Structural Sensitivity of Neural and Genetic Networks

International audienceThis paper aims at giving new results on the structural sensitivity of biological networks represented by threshold Boolean networks and ruled by Hopfield-like evolution laws classically used in the context of neural and genetic networks. Indeed, the objective is to present how certain changes and/or perturbations in such networks can modify signicantly their asymptotic behaviour. More precisely, this work has been focused on three diferent kinds of what we think to be relevant in the biological area of robustness (in both theoretical and applied frameworks): the boundary sensitivity (external fields, hormone flows, ...), the state sensitivity (axonal or somatic modulations, microRNAs actions, ...) and the updating sensitivity

On the complexity of acyclic modules in automata networks

Modules were introduced as an extension of Boolean automata networks. They have inputs which are used in the computation said modules perform, and can be used to wire modules with each other. In the present paper we extend this new formalism and study the specific case of acyclic modules. These modules prove to be well described in their limit behavior by functions called output functions. We provide other results that offer an upper bound on the number of attractors in an acyclic module when wired recursively into an automata network, alongside a diversity of complexity results around the difficulty of deciding the existence of cycles depending on the number of inputs and the size of said cycle.Comment: 21 page

Implementation and Performance of the ATLAS Second Level Jet Trigger

ATLAS is one of the four major LHC experiments, designed to cover a wide range of physics topics. In order to cope with a rate of 40 MHz and 25 interactions per bunch crossing, the ATLAS trigger system is divided in three different levels. The first one (LVL1, hardware based) identifies signatures in 2 microseconds that are confirmed by the the following trigger levels (software based). The Second Level Trigger (LVL2) only looks at a region of the space around the LVL1 signature (called Region of Interest or ROI), confirming/rejecting the event in about 10 ms, while the Event Filter (Third Level Trigger, EF) has potential full event access and larger processing times, of the order of 1 s. The jet selection starts at the LVL1 with dedicated processors that search for high ET hadronic energy depositions. At the LVL2, the jet signatures are verified with the execution of a dedicated, fast jet reconstruction algorithm. Given the fact that the main jet's background are jets,the energy calibration at the LVL2 is one of the major dificulties of this trigger, allowing to distinguish low/high energy jets. The algorithm for the calibration has been chosen to be fast and robust, with a good performance. The other major dificulty is the execution time of the algorithm,dominated by the data unpacking time due to the large sizes of the jet ROI. In order to reduce the execution time, three possible granularities have been proposed and are being evaluated: cell based (standard), energy sums calculated at each Fron-End Board (FEB) and the use of the LVL1 Trigger Towers. The FEB and Trigger Tower granularities are also being used/evaluated for the reconstruction of the missing ET triggers at the Event Filter, given the short times available to process the full event. In this presentation, the design and implementation of the jet trigger of ATLAS will be discussed in detail, emphasasing the major dificulties of each selection step. The performance of the jet algorithm, including timing, eficiencies and rates will also be shown, with detailed comparisons of the different unpacking modes

Characterization of Reachable Attractors Using Petri Net Unfoldings

International audienceAttractors of network dynamics represent the long-term behaviours of the modelled system. Their characterization is therefore crucial for understanding the response and differentiation capabilities of a dynamical system. In the scope of qualitative models of interaction networks, the computation of attractors reachable from a given state of the network faces combinatorial issues due to the state space explosion. In this paper, we present a new algorithm that exploits the concurrency between transitions of parallel acting components in order to reduce the search space. The algorithm relies on Petri net unfoldings that can be used to compute a compact representation of the dynamics. We illustrate the applicability of the algorithm with Petri net models of cell signalling and regulation networks, Boolean and multi-valued. The proposed approach aims at being complementary to existing methods for deriving the attractors of Boolean models, while being %so far more generic since it applies to any safe Petri net

Association Between Toenail Selenium and Risk of Acute Myocardial Infarction in European Men: The EURAMIC Study

The association between selenium status and risk of acute myocardial infarction was examined in a multicenter case-control study in 10 centers from Europe and Israel in 1991-1992. Selenium in toenails was assessed for 683 nonfatal male cases with first acute myocardial infarction and 729 controls less than 70 years of age. Median toenail selenium content was 0.553 μg/g for cases and 0.590 μg/g for controls. After adjustment for age, center, and smoking, the odds ratio for myocardial infarction in the highest quintile of selenium as compared with the lowest was 0.63 . The observed inverse trend was somewhat stronger when the authors adjusted for vitamin E status (p = 0.05). Analysis stratified for smoking habits showed an inverse association in former smokers (odds ratio for the 75th-25th percentile contrast = 0.63 (95 percent confidence interval 0.43-0.94)), but not in current smokers (odds ratio = 0.97 ( 0.71-1.32)) or in those who had never smoked (odds ratio = 1.55 (0.87-2.76)). Analysis stratified by center showed a significant inverse association between selenium levels and risk of myocardial infarction for Germany (Berlin) only (75th to 25th percentile odds ratio = 0.62 (95 percent confidence interval 0.42-0.91)), which was the center with the lowest selenium levels. It appears that the increased risk of acute myocardial infarction at low levels of selenium intake is largely explained by cigarette smoking; selenium status does not appear to be an important determinant of risk of myocardial infarction at the levels observed in a large part of Europe. Am J Epidemiol 1997; 145: 373-

Measurements of Higgs boson production and couplings in diboson final states with the ATLAS detector at the LHC

Measurements are presented of production properties and couplings of the recently discovered Higgs boson using the decays into boson pairs, H →γ γ, H → Z Z∗ →4l and H →W W∗ →lνlν. The results are based on the complete pp collision data sample recorded by the ATLAS experiment at the CERN Large Hadron Collider at centre-of-mass energies of √s = 7 TeV and √s = 8 TeV, corresponding to an integrated luminosity of about 25 fb−1. Evidence for Higgs boson production through vector-boson fusion is reported. Results of combined fits probing Higgs boson couplings to fermions and bosons, as well as anomalous contributions to loop-induced production and decay modes, are presented. All measurements are consistent with expectations for the Standard Model Higgs boson

Standalone vertex finding in the ATLAS muon spectrometer

A dedicated reconstruction algorithm to find decay vertices in the ATLAS muon spectrometer is presented. The algorithm searches the region just upstream of or inside the muon spectrometer volume for multi-particle vertices that originate from the decay of particles with long decay paths. The performance of the algorithm is evaluated using both a sample of simulated Higgs boson events, in which the Higgs boson decays to long-lived neutral particles that in turn decay to bbar b final states, and pp collision data at √s = 7 TeV collected with the ATLAS detector at the LHC during 2011