MetAssimulo:Simulation of Realistic NMR Metabolic Profiles

<p>Abstract</p> <p>Background</p> <p>Probing the complex fusion of genetic and environmental interactions, metabolic profiling (or metabolomics/metabonomics), the study of small molecules involved in metabolic reactions, is a rapidly expanding 'omics' field. A major technique for capturing metabolite data is ¹H-NMR spectroscopy and this yields highly complex profiles that require sophisticated statistical analysis methods. However, experimental data is difficult to control and expensive to obtain. Thus data simulation is a productive route to aid algorithm development.</p> <p>Results</p> <p>MetAssimulo is a MATLAB-based package that has been developed to simulate ¹H-NMR spectra of complex mixtures such as metabolic profiles. Drawing data from a metabolite standard spectral database in conjunction with concentration information input by the user or constructed automatically from the Human Metabolome Database, MetAssimulo is able to create realistic metabolic profiles containing large numbers of metabolites with a range of user-defined properties. Current features include the simulation of two groups ('case' and 'control') specified by means and standard deviations of concentrations for each metabolite. The software enables addition of spectral noise with a realistic autocorrelation structure at user controllable levels. A crucial feature of the algorithm is its ability to simulate both intra- and inter-metabolite correlations, the analysis of which is fundamental to many techniques in the field. Further, MetAssimulo is able to simulate shifts in NMR peak positions that result from matrix effects such as pH differences which are often observed in metabolic NMR spectra and pose serious challenges for statistical algorithms.</p> <p>Conclusions</p> <p>No other software is currently able to simulate NMR metabolic profiles with such complexity and flexibility. This paper describes the algorithm behind MetAssimulo and demonstrates how it can be used to simulate realistic NMR metabolic profiles with which to develop and test new data analysis techniques. MetAssimulo is freely available for academic use at <url>http://cisbic.bioinformatics.ic.ac.uk/metassimulo/</url>.</p

The possibilities and perils of critical performativity: learning from four case studies

The relevance of academic research to organizational practice is increasingly a concern for management scholars (Currie, Knights, & Starkey, 2010; Starkey & Madan, 2001), and wider social scientists (Chatterton, Hodkinson, & Pickerill, 2010). In particular there have been calls for "engaged scholarship" (Van de Ven, 2007) to "bridge the relevance gap" (Rynes, Bartunek, & Daft, 2001) and create meaningful knowledge that is relevant and useful for practice (for a debate see Boyer, 1997; Deetz, 2008; Learmonth, Lockett, & Dowd, 2012; Van de Ven, 2007; Zundel & Kokkalis, 2010). Such concerns about relevance have also become prevalent within critical management studies (CMS), with regular calls for critical academics to intervene in organizational practice (see for instance Alvesson & Spicer, 2012; Clegg, Kornberger, Carter, & Rhodes, 2006; Koss Hartmann, 2014; Voronov, 2008; Walsh & Weber, 2002; Willmott, 2008; Wolfram Cox, Voronov, LeTrent-Jones, & Weir, 2009). Recently this has been labelled the "performative turn" (Spicer, Alvesson, & Kärreman, 2009) in which critical scholars seek make their work more relevant to organizations (Wickert & Schaefer, 2014, p. 19). Yet, despite the regularity of these calls for intervention, there have been few actual examples of engagement by critical scholars directly into management practice. Without such examples, our understanding of the possibilities of engagement by critical scholars into practice is thus limited, and CMS is left susceptible to the criticism that it is more comfortable discussing radicalism than actually intervening (Koss Hartmann, 2014)

Actes du 29e Colloque de l'AQPC

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Final reading outcomes of the national randomized field trial of Success for All

Using a cluster randomization design, schools were randomly assigned to implement Success for All, a comprehensive reading reform model, or control methods. This article reports final literacy outcomes for a 3-year longitudinal sample of children who participated in the treatment or control condition from kindergarten through second grade and a combined longitudinal and in-mover student sample, both of which were nested within 35 schools. Hierarchical linear model analyses of all three outcomes for both samples revealed statistically significant school-level effects of treatment assignment as large as one third of a standard deviation. The results correspond with the Success for All program theory, which emphasizes both comprehensive school-level reform and targeted student-level achievement effects through a multi-year sequencing of literacy instruction