An evaluation of exact matching and propensity score methods as applied in a comparative effectiveness study of inhaled corticosteroids in asthma

Peer reviewedPublisher PD

Solar System Objects in the ISOPHOT 170 micron Serendipity Survey

The ISOPHOT Serendipity Survey (ISOSS) covered approximately 15 % of the sky at a wavelength of 170 micron while the ISO satellite was slewing from one target to the next. By chance ISOSS slews went over many solar system objects (SSOs). We identified the comets, asteroids and planets in the slews through a fast and effective search procedure based on N-body ephemeris and flux estimates. The detections were analysed from a calibration and scientific point of view. Through the measurements of the well-known asteroids Ceres, Pallas, Juno and Vesta and the planets Uranus and Neptune it was possible to improve the photometric calibration of ISOSS and to extend it to higher flux regimes. We were also able to establish calibration schemes for the important slew end data. For the other asteroids we derived radiometric diameters and albedos through a recent thermophysical model. The scientific results are discussed in the context of our current knowledge of size, shape and albedos, derived from IRAS observations, occultation measurements and lightcurve inversion techniques. In all cases where IRAS observations were available we confirm the derived diameters and albedos. For the five asteroids without IRAS detections only one was clearly detected and the radiometric results agreed with sizes given by occultation and HST observations. Four different comets have clearly been detected at 170 micron and two have marginal detections. The observational results are presented to be used by thermal comet models in the future. The nine ISOSS slews over Hale-Bopp revealed extended and asymmetric structures related to the dust tail. We attribute the enhanced emission in post-perihelion observations to large particles around the nucleus. The signal patterns are indicative of a concentration of the particles in trail direction.Comment: 15 pages, 6 figures, 6 tables; Accepted for publication in Astronomy and Astrophysic

Impact of EMA regulatory label changes on systemic diclofenac initiation, discontinuation, and switching to other pain medicines in Scotland, England, Denmark, and The Netherlands

Purpose: In June 2013 a European Medicines Agency referral procedure concluded that diclofenac was associated with an elevated risk of acute cardiovascular events and contraindications, warnings, and changes to the product information were implemented across the European Union. This study measured the impact of the regulatory action on the prescribing of systemic diclofenac in Denmark, The Netherlands, England, and Scotland. Methods: Quarterly time series analyses measuring diclofenac prescription initiation, discontinuation and switching to other systemic nonsteroidal anti-inflammatory (NSAIDs), topical NSAIDs, paracetamol, opioids, and other chronic pain medication in those who discontinued diclofenac. Absolute effects were estimated using interrupted time series regression. Results: Overall, diclofenac prescription initiations fell during the observation periods of all countries. Compared with Denmark where there appeared to be amore limited effect, the regulatory action was associated with significant immediate reductions in diclofenac initiation in The Netherlands (−0.42%, 95% CI, −0.66% to −0.18%), England (−0.09%, 95% CI, −0.11% to −0.08%), and Scotland (−0.67%, 95% CI, −0.79% to −0.55%); and falling trends in diclofenac initiation in the Netherlands (−0.03%, 95% CI, −0.06% to −0.01% per quarter) and Scotland (−0.04%, 95% CI, −0.05% to −0.02% per quarter). There was no significant impact on diclofenac discontinuation in any country. The regulatory action was associated with modest differences in switching to other pain medicines following diclofenac discontinuation. Conclusions: The regulatory action was associated with significant reductions in overall diclofenac initiation which varied by country and type of exposure. There was no impact on discontinuation and variable impact on switching

Self-similarity of complex networks

Complex networks have been studied extensively due to their relevance to many real systems as diverse as the World-Wide-Web (WWW), the Internet, energy landscapes, biological and social networks \cite{ab-review,mendes,vespignani,newman,amaral}. A large number of real networks are called ``scale-free'' because they show a power-law distribution of the number of links per node \cite{ab-review,barabasi1999,faloutsos}. However, it is widely believed that complex networks are not {\it length-scale} invariant or self-similar. This conclusion originates from the ``small-world'' property of these networks, which implies that the number of nodes increases exponentially with the ``diameter'' of the network \cite{erdos,bollobas,milgram,watts}, rather than the power-law relation expected for a self-similar structure. Nevertheless, here we present a novel approach to the analysis of such networks, revealing that their structure is indeed self-similar. This result is achieved by the application of a renormalization procedure which coarse-grains the system into boxes containing nodes within a given "size". Concurrently, we identify a power-law relation between the number of boxes needed to cover the network and the size of the box defining a finite self-similar exponent. These fundamental properties, which are shown for the WWW, social, cellular and protein-protein interaction networks, help to understand the emergence of the scale-free property in complex networks. They suggest a common self-organization dynamics of diverse networks at different scales into a critical state and in turn bring together previously unrelated fields: the statistical physics of complex networks with renormalization group, fractals and critical phenomena.Comment: 28 pages, 12 figures, more informations at http://www.jamlab.or

Many-body interactions and melting of colloidal crystals

We study the melting behavior of charged colloidal crystals, using a simulation technique that combines a continuous mean-field Poisson-Boltzmann description for the microscopic electrolyte ions with a Brownian-dynamics simulation for the mesoscopic colloids. This technique ensures that many-body interactions between the colloids are fully taken into account, and thus allows us to investigate how many-body interactions affect the solid-liquid phase behavior of charged colloids. Using the Lindemann criterion, we determine the melting line in a phase-diagram spanned by the colloidal charge and the salt concentration. We compare our results to predictions based on the established description of colloidal suspensions in terms of pairwise additive Yukawa potentials, and find good agreement at high-salt, but not at low-salt concentration. Analyzing the effective pair-interaction between two colloids in a crystalline environment, we demonstrate that the difference in the melting behavior observed at low salt is due to many-body interactions

The electrical double layer for a fully asymmetric electrolyte around a spherical colloid: an integral equation study

The hypernetted chain/mean spherical approximation (HNC/MSA) integral equation is obtained and solved numerically for a totally asymmetric primitive model electrolyte around a spherical macroparticle. The ensuing radial distribution functions show a very good agreement when compared to our Monte Carlo and molecular dynamics simulations for spherical geometry and with respect to previous anisotropic reference HNC calculations in the planar limit. We report an analysis of the potential vs charge relationship, radial distribution functions, mean electrostatic potential and cumulative reduced charge for representative cases of 1:1 and 2:2 salts with a size asymmetry ratio of 2. Our results are collated with those of the Modified Gouy-Chapman (MGC) and unequal radius Modified Gouy-Chapman (URMGC) theories and with those of HNC/MSA in the restricted primitive model (RPM) to assess the importance of size asymmetry effects. One of the most striking characteristics found is that,\textit{contrary to the general belief}, away from the point of zero charge the properties of an asymmetric electrical double layer (EDL) are not those corresponding to a symmetric electrolyte with the size and charge of the counterion, i.e. \textit{counterions do not always dominate}. This behavior suggests the existence of a new phenomenology in the EDL that genuinely belongs to a more realistic size-asymmetric model where steric correlations are taken into account consistently. Such novel features can not be described by traditional mean field theories like MGC, URMGC or even by enhanced formalisms, like HNC/MSA, if they are based on the RPM.Comment: 29 pages, 13 figure

Reconciling gene expression data with regulatory network models – a stimulon-based approach for integrated metabolic and regulatory modeling of Bacillus subtilis

The reconstruction of genome-scale metabolic models from genome annotations has become a routine practice in Systems Biology research. The potential of metabolic models for predictive biology is widely accepted by the scientific community, but these same models still lack the capability to account for the effect of gene regulation on metabolic activity. Our focus organism, Bacillus subtilis is most commonly found in soil, being subject to a wide variety of external environmental conditions. This reinforces the importance of the regulatory mechanisms that allow the bacteria to survive and adapt to such conditions. Existing integrated metabolic regulatory models are currently available for only a small number of well-known organisms (e.g E. coli and B. subtilis). The E. coli integrated model was proposed by Covert et al in 2004 and has slowly improved over the years. Goelzer et al. introduced the B. subtilis integrated model in 2008, covering only the central metabolic pathways. Different strategies were used in the two modeling efforts. The E. coli model is defined by a set of Boolean rules (turning genes ON and OFF) accounting mostly for transcription factors, gene interactions, involved metabolites, and some external conditions such as heat shock. The B. subtilis model introduces a set of more complex rules and also incorporates sigma factor activity into the modeling abstraction. Here we propose a genome-scale model for the regulatory network of B. subtilis, using a new stimulon-based approach. A stimulon is defined as the set of genes (that can be a part of the same operon(s) and regulon(s)) that respond in the same set of stimuli. The proposed stimulon-based approach allows for the inclusion of more types of regulation in the model. This methodology also abstracts away much of the complexity of regulatory mechanisms by directly connecting the activity of genes to the presence or absence of associated stimuli, a necessity in the many cases where details of regulatory mechanisms are poorly understood. Our model integrates regulatory network data from the Goelzer et al model, in addition to other available literature data. We then reconciled our model against a large set of high-quality gene expression data (tiled microarrays for 104 different conditions). The stimulons in our model were split or extended to improve consistency with our expression data, and the stimuli in our model were adjusted to improve consistency with the conditions of our expression experiments. The reconciliation with gene expression data revealed a significant number of exact or nearly exact matches between the manually curated regulons/stimulons and pure correlation-based regulons. Our reconciliation analysis of the 2011 SubtiWiki regulon release suggested many gene candidates for regulon extension that were subsequently included in the 2013 SubtiWiki update. Our enhanced model also includes an improved coverage of a wide range of different stress conditions. We then integrated our regulatory model with the latest metabolic reconstruction for B. subtilis, the iBsu1103V2 model (Tanaka et al. 2012). We applied this integrated metabolic regulatory model to the simulation of all growth phenotype data currently available for B. subtilis, demonstrating how the addition of regulatory constraints improved consistency of model predictions with experimentally observed phenotype data. This analysis of growth phenotype data unveiled phenotypes that could only be characterized with the addition of regulatory network constraints. All tools applied in the reconstruction, simulation, and curation of our new regulatory model are now publicly available as a part of the KBase framework. These tools permit the direct simulation of gene expression data using the regulon model alone, as well as the simulation of phenotypes and growth conditions using an integrated metabolic and regulatory model. We will highlight these new tools in the context of our reconstruction and analysis of the B. subtilis regulatory model

Metagenomic analysis of the saliva microbiome with merlin

In recent years, metagenomics has demonstrated to play an essential role on the study of the microorganisms that live in microbial communities, particularly those who inhabit the human body. Several bioinformatics tools and pipelines have been developed for the analysis of these data, but they usually only address one topic: to identify the taxonomic composition or to address the metabolic functional profile. This work aimed to implement a computational framework able to answer the two questions simultaneously. Merlin, a previously released software aiming at the reconstruction of genome-scale metabolic models for single organisms, was extended to deal with metagenomics data. It has an user-friendly and intuitive interface, being suitable for those with limited bioinformatics skills. The performance of the tool was evaluated with samples from the Human Microbiome Project, particularly from saliva. Overall, the results show the same patterns reported before: while the pathways needed for microbial life remain relatively stable, the community composition varies extensively among individuals

Reconciling gene expression data with regulatory network models

The reconstruction of genome-scale metabolic models from genome annotations has become a routine practice in Systems Biology research. The potential of metabolic models for predictive biology is widely accepted by the scientific community, but these same models still lack the capability to account for the effect of gene regulation on metabolic activity. Our focus organism, Bacillus subtilis is most commonly found in soil, being subject to a wide variety of external environmental conditions. This reinforces the importance of the regulatory mechanisms that allow the bacteria to survive and adapt to such conditions. We introduce a manually curated regulatory network for Bacillus subtilis, tapping into the notable resources for B. subtilis regulation. We propose the concept of Atomic Regulon, as a set of genes that share the same ON and OFF gene expression profile across multiple samples of experimental data. Atomic regulon inference uses prior knowledge from curated SEED subsystems, in addition to expression data to infer regulatory interactions. We show how atomic regulons for B. subtilis are able to capture many sets of genes corresponding to regulated operons in our manually curated network. Additionally, we demonstrate how atomic regulons can be used to help expand/ validate the knowledge of the regulatory networks and gain insights into novel biology