Multivariate paired data analysis: multilevel PLSDA versus OPLSDA

Metabolomics data obtained from (human) nutritional intervention studies can have a rather complex structure that depends on the underlying experimental design. In this paper we discuss the complex structure in data caused by a cross-over designed experiment. In such a design, each subject in the study population acts as his or her own control and makes the data paired. For a single univariate response a paired t-test or repeated measures ANOVA can be used to test the differences between the paired observations. The same principle holds for multivariate data. In the current paper we compare a method that exploits the paired data structure in cross-over multivariate data (multilevel PLSDA) with a method that is often used by default but that ignores the paired structure (OPLSDA). The results from both methods have been evaluated in a small simulated example as well as in a genuine data set from a cross-over designed nutritional metabolomics study. It is shown that exploiting the paired data structure underlying the cross-over design considerably improves the power and the interpretability of the multivariate solution. Furthermore, the multilevel approach provides complementary information about (I) the diversity and abundance of the treatment effects within the different (subsets of) subjects across the study population, and (II) the intrinsic differences between these study subjects

Strategic responses to global challenges: The case of European banking, 1973–2000

In applying a strategy, structure, ownership and performance (SSOP) framework to three major clearing banks (ABN AMRO, UBS, Barclays), this article debates whether the conclusions generated by Whittington and Mayer about European manufacturing industry can be applied to the financial services sector. While European integration plays a key role in determining strategy, it is clear that global factors were far more important in determining management actions, leading to significant differences in structural adaptation. The article also debates whether this has led to improved performance, given the problems experienced with both geographical dispersion and diversification, bringing into question the quality of decision-making over the long term

Double-check: validation of diagnostic statistics for PLS-DA models in metabolomics studies

Partial Least Squares-Discriminant Analysis (PLS-DA) is a PLS regression method with a special binary ‘dummy’ y-variable and it is commonly used for classification purposes and biomarker selection in metabolomics studies. Several statistical approaches are currently in use to validate outcomes of PLS-DA analyses e.g. double cross validation procedures or permutation testing. However, there is a great inconsistency in the optimization and the assessment of performance of PLS-DA models due to many different diagnostic statistics currently employed in metabolomics data analyses. In this paper, properties of four diagnostic statistics of PLS-DA, namely the number of misclassifications (NMC), the Area Under the Receiver Operating Characteristic (AUROC), Q2 and Discriminant Q2 (DQ2) are discussed. All four diagnostic statistics are used in the optimization and the performance assessment of PLS-DA models of three different-size metabolomics data sets obtained with two different types of analytical platforms and with different levels of known differences between two groups: control and case groups. Statistical significance of obtained PLS-DA models was evaluated with permutation testing. PLS-DA models obtained with NMC and AUROC are more powerful in detecting very small differences between groups than models obtained with Q2 and Discriminant Q2 (DQ2). Reproducibility of obtained PLS-DA models outcomes, models complexity and permutation test distributions are also investigated to explain this phenomenon. DQ2 and Q2 (in contrary to NMC and AUROC) prefer PLS-DA models with lower complexity and require higher number of permutation tests and submodels to accurately estimate statistical significance of the model performance. NMC and AUROC seem more efficient and more reliable diagnostic statistics and should be recommended in two group discrimination metabolomic studies

Dynamic metabolomic data analysis: a tutorial review

In metabolomics, time-resolved, dynamic or temporal data is more and more collected. The number of methods to analyze such data, however, is very limited and in most cases the dynamic nature of the data is not even taken into account. This paper reviews current methods in use for analyzing dynamic metabolomic data. Moreover, some methods from other fields of science that may be of use to analyze such dynamic metabolomics data are described in some detail. The methods are put in a general framework after providing a formal definition on what constitutes a ‘dynamic’ method. Some of the methods are illustrated with real-life metabolomics examples

Have Anglo-Saxon concepts really influenced the development of European qualifications policy?

This paper considers how far Anglo-Saxon conceptions of have influenced European Union vocational education and training policy, especially given the disparate approaches to VET across Europe. Two dominant approaches can be identified: the dual system (exemplified by Germany); and output based models (exemplified by the NVQ ‘English style’). Within the EU itself, the design philosophy of the English output-based model proved in the first instance influential in attempts to develop tools to establish equivalence between vocational qualifications across Europe, resulting in the learning outcomes approach of the European Qualifications Framework, the credit-based model of European VET Credit System and the task-based construction of occupation profiles exemplified by European Skills, Competences and Occupations. The governance model for the English system is, however, predicated on employer demand for ‘skills’ and this does not fit well with the social partnership model encompassing knowledge, skills and competences that is dominant in northern Europe. These contrasting approaches have led to continual modifications to the tools, as these sought to harmonise and reconcile national VET requirements with the original design. A tension is evident in particular between national and regional approaches to vocational education and training, on the one hand, and the policy tools adopted to align European vocational education and training better with the demands of the labour market, including at sectoral level, on the other. This paper explores these tensions and considers the prospects for the successful operation of these tools, paying particular attention to the European Qualifications Framework, European VET Credit System and European Skills, Competences and Occupations tool and the relationships between them and drawing on studies of the construction and furniture industries

Panorganismal metabolic response modeling of an experimental Echinostoma caproni infection in the mouse.

Published versio

European skills framework? - but what are skills? Anglo-Saxon versus German concepts

With the proposed introduction of a common framework for comparing qualifications within the European Union (EU), as a result of the Lisbon agreement of 2000, the question of commonly agreed transnational concepts of skills and qualifications is has become a pressing political and practical issue. The paper argues that there are grounds for doubting that there is a ready translation of the English terms 'skill'and 'qualification' in a way that avoids problems of comparing and calibrating German and English vocational qualifications. Reasons for this difficulty are explored, the most important of which relate to: a) the conceptual structure of skill and its cognates in the two languages; b) the differing socio-political role of qualifications; c) different industrial structures and labour processes; d) differences in institutions regulating vocational education and training (VET). These problems are discussed in relation to examples of similar industries and occupations and apparently similar levels of qualification in England and Germany

Simplivariate Models: Ideas and First Examples

One of the new expanding areas in functional genomics is metabolomics: measuring the metabolome of an organism. Data being generated in metabolomics studies are very diverse in nature depending on the design underlying the experiment. Traditionally, variation in measurements is conceptually broken down in systematic variation and noise where the latter contains, e.g. technical variation. There is increasing evidence that this distinction does not hold (or is too simple) for metabolomics data. A more useful distinction is in terms of informative and non-informative variation where informative relates to the problem being studied. In most common methods for analyzing metabolomics (or any other high-dimensional x-omics) data this distinction is ignored thereby severely hampering the results of the analysis. This leads to poorly interpretable models and may even obscure the relevant biological information. We developed a framework from first data analysis principles by explicitly formulating the problem of analyzing metabolomics data in terms of informative and non-informative parts. This framework allows for flexible interactions with the biologists involved in formulating prior knowledge of underlying structures. The basic idea is that the informative parts of the complex metabolomics data are approximated by simple components with a biological meaning, e.g. in terms of metabolic pathways or their regulation. Hence, we termed the framework ‘simplivariate models’ which constitutes a new way of looking at metabolomics data. The framework is given in its full generality and exemplified with two methods, IDR analysis and plaid modeling, that fit into the framework. Using this strategy of ‘divide and conquer’, we show that meaningful simplivariate models can be obtained using a real-life microbial metabolomics data set. For instance, one of the simple components contained all the measured intermediates of the Krebs cycle of E. coli. Moreover, these simplivariate models were able to uncover regulatory mechanisms present in the phenylalanine biosynthesis route of E. coli

Effects of the fatty acid amide hydrolase inhibitor URB597 on coping behavior under challenging conditions in mice

RATIONALE: Recent evidence suggests that in addition to controlling emotional behavior in general, endocannabinoid signaling is engaged in shaping behavioral responses to challenges. This important function of endocannabinoids is still poorly understood. OBJECTIVES: Here we investigated the impact of blockade of fatty acid amide hydrolase (FAAH), the degrading enzyme of anandamide on behavioral responses induced by challenges of different intensity. METHODS: Mice treated with FAAH inhibitor URB597 were either manually restrained on their backs (back test) or received foot-shocks. RESULTS: The behavior of mice showed bimodal distribution in the back test: they either predominantly showed escape attempts or equally distributed time between passivity and escape. URB597 increased escapes in animals with low escape scores. No effects were noticed in mice showing high escape scores, which is likely due to a ceiling effect. We hypothesized that stronger stressors would wash out individual differences in coping; therefore, we exposed mice to foot-shocks that decreased locomotion and increased freezing in all mice. URB597 ameliorated both responses. The re-exposure of mice to the shock cage 14 days later without delivering shocks or treatment was followed by reduced and fragmented sleep as shown by electrophysiological recordings. Surprisingly, sleep was more disturbed after the reminder than after shocks in rats receiving vehicle before foot-shocks. These reminder-induced disturbances were abolished by URB597 administered before shocks. CONCLUSIONS: These findings suggest that FAAH blockade has an important role in the selection of behavioral responses under challenging conditions and-judging from its long-term effects-that it influences the cognitive appraisal of the challenge

Discovery of early-stage biomarkers for diabetic kidney disease using ms-based metabolomics (FinnDiane study)

Diabetic kidney disease (DKD) is a devastating complication that affects an estimated third of patients with type 1 diabetes mellitus (DM). There is no cure once the disease is diagnosed, but early treatment at a sub-clinical stage can prevent or at least halt the progression. DKD is clinically diagnosed as abnormally high urinary albumin excretion rate (AER). We hypothesize that subtle changes in the urine metabolome precede the clinically significant rise in AER. To test this, 52 type 1 diabetic patients were recruited by the FinnDiane study that had normal AER (normoalbuminuric). After an average of 5.5 years of follow-up half of the subjects (26) progressed from normal AER to microalbuminuria or DKD (macroalbuminuria), the other half remained normoalbuminuric. The objective of this study is to discover urinary biomarkers that differentiate the progressive form of albuminuria from non-progressive form of albuminuria in humans. Metabolite profiles of baseline 24 h urine samples were obtained by gas chromatography–mass spectrometry (GC–MS) and liquid chromatography–mass spectrometry (LC–MS) to detect potential early indicators of pathological changes. Multivariate logistic regression modeling of the metabolomics data resulted in a profile of metabolites that separated those patients that progressed from normoalbuminuric AER to microalbuminuric AER from those patients that maintained normoalbuminuric AER with an accuracy of 75% and a precision of 73%. As this data and samples are from an actual patient population and as such, gathered within a less controlled environment it is striking to see that within this profile a number of metabolites (identified as early indicators) have been associated with DKD already in literature, but also that new candidate biomarkers were found. The discriminating metabolites included acyl-carnitines, acyl-glycines and metabolites related to tryptophan metabolism. We found candidate biomarkers that were univariately significant different. This study demonstrates the potential of multivariate data analysis and metabolomics in the field of diabetic complications, and suggests several metabolic pathways relevant for further biological studies