Kamel Mazouzi

What It Takes to Be a Pseudomonas aeruginosa? The Core Genome of the Opportunistic Pathogen Updated

Pseudomonas aeruginosa is an opportunistic bacterial pathogen able to thrive in highly diverse ecological niches and to infect compromised patients. Its genome exhibits a mosaic structure composed of a core genome into which accessory genes are inserted en bloc at specific sites. The size and the content of the core genome are open for debate as their estimation depends on the set of genomes considered and the pipeline of gene detection and clustering. Here, we redefined the size and the content of the core genome of P. aeruginosa from fully re-analyzed genomes of 17 reference strains. After the optimization of gene detection and clustering parameters, the core genome was defined at 5,233 orthologs, which represented ~ 88% of the average genome. Extrapolation indicated that our panel was suitable to estimate the core genome that will remain constant even if new genomes are added. The core genome contained resistance determinants to the major antibiotic families as well as most metabolic, respiratory, and virulence genes. Although some virulence genes were accessory, they often related to conserved biological functions. Long-standing prophage elements were subjected to a genetic drift to eventually display a G+C content as higher as that of the core genome. This contrasts with the low G+C content of highly conserved ribosomal genes. The conservation of metabolic and respiratory genes could guarantee the ability of the species to thrive on a variety of carbon sources for energy in aerobiosis and anaerobiosis. Virtually all the strains, of environmental or clinical origin, have the complete toolkit to become resistant to the major antipseudomonal compounds and possess basic pathogenic mechanisms to infect humans. The knowledge of the genes shared by the majority of the P. aeruginosa isolates is a prerequisite for designing effective therapeutics to combat the wide variety of human infections.

Jace: a Java environment for distributed asynchronous iterative computations

Distributed computing over large networks often suffers poor performances due to architecture heterogeneity and synchronization delays. This is the case of classical computing libraries which are dedicated to parallel machines or clusters. To achieve a better efficiency on multisites heterogeneous networks, one can use asynchronous algorithms which are less sensible to communication delays and loss of messages. Implementing such algorithms with classical MPI versions is not the best choice. We propose Jace, a Java environment dedicated to distributed asynchronous computations, and more especially to asynchronous iterations-asynchronous communications algorithms. It contains all facilities to build a parallel virtual machine and to implement computing tasks in a message passing style. Communications have a special semantic adapted to asynchronism. First tests on a simple Jacobi algorithm clearly show the benefits of our environment to compute on a multisite heterogeneous network, that is, the grid.

Combination of Java and asynchronism for the grid : a comparative study based on a parallel power method

Summary form only given. Nowadays, distributed computing over large networks is a popular and good alternative to dedicated parallel machines or clusters. Nevertheless, grid computing often suffers poor performances due to the heterogeneity of the machines and bottlenecks on the network. This is especially true for iterative computations when a lot of iterations are needed to converge to the problem solution. We evaluate Jace, a Java environment we have developed to easily implement and execute asynchronous iterations-asynchronous communications algorithms on the grid. Our evaluation is based on a typical iterative algorithm : the power method.

Using GPU for Multi-agent Multi-scale Simulations

Multi-Agent System (MAS) is an interesting way to create models and simulators and is widely used to model complex systems. As the complex system community tends to build up larger models to fully represent real systems, the need for computing power raise significantly. Thus MAS often lead to long computing intensive simulations. Parallelizing such a simulation is complex and it execution requires the access to large computing resources. In this paper, we present the adaptation of a MAS system, Sworm, to a Graphical Processing Unit.We show that such an adaptation can improve the performance of the simulator and advocate for a more wider use of the GPU in Agent Based Models in particular for simple agents.

MCMAS: A Toolkit to Benefit from Many-Core Architecure in Agent-Based Simulation

Multi-agent models and simulations are used to describe complex systems in domains such as biological, geographical or ecological sciences. The increasing model complexity results in a growing need for computing resources and motivates the use of new architectures such as multi-cores and many-cores. Using them efficiently however remains a challenge in many models as it requires adaptations tailored to each program, using low-level code and libraries. In this paper we present MCMAS a generic toolkit allowing an efficient use of many-core architectures through already defined data structures and kernels. This toolkit promotes few famous algorithms (diffusion, path-finding, population dynamics) which are ready to be used in an Agent Based Model. For other needs, MCMAS is based on a flexible architecture and can easily be enriched by new algorithms thanks to development features. The use of the library is illustrated with two models and their performance analysis.

Using GPU for Multi-Agent Soil Simulation

Multi-Agent Systems (MAS) can be used to model systems where the global behavior cannot be uniformly represented by standard techniques such as partial differential equations or linear systems because the system elements have their own independent behavior. This is, for instance, the case in complex systems such as daily mobility in a city for example. Depending on the system size the computing power needs for the MAS may be as big as for more traditional linear numerical systems and may need to be parallelized to fully represent real systems. Graphical Processing Units (GPU) have already proven to be an efficient support to execute large linear programs. In this paper we present the use of GPU for the execution of Sworm, a multi-scale MAS system. We show that GPU computing can be efficient in that less regular case and when the agent behavior is simple. We advocate for a wider use of the GPU in Agent Based Models in particular for multi-scale systems with work distribution between the CPU and GPU.

Computational investigations of folded self-avoiding walks related to protein folding

Various subsets of self-avoiding walks naturally appear when investigating existing methods designed to predict the 3D conformation of a protein of interest. Two such subsets, namely the folded and the unfoldable self-avoiding walks, are studied computationally in this article. We show that these two sets are equal and correspond to the whole n-step self-avoiding walks for n≤14, but that they are different for numerous n≥108, which are common protein lengths. Concrete counterexamples are provided and the computational methods used to discover them are completely detailed. A tool for studying these subsets of walks related to both pivot moves and protein conformations is finally presented.

More on JACE: new functionalities, new experiments

Java is often criticized for its poor performances compared to native codes. Nevertheless, this language provides lots of interesting functionalities to easily implement scientific applications on a widely distributed architecture (i.e. grid). The context of this paper is that of iterative algorithms. In order to increase the efficiency of the code, we suggest to use a special class of algorithms called AIACs (asynchronous iterations, asynchronous computations). This paper presents new results on our works to combine Java and asynchronism within a programming/execution environment called JACE. New functionalities have been added and interesting comparisons with C/MPI and on the impact of overlap techniques are given

MCMAS: A toolkit for developing agent-based simulations on many-core architectures

Multi-agent models and simulations are used to describe complex systems in domains such as biological, geographical or ecological sciences. The increasing model complexity results in a growing need for computing resources and motivates the use of new architectures such as multi-cores and many-cores. Using them efficiently however remains a challenge in many models as it requires adaptations tailored to each program, using low-level code and libraries. In this paper we present MCMAS a generic toolkit allowing an efficient use of many-core architectures through already defined data structures and kernels. The toolkit provides few famous algorithms as diffusion, path-finding or population dynamics that are frequently used in an agent based models. For further needs, MCMAS is based on a flexible architecture that can easily be enriched by new algorithms thanks to development features. The use of the library is illustrated with three models and their performance analysis.

Quality Studies of an Invisible Chaos-Based Watermarking Scheme with Message Extraction

This paper takes place in the field of invisible chaos-based watermarking schemes. It addresses the quality study of an already pyblished algorithm by focusing on three class of properties. Its robustness is experimentally shown against classical attacks on a large set of image instances and image transformations. It correctness and completness are formally proven. Due to this main advantages, this process is fitted for practical use.