Determining factors of the innovative behavior of cooperatives: an analysis of the case of Castilla y León

Cooperatives have a long historical experience in the Spanish economy and have demonstrated their ability to compete against traditional firms in the market. To maintain this capability, while taking advantage of the competitive advantages associated with their idiosyncrasies as social economy enterprises, they should take into consideration that the economy is increasingly globalized and increasingly knowledge-based, especially with regards to technological content. As a consequence, the innovative capacity appears to be a key aspect in order to be able to challenge competitors. This article characterizes the innovative behavior of cooperatives in the region of Castile and Leon and analyses the internal and external factors affecting their innovative performance, based on data from a survey of 581 cooperatives. The results of the empirical analysis, which is performed by multivariate binary logistic regression on various types of innovation, lead us to identify the size of the organizations, the existence of planning, the R & D activities and the human capital as the main determining factors.

Multivariate prediction of clarified butter composition using Raman spectroscopy

Raman spectroscopy has been used to predict the abundance of the FA in clarified butterfat that was obtained from dairy cows fed a range of levels of rapeseed oil in their diet. Partial least squares regression of the Raman spectra against FA compositions obtained by GC showed good prediction for the five major (abundance >5%) FA with R-2=0.74-0.92 and a root mean SE of prediction (RMSEP) that was 5-7% of the mean. In general, the prediction accuracy fell with decreasing abundance in the sample, but the RMSEP was 1.25%. The Raman method has the best prediction ability for unsaturated FA (R-2=0.85-0.92), and in particular trans unsaturated FA (best-predicted FA was 18:1 tDelta9). This enhancement was attributed to the isolation of the unsaturated modes from the saturated modes and the significantly higher spectral response of unsaturated bonds compared with saturated bonds. Raman spectra of the melted butter samples could also be used to predict bulk parameters calculated from standard analyzes, such as iodine value (R-2=0.80) and solid fat content at low temperature (R-2=0.87). For solid fat contents determined at higher temperatures, the prediction ability was significantly reduced (R-2=0.42), and this decrease in performance was attributed to the smaller range of values in solid fat content at the higher temperatures. Finally, although the prediction errors for the abundances of each of the FA in a given sample are much larger with Raman than with full GC analysis, the accuracy is acceptably high for quality control applications. This, combined with the fact that Raman spectra can be obtained with no sample preparation and with 60-s data collection times, means that high-throughput, on-line Raman analysis of butter samples should be possible.

Analysis of DNA processing reactions in bacterial conjugation by using suicide oligonucleotides

Protein TrwC is the conjugative relaxase responsible for DNA processing in plasmid R388 bacterial conjugation. TrwC has two catalytic tyrosines, Y18 and Y26, both able to carry out cleavage reactions using unmodified oligonucleotide substrates. Suicide substrates containing a 30-Sphosphorothiolate linkage at the cleavage site displaced TrwC reaction towards covalent adducts and thereby enabled intermediate steps in relaxase reactions to be investigated. Two distinct covalent TrwC–oligonucleotide complexes could be separated from noncovalently bound protein by SDS–PAGE. As observed by mass spectrometry, one complex contained a single, cleaved oligonucleotide bound to Y18, whereas the other contained two cleaved oligonucleotides, bound to Y18 and Y26. Analysis of the cleavage reaction using suicide substrates and Y18F or Y26F mutants showed that efficient Y26 cleavage only occurs after Y18 cleavage. Strand-transfer reactions carried out with the isolated Y18–DNA complex allowed the assignment of specific roles to each tyrosine. Thus, only Y18 was used for initiation. Y26 was specifically used in the second transesterification that leads to strand transfer, thus catalyzing the termination reaction that occurs in the recipient cell.

Fault reconstruction in linear dynamic systems using multivariate statistics

Treasure et al. (2004) recently proposed a new sub space-monitoring technique, based on the N4SID algorithm, within the multivariate statistical process control framework. This dynamic-monitoring method requires considerably fewer variables to be analysed when compared with dynamic principal component analysis (PCA). The contribution charts and variable reconstruction, traditionally employed for static PCA, are analysed in a dynamic context. The contribution charts and variable reconstruction may be affected by the ratio of the number of retained components to the total number of analysed variables. Particular problems arise if this ratio is large and a new reconstruction chart is introduced to overcome these. The utility of such a dynamic contribution chart and variable reconstruction is shown in a simulation and by application to industrial data from a distillation unit.

Improved reliability in diagnosing faults using multivariate statistics

This paper analyses multivariate statistical techniques for identifying and isolating abnormal process behaviour. These techniques include contribution charts and variable reconstructions that relate to the application of principal component analysis (PCA). The analysis reveals firstly that contribution charts produce variable contributions which are linearly dependent and may lead to an incorrect diagnosis, if the number of principal components retained is close to the number of recorded process variables. The analysis secondly yields that variable reconstruction affects the geometry of the PCA decomposition. The paper further introduces an improved variable reconstruction method for identifying multiple sensor and process faults and for isolating their influence upon the recorded process variables. It is shown that this can accommodate the effect of reconstruction, i.e. changes in the covariance matrix of the sensor readings and correctly re-defining the PCA-based monitoring statistics and their confidence limits. (c) 2006 Elsevier Ltd. All rights reserved.

Genetic analysis of coronary artery disease single nucleotide polymorphisms in diabetic nephropathy

Background. Diabetic nephropathy is a leading cause of end-stage renal disease. Premature mortality is common in patients with nephropathy, largely due to cardiovascular disease. Genetic variants implicated in macrovascular disease are therefore excellent candidates to assess for association with diabetic nephropathy. Recent genome-wide association studies have identified a total of 15 single-nucleotide polymorphisms (SNPs) that are reproducibly associated with cardiovascular disease.

Methods. We initially assessed these SNPs for association in UK type 1 diabetic patients with (cases; n = 597) and without (controls; n = 502) nephropathy using iPLEXTM and TaqMan® assays. Replication studies were performed with DNA genotyped in a total of 2668 individuals from the British Isles.

Results. One SNP (rs4420638) on chromosome 19q13 was found to be significantly associated with diabetic nephropathy before (P = 0.0002) and after correction for multiple testing (Pcorrected = 0.002). We replicated this finding in a phenotypically similar case–control collection comprising 709 individuals with type 1 diabetes (P = 0.002; combined P < 0.00001; OR = 1.54, 95% CI: 1.29–1.84).

Conclusions. Our case–control data suggest that rs4420638, or a functional SNP in linkage disequilibrium with this SNP, may be associated with diabetic nephropathy.

Improved fault diagnosis in multivariate systems using regression-based reconstruction

Subspace monitoring has recently been proposed as a condition monitoring tool that requires considerably fewer variables to be analysed compared to dynamic principal component analysis (PCA). This paper analyses subspace monitoring in identifying and isolating fault conditions, which reveals that the existing work suffers from inherent limitations if complex fault senarios arise. Based on the assumption that the fault signature is deterministic while the monitored variables are stochastic, the paper introduces a regression-based reconstruction technique to overcome these limitations. The utility of the proposed fault identification and isolation method is shown using a simulation example and the analysis of experimental data from an industrial reactive distillation unit.

Modelling the impact of antibiotic use and infection control practices on the incidence of hospital-acquired methicillin-resistant Staphylococcus aureus: a time-series analysis

Objectives: Methicillin-resistant Staphylococcus aureus (MRSA) is a major nosocomial pathogen worldwide. A wide range of factors have been suggested to influence the spread of MRSA. The objective of this study was to evaluate the effect of antimicrobial drug use and infection control practices on nosocomial MRSA incidence in a 426-bed general teaching hospital in Northern Ireland.

Methods: The present research involved the retrospective collection of monthly data on the usage of antibiotics and on infection control practices within the hospital over a 5 year period (January 2000–December 2004). A multivariate ARIMA (time-series analysis) model was built to relate MRSA incidence with antibiotic use and infection control practices.

Results: Analysis of the 5 year data set showed that temporal variations in MRSA incidence followed temporal variations in the use of fluoroquinolones, third-generation cephalosporins, macrolides and amoxicillin/clavulanic acid (coefficients = 0.005, 0.03, 0.002 and 0.003, respectively, with various time lags). Temporal relationships were also observed between MRSA incidence and infection control practices, i.e. the number of patients actively screened for MRSA (coefficient = -0.007), the use of alcohol-impregnated wipes (coefficient = -0.0003) and the bulk orders of alcohol-based handrub (coefficients = -0.04 and -0.08), with increased infection control activity being associated with decreased MRSA incidence, and between MRSA incidence and the number of new patients admitted with MRSA (coefficient = 0.22). The model explained 78.4% of the variance in the monthly incidence of MRSA.

Conclusions: The results of this study confirm the value of infection control policies as well as suggest the usefulness of restricting the use of certain antimicrobial classes to control MRSA.

Unite and conquer: univariate and multivariate approaches for finding differentially expressed gene sets

Motivation: Recently, many univariate and several multivariate approaches have been suggested for testing differential expression of gene sets between different phenotypes. However, despite a wealth of literature studying their performance on simulated and real biological data, still there is a need to quantify their relative performance when they are testing different null hypotheses.

Results: In this article, we compare the performance of univariate and multivariate tests on both simulated and biological data. In the simulation study we demonstrate that high correlations equally affect the power of both, univariate as well as multivariate tests. In addition, for most of them the power is similarly affected by the dimensionality of the gene set and by the percentage of genes in the set, for which expression is changing between two phenotypes. The application of different test statistics to biological data reveals that three statistics (sum of squared t-tests, Hotelling's T2, N-statistic), testing different null hypotheses, find some common but also some complementing differentially expressed gene sets under specific settings. This demonstrates that due to complementing null hypotheses each test projects on different aspects of the data and for the analysis of biological data it is beneficial to use all three tests simultaneously instead of focusing exclusively on just one.

Stratification and Monitoring of Juvenile Idiopathic Arthritis Patients by Synovial Proteome Analysis

Juvenile idiopathic arthritis (JIA) comprises a poorly understood group of chronic, childhood onset, autoimmune diseases with variable clinical outcomes. We investigated whether profiling of the synovial fluid (SF) proteome by a fluorescent dye based, two-dimensional gel (DIGE) approach could distinguish patients in whom inflammation extends to affect a large number of joints, early in the disease process. SF samples from 22 JIA patients were analyzed: 10 with oligoarticular arthritis, 5 extended oligoarticular and 7 polyarticular disease. SF samples were labeled with Cy dyes and separated by two-dimensional electrophoresis. Multivariate analyses were used to isolate a panel of proteins which distinguish patient subgroups. Proteins were identified using MALDI-TOF mass spectrometry with expression further verified by Western immunoblotting and immunohistochemistry. Hierarchical clustering based on the expression levels of a set of 40 proteins segregated the extended oligoarticular from the oligoarticular patients (p <0.05). Expression patterns of the isolated protein panel have also been observed over time, as disease spreads to multiple joints. The data indicates that synovial fluid proteome profiles could be used to stratify patients based on risk of disease extension. These protein profiles may also assist in monitoring therapeutic responses over time and help predict joint damage. © 2009 American Chemical Society.

Statistical-based monitoring of multivariate non-Gaussian systems

The monitoring of multivariate systems that exhibit non-Gaussian behavior is addressed. Existing work advocates the use of independent component analysis (ICA) to extract the underlying non-Gaussian data structure. Since some of the source signals may be Gaussian, the use of principal component analysis (PCA) is proposed to capture the Gaussian and non-Gaussian source signals. A subsequent application of ICA then allows the extraction of non-Gaussian components from the retained principal components (PCs). A further contribution is the utilization of a support vector data description to determine a confidence limit for the non-Gaussian components. Finally, a statistical test is developed for determining how many non-Gaussian components are encapsulated within the retained PCs, and associated monitoring statistics are defined. The utility of the proposed scheme is demonstrated by a simulation example, and the analysis of recorded data from an industrial melter.

Drivers of sales performance: a contemporary meta-analysis. Have salespeople become knowledge brokers?

It has been 25 years since the publication of a comprehensive review of the full spectrum of salesperformance drivers. This study takes stock of the contemporary field and synthesizes empirical evidence from the period 1982–2008. The authors revise the classification scheme for sales performance determinants devised by Walker et al. (1977) and estimate both the predictive validity of its sub-categories and the impact of a range of moderators on determinant-sales performance relationships. Based on multivariate causal model analysis, the results make two major observations: (1) Five sub-categories demonstrate significant relationships with sales performance: selling-related knowledge (ß=.28), degree of adaptiveness (ß=.27), role ambiguity (ß=-.25), cognitive aptitude (ß=.23) and work engagement (ß=.23). (2) These sub-categories are moderated by measurement method, research context, and salestype variables. The authors identify managerial implications of the results and offer suggestions for further research, including the conjecture that as the world is moving toward a knowledge-intensive economy, salespeople could be functioning as knowledge-brokers. The results seem to back this supposition and indicate how it might inspire future research in the field of personal selling.

Association Analysis of the PIP4K2A Gene on Chromosome 10p12 and Schizophrenia in the Irish Study of High Density Schizophrenia Families (ISHDSF) and the Irish Case-Control Study of Schizophrenia (ICCSS)

Molecular studies support pharmacological evidence that phosphoinositide signaling is perturbed in schizophrenia and bipolar disorder. The phosphatidylinositol-4-phosphate-5-kinase type-II alpha (PIP4K2A) gene is located on chromosome 10p12. This region has been implicated in both diseases by linkage, and PIP4K2A directly by association. Given linkage evidence in the Irish Study of High Density Schizophrenia Families (ISHDSF) to a region including 10p12, we performed an association study between genetic variants at PIP4K2A and disease. No association was detected through single-marker or haplotype analysis of the whole sample. However, stratification into families positive and negative for the ISHDSF schizophrenia high-risk haplotype (HRH) in the DTNBP1 gene and re-analysis for linkage showed reduced amplitude of the 10p12 linkage peak in the DTNBP1 HRH positive families. Association analysis of the stratified sample showed a trend toward association of PIP4K2A SNPs rs1417374 and rs1409395 with schizophrenia in the DTNBP1 HRH positive families. Despite this apparent paradox, our data may therefore suggest involvement of PIP4K2A in schizophrenia in those families for whom genetic variation in DTNBP1 appears also to be a risk factor. This trend appears to arise from under-transmission of common alleles to female cases. Follow-up association analysis in a large Irish schizophrenia case-control control sample (ICCSS) showed significant association with disease of a haplotype comprising these same SNPs rs1417374-rs1409395, again more so in affected females, and in cases with negative family history of the disease. This study supports a minor role for PIP4K2A in schizophrenia etiology in the Irish population. (C) 2009 Wiley-Liss, Inc.

The impact of biomarkers in multivariate algorithms for bladder cancer diagnosis in patients with hematuria

BACKGROUND: We appraised 23 biomarkers previously associated with urothelial cancer in a case-control study. Our aim was to determine whether single biomarkers and/or multivariate algorithms significantly improved on the predictive power of an algorithm based on demographics for prediction of urothelial cancer in patients presenting with hematuria. METHODS: Twenty-two biomarkers in urine and carcinoembryonic antigen (CEA) in serum were evaluated using enzyme-linked immunosorbent assays (ELISAs) and biochip array technology in 2 patient cohorts: 80 patients with urothelial cancer, and 77 controls with confounding pathologies. We used Forward Wald binary logistic regression analyses to create algorithms based on demographic variables designated prior predicted probability (PPP) and multivariate algorithms, which included PPP as a single variable. Areas under the curve (AUC) were determined after receiver-operator characteristic (ROC) analysis for single biomarkers and algorithms. RESULTS: After univariate analysis, 9 biomarkers were differentially expressed (t test; P

Job strain as a risk factor for coronary heart disease:a collaborative meta-analysis of individual participant data

BACKGROUND: Published work assessing psychosocial stress (job strain) as a risk factor for coronary heart disease is inconsistent and subject to publication bias and reverse causation bias. We analysed the relation between job strain and coronary heart disease with a meta-analysis of published and unpublished studies. METHODS: We used individual records from 13 European cohort studies (1985-2006) of men and women without coronary heart disease who were employed at time of baseline assessment. We measured job strain with questions from validated job-content and demand-control questionnaires. We extracted data in two stages such that acquisition and harmonisation of job strain measure and covariables occurred before linkage to records for coronary heart disease. We defined incident coronary heart disease as the first non-fatal myocardial infarction or coronary death. FINDINGS: 30?214 (15%) of 197?473 participants reported job strain. In 1·49 million person-years at risk (mean follow-up 7·5 years [SD 1·7]), we recorded 2358 events of incident coronary heart disease. After adjustment for sex and age, the hazard ratio for job strain versus no job strain was 1·23 (95% CI 1·10-1·37). This effect estimate was higher in published (1·43, 1·15-1·77) than unpublished (1·16, 1·02-1·32) studies. Hazard ratios were likewise raised in analyses addressing reverse causality by exclusion of events of coronary heart disease that occurred in the first 3 years (1·31, 1·15-1·48) and 5 years (1·30, 1·13-1·50) of follow-up. We noted an association between job strain and coronary heart disease for sex, age groups, socioeconomic strata, and region, and after adjustments for socioeconomic status, and lifestyle and conventional risk factors. The population attributable risk for job strain was 3·4%. INTERPRETATION: Our findings suggest that prevention of workplace stress might decrease disease incidence; however, this strategy would have a much smaller effect than would tackling of standard risk factors, such as smoking. FUNDING: Finnish Work Environment Fund, the Academy of Finland, the Swedish Research Council for Working Life and Social Research, the German Social Accident Insurance, the Danish National Research Centre for the Working Environment, the BUPA Foundation, the Ministry of Social Affairs and Employment, the Medical Research Council, the Wellcome Trust, and the US National Institutes of Health.