Probabilistic simulation for the certification of railway vehicles

The present dynamic certification process that is based on experiments has been essentially built on the basis of experience. The introduction of simulation techniques into this process would be of great interest. However, an accurate simulation of complex, nonlinear systems is a difficult task, in particular when rare events (for example, unstable behaviour) are considered. After analysing the system and the currently utilized procedure, this paper proposes a method to achieve, in some particular cases, a simulation-based certification. It focuses on the need for precise and representative excitations (running conditions) and on their variable nature. A probabilistic approach is therefore proposed and illustrated using an example. First, this paper presents a short description of the vehicle / track system and of the experimental procedure. The proposed simulation process is then described. The requirement to analyse a set of running conditions that is at least as large as the one tested experimentally is explained. In the third section, a sensitivity analysis to determine the most influential parameters of the system is reported. Finally, the proposed method is summarized and an application is presented

SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo

The scientific interest attracted by Spiking Neural Networks (SNN) has lead to the development of tools for the simulation and study of neuronal dynamics ranging from phenomenological models to the more sophisticated and biologically accurate Hodgkin-and-Huxley-based and multi-compartmental models. However, despite the multiple features offered by neural modelling tools, their integration with environments for the simulation of robots and agents can be challenging and time consuming. The implementation of artificial neural circuits to control robots generally involves the following tasks: (1) understanding the simulation tools, (2) creating the neural circuit in the neural simulator, (3) linking the simulated neural circuit with the environment of the agent and (4) programming the appropriate interface in the robot or agent to use the neural controller. The accomplishment of the above-mentioned tasks can be challenging, especially for undergraduate students or novice researchers. This paper presents an alternative tool which facilitates the simulation of simple SNN circuits using the multi-agent simulation and the programming environment Netlogo (educational software that simplifies the study and experimentation of complex systems). The engine proposed and implemented in Netlogo for the simulation of a functional model of SNN is a simplification of integrate and fire (I&F) models. The characteristics of the engine (including neuronal dynamics, STDP learning and synaptic delay) are demonstrated through the implementation of an agent representing an artificial insect controlled by a simple neural circuit. The setup of the experiment and its outcomes are described in this work

A cDNA microarray approach to decipher sunflower (Helianthus annuus) responses to the necrotrophic fungus Phoma macdonaldii

To identify the genes involved in the partial resistance of sunflower (Helianthus annuus) to the necrotrophic fungus Phoma macdonaldii, we developed a 1000‐element cDNA microarray containing carefully chosen genes putatively involved in primary metabolic pathways, signal transduction and biotic stress responses. A two‐pass general linear model was used to normalize the data and then to detect differentially expressed genes. This method allowed us to identify 38 genes differentially expressed among genotypes, treatments and times, mainly belonging to plant defense, signaling pathways and amino acid metabolism. Based on a set of genes whose differential expression was highly significant, we propose a model in which negative regulation of a dual‐specificity MAPK phosphatase could be implicated in sunflower defense mechanisms against the pathogen. The resulting activation of the MAP kinase cascade could subsequently trigger defense responses (e.g. thaumatin biosynthesis and phenylalanine ammonia lyase activation), under the control of transcription factors belonging to MYB and WRKY families. Concurrently, the activation of protein phosphatase 2A (PP2A), which is implicated in cell death inhibition, could limit pathogen development. The results reported here provide a valuable first step towards the understanding and analysis of the P. macdonaldii–sunflower interaction

Effect of arsenic-phosphorus interaction on arsenic-induced oxidative stress in chickpea plants

Arsenic-induced oxidative stress in chickpea was investigated under glasshouse conditions in response to application of arsenic and phosphorus. Three levels of arsenic (0, 30 and 60 mg kg−1) and four levels of P (50, 100, 200, and 400 mg kg−1) were applied to soil-grown plants. Increasing levels of both arsenic and P significantly increased arsenic concentrations in the plants. Shoot growth was reduced with increased arsenic supply regardless of applied P levels. Applied arsenic induced oxidative stress in the plants, and the concentrations of H2O2 and lipid peroxidation were increased. Activity of superoxide dismutase (SOD) and concentrations of non-enzymatic antioxidants decreased in these plants, but activities of catalase (CAT) and ascorbate peroxidase (APX) were significantly increased under arsenic phytotoxicity. Increased supply of P decreased activities of CAT and APX, and decreased concentrations of non-enzymatic antioxidants, but the high-P plants had lowered lipid peroxidation. It can be concluded that P increased uptake of arsenic from the soil, probably by making it more available, but although plant growth was inhibited by arsenic the P may have partially protected the membranes from arsenic-induced oxidative stress

Designing an automated clinical decision support system to match clinical practice guidelines for opioid therapy for chronic pain

Abstract Background Opioid prescribing for chronic pain is common and controversial, but recommended clinical practices are followed inconsistently in many clinical settings. Strategies for increasing adherence to clinical practice guideline recommendations are needed to increase effectiveness and reduce negative consequences of opioid prescribing in chronic pain patients. Methods Here we describe the process and outcomes of a project to operationalize the 2003 VA/DOD Clinical Practice Guideline for Opioid Therapy for Chronic Non-Cancer Pain into a computerized decision support system (DSS) to encourage good opioid prescribing practices during primary care visits. We based the DSS on the existing ATHENA-DSS. We used an iterative process of design, testing, and revision of the DSS by a diverse team including guideline authors, medical informatics experts, clinical content experts, and end-users to convert the written clinical practice guideline into a computable algorithm to generate patient-specific recommendations for care based upon existing information in the electronic medical record (EMR), and a set of clinical tools. Results The iterative revision process identified numerous and varied problems with the initially designed system despite diverse expert participation in the design process. The process of operationalizing the guideline identified areas in which the guideline was vague, left decisions to clinical judgment, or required clarification of detail to insure safe clinical implementation. The revisions led to workable solutions to problems, defined the limits of the DSS and its utility in clinical practice, improved integration into clinical workflow, and improved the clarity and accuracy of system recommendations and tools. Conclusions Use of this iterative process led to development of a multifunctional DSS that met the approval of the clinical practice guideline authors, content experts, and clinicians involved in testing. The process and experiences described provide a model for development of other DSSs that translate written guidelines into actionable, real-time clinical recommendations.http://deepblue.lib.umich.edu/bitstream/2027.42/78267/1/1748-5908-5-26.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/2/1748-5908-5-26.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/3/1748-5908-5-26-S3.TIFFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/4/1748-5908-5-26-S2.TIFFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/5/1748-5908-5-26-S1.TIFFPeer Reviewe

ruvA Mutants that resolve Holliday junctions but do not reverse replication forks

RuvAB and RuvABC complexes catalyze branch migration and resolution of Holliday junctions (HJs) respectively. In addition to their action in the last steps of homologous recombination, they process HJs made by replication fork reversal, a reaction which occurs at inactivated replication forks by the annealing of blocked leading and lagging strand ends. RuvAB was recently proposed to bind replication forks and directly catalyze their conversion into HJs. We report here the isolation and characterization of two separation-of-function ruvA mutants that resolve HJs, based on their capacity to promote conjugational recombination and recombinational repair of UV and mitomycin C lesions, but have lost the capacity to reverse forks. In vivo and in vitro evidence indicate that the ruvA mutations affect DNA binding and the stimulation of RuvB helicase activity. This work shows that RuvA's actions at forks and at HJs can be genetically separated, and that RuvA mutants compromised for fork reversal remain fully capable of homologous recombination

A Tale of Two Current Sheets

I outline a new model of particle acceleration in the current sheet separating the closed from the open field lines in the force-free model of pulsar magnetospheres, based on reconnection at the light cylinder and "auroral" acceleration occurring in the return current channel that connects the light cylinder to the neutron star surface. I discuss recent studies of Pulsar Wind Nebulae, which find that pair outflow rates in excess of those predicted by existing theories of pair creation occur, and use those results to point out that dissipation of the magnetic field in a pulsar's wind upstream of the termination shock is restored to life as a viable model for the solution of the "$\sigma$" problem as a consequence of the lower wind 4-velocity implied by the larger mass loading.Comment: 17 pages, 6 figures, Invited Review, Proceedings of the "ICREA Workshop on The High-Energy Emission from Pulsars and their Systems", Sant Cugat, Spain, April 12-16, 201

Blood Cholinesterases from Washington State Orchard Workers

Court-ordered monitoring of blood cholinesterases (ChEs) from orchard workers in Washington State is underway. In 2008, the mean red blood cell acetylcholinesterase (AChE, EC 3.1.1.7) activity was 9.65 ± 1.11 μmoles/min/ml (n = 1,793) and the mean serum (BChE, 3.1.1.6) activity was 5.19 ± 0.90 μmoles/min/ml (n = 1,811). Determinations were made using the Ellman assay and automated equipment of Pathology Associates Medical Laboratories (PAML), Spokane, Washington

Complete mitochondrial DNA sequences provide new insights into the Polynesian motif and the peopling of Madagascar

More than a decade of mitochondrial DNA (mtDNA) studies have given the 'Polynesian motif' renowned status as a marker for tracing the late-Holocene expansion of Austronesian speaking populations. Despite considerable research on the Polynesian motif in Oceania, there has been little equivalent work on the western edge of its expansion - leaving major issues unresolved regarding the motif's evolutionary history. This has also led to considerable uncertainty regarding the settlement of Madagascar. In this study, we assess mtDNA variation in 266 individuals from three Malagasy ethnic groups: the Mikea, Vezo, and Merina. Complete mtDNA genome sequencing reveals a new variant of the Polynesian motif in Madagascar; two coding region mutations define a Malagasy-specific sub-branch. This newly defined 'Malagasy motif' occurs at high frequency in all three ethnic groups (13-50%), and its phylogenetic position, geographic distribution, and estimated age all support a recent origin, but without conclusively identifying a specific source region. Nevertheless, the haplotype's limited diversity, similar to those of other mtDNA haplogroups found in our Malagasy groups, best supports a small number of initial settlers arriving to Madagascar through the same migratory process. Finally, the discovery of this lineage provides a set of new polymorphic positions to help localize the Austronesian ancestors of the Malagasy, as well as uncover the origin and evolution of the Polynesian motif itself

Emergent global patterns of ecosystem structure and function from a mechanistic general ecosystem model

Anthropogenic activities are causing widespread degradation of ecosystems worldwide, threatening the ecosystem services upon which all human life depends. Improved understanding of this degradation is urgently needed to improve avoidance and mitigation measures. One tool to assist these efforts is predictive models of ecosystem structure and function that are mechanistic: based on fundamental ecological principles. Here we present the first mechanistic General Ecosystem Model (GEM) of ecosystem structure and function that is both global and applies in all terrestrial and marine environments. Functional forms and parameter values were derived from the theoretical and empirical literature where possible. Simulations of the fate of all organisms with body masses between 10 µg and 150,000 kg (a range of 14 orders of magnitude) across the globe led to emergent properties at individual (e.g., growth rate), community (e.g., biomass turnover rates), ecosystem (e.g., trophic pyramids), and macroecological scales (e.g., global patterns of trophic structure) that are in general agreement with current data and theory. These properties emerged from our encoding of the biology of, and interactions among, individual organisms without any direct constraints on the properties themselves. Our results indicate that ecologists have gathered sufficient information to begin to build realistic, global, and mechanistic models of ecosystems, capable of predicting a diverse range of ecosystem properties and their response to human pressures