MetAssign: probabilistic annotation of metabolites from LC–MS data using a Bayesian clustering approach

Motivation: The use of liquid chromatography coupled to mass spectrometry (LC–MS) has enabled the high-throughput profiling of the metabolite composition of biological samples. However, the large amount of data obtained can be difficult to analyse and often requires computational processing to understand which metabolites are present in a sample. This paper looks at the dual problem of annotating peaks in a sample with a metabolite, together with putatively annotating whether a metabolite is present in the sample. The starting point of the approach is a Bayesian clustering of peaks into groups, each corresponding to putative adducts and isotopes of a single metabolite.<p></p> Results: The Bayesian modelling introduced here combines information from the mass-to-charge ratio, retention time and intensity of each peak, together with a model of the inter-peak dependency structure, to increase the accuracy of peak annotation. The results inherently contain a quantitative estimate of confidence in the peak annotations and allow an accurate trade off between precision and recall. Extensive validation experiments using authentic chemical standards show that this system is able to produce more accurate putative identifications than other state-of-the-art systems, while at the same time giving a probabilistic measure of confidence in the annotations.<p></p> Availability: The software has been implemented as part of the mzMatch metabolomics analysis pipeline, which is available for download at http://mzmatch.sourceforge.net/

Adverse events during anaesthesia at an Ethiopian referral hospital : a prospective observational study

Peer reviewedPublisher PD

Models of Deterministic and Stochastic Comparison: Two Studies in Applied Operations Research

This dissertation includes two essays on applications of management science methods to modelling service systems and developing novel improvements to sports team ranking systems. The first essay proposes a novel approach to modelling changes in business procedures that have neither explicitly positive nor explicitly negative effects on operational performance, but are changes to operating rules; we call these procedure changes Operational Protocol Modifications (OPMs). Our approach is to model these OPMs via distributional censoring. Using the scenario of a technical support employee at a SaaS firm, we model changes in OPMs as censoring effects on the distributions of both service quality and service time. We demonstrate the nonlinear effects OPMs can have on the optimal service contract and the employer's (principal's) expected utility in hiring the technical support employee (agent), under certain distributional assumptions. This modelling approach arms operations management analysts with a new tool to better capture the impact of OPMs and their non-linear impacts on operational performance. The second essay proposes a number of additions to both static and dynamic network ranking models for professional soccer teams. We introduce ways to incorporate relevant home/away game status and goal difference information. Further, we introduce a collection of methods to measure the competitive similarity between teams, which we then integrate into the ranking systems. We demonstrate, using a large collection of data on five of the top European professional soccer leagues, that our methods produce superior empirical performance when compared to comparable approaches. Importantly, our work is the first to integrate the competitive similarity notion directly into network ranking models, providing the first direct link between two related bodies of literature

The Effect of Large Scale Magnetic Turbulence on the Acceleration of Electrons by Perpendicular Collisionless Shocks

We study the physics of electron acceleration at collisionless shocks that move through a plasma containing large-scale magnetic fluctuations. We numerically integrate the trajectories of a large number of electrons, which are treated as test particles moving in the time dependent electric and magnetic fields determined from 2-D hybrid simulations (kinetic ions, fluid electron). The large-scale magnetic fluctuations effect the electrons in a number of ways and lead to efficient and rapid energization at the shock front. Since the electrons mainly follow along magnetic lines of force, the large-scale braiding of field lines in space allows the fast-moving electrons to cross the shock front several times, leading to efficient acceleration. Ripples in the shock front occuring at various scales will also contribute to the acceleration by mirroring the electrons. Our calculation shows that this process favors electron acceleration at perpendicular shocks. The current study is also helpful in understanding the injection problem for electron acceleration by collisionless shocks. It is also shown that the spatial distribution of energetic electrons is similar to in-situ observations (e.g., Bale et al. 1999; Simnett et al. 2005). The process may be important to our understanding of energetic electrons in planetary bow shocks and interplanetary shocks, and explaining herringbone structures seen in some type II solar radio bursts.Comment: 23 pages, 6 figures, accepted by Ap

Topic modeling for untargeted substructure exploration in metabolomics

The potential of untargeted metabolomics to answer important questions across the life sciences is hindered due to a paucity of computational tools that enable extraction of key biochemically relevant information. Available tools focus on using mass spectrometry fragmentation spectra to identify molecules whose behavior suggests they are relevant to the system under study. Unfortunately, fragmentation spectra cannot identify molecules in isolation, but require authentic standards or databases of known fragmented molecules. Fragmentation spectra are, however, replete with information pertaining to the biochemical processes present; much of which is currently neglected. Here we present an analytical workflow that exploits all fragmentation data from a given experiment to extract biochemically-relevant features in an unsupervised manner. We demonstrate that an algorithm originally utilized for text-mining, Latent Dirichlet Allocation, can be adapted to handle metabolomics datasets. Our approach extracts biochemically-relevant molecular substructures (‘Mass2Motifs’) from spectra as sets of co-occurring molecular fragments and neutral losses. The analysis allows us to isolate molecular substructures, whose presence allows molecules to be grouped based on shared substructures regardless of classical spectral similarity. These substructures in turn support putative de novo structural annotation of molecules. Combining this spectral connectivity to orthogonal correlations (e.g. common abundance changes under system perturbation) significantly enhances our ability to provide mechanistic explanations for biological behavior

A Previously Unreported Arterial Variant of the Suboccipital Region Based on Cadaveric Dissection.

Introduction Several arterial variants have been reported to occur around the posterior arch of the atlas. Understanding the various anomalies and diagnosing them preoperatively can dramatically reduce the risk of surgical insult during neurosurgical procedures. Herein we report a case of an arterial variant found just below the posterior arch of C1. Case Report During the routine dissection of the suboccipital region via a posterior approach, an unusual bulge was identified just inferior to the inferior capitis oblique muscle. With further dissection, the structure was identified as a tortuous internal carotid artery. Conclusion Arterial variants around the posterior arch of C1 are surgically significant and can result in catastrophic injuries if unappreciated. Most of these variants will be related to the vertebral artery. To our knowledge, an arterial variant of the internal carotid artery in this location, as reported herein, has not been previously reported

PiMP my metabolome:An integrated, web-based tool for LC-MS metabolomics data

Summary: The Polyomics integrated Metabolomics Pipeline (PiMP) fulfils an unmet need in metabolomics data analysis. PiMP offers automated and user-friendly analysis from mass spectrometry data acquisition to biological interpretation. Our key innovations are the Summary Page, which provides a simple overview of the experiment in the format of a scientific paper, containing the key findings of the experiment along with associated metadata; and the Metabolite Page, which provides a list of each metabolite accompanied by ‘evidence cards’, which provide a variety of criteria behind metabolite annotation including peak shapes, intensities in different sample groups and database information. Availability: PiMP is available at http://polyomics.mvls.gla.ac.uk, and access is freely available on request. 50 GB of space is allocated for data storage, with unrestricted number of samples and analyses per user. Source code is available at https://github.com/RonanDaly/pimp and licensed under the GPL

Ranking metabolite sets by their activity levels

Related metabolites can be grouped into sets in many ways, e.g., by their participation in series of chemical reactions (forming metabolic pathways), or based on fragmentation spectral similarities or shared chemical substructures. Understanding how such metabolite sets change in relation to experimental factors can be incredibly useful in the interpretation and understanding of complex metabolomics data sets. However, many of the available tools that are used to perform this analysis are not entirely suitable for the analysis of untargeted metabolomics measurements. Here, we present PALS (Pathway Activity Level Scoring), a Python library, command line tool, and Web application that performs the ranking of significantly changing metabolite sets over different experimental conditions. The main algorithm in PALS is based on the pathway level analysis of gene expression (PLAGE) factorisation method and is denoted as mPLAGE (PLAGE for metabolomics). As an example of an application, PALS is used to analyse metabolites grouped as metabolic pathways and by shared tandem mass spectrometry fragmentation patterns. A comparison of mPLAGE with two other commonly used methods (overrepresentation analysis (ORA) and gene set enrichment analysis (GSEA)) is also given and reveals that mPLAGE is more robust to missing features and noisy data than the alternatives. As further examples, PALS is also applied to human African trypanosomiasis, Rhamnaceae, and American Gut Project data. In addition, normalisation can have a significant impact on pathway analysis results, and PALS offers a framework to further investigate this. PALS is freely available from our project Web site