and reduced physical activity in the third trimester of pregnancy. Professionals caring for women during pregnancy have an important role in promoting and supporting more active lifestyles amongst women who are predicted to deliver a macrosomic infant given the known associated risks.