Analysis of DNA Replication by Optical Mapping in Nanochannels

DNA replication is essential to maintain genome integrity in S phase of the cell division cycle. Accumulation of stalled replication forks is a major source of genetic instability, and likely constitutes a key driver of tumorigenesis. The mechanisms of regulation of replication fork progression have therefore been extensively investigated, in particular with DNA combing, an optical mapping technique that allows the stretching of single molecules and the mapping of active region for DNA synthesis by fluorescence microscopy. DNA linearization in nanochannels has been successfully used to probe genomic information patterns along single chromosomes, and has been proposed to be a competitive alternative to DNA combing. Yet this conjecture remains to be confirmed experimentally. Here, two complementary techniques are established to detect the genomic distribution of tracks of newly synthesized DNA in human cells by optical mapping in nanochannels. Their respective advantages and limitations are compared, and applied them to detect deregulations of the replication program induced by the antitumor drug hydroxyurea. The developments here thus broaden the field of applications accessible to nanofluidic technologies, and can be used in the future as part for molecular diagnostics in the context of high throughput cancer drug screening.     

Anisotropic metrics for finite element meshes using a posteriori error estimates: Poisson and Stokes equations

In this paper, metrics derived from a posteriori error estimates for the Poisson problem and for the Stokes system solved by some finite element methods are presented. Numerical examples of mesh adaptation in two dimensions of the space are given and show that these metrics detect the singular behavior of the solution, in particular its anisotropy.     

A hybrid meta-heuristic for multi-objective vehicle routing problems with time windows

The Capacitated Vehicle Routing Problem with Time Windows is an important combinatorial optimization problem consisting in the determination of the set of routes of minimum distance to deliver goods, using a fleet of identical vehicles with restricted capacity, so that vehicles must visit customers within a time frame. A large number of algorithms have been proposed to solve single-objective formulations of this problem, including meta-heuristic approaches, which provide high quality solutions in reasonable runtimes. Nevertheless, in recent years some authors have analyzed multi-objective variants that consider additional objectives to the distance travelled. This paper considers not only the minimum distance required to deliver goods, but also the workload imbalance in terms of the distances travelled by the used vehicles and their loads. Thus, MMOEASA, a Pareto-based hybrid algorithm that combines evolutionary computation and simulated annealing, is here proposed and analyzed for solving these multi-objective formulations of the VRPTW. The results obtained when solving a subset of Solomon’s benchmark problems show the good performance of this hybrid approach.     

Parallel Ant Colony Optimization on Graphics Processing Units

The purpose of this paper is to propose effective parallelization strategies for the Ant Colony Optimization (ACO) metaheuristic on Graphics Processing Units (GPUs). The Max–Min Ant System (MMAS) algorithm augmented with 3-opt local search is used as a framework for the implementation of the parallel ants and multiple ant colonies general parallelization approaches. The four resulting GPU algorithms are extensively evaluated and compared on both speedup and solution quality on a state-of-the-art Fermi GPU architecture. A rigorous effort is made to keep parallel algorithms true to the original MMAS applied to the Traveling Salesman Problem. We report speedups of up to 23.60 with solution quality similar to the original sequential implementation. With the intent of providing a parallelization framework for ACO on GPUs, a comparative experimental study highlights the performance impact of ACO parameters, GPU technical configuration, memory structures and parallelization granularity.     

Knowledge base specification to automate the fluid critical point of fluids

In this paper an analytical procedure implemented to structure the knowledge required by an expert system applied for controlling a chemical process is described. The analytical approach is based on L ifecycle of Systems and on Task Model approaches. It was validated with an experimental procedure related to the determination of the critical point of pure fluids (CO₂, N₂O, SF₆) and mixtures of low concentration (CO₂-limonene). For the implementation of the control system an expert system shell (CHRONOS?) was used.     

A MIXTURE MODEL CLASSIFIER AND ITS APPLICATION ON THE BIOMEDICAL TIME SERIES

This article presents a methodology based on the mixture model to classify the real biomedical time series. The mixture model is shown to be an efficient probabilistic density estimation scheme aimed at approximating the posterior probability distribution of a certain class of data. The approximation is conducted by employing a weighted mixture of a finite number of Gaussian kernels whose parameters and mixing coefficients are estimated iteratively through a maximum likelihood method. A database of the real electrocardiogram (ECG) time series of out-of-hospital cardiac arrest patients suffering ventricular fibrillation (VF) with known defibrillation outcomes was adopted to evaluate the performance of this model and confirm its efficiency compared with other classification methods.     

Multiprocessor schedulability of arbitrary-deadline sporadic tasks: complexity and antichain algorithm

Baker and Cirinei (Lecture Notes in Computer Science, vol. 4878, Springer, pp. 62–75, 2007) have introduced an exact but naive algorithm, that consists in solving a state reachability problem in a finite automaton, to check whether a set of sporadic hard real-time tasks is schedulable on an identical multiprocessor platform. However, this algorithm suffers from poor performance due to the exponential size of the automaton relative to the size of the task set. In this paper, we build on the work of Baker and Cirinei, and rely on their formalism to characterise the complexity of this problem. We prove that it is PSpace-complete. In order to obtain an algorithm that is applicable in practice to systems of realistic sizes, we successfully apply techniques developed by the formal verification community, specifically antichain techniques (Doyen and Raskin in Lecture Notes in Computer Science, vol. 6015, Springer, pp. 2–22, 2010) to this scheduling problem. For that purpose, we define and prove the correctness of a simulation relation on Baker and Cirinei’s automaton. We show that our improved algorithm yields dramatically improved performance for the schedulability test and opens for many further improvements. This work is an extended and revised version of a previous conference paper by the same authors (Lindström et al., Proceedings of the 19th International Conference on Real-Time and Network Systems (RTNS 2011), pp. 25–34, 2011).     

Electromagnetic MEMS microspeaker for portable electronic devices

This work presents the conception, the microfabrication, and the electroacoustic characterization of a new electromagnetic microspeaker based on silicon. The objectives are to get improved sound quality compared to that of conventional microspeakers, while keeping the electroacoustic efficiency as high as possible. An optimized stiffening silicon microstructure let the sound radiator be extremely light and rigid. The mobile part is suspended to the fixed part by silicon suspension springs, which enable large out-of-plane displacement. The acoustic radiator is actuated by an electromagnetic motor, composed of a fixed permanent magnet and a planar coil located on top of the silicon radiator. The piston-like motion of the radiator favored by this structure is very beneficial for the sound quality. Electro–mechano–acoustic characterization of the microfabricated microspeaker showed that the radiator surface could run out-of-plane with displacements higher than ±400 μm, with no mechanical and electrical failure. For an electrical power of 0.5 W, the microspeaker was capable to generate a sound pressure level of 80 dB at 10 cm, from 330 Hz up to 20 kHz frequency. The efficiency reaches 3 × 10^?5, that is to say three times more than typical efficiency of conventional microspeakers. Moreover, as characterization results showed, the existence of very few structural modes and the low electroacoustic distortions evidence the high sound quality of the microspeaker.     

Prognostic normative reasoning

Human users planning for multiple objectives in complex environments are subjected to high levels of cognitive workload, which can severely impair the quality of the plans created. This paper describes a software agent that can proactively assist cognitively overloaded users by providing normative reasoning about prohibitions and obligations so that the user can focus on her primary objectives. In order to provide proactive assistance, we develop the notion of prognostic normative reasoning (PNR) that consists of the following steps: (1) recognizing the user's planned activities, (2) reasoning about norms to evaluate those predicted activities, and (3) providing necessary assistance so that the user's activities are consistent with norms. The idea of PNR integrates various AI techniques, namely, user intention recognition, normative reasoning over a user's intention, and planning, execution and replanning for assistive actions. In this paper, we describe an agent architecture for PNR and discuss practical applications.     

A keyword-driven approach for generating OWL DL conformance test data

The conformance of semantic technologies has to be systematically evaluated to measure and verify the real adherence of these technologies to the Semantic Web standards. Current evaluations of semantic technology conformance are not exhaustive enough and do not directly cover user requirements and use scenarios, which raises the need for a simple, extensible and parameterizable method to generate test data for such evaluations. To address this need, this paper presents a keyword-driven approach for generating ontology language conformance test data that can be used to evaluate semantic technologies, details the definition of a test suite for evaluating OWL DL conformance using this approach, and describes the use and extension of this test suite during the evaluation of some tools.