Performance of the ALICE experiment at the CERN LHC

ALICE is the heavy-ion experiment at the CERN Large Hadron Collider. The experiment continuously took data during the first physics campaign of the machine from fall 2009 until early 2013, using proton and lead-ion beams. In this paper we describe the running environment and the data handling procedures, and discuss the performance of the ALICE detectors and analysis methods for various physics observables

Threats of Zika virus transmission for Asia and its Hindu-Kush Himalayan region

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.No specific funding was received for this research. However, the work of RM, UK and DAG was funded by the Federal Ministry of Education and Research of Germany (BMBF) under the project AECO (number 01Kl1717) as part of the National Research Network on Zoonotic Infectious Diseases of Germany

Elliptic flow of identified hadrons in Pb-Pb collisions at 1asNN = 2.76 TeV

The elliptic flow coefficient (v2) of identified particles in Pb-Pb collisions at 1asNN = 2.76 TeV was measured with the ALICE detector at the Large Hadron Collider (LHC). The results were obtained with the Scalar Product method, a two-particle corre- lation technique, using a pseudo-rapidity gap of | 06\u3b7| > 0.9 between the identified hadron under study and the reference particles. The v2 is reported for \u3c0\ub1, K\ub1, K0S, p+p, \u3c6, \u39b+\u39b, \u39e 12+\u39e+ and \u3a9 12+\u3a9+ in several collision centralities. In the low transverse momentum (pT) region, pT 3 GeV/c

Model checking optimal finite-horizon control for probabilistic gene regulatory networks

Abstract Background Probabilistic Boolean networks (PBNs) have been proposed for analyzing external control in gene regulatory networks with incorporation of uncertainty. A context-sensitive PBN with perturbation (CS-PBNp), extending a PBN with context-sensitivity to reflect the inherent biological stability and random perturbations to express the impact of external stimuli, is considered to be more suitable for modeling small biological systems intervened by conditions from the outside. In this paper, we apply probabilistic model checking, a formal verification technique, to optimal control for a CS-PBNp that minimizes the expected cost over a finite control horizon. Results We first describe a procedure of modeling a CS-PBNp using the language provided by a widely used probabilistic model checker PRISM. We then analyze the reward-based temporal properties and the computation in probabilistic model checking; based on the analysis, we provide a method to formulate the optimal control problem as minimum reachability reward properties. Furthermore, we incorporate control and state cost information into the PRISM code of a CS-PBNp such that automated model checking a minimum reachability reward property on the code gives the solution to the optimal control problem. We conduct experiments on two examples, an apoptosis network and a WNT5A network. Preliminary experiment results show the feasibility and effectiveness of our approach. Conclusions The approach based on probabilistic model checking for optimal control avoids explicit computation of large-size state transition relations associated with PBNs. It enables a natural depiction of the dynamics of gene regulatory networks, and provides a canonical form to formulate optimal control problems using temporal properties that can be automated solved by leveraging the analysis power of underlying model checking engines. This work will be helpful for further utilization of the advances in formal verification techniques in system biology