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40 results on '"ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES"'

1. A Logical Framework for Systems Biology

Author

Amy P. Felty, Elisabetta De Maria, Joëlle Despeyroux, Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe BIOINFO, Modèles Discrets pour les Systèmes Complexes (Laboratoire I3S - MDSC), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria), School of Electrical Engineering and Computer Science, Univ. of Ottawa (EECS, Ottawa), University of Ottawa [Ottawa], Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), Mathematical, Reasoning and Software (MARELLE), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), François Fages, and Carla Piazza

Subjects
FOS: Computer and information sciences, Computer Science - Logic in Computer Science, Horn clause, Theoretical computer science, Computer science, [SDV]Life Sciences [q-bio], logical frameworks, computer.software_genre, Prolog, Description logic, Linear temporal logic, Computer Science::Logic in Computer Science, computer.programming_language, temporal properties, Programming language, Computational logic, Multimodal logic, Sequent calculus, Modal logic, [INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO], systems biology, Non-classical logic, Other Quantitative Biology (q-bio.OT), [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Signature (logic), Linear logic, Quantitative Biology - Other Quantitative Biology, First-order logic, Logical framework, biological networks, ACM: F.: Theory of Computation/F.4: MATHEMATICAL LOGIC AND FORMAL LANGUAGES, Computation tree logic, Interval temporal logic, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, Mathematical proof, theorem proving, linear logic, Temporal logic, Bunched logic, formal verification, Formal verification, modal logic, Proof assistant, [SDV.BBM.BM]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Molecular biology, ACM: F.: Theory of Computation/F.4: MATHEMATICAL LOGIC AND FORMAL LANGUAGES/F.4.1: Mathematical Logic, Logic in Computer Science (cs.LO), Automated theorem proving, TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES, FOS: Biological sciences, Dynamic logic (modal logic), Temporal logic of actions, computer, ACM: I.: Computing Methodologies/I.1: SYMBOLIC AND ALGEBRAIC MANIPULATION
Abstract

We propose a novel approach for the formal verification of biological systems based on the use of a modal linear logic. We show how such a logic can be used, with worlds as instants of time, as an unified framework to encode both biological systems and temporal properties of their dynamic behaviour. To illustrate our methodology, we consider a model of the P53/Mdm2 DNA-damage repair mechanism. We prove several properties that are important for such a model to satisfy and serve to illustrate the promise of our approach. We formalize the proofs of these properties in the Coq Proof Assistant, with the help of a Lambda Prolog prover for partial automation of the proofs., Comment: (2014)

Published
2014
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2. Taming the complexity of 'n-ary' relations in comparative genomics

Author

David James Sherman, Models and Algorithms for the Genome ( MAGNOME), Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Modèles et algorithmes pour la Bioinformatique et la Visualisation d'informations, (MABIOVIS), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Georgia Tech and Emory University, Mark Borodovsky, plafrim, Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), and Sherman, David James

Subjects
[SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], data-mining, Map-reduce, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, NoSQL, comparative genomics, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract

International audience; Microbial genomes used in biotechnology applications are now routinely sequenced in groups rather than individually, in order to more unambiguously identify specific variations that are linked to phenotype. These 'paraphyletic' sequencing strategies certainly result in growing volumes of sequence data, but these in turn are dominated by the n-ary relations between genomes obtained from systematic comparison, classification, and network inference. In the worse case, relations can grow geometrically while the genomes grow arithmetically. Comparative genomics is increasingly becoming a question of taming the complexity of these n-ary relations, and requires rethinking analyses in terms of new distributed computing paradigms. We will discuss a number of examples of large-scale comparative genomics in biotechnologically interesting hemiascomycete yeasts, and see how the MapReduce and NoSQL paradigms can be used to rethink representation, querying, and analysis of large groups of closely related genomes. I will further illustrate how reference-agnostic systematic comparisons can produce comprehensive views of the genomes as a group, and can drive comparative inference of metabolic models.

Published
2013

3. Towards a better understanding of pelvic system disorders using numerical simulation

Author

Pauline Lecomte-Grosbras, Mouhamadou Nassirou - Diallo, Jean-François Witz, Damien Marchal, Jeremie Dequidt, Stéphane Cotin, Michel Cosson, Christian Duriez, Mathias Brieu, Laboratoire de Mécanique de Lille - FRE 3723 (LML), Université de Lille, Sciences et Technologies-Centrale Lille-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Mécanique et Technologie (LMT), École normale supérieure - Cachan (ENS Cachan)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS), Simulation in Healthcare using Computer Research Advances (SHACRA), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria), Service de gynécologie obstétrique, Hôpital Jeanne de Flandre [Lille], and Université de Lille, Sciences et Technologies-Ecole Centrale de Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)

Subjects
Models, Anatomic, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, [SPI.MECA.BIOM]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph], Magnetic Resonance Imaging, Models, Biological, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Pelvic Organ Prolapse, Pelvis, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES/J.3.1: Health, Elastic Modulus, Tensile Strength, Cadaver, Humans, Computer Simulation, Female, Stress, Mechanical, [PHYS.MECA.BIOM]Physics [physics]/Mechanics [physics]/Biomechanics [physics.med-ph], Aged
Abstract

International audience; Genital prolapse is a pathologic hyper-mobility of the organs that forms the pelvic system. Although this is common condition, the pathophysiology of this disorder is not well known. In order to improve the understanding of its origins, we recreate - virtually - this biomechanical pathology using numerical simulation. The approach builds on a finite element model with parameters measured on several fresh cadavers. The meshes are created from a MRI of a healthy woman and the simulation includes the mechanical interactions between organs (contacts, ligaments, adhesion...). The model is validated through comparison of functional mobilities of the pelvic system observed on a dynamic MRI. We then propose to modify, step by step, the model and its parameters to produce a pathologic situation and have a better understanding of the process. It is not a formal proof but the numerical experiments reinforce the clinical hypothesis on the multifactorial origins of the pathology.

Published
2013

4. Minimal Intervention Strategies in Logical Signaling Networks with ASP

Author

Roland Kaminski, Santiago Videla, Torsten Schaub, Anne Siegel, Institut für informatik [Potsdam], University of Potsdam = Universität Potsdam, Dynamics, Logics and Inference for biological Systems and Sequences (Dyliss), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Universität Potsdam, CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), and University of Potsdam

Subjects
Fixpoint semantics, Biological signaling, Theoretical computer science, Computer science, Systems biology, Computation, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, 02 engineering and technology, Transduction (psychology), Computer security, computer.software_genre, Theoretical Computer Science, 03 medical and health sciences, Answer set programming, Artificial Intelligence, Intervention (counseling), 0202 electrical engineering, electronic engineering, information engineering, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.4: Knowledge Representation Formalisms and Methods, 030304 developmental biology, 0303 health sciences, Institut für Informatik und Computational Science, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], 3. Good health, Computational Theory and Mathematics, Hardware and Architecture, 020201 artificial intelligence & image processing, ddc:004, Mathematisch-Naturwissenschaftliche Fakultät, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], computer, Software
Abstract

Proposing relevant perturbations to biological signaling networks is central to many problems in biology and medicine because it allows for enabling or disabling certain biological outcomes. In contrast to quantitative methods that permit fine-grained (kinetic) analysis, qualitative approaches allow for addressing large-scale networks. This is accomplished by more abstract representations such as logical networks. We elaborate upon such a qualitative approach aiming at the computation of minimal interventions in logical signaling networks relying on Kleene's three-valued logic and fixpoint semantics. We address this problem within answer set programming and show that it greatly outperforms previous work using dedicated algorithms.

Published
2013

5. A framework for inter-subject prediction of functional connectivity from structural networks

Author

Daniel Rueckert, Robert Leech, Fani Deligianni, Gaël Varoquaux, Bertrand Thirion, Christian Ledig, David J. Sharp, Department of Computing [London], Biomedical Image Analysis Group [London] (BioMedIA), Imperial College London-Imperial College London, Imaging and Biophysics Unit, University College of London [London] (UCL), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Service NEUROSPIN (NEUROSPIN), Computational, Cognitive and Clinical Neuroimaging Laboratory (C3NL), Imperial College London, The Computational, Cognitive and Clinical Neuroimaging Lab, Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)

Subjects
Computer science, structural brain connectivity, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, Machine learning, computer.software_genre, functional brain connectivity, statistical associations, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, symbols.namesake, 0302 clinical medicine, Salience (neuroscience), medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Graphical model, Electrical and Electronic Engineering, Gaussian process, Default mode network, Radiological and Ultrasound Technology, medicine.diagnostic_test, Artificial neural network, business.industry, Functional connectivity, [SCCO.NEUR]Cognitive science/Neuroscience, Computer Science Applications, symbols, ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.1: Models/I.5.1.4: Statistical, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Artificial intelligence, business, Functional magnetic resonance imaging, computer, predictive modeling, 030217 neurology & neurosurgery, Software
Abstract

Functional connections between brain regions are supported by structural connectivity. Both functional and structural connectivity are estimated from in vivo magnetic resonance imaging and offer complementary information on brain organization and function. However, imaging only provides noisy measures, and we lack a good neuroscientific understanding of the links between structure and function. Therefore, inter-subject joint modeling of structural and functional connectivity, the key to multimodal biomarkers, is an open challenge. We present a probabilistic framework to learn across subjects a mapping from structural to functional brain connectivity. Expanding on our previous work [1], our approach is based on a predictive framework with multiple sparse linear regression. We rely on the randomized LASSO to identify relevant anatomo-functional links with some confidence interval. In addition, we describe resting-state functional magnetic resonance imaging in the setting of Gaussian graphical models, on the one hand imposing conditional independences from structural connectivity and on the other hand parameterizing the problem in terms of multivariate autoregressive models. We introduce an intrinsic measure of prediction error for functional connectivity that is independent of the parameterization chosen and provides the means for robust model selection. We demonstrate our methodology with regions within the default mode and the salience network as well as, atlas-based cortical parcellation.

Published
2013

6. Knowledge-based generalization of metabolic networks: An applicational study

Author

Anna Zhukova, David James Sherman, Models and Algorithms for the Genome ( MAGNOME), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), and Sherman, David James

Subjects
[SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, metabolic models, bioinformatics, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], knowledge engineering, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract

International audience; Genome-scale metabolic networks are complex systems that describe thousands of reactions participating in the organism's metabolism. During the process of genome network reconstruction these reactions are automatically inferred from pathway and reaction databases, and existing models for similar organisms, using genomic data [1]. The inferred draft network is then refined during several iterations of error detection, gap filling, analysis and improvement [2]. Although automatic tools for model inference and analysis are becoming more and more powerful, they may still miss some reactions or add those ones that should not belong to the network of the target organism. That is why the analysis by a human expert is needed during the network refinement process. However, being tailored for a computer simulation, and thus including all the reactions thought to participate in the organism's metabolism, genome-scale networks can be too complicated and detailed for a human. The errors may be hidden in the multitude of reactions. To help a human expert understanding these detailed networks, we developed a method for knowledge-based generalization that focusses on the higher-level relationships in the network, while omitting the details [3]. The generalization process groups chemical species present in the network into semantically equivalent classes, based on their hierarchical relationships in the ChEBI ontology [4], and merges them into a generalized chemical species. For instance, butyryl-CoA, hexanoyl-CoA and octanoyl-CoA species can be generalized into fatty acyl-CoA. After the species generalization, reactions that share the same generalized reactants and the same generalized products, are factored together into a generalized reaction. This provides a higher-level view of the network. In this poster, we show the application of this generalization method to the network of the yeast Y. lypolitica [5]. We analyze the generalized network, and illustrate how it can be used for easier error detection: We show the changes that both initial and generalized networks undergo if the catalyzing enzyme for some of the reactions is missing. The application of the generalization procedure also facilitates network comparison, which we show by comparing the generalized Y. lypolitica network to the networks of several other organisms.

Published
2013

7. Spatial vs. temporal features in ICA of resting-state fMRI - A quantitative and qualitative investigation in the context of response inhibition

Author

Yu-Feng Zang, Stephen M. Smith, Lixia Tian, Gaël Varoquaux, Yazhuo Kong, Juejing Ren, Department of Biomedical Engineering, Beijing Jiaotong University (BJTU), Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford [Oxford], State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University (BNU), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Center for Cognition and Brain Disorders, Hangzhou Normal University, University of Oxford, Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
Male, Central Nervous System, Anatomy and Physiology, lcsh:Medicine, Bioinformatics, Standard deviation, Behavioral Neuroscience, Engineering, 0302 clinical medicine, Neural Pathways, lcsh:Science, Physics, Brain Mapping, 0303 health sciences, Multidisciplinary, medicine.diagnostic_test, fMRI, Magnetic Resonance Imaging, Smoothing, Research Article, Adult, Neural Networks, Rest, Cognitive Neuroscience, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, Biomedical Engineering, Neuroimaging, Bioengineering, Context (language use), Neurological System, Young Adult, 03 medical and health sciences, Reaction Time, medicine, Humans, Time series, Biology, Spatial analysis, 030304 developmental biology, Computational Neuroscience, Resting state fMRI, business.industry, [SCCO.NEUR]Cognitive science/Neuroscience, lcsh:R, Pattern recognition, Connectomics, Independent component analysis, Neuroanatomy, Signal Processing, lcsh:Q, Artificial intelligence, Functional magnetic resonance imaging, business, 030217 neurology & neurosurgery, Neuroscience
Abstract

International audience; Independent component analysis (ICA) can identify covarying functional networks in the resting brain. Despite its relatively widespread use, the potential of the temporal information (unlike spatial information) obtained by ICA from resting state fMRI (RS-fMRI) data is not always fully utilized. In this study, we systematically investigated which features in ICA of resting-state fMRI relate to behaviour, with stop signal reaction time (SSRT) in a stop-signal task taken as a test case. We did this by correlating SSRT with the following three kinds of measure obtained from RS-fMRI data: (1) the amplitude of each resting state network (RSN) (evaluated by the standard deviation of the RSN timeseries), (2) the temporal correlation between every pair of RSN timeseries, and (3) the spatial map of each RSN. For multiple networks, we found significant correlations not only between SSRT and spatial maps, but also between SSRT and network activity amplitude. Most of these correlations are of functional interpretability. The temporal correlations between RSN pairs were of functional significance, but these correlations did not appear to be very sensitive to finding SSRT correlations. In addition, we also investigated the effects of the decomposition dimension, spatial smoothing and Z-transformation of the spatial maps, as well as the techniques for evaluating the temporal correlation between RSN timeseries. Overall, the temporal information acquired by ICA enabled us to investigate brain function from a complementary perspective to the information provided by spatial maps.

Published
2013

8. Metabolic Model Generalization

Author

Zhukova, Anna, Sherman, David James, Models and Algorithms for the Genome ( MAGNOME), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)

Subjects
[SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract

International audience; Genome-scale metabolic models for new organisms include thousands of reactions that are generated automatically: by inferring them from databases of reactions and pathways, existing models for similar organisms, etc. This process includes several iterations of the draft model analysis, error detection, and improvement; starting from more general issues and going deeper into details. Especially in the first iterations model evaluation by a human expert is important. But genome-scale models are targeted for computer simulation and analysis, and are too detailed and complicated to be easily understood by a human. For example, in the beta-oxidation of fatty acids pathway, a reaction missing for a particular fatty-acyl-CoA type (e.g. decanoyl-CoA) is a more specific issue than missing a whole enoyl-CoA hydratase step (which could happen if the corresponding enzyme is not found). But the abundance of reactions in the model (e.g. those corresponding to other beta-oxidation steps and presented for each of the different types of fatty-acyls-CoA) may hide this fact from the human. That is why we developed a method for knowledge-based scaling of metabolic models, providing a higher-level view of a model, keeping its essential structure and omitting the details.

Published
2013

9. Processing Stages of Visual Stimuli and Event-Related Potentials

Author

Saavedra, Carolina, Bougrain, Laurent, Neuromimetic intelligence (CORTEX), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), equipe associée KEOpS, Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SCCO.NEUR]Cognitive science/Neuroscience, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES
Abstract

International audience; Event-evoked potentials (ERP) in electroencephalograms reflect various visual processing stages according to their latencies and locations. Thus, ERP components such as the N100, N170 and the N200 which appears 100, 170 and 200 ms after the onset of a visual stimulus correspond respectively to a selective attention, the processing of color, shape and rotation (e.g. processing of human faces) and a degree of attention.

Published
2012

10. Inverse reinforcement learning to control a robotic arm using a Brain-Computer Interface

Author

Bougrain, Laurent, Duvinage, Matthieu, Klein, Edouard, Analysis and modeling of neural systems by a system neuroscience approach (NEUROSYS), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), TCTS Lab, Circuit Theory and Signal Processing Lab (TCTS), Faculté polytechnique de Mons, Université de Mons (UMons)-Université de Mons (UMons)-Faculté polytechnique de Mons, Université de Mons (UMons)-Université de Mons (UMons), IMS : Information, Multimodalité & Signal, SUPELEC-Campus Metz, Ecole Supérieure d'Electricité - SUPELEC (FRANCE)-Ecole Supérieure d'Electricité - SUPELEC (FRANCE), European Project, Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)

Subjects
[SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Robotic arm, ACM: H.: Information Systems/H.5: INFORMATION INTERFACES AND PRESENTATION (e.g., HCI)/H.5.2: User Interfaces/H.5.2.4: Graphical user interfaces (GUI), Inverse reinforcement learning, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Motor imaginary, [SDV.IB]Life Sciences [q-bio]/Bioengineering, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.9: Robotics/I.2.9.1: Commercial robots and applications, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Brain- Computer Interfaces, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning
Abstract

The goal of this project is to use inverse reinforce- ment learning to better control a JACO robotic arm developed by Kinova in a Brain-Computer Interface (BCI). A self-paced BCI such as a motor imagery based-BCI allows the subject to give orders at any time to freely control a device. But using this paradigm, even after a long training, the accuracy of the classifier used to recognize the order is not 100%. While a lot of studies try to improve the accuracy using a preprocessing stage that improves the feature extraction, we work on a post- processing solution. The classifier used to recognize the mental commands will provide as outputs a value for each command such as the posterior probability. But the executed action will not only depend on this information. A decision process will also take into account the position of the robotic arm and previous trajectories. More precisely, the decision process will be obtained applying an inverse reinforcement learning (IRL) on a subset of trajectories specified by an expert. At the end of the workshop, the convergence of the inverse reinforcement algorithm has not been achieved. Nevertheless, we developed a whole processing chain based on OpenViBE for controlling 2D- movements and we present how to deal with this high dimensional time series problem with a lot of noise which is unusual for the IRL community.

Published
2012

11. Using Neuronal States for Transcribing Cortical Activity into Muscular Effort

Author

Laurent Bougrain, Octave Boussaton, Neuromimetic intelligence (CORTEX), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)

Subjects
ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, Thumb, Biology, Fingers, 03 medical and health sciences, 0302 clinical medicine, Neuromuscular stimulation, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], medicine, Animals, 030304 developmental biology, Neurons, 0303 health sciences, Hand Strength, Motor Cortex, Haplorhini, Anatomy, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.1: Models, medicine.anatomical_structure, nervous system, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.IB]Life Sciences [q-bio]/Bioengineering, Primary motor cortex, Neuroscience, 030217 neurology & neurosurgery
Abstract

International audience; We study the relations between the ac- tivity of corticomotoneuronal (CM) cells and the forces exerted by fingers. The activity of CM cells, located in the primary motor cortex is recorded in the thumb and index fingers area of a monkey. The activity of the fingers is recorded as they press two levers. The main idea of this work is to establish and use a collection of neuronal states. At any time, the neuronal state is defined by the firing rates of the recorded neurons. We assume that any such neuronal state is related to a typical variation (or absence of variation) in the muscular effort. Our forecasting model uses a linear combination of the firing rates, some synchrony information between spike trains and averaged variations of the positions of the levers.

Published
2012

12. Scale-Free and Multifractal Time Dynamics of fMRI Signals during Rest and Task

Author

Gaël Varoquaux, Sepideh Sadaghiani, Philippe Ciuciu, Andreas Kleinschmidt, Patrice Abry, Laboratoire de Neuroimagerie Assistée par Ordinateur (LNAO), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)

Subjects
Property (programming), Computer science, Physiology, scale invariance, evoked activity, computer.software_genre, 0302 clinical medicine, Wavelet, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Voxel, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Feature (machine learning), rest, Original Research, self-similarity, 05 social sciences, fMRI, Multifractal system, ongoing activity, multifractality, wavelet Leader, Neurons and Cognition (q-bio.NC), [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, scale-free, ACM: G.: Mathematics of Computing/G.3: PROBABILITY AND STATISTICS, ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.9: Applications, Self-similarity, task, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, Mathematics - Statistics Theory, Statistics Theory (math.ST), wavelets, 050105 experimental psychology, 03 medical and health sciences, Physiology (medical), brain dynamics, FOS: Mathematics, 0501 psychology and cognitive sciences, Set (psychology), Hurst exponent, business.industry, [SCCO.NEUR]Cognitive science/Neuroscience, Pattern recognition, [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], FOS: Biological sciences, Quantitative Biology - Neurons and Cognition, Artificial intelligence, business, computer, 030217 neurology & neurosurgery
Abstract

International audience; Scaling temporal dynamics in functional MRI (fMRI) signals have been evidenced for a decade as intrinsic characteristics of ongoing brain activity (Zarahn et al., 1997). Recently, scaling properties were shown to fluctuate across brain networks and to be modulated between rest and task (He, 2011): notably, Hurst exponent, quantifying long memory, decreases under task in activating and deactivating brain regions. In most cases, such results were obtained: First, from univariate (voxelwise or regionwise) analysis, hence focusing on specific cognitive systems such as Resting-State Networks (RSNs) and raising the issue of the specificity of this scale-free dynamics modulation in RSNs. Second, using analysis tools designed to measure a single scaling exponent related to the second order statistics of the data, thus relying on models that either implicitly or explicitly assume Gaussianity and (asymptotic) self-similarity, while fMRI signals may significantly depart from those either of those two assumptions (Ciuciu et al., 2008; Wink et al., 2008). To address these issues, the present contribution elaborates on the analysis of the scaling properties of fMRI temporal dynamics by proposing two significant variations. First, scaling properties are technically investigated using the recently introduced Wavelet Leader-based Multifractal formalism (WLMF; Wendt et al., 2007). This measures a collection of scaling exponents, thus enables a richer and more versatile description of scale invariance (beyond correlation and Gaussianity), referred to as multifractality. Also, it benefits from improved estimation performance compared to tools previously used in the literature. Second, scaling properties are investigated in both RSN and non-RSN structures (e.g., artifacts), at a broader spatial scale than the voxel one, using a multivariate approach, namely the Multi-Subject Dictionary Learning (MSDL) algorithm (Varoquaux et al., 2011) that produces a set of spatial components that appear more sparse than their Independent Component Analysis (ICA) counterpart. These tools are combined and applied to a fMRI dataset comprising 12 subjects with resting-state and activation runs (Sadaghiani et al., 2009). Results stemming from those analysis confirm the already reported task-related decrease of long memory in functional networks, but also show that it occurs in artifacts, thus making this feature not specific to functional networks. Further, results indicate that most fMRI signals appear multifractal at rest except in non-cortical regions. Task-related modulation of multifractality appears only significant in functional networks and thus can be considered as the key property disentangling functional networks from artifacts. These finding are discussed in the light of the recent literature reporting scaling dynamics of EEG microstate sequences at rest and addressing non-stationarity issues in temporally independent fMRI modes.

Published
2012

13. Control of Cell Fate in the Circulatory and Ventilatory Systems

Author

Thiriet, Marc, Numerical simulation of biological flows (REO), Laboratoire Jacques-Louis Lions (LJLL), Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)-Inria Paris-Rocquencourt, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
0303 health sciences, 03 medical and health sciences, ACM: J.: Computer Applications, 0302 clinical medicine, 030220 oncology & carcinogenesis, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, Other Fields of Physics, [SPI.MECA.BIOM]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph], [PHYS.MECA.BIOM]Physics [physics]/Mechanics [physics]/Biomechanics [physics.med-ph], 030304 developmental biology
Abstract

The volumes in this authoritative series present a multidisciplinary approach to modeling and simulation of flows in the cardiovascular and ventilatory systems, especially multiscale modeling and coupled simulations. The cardiovascular and respiratory systems are tightly coupled, as their primary function is to supply oxygen to and remove carbon dioxide from the body's cells. Because physiological conduits have deformable and reactive walls, macroscopic flow behavior and prediction must be coupled to nano- and microscopic events in a corrector scheme of regulated mechanisms. Therefore, investigation of flows of blood and air in physiological conduits requires an understanding of the biology, chemistry, and physics of these systems together with the mathematical tools to describe their functioning. Volumes 1 and 2 are devoted to cell organization and fate, as well as activities that are autoregulated and/or controlled by the cell environment. Volume 1 examined cellular features that allow adaptation to environmental conditions. Volume 2 begins with a survey of the cell types of the nervous and endocrine systems involved in the regulation of the vasculature and respiratory tract and growth factors. It then describes major cell events in the circulatory and ventilatory systems, such as cell growth, proliferation, migration, and death. Circadian cycles that drive rhythmic gene transcription are also covered. Describes cell types, functions, and fate in the regulated activities of the circulatory and respiratory systems Presents applications of mechanics and mathematics for an understanding and prediction of function in health and disease Integrates biology, chemistry, and physics for a multidisciplinary understanding of physiological flows

Published
2012

14. A Comparative Study of Algorithms for Intra- and Inter-subjects fMRI Decoding

Author

Evelyn Eger, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Laboratoire de Neuroimagerie Assistée par Ordinateur (LNAO), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Neuroimagerie cognitive (LCogn), Université Paris-Sud - Paris 11 (UP11)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM), Georg Langs, Irina Rish, Moritz Grosse-Wentrup, Brain Murphy, Varoquaux, Gaël, Georg Langs, Irina Rish, Moritz Grosse-Wentrup, Brain Murphy, Laboratoire de Neuroimagerie Assistée par Ordinateur ( LNAO ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ), Modelling brain structure, function and variability based on high-field MRI data ( PARIETAL ), Service NEUROSPIN ( NEUROSPIN ), Direction de Recherche Fondamentale (CEA) ( DRF (CEA) ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) ( DRF (CEA) ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ), Neuroimagerie cognitive ( LCogn ), Université Paris-Sud - Paris 11 ( UP11 ) -Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay

Subjects
Elementary cognitive task, decoding, Computer science, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, ACM: G.: Mathematics of Computing/G.3: PROBABILITY AND STATISTICS/G.3.5: Multivariate statistics, ACM : J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Inference, Machine learning, computer.software_genre, Brain mapping, Regularization (mathematics), 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, ACM : I.: Computing Methodologies/I.5: PATTERN RECOGNITION, ACM : G.: Mathematics of Computing/G.3: PROBABILITY AND STATISTICS/G.3.5: Multivariate statistics, medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION, medicine.diagnostic_test, [ INFO.INFO-IM ] Computer Science [cs]/Medical Imaging, business.industry, fMRI, Total variation denoising, machine learning, brain mapping, Artificial intelligence, business, Neural coding, Functional magnetic resonance imaging, computer, 030217 neurology & neurosurgery, Decoding methods
Abstract

International audience; Functional Magnetic Resonance Imaging (fMRI) provides a unique opportunity to study brain functional architecture, while being minimally invasive. Reverse inference, a.k.a. decoding, is a recent statistical analysis approach that has been used with success for deciphering activity patterns that are thought to fit the neuroscientific concept of population coding. Decoding relies on the selection of brain regions in which the observed activity is predictive of certain cognitive tasks. The accuracy of such a procedure is quantified by the prediction of the behavioral variable of interest - the target. In this paper, we discuss the optimality of decoding methods in two different settings, namely intra- and inter-subject kind of decoding. While inter-subject prediction aims at finding predictive regions that are stable across subjects, it is plagued by the additional inter-subject variability (lack of voxel-to-voxel correspondence), so that the best suited prediction algorithms used in reverse inference may not be the same in both cases. We benchmark different prediction algorithms in both intra- and inter-subjects analysis, and we show that using spatial regularization improves reverse inference in the challenging context of inter-subject prediction. Moreover, we also study the different maps of weights, and show that methods with similar accuracy may yield maps with very different spatial layout of the predictive regions.

Published
2011

15. Three-dimensional simulations in Glenn patients: clinically based boundary conditions, hemodynamic results and sensitivity to input data

Author

Guillaume Troianowski, Charles A. Taylor, Jeffrey A. Feinstein, Irene E. Vignon-Clementel, Institute for Computational and Mathematical Engineering [Stanford] (ICME), Stanford University, Department of Bioengineering [Stanford], Department of Surgery [Stanford], Stanford Medicine, Stanford University-Stanford University, Department of Pediatrics [Stanford], Numerical simulation of biological flows (REO), Laboratoire Jacques-Louis Lions (LJLL), Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)-Inria Paris-Rocquencourt, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
Heart Defects, Congenital, Models, Anatomic, medicine.medical_specialty, Vena Cava, Superior, 0206 medical engineering, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, Biomedical Engineering, Cardiac index, Hemodynamics, Context (language use), 02 engineering and technology, 030204 cardiovascular system & hematology, Anastomosis, Pulmonary Artery, Fontan Procedure, numerical simulations, 03 medical and health sciences, 0302 clinical medicine, Imaging, Three-Dimensional, sensitivity analysis, Physiology (medical), Internal medicine, medicine.artery, boundary conditions, ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING/I.6.3: Applications, Medicine, Humans, Computer Simulation, Sensitivity (control systems), [PHYS.MECA.BIOM]Physics [physics]/Mechanics [physics]/Biomechanics [physics.med-ph], Simulation, business.industry, Models, Cardiovascular, [SPI.MECA.BIOM]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph], Left pulmonary artery, ACM: G.: Mathematics of Computing/G.1: NUMERICAL ANALYSIS/G.1.8: Partial Differential Equations/G.1.8.7: Iterative solution techniques, 020601 biomedical engineering, Right pulmonary artery, congenital heart disease, Biomechanical Phenomena, ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING/I.6.4: Model Validation and Analysis, ACM: G.: Mathematics of Computing/G.1: NUMERICAL ANALYSIS/G.1.8: Partial Differential Equations/G.1.8.3: Finite element methods, Child, Preschool, Pulmonary artery, Cardiology, clinical data, business, [MATH.MATH-NA]Mathematics [math]/Numerical Analysis [math.NA]
Abstract

International audience; While many congenital heart defects can be treated without significant long term sequelae, some achieve successful palliation as their definitive endpoints. The single-ventricle defect is one such defect and leaves the child with only one operational ventricle, requiring the systemic and the pulmonary circulations to be placed in series through several operations performed during early childhood. Numerical simulations may be used to investigate these hemodynamic conditions and their relation to post-operative sequelae; however, they rely heavily on boundary condition prescription. In this study, we investigate the impact of hemodynamic input data uncertainties on simulation results. Imaged- based patient-specific models of the multi-branched pulmonary arteries and superior vena cava were built for five cavopulmonary connection (i.e. Glenn) patients. Magnetic resonance imaging and catheterization data were acquired for each patient prior to their Fontan surgery. Inflow and outflow boundary conditions were constructed to match available clinical data and resulted in the development of a framework to incorporate these types of clinical data into patient-specific simulations. Three-dimensional computational fluid dynamics simulations were run and hemodynamic indicators were computed. Power loss was low (and efficiency very high) and a linear correlation was found between power loss and cardiac index among the five patients. Other indicators such as low wall shear stress were considered to better characterize these patients. Flow was complex and oscillatory near the anastomosis, and laminar in the smaller branches. While common trends were seen among patients, results showed differences among patients, especially in the 3D maps, strengthening the importance of patient-specific simulations. A sensitivity analysis was performed to investigate the impact of input data (clinical and modeling) to construct boundary conditions on several indicators. Overall, the sensitivity of the output indicators to the input data was small but non-negligible. The sensitivity of commonly used hemodynamic indicators to compare patients is discussed in this context. Power efficiency was much more sensitive to pressure variation than power loss. To increase the precision of such indicators, mean flow split between right and left lungs needs to be measured with more accuracy with higher priority than refining the model of how the flow is distributed on average among the smaller branches. Although 6-10% flow split imprecision seemed reasonable in terms of patient comparison, this study suggests that the common practice of imposing a right pulmonary artery/left pulmonary artery flow split of 55%/45% when performing patient specific simulations should be avoided. This study constitutes a first step towards understanding the hemodynamic differences between pre- and post Fontan surgery, predicting these differences, and evaluating surgical outcomes based on preoperative data.

Published
2011

16. Connectivity-informed fMRI Activation Detection

Author

Jean-Baptiste Poline, Bertrand Thirion, Bernard Ng, Rafeef Abugharbieh, Gaël Varoquaux, Biomedical Signal and Image Computing Laboratory (BiSICL), University of British Columbia (UBC), Laboratoire de Neuroimagerie Assistée par Ordinateur (LNAO), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Neuroimagerie cognitive - Psychologie cognitive expérimentale (UNICOG-U992), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, Gabor Fichtinger and Anne Martel and Terry Peters, Imagen, Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Saclay (COmUE)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Sensory gating, medicine.diagnostic_test, Resting state fMRI, Computer science, business.industry, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, ACM: G.: Mathematics of Computing/G.3: PROBABILITY AND STATISTICS/G.3.5: Multivariate statistics, Univariate, Pattern recognition, Magnetic resonance imaging, Machine learning, computer.software_genre, Brain mapping, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Generative model, 0302 clinical medicine, medicine.anatomical_structure, Lasso (statistics), medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Artificial intelligence, business, computer, 030217 neurology & neurosurgery
Abstract

International audience; A growing interest has emerged in studying the correlation structure of spontaneous and task-induced brain activity to elucidate the functional architecture of the brain. In particular, functional networks estimated from resting state (RS) data were shown to exhibit high resemblance to those evoked by stimuli. Motivated by these findings, we propose a novel generative model that integrates RS-connectivity and stimulus-evoked responses under a unified analytical framework. Our model permits exact closed-form solutions for both the posterior activation effect estimates and the model evidence. To learn RS networks, graphical LASSO and the oracle approximating shrinkage technique are deployed. On a cohort of 65 subjects, we demonstrate increased sensitivity in fMRI activation detection using our connectivity-informed model over the standard univariate approach. Our results thus provide further evidence for the presence of an intrinsic relationship between brain activity during rest and task, the exploitation of which enables higher detection power in task-driven studies.

Published
2011

17. A probabilistic framework to infer brain functional connectivity from anatomical connections

Author

Bertrand Thirion, Daniel Rueckert, Emma C. Robinson, Gaël Varoquaux, David J. Sharp, Fani Deligianni, A. David Edwards, Department of Computing [London], Biomedical Image Analysis Group [London] (BioMedIA), Imperial College London-Imperial College London, Laboratoire de Neuroimagerie Assistée par Ordinateur (LNAO), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Neuroimagerie cognitive - Psychologie cognitive expérimentale (UNICOG-U992), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Saclay (COmUE)-Institut National de la Santé et de la Recherche Médicale (INSERM), The Computational, Cognitive and Clinical Neuroimaging Lab, Imperial College London, Institute of Clinical Sciences, Gábor Székely, Horst Hahn, Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, and Varoquaux, Gaël

Subjects
Computer science, Brain activity and meditation, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, brain, ACM: G.: Mathematics of Computing/G.3: PROBABILITY AND STATISTICS/G.3.5: Multivariate statistics, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Linear prediction, Machine learning, computer.software_genre, Brain mapping, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, learning, Basis (linear algebra), business.industry, Model selection, functional connectivity, Function (mathematics), Covariance, anatomical connectivity, Conditional independence, Artificial intelligence, business, computer, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 030217 neurology & neurosurgery, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning
Abstract

International audience; We present a novel probabilistic framework to learn across several subjects a mapping from brain anatomical connectivity to functional connectivity, i.e. the covariance structure of brain activity. This prediction problem must be formulated as a structured-output learning task, as the predicted parameters are strongly correlated. We introduce a model selection framework based on cross-validation with a parametrization-independent loss function suitable to the manifold of covariance matrices. Our model is based on constraining the conditional independence structure of functional activity by the anatomical connectivity. Subsequently, we learn a linear predictor of a stationary multivariate autoregressive model. This natural parameterization of functional connectivity also enforces the positive-definiteness of the predicted covariance and thus matches the structure of the output space. Our results show that functional connectivity can be explained by anatomical connectivity on a rigorous statistical basis, and that a proper model of functional connectivity is essential to assess this link.

Published
2011

18. Cell and Tissue Organization in the Circulatory and Ventilatory Systems

Author

Thiriet, Marc, Numerical simulation of biological flows (REO), Laboratoire Jacques-Louis Lions (LJLL), Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)-Inria Paris-Rocquencourt, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
0303 health sciences, 03 medical and health sciences, ACM: J.: Computer Applications, 0302 clinical medicine, 030302 biochemistry & molecular biology, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, Other Fields of Physics, [SPI.MECA.BIOM]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph], 030206 dentistry, [PHYS.MECA.BIOM]Physics [physics]/Mechanics [physics]/Biomechanics [physics.med-ph]
Abstract

The volumes in this authoritative series present a multidisciplinary approach to modeling and simulation of flows in the cardiovascular and ventilatory systems, especially multiscale modeling and coupled simulations. The cardiovascular and respiratory systems are tightly coupled, as their primary function is to supply oxygen to and remove carbon dioxide from the body's cells. Because physiological conduits have deformable and reactive walls, macroscopic flow behavior and prediction must be coupled to nano- and microscopic events in a corrector scheme of regulated mechanisms. Therefore, investigation of flows of blood and air in physiological conduits requires an understanding of the biology, chemistry, and physics of these systems together with the mathematical tools to describe their functioning. The present volume is devoted to cellular events that allow adaptation to environmental conditions, particularly mechanotransduction. It begins with cell organization and a survey of cell types in the vasculature and respiratory tract. It then addresses cell structure and functions, especially in interactions with adjoining cells and matrix. Describes cell types, functions, and fate in the regulated activities of the circulatory and respiratory systems Presents applications of mechanics and mathematics for an understanding and prediction of function in health and disease Integrates biology, chemistry, and physics for a multidisciplinary understanding of physiological flows

Published
2011

19. Modelling the spatial organization of cell proliferation in the developing central nervous system

Author

Clairambault, Jean, Flores, Vladimir, Perthame, Benoît, Rapacioli, Melina, Rofman, Edmundo, Verdes, Rafael, Nonlinear Analysis for Biology and Geophysical flows (BANG), Laboratoire Jacques-Louis Lions (LJLL), Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)-Inria Paris-Rocquencourt, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Department of Biostructural Sciences, Universidad Favaloro, Favaloro Foundation-Favaloro Foundation, Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Instituto Argentino de Matemática (IAM), Consejo Nacional de Investigaciones Científicas y Técnicas [Buenos Aires] (CONICET), Instituto de Matemática Beppo Levi, and Universidad Nacional de Rosario [Santa Fe]

Subjects
cell proliferation, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, [MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP], developmental neurobiology, structured partial differential equations, optical tectum, [SDV.BDD]Life Sciences [q-bio]/Development Biology, spatial organization
Abstract

How far is neuroepithelial cell proliferation in the developing central nervous system a deterministic process? Or, to put it in a more precise way, how accurately can it be described by a deterministic mathematical model? To provide tracks to answer this question, a deterministic system of transport and diffusion partial differential equations, both physiologically and spatially structured, is introduced as a model to describe the spatially organized process of cell proliferation during the development of the central nervous system. As an initial step towards dealing with the three-dimensional case, a unidimensional version of the model is presented. Numerical analysis and numerical tests are performed. In this work we also achieve a first experimental validation of the proposed model, by using cell proliferation data recorded from histological sections obtained during the development of the optic tectum in the chick embryo.

Published
2010

20. Combattre les douleurs fantômes

Author

Guiraud, David, Artificial movement and gait restoration (DEMAR), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)

Subjects
ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.9: Robotics, [INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, [SDV.IB]Life Sciences [q-bio]/Bioengineering
Abstract

National audience; Toutes sortes de traitements ont été tentés, sans grand succès sur la durée, pour soulager la douleur des patients ayant subi l'amputation d'un membre. Une nouvelle approche, fondée sur une technique originale de stimulation électrique, pourrait offrir une alternative.

Published
2010

21. Technical Report on Formal Development of Two-Electrode Cardiac Pacing System

Author

Méry, Dominique, Singh, Neeraj Kumar, Proof-oriented development of computer-based systems (MOSEL), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), and ANR-06-SETI-0015,RIMEL,Raffinement Incrémental de Modèles EvènementieLs(2006)

Subjects
modelling, ACM: F.: Theory of Computation/F.3: LOGICS AND MEANINGS OF PROGRAMS, grand challenge, ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING, medical device, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], refinement, formal method, Event B, pacemaker
Abstract

To build a high quality and zero defects medical devices and softwares is a crucial task. Formal modeling techniques help to achieve this target at certain level. Formal modeling of High-Confidence Medical devices those are too much error prone in operating, are an International Grand Challenge in the area of Verified Software. Formal modeling of an artificial pacemaker is also one of the proposed challenge. The architecture and functional behaviour of the double electrode pacemaker is more complex than the single electrode pacemaker. Proof-based an incremental approach, we use to develop the formal model of functional behaviour of the double electrode pacemaker. The incremental proof-based development is mainly driven by the refinement between an abstract model of the system and its detailed design through a series of refinements, which adds parametric based functional properties to the abstract system-level specifications using some intermediate models. The properties express system architecture and action-reaction under real-time constraints. This technical report focuses on the formal development of the double electrode operating modes and finds the common architecture of operating modes in tree form that helps to make the consistent system. The \eventb modeling language is used to express the double electrode pacemaker and generated proof obligations are proved by RODIN platform. Finally, the pacemaker model has been validated by an Event B animator ProB tool.

Published
2010

22. Acquisition multimodale de données articulatoires

Author

Aron, Michael, Berger, Marie-Odile, Kerrien, Erwan, Laprie, Yves, Visual Augmentation of Complex Environments (MAGRIT), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), Analysis, perception and recognition of speech (PAROLE), and Alain Marchal et Christian Cavé

Subjects
ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
Abstract

Ce chapitre décrit le système d'acquisition multimodale de données articulatoires élaboré dans le cadre du projet européen ASPI. Ce système intègre un échographe pour obtenir les contours de la langue, un dispositif de localisation électromagnétique pour localiser la sonde et l'apex de langue et des caméras de stréovision pour suivre les marqueurs peints sur le visage. Comme les concepteurs du système Hocus, nous avons choisi de ne pas imposer de contention au locuteur. Ce chapitre décrit la configuration du sytème et les procédures de synchronisation et de calibrage qui permettent de fusionner les informations sur les articulateurs disponibles dans les images et les mesures electromagnétiques. Diverses applications de ce sytème sont proposées, notamment l'étude de la variabilité des articulateurs durant la phonation de sons tenus.

Published
2009

23. On the Characterization and Selection of Diverse Conformational Ensembles, with Applications to Flexible Docking

Author

Loriot, Sebastien, Sachdeva, Sushant, Bastard, Karine, Prevost, Chantal, Cazals, Frédéric, Algorithms, Biology, Structure (ABS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Indian Institute of Technology Bombay (IIT Bombay), Unité de Biotechnologie, Biocatalyse et Biorégulation (U3B), Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS), Institut de biologie physico-chimique (IBPC (FR_550)), Centre National de la Recherche Scientifique (CNRS), INRIA, and Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Quantitative Biology::Biomolecules, conformer selection, Van der Waals models, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, geometric optimization, flexible docking, Flexibility, [INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG]
Abstract

To address challenging flexible docking problems, a number of docking algorithms pre-generate large collections of candidate conformers. To remove the redundancy from such ensembles, a central problem in this context is to report a selection of conformers maximizing some geometric diversity criterion. We make three contributions to this problem. First, we resort to geometric optimization so as to report selections maximizing the molecular volume or molecular surface area (MSA) of the selection. Greedy strategies are developed, together with approximation bounds. Second, to assess the efficacy of our algorithms, we investigate two conformer ensembles corresponding to a flexible loop of four protein complexes. By focusing on the MSA of the selection, we show that our strategy matches the MSA of standard selection methods, but resorting to a number of conformers between one and two orders of magnitude smaller. This observation is qualitatively explained using the Betti numbers of the union of balls of the selection. Finally, we replace the conformer selection problem in the context of multiple-copy flexible docking. On the afore-mentioned systems, we show that using the loops selected by our strategy can improve the result of the docking process.

Published
2009

24. Bioinformatique : de la cellule à la puce. Alchimistes de l'informatique, entretien avec Hugues Berry et Olivier Temam, propos recueillis par Dominique Chouchan

Author

Berry, Hugues, Temam, Olivier, Architectures, Languages and Compilers to Harness the End of Moore Years (ALCHEMY), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Comte, Marie-Hélène

Subjects
[SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract

National audience; Les systèmes de régulation chez les bactéries peuvent être analysés comme des circuits intégrés. Seront-ils une source d'inspiration pour les ordinateurs de demain ?

Published
2008

25. Parallel Reconfigurable Operator for Genomic Sequence Comparison: Architecture and Performance Analyses

Author

Cornu, Alexandre, Dussaugey, Francois, Lavenier, Dominique, Biological systems and models, bioinformatics and sequences (SYMBIOSE), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), INRIA, BULL - Les Clayes sous Bois, ANR-05-CIGC-0001,PARA,Parallélisme et Amélioration du Rendement des Apllications(2005), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, and ANR-05-CIGC-001-08,PARA,Parallélisme et Amélioration du Rendement des Applications(2005)

Subjects
[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR], ACM: J.: Computer Applications, ComputingMethodologies_PATTERNRECOGNITION, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, Parallelisation, similarity search, TBLASTN, indexing
Abstract

This document describes a parallel genomic sequence comparison operator for reconfigurable platforms. It has been implemented for speeding up the time consuming parts of the IRISA-TBLASTN program designed for comparing full genomes with large protein banks. Speed-up ranging from 25 to 200 has been measured on real genomic data.

Published
2008

26. Simulation : Le coeur numérique par Hervé Delingette et Miguel Fernandez. De l'ordinateur à la clinique, entretien avec Jérôme Garot, propos recueillis par Dominique Chouchan. Améliorer les performances des implants cardiaques par Alain Ripart

Author

Delingette, Hervé, Fernández, Miguel Angel, Garot, Jérôme, Ripart, Alain, Comte, Marie-Hélène, Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Numerical simulation of biological flows (REO), Laboratoire Jacques-Louis Lions (LJLL), Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)-Inria Paris-Rocquencourt, Service de cardiologie, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Henri Mondor-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), and Ela Médical S.A.

Subjects
ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [SDV.MHEP.CSC] Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system
Abstract

National audience; Un modèle numérique du coeur et des fonctions cardiaques, c'est presque pour demain. Médecins et informaticiens s'associent pour s'attaquer aux maladies cardiovasculaires, la première cause de mortalité dans le monde.

Published
2008

27. NORINE: a database of nonribosomal peptides

Author

Valérie Leclère, Philippe Jacques, Ségolène Caboche, Gregory Kucherov, Arnaud Fontaine, Maude Pupin, Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS), Sequential Learning (SEQUOIA), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire des Procédés Biologiques Génie Enzymatique et Microbien (PROBIOGEM), Université de Lille, Sciences et Technologies-Fédération Universitaire et Polytechnique de Lille, and PPF bioinformatique of Lille INRIA/Région Nord-Pas-de-Calais fellowship

Subjects
nonribosomal peptide database, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, Peptide, Biology, computer.software_genre, 01 natural sciences, 03 medical and health sciences, User-Computer Interface, Nonribosomal peptide, Genetics, Peptide Synthases, Databases, Protein, 030304 developmental biology, chemistry.chemical_classification, 0303 health sciences, Internet, Database, 010405 organic chemistry, Drug discovery, Protein primary structure, Articles, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], 0104 chemical sciences, chemistry, Biochemistry, Peptide Biosynthesis, Nucleic Acid-Independent, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Peptides, computer
Abstract

International audience; Norine is the first database entirely dedicated to nonribosomal peptides (NRPs). In bacteria and fungi, in addition to the traditional ribosomal proteic biosynthesis, an alternative ribosome-independent pathway called NRP synthesis allows peptide production. It is performed by huge protein complexes called nonribosomal peptide synthetases (NRPSs). The molecules synthesized by NRPS contain a high proportion of nonproteogenic amino acids. The primary structure of these peptides is not always linear but often more complex and may contain cycles and branchings. In recent years, NRPs attracted a lot of attention because of their biological activities and pharmacological properties (antibiotic, immunosuppressor, antitumor, etc.). However, few computational resources and tools dedicated to those peptides have been available so far. Norine is focused on NRPs and contains more than 700 entries. The database is freely accessible at http://bioinfo.lifl.fr/norine/. It provides a complete computational tool for systematic study of NRPs in numerous species, and as such, should permit to obtain a better knowledge of these metabolic products and underlying biological mechanisms, and ultimately to contribute to the redesigning of natural products in order to obtain new bioactive compounds for drug discovery.

Published
2008

28. Lagrangian approaches for a class of matching problems in computational biology

Author

Rumen Andonov, Nicola Yanev, Philippe Veber, Stefan Balev, Department Probability, Operations Research and Statistics, University of Sofia, Institute of Mathematics and Computer Science, University of Debrecen, Biological systems and models, bioinformatics and sequences (SYMBIOSE), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire d'informatique du Havre (LIH), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU), Софийски университет = Sofia University, University of Debrecen Egyetem [Debrecen], Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique

Subjects
Mathematical optimization, Theoretical computer science, Matching (graph theory), ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, 0102 computer and information sciences, 01 natural sciences, Protein threading, Quantitative Biology - Quantitative Methods, 03 medical and health sciences, symbols.namesake, Modelling and Simulation, 3-dimensional matching, Graph matching, Special case, Integer programming, integer programming, Quantitative Methods (q-bio.QM), 030304 developmental biology, Mathematics, Sequence-structure alignment, 0303 health sciences, Quantitative Biology::Biomolecules, Lagrangian relaxation, Cost splitting, Computational Mathematics, Computational Theory and Mathematics, 010201 computation theory & mathematics, Cutting stock problem, Modeling and Simulation, FOS: Biological sciences, symbols, Graph (abstract data type), complexity, Network optimization, Lagrangian
Abstract

International audience; This paper presents efficient algorithms for solving the problem of aligning a protein structure template to a query amino-acid sequence, known as protein threading problem. We consider the problem as a special case of graph matching problem. We give formal graph and integer programming models of the problem. After studying the properties of these models, we propose two kinds of Lagrangian relaxation for solving them. We present experimental results on real-life instances showing the efficiency of our approaches.

Published
2008

29. GridR: An R-based grid-enabled tool for data analysis in ACGT clinico-genomics trials

Author

A. Assi, Thierry Sengstag, Dennis Wegener, Stelios Sfakianakis, Stefan Rüping, Fraunhofer Institute for Intelligent Analysis and Information Systems (Fraunhofer IAIS), Fraunhofer (Fraunhofer-Gesellschaft), Swiss Institute of Bioinformatics [Lausanne] (SIB), Université de Lausanne = University of Lausanne (UNIL), Swiss Institute for Experimental Cancer Research - Lausanne (ISREC), Swiss Institute for Experimental Cancer Research, Biomedical Informatics Laboratory, Foundation for Research and Technology - Hellas (FORTH), Biological systems and models, bioinformatics and sequences (SYMBIOSE), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), ACGT, IEEE, Assi, Anthony, Université de Lausanne (UNIL), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique

Subjects
[SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Computer science, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, Context (language use), Genomics, 02 engineering and technology, Grid, computer.software_genre, 01 natural sciences, Data science, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Microarray-Based Analysis, 3. Good health, 010104 statistics & probability, ACM: J.: Computer Applications, Grid computing, Proof of concept, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Statistical analysis, 0101 mathematics, Web service, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], computer, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract

International audience; In this paper, we describe an analysis tool based on the statistical environment R, GridR, which allows using the collection of methodologies available as R packages in a grid environment. The aim of GridR, which was initiated in the context of the EU project Advancing Clinico-Genomics Trials on Cancer (ACGT), is to provide a powerful framework for the analysis of clinico-genomic trials involving large amount of data (e.g. microarray-based clinical trials). As a proof of concept, an example of microarray-based analysis taken from the literature was reproduced using GridR. As GridR will ultimately be made available to the ACGT community as a web service, we are sketching the ACGT project and its architecture in the present article as well.

Published
2007

30. Recent Advances in Solving the Protein Threading Problem

Author

Andonov, Rumen, Collet, Guillaume, Gibrat, Jean-François, Marin, Antoine, Poirriez, Vincent, Yanev, Nicola, Biological systems and models, bioinformatics and sequences (SYMBIOSE), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Unité Mathématique Informatique et Génome (MIG), Institut National de la Recherche Agronomique (INRA), Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201 (LAMIH), Université Polytechnique Hauts-de-France (UPHF)-Université Polytechnique Hauts-de-France (UPHF)-Centre National de la Recherche Scientifique (CNRS), INRIA, Unité Mathématique, Informatique et Génome (MIG), Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Centre National de la Recherche Scientifique (CNRS)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Department Probability, Operations Research and Statistics, University of Sofia, Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Centre National de la Recherche Scientifique (CNRS), and Софийски университет = Sofia University

Subjects
Protein Threading Problem, Protein Structure, ComputingMethodologies_PATTERNRECOGNITION, Parallel Processing, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
Abstract

The fold recognition methods are promissing tools for capturing the structure of a protein by its amino acid residues sequence but their use is still restricted by the needs of huge computational resources and suitable efficient algorithms as well. In the recent version of FROST (Fold Recognition Oriented Search Tool) package the most efficient algorithm for solving the Protein Threading Problem (PTP) is implemented due to the strong collaboration between the SYMBIOSE group in IRISA and MIG in Jouy-en-Josas. In this paper, we present the diverse components of FROST, emphasizing on the recent advances in formulating and solving new versions of the PTP and on the way of solving on a computer cluster a million of instances in a easonable time.

Published
2007

31. A Novel Algorithm for Finding Maximum Common Ordered Subgraph

Author

Yanev, Nicola, Andonov, Rumen, Biological systems and models, bioinformatics and sequences (SYMBIOSE), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Department Probability, Operations Research and Statistics, University of Sofia, INRIA, Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, and Софийски университет = Sofia University

Subjects
graph algorithms, proteins structures comparison, Combinatorial optimization, ACM: G.: Mathematics of Computing/G.2: DISCRETE MATHEMATICS, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
Abstract

In this paper, we study the following problem: given are adjacency matrices of two simple graphs. Find two principal matrices (though they are vectors) having the maximum inner product. When used for computing the similarity of two protein structures this problem is called contact map overlap and for the later, we give an exact B&B algorithm with bounds computed by solving Lagrangian relaxation of the problem. The efficiency of the approach is demonstrated on a popular benchmark set of instances together with a comparison with the best existing algorithm.

Published
2007

32. Biological model of motion integration and segmentation based on form cues

Author

Tlapale, Émilien, Masson, Guillaume, Kornprobst, Pierre, Computer and biological vision (ODYSSEE), Département d'informatique - ENS Paris (DI-ENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Inria Paris-Rocquencourt, Institut National de Recherche en Informatique et en Automatique (Inria)-École des Ponts ParisTech (ENPC), Institut de neurosciences cognitives de la méditerranée - UMR 6193 (INCM), Université de la Méditerranée - Aix-Marseille 2-Centre National de la Recherche Scientifique (CNRS), This work was partially supported by the EC IP project FP6-015879, FACETS., INRIA, Département d'informatique de l'École normale supérieure (DI-ENS), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Inria Sophia Antipolis - Méditerranée (CRISAM)

Subjects
motion estimation, optical flow, bio-inspired vision, genetic structures, MT area, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], shunting inhibition, center-surround integration, ACM: G.: Mathematics of Computing, feedbacks
Abstract

Active vision is an essential part of every biological organism possessing an eye system: posture, eye movements, visual research, ... All require a motion percept to operate. At the basis of active vision lays the ability to calculate movements of objects in the scene, at least on a sufficient level to react correctly. In this report we present a model of motion integration and segmentation in the first visual cortex areas. Specifically we modeled the first two cortex areas involved in motion processing in the primate: V1 and MT. To be able to process motion correctly a visual system also need to deal with form information. We investigate how form cues coming from the ventral pathway can be used by the V1/MT dorsal pathway to solve some perception problems. By using a recurrent dynamical system between the V1 and MT layers we are able to find out psychophysical results such as motion integration and center-surround effects due to the feedback connections, or end-of-line and 2D features detectors thanks to the shunting inhibition. We propose to modulate this system by a form information coming from the ventral stream and are thus able to explain asymmetric center-surround effects as well as motion segmentation and segregation between extrinsic and intrinsic junctions.

Published
2007

33. Functional Brain Imaging with Multi-Objective Multi-Modal Evolutionary Optimization

Author

Michèle Sebag, Vojtech Krmicek, Algorithmic number theory for cryptology (TANC), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX), Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Th. Runarsson et al., Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
Input/output, Computer Science - Artificial Intelligence, Computer science, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, Parallel algorithm, Evolutionary algorithm, InformationSystems_DATABASEMANAGEMENT, 02 engineering and technology, computer.software_genre, Multi-objective optimization, Evolutionary computation, Temporal database, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE, Information extraction, Modal, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Data mining, computer
Abstract

Functional brain imaging is a source of spatio-temporal data mining problems. A new framework hybridizing multi-objective and multi-modal optimization is proposed to formalize these data mining problems, and addressed through Evolutionary Computation (EC). The merits of EC for spatio-temporal data mining are demonstrated as the approach facilitates the modelling of the experts' requirements, and flexibly accommodates their changing goals.

Published
2006

34. Restoration of 3D medical images with total variation scheme on wavelet domains (TVW)

Author

Arnaud Ogier, Pierre Hellier, Christian Barillot, Vision, Action et Gestion d'informations en Santé (VisAGeS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Pluim J, Reinhardt J.M., CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), and Barillot, Christian

Subjects
medicine.diagnostic_test, business.industry, Stationary wavelet transform, Noise reduction, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Wavelet transform, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 02 engineering and technology, Regularization (mathematics), 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Noise, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 0302 clinical medicine, Wavelet, 0202 electrical engineering, electronic engineering, information engineering, Medical imaging, medicine, 020201 artificial intelligence & image processing, Computer vision, 3D ultrasound, Artificial intelligence, business, Mathematics
Abstract

http://bookstore.spie.org/index.cfm?fuseaction=detailpaper&cachedsearch=1&productid=651235&producttype=&CFID=570931&CFTOKEN=59424777; The multiplicity of sensors used in medical imaging leads to different noises. Non informative noise can damage the image interpretation process and the performance of automatic analysis. The method proposed in this paper allows compensating highly noisy image data from non informative noise without sophisticated modeling of the noise statistics. This generic approach uses jointly a wavelet decomposition scheme and a non-isotropic Total Variation filtering of the transform coefficients. This framework benefits from both the hierarchical capabilities of the wavelet transform and the well-posed regularization scheme of the Total Variation. This algorithm has been tested and validated on test-bed data, as well as different clinical MR and 3D ultrasound images, enhancing the capabilities of the proposed method to cope with different noise models.

Published
2006

35. Analyse logique, combinatoire et statistique de la construction d'une hiérarchie binaire implicative ; niveaux et noeuds significatifs

Author

Lerman, Israël-César, Biological systems and models, bioinformatics and sequences (SYMBIOSE), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique

Subjects
classification ascendante hiérarchique orientée, ``significant'' nodes, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, Analyse implicative, niveaux et noeuds ``significatifs'' \\ Implicative analysis, ``significant'' levels, implicative tree, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], arbre implicatif, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], oriented ascendant hierarchical classification
Abstract

Nous reprenons ici d'une façon nouvelle et systématique l'étude d'un type spécifique d'analyse des données fondé sur la classification ascendante hiérarchique binaire. L'ensemble organisé est un ensemble d'attributs de description, généralement booléens. La relation valuée de similarité représentée est de nature implicative et l'arbre obtenu est un arbre ``implicatif''. Ce type d'analyse des données a été introduit et développé par R. Gras et ses collaborateurs. Nous en étudions les fondements et proposons une axiomatique nouvelle qui conduit à des aspects constructifs et énumératifs. Une interprétation différente de celle des auteurs mentionnés, des résultats de ce type d'analyse des données est proposée et justifiée. Les propriétés mathématiques de la construction statistique de l'arbre implicatif sont étudiées. Enfin, nous analysons de façon précise et complète l'adaptation de nos critères de reconnaissance des niveaux et noeuds les plus ``significatifs'' d'un arbre de classification classique au cas d'un arbre binaire implicatif. \\ Implicative data analysis is a specific method of data analysis, employing asymmetrical similarities and based on a construction of an ascendant binary hierarchical classification. This method has been introduced and developed by R. Gras and collaborators. Statistical descriptive attributes (generally, boolean attributes) are organized according to the latter structure, which is called: a binary implicative tree. In this technical report we reconsider in a new and systematic manner this approach. The analysis of its foundations leads us to a new axiomatic and to a new constructive and combinatorial properties. We give and justify a new interpretation of the results obtained by this type of data analysis method.The mathematical properties of the statistical construction of the binary implicative tree are studied. Finally, we analyze in a precise and complete manner our criteria for recognizing the most``significant'' levels and the most ``significant'' nodes of the implicative binary tree.

Published
2006

36. Lagrangian Approaches for a class of Matching Problems in Computational Biology

Author

Yanev, Nicola, Andonov, Rumen, Veber, Philippe, Balev, Stefan, Biological systems and models, bioinformatics and sequences (SYMBIOSE), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire d'informatique du Havre (LIH), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, and INRIA

Subjects
Quantitative Biology::Biomolecules, Alignement séquence-structure, Lagrangian relaxation, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, sequence-structure alignment, relaxation lagrangienne \\ Sequence-structure alignment, Lagrangian relaxatio, programmation en nombres entiers, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], complexité, complexity, integer programming, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
Abstract

Cet article propose des algorithmes efficaces pour déterminer l'alignement optimal entre une structure et une séquence protéique, problème connu sous le nom de protein threading. Nous posons ce problème comme un cas particulier d'appariement. Nous présentons un modèle formel du problème sous la forme d'une famille de graphes, et des programmes en nombre entiers correspondants. Nous étudions dans un premier temps les propriétés de ces modèles, pour ensuite proposer deux approches de relaxation lagrangienne pour la résolution. Enfin, nous montrons, à l'aide de données expérimentales sur des instances réelles, l'efficacité de ces approches. \\ This paper presents efficient algorithms for solving the problem of aligning a protein structure template to a query amino-acid sequence, known as protein threading problem. We consider the problem as a special case of graph matching problem. We give formal graph and integer programming models of the problem. After studying the properties of these models, we propose two kinds of Lagrangian relaxation for solving them. We present experimental results on real life instances showing the efficiency of our approaches.

Published
2006

37. Genetic genealogical models in rare event analysis

Author

CÉROU, Frédéric, DEL MORAL, Pierre, LE GLAND, François, LEZAUD, Pascal, Applications of interacting particle systems to statistics (ASPI), Université de Rennes (UR)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Jean Alexandre Dieudonné (JAD), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS), Centre d'études de la Navigation Aérienne (CENA), Direction Générale de l'Aviation Civile (DGAC), INRIA, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Inria Rennes – Bretagne Atlantique, and Université Nice Sophia Antipolis (... - 2019) (UNS)

Subjects
ACM: G.: Mathematics of Computing/G.3: PROBABILITY AND STATISTICS, GENETIC ALGORITHMS, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], arbres généalogiques // Interacting particle systems, algorithmes génétiques, modèles de Feynman-Kac, [MATH.MATH-PR]Mathematics [math]/Probability [math.PR], RARE EVENTS, FEYNMAN-KAC MODELS, INTERACTING PARTICLE SYSTEMS, Systèmes de particules en interaction, événements rares, GENEALOGICAL TREES
Abstract

Nous présentons dans cet article un algorithme de particules en interaction de type génétique et un modèle généalogique pour estimer une classe d'événements rares provenant de la physique et de l'analyse des réseaux. Nous exprimons la loi d'un processus de Markov qui atteint un événement rare comme un modèle de Feynman--Kac dans l'espace des trajectoires. Nous montrons comment ces modèles de branchement de particules décrits dans des travaux précédents peuvent être utilisés pour estimer la probabilité d'événements rares, ainsi que la loi du processus dans ce régime. \\ We present in this article a genetic type interacting particle systems algorithm and a genealogical model for estimating a class of rare events arising in physics and network analysis. We represent the distribution of a Markov process hitting a rare target in terms of a Feynman--Kac model in path space. We show how these branching particle models described in previous works can be used to estimate the probability of the corresponding rare events as well as the distribution of the process in this regime.

Published
2006

38. An algorithm for robust and efficient location of T-wave ends in electrocardiogram

Author

Zhang, Qinghua, Illanes Manriquez, Alfredo, Médigue, Claire, Papelier, Yves, Sorine, Michel, Applications and Tools of Automatic Control (SOSSO2), Inria Paris-Rocquencourt, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION, traitement de signal, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], signal processing} \\ Electrocardiogramme (ECG), Electrocardiogram (ECG), T-wave end location, localisation de fins d'ondes T, ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.4: Applications
Abstract

Computer-aided analysis of electrocardiogram (ECG) is widely used in cardiac disease diagnosis. The purpose of this paper is to propose a new algorithm for T-wave end location, which is known to be the most difficult one among ECG wave form detection and location problems. The proposed algorithm mainly consists of the computation of an indicator related to the area covered by the T-wave curve and delimited in a special manner. Based on simple assumptions, essentially on the concavity of the T-wave form, it is formally proved that the maximum of the computed indicator inside each cardiac cycle coincides with the T-wave end. Moreover, the algorithm is robust to measurement noise, to wave form morphological variations and to baseline wander. It is also computationally very simple: the main computation can be implemented as a simple finite impulse response (FIR) filter. When evaluated with the PhysioNet QT database in terms of the mean and the standard deviation of the \mbox{T-wave} end location errors, the proposed algorithm outperforms the other algorithms evaluated with the same data base, according to the most recent available publications up to our knowledge. \\ L'analyse de l'électrocardiogramme (ECG) assistée par l'ordinateur est largement utilisée en diagnostic de maladies cardiaques. Le but de cet article est de proposer un nouveau algorithme pour la localisation de fins d'ondes T, connu d'être le plus difficile des problèmes concernant la détection et localisation d'ondes dans l'ECG. L'algorithme proposé consiste principalement à calculer un indicateur lié à la surface couverte par l'onde T délimitée d'une manière spéciale. Sous des hypothèses simples, principalement sur la concavité de l'onde T, il est démontré que le maximum de l'indicateur calculé dans chaque cycle cardiaque coïncide avec la fin de l'onde T. En plus, l'algorithme est robuste au bruit de mesure, aux variations morphologiques et à la dérive de la ligne de base. Le calcul numérique est très simple : le calcul principal peut être implémenté sous forme d'un filtre à réponse impulsionnelle finie. Quand cet algorithme est évalué sur la base de données QT de PhysioNet en termes de la moyenne et l'écart type des erreurs de localisation de l'onde T, il obtient de meilleurs résultats par rapport à d'autres algorithmes évalués avec la même base de données, d'après les publications les plus récentes à notre connaissance.

Published
2005

39. État de l'art des méthodes de correction des déformations cérébrales per-opératoires

Author

Coupé, Pierrick, Hellier, Pierre, Barillot, Christian, Vision, Action et Gestion d'informations en Santé (VisAGeS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), INRIA, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)

Subjects
Registration, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], Brain Shift, Recalage, Neuronavigation
Abstract

L'utilisation croissante de systèmes de navigation pour l'aide à la chirurgie a permis de faciliter les interventions ainsi que la planification des gestes chirurgicaux. Néanmoins, dans le cas de la neurochirurgie où le geste opératoire doit être très précis, les systèmes actuels sont limités à cause de déformations per-opératoires nommées ``Brain Shift''. Le terme de 'Brain Shift' traduit le mouvement des structures cérébrales arrivant après ouverture de la boite crânienne (jusqu'à 25mm). Le recalage rigide réalisé par le système de neuronavigation entre les examens préopératoires et la position du patient en salle d'opération est donc entaché d'une imprécision. Ainsi, les informations fournies par le système de navigation deviennent partiellement obsolètes. Ce document propose une présentation des différentes techniques de mesure et de compensation du 'Brain Shift'. Les avantages et inconvénients de chaque approche seront soulignés avant de conclure par une brève présentation des méthodes de validation existantes. / Navigation systems become a very attractive tool in surgical planning and procedure. However, the accuracy and usefulness of such systems is limited in presence of soft-tissue deformations. In neurosurgery, this phenomenon is called ``Brain Shift''. The ``Brain shift'' is the motion of cerebral structures occurring after the craniotomy (up to 25mm). The neuronavigation system matches rigidly the pre-operative images with the surgical field. The hypothesis of a rigid registration is no longer valid because of deformations. This document presents a survey with classification of published methods to measure and compensate for the brain shift. The various validation framework are also presented.

Published
2005

40. Probe Trajectory Interpolation for 3D Reconstruction of Freehand Ultrasound

Author

Pierre Hellier, Christian Barillot, Xavier Morandi, Pierrick Coupé, Vision, Action et Gestion d'informations en Santé (VisAGeS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), and Coupé, Pierrick

Subjects
[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Computer science, ACM: J.: Computer Applications/J.3: LIFE AND MEDICAL SCIENCES, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Health Informatics, Brain imaging, Sensitivity and Specificity, Imaging phantom, Freehand Ultrasound, Imaging, Three-Dimensional, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Image Interpretation, Computer-Assisted, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Computer vision algorithms, Humans, Radiology, Nuclear Medicine and imaging, Computer vision, Segmentation, 3D reconstruction, Probe Trajectory, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Ultrasonography, Brain Diseases, Radiological and Ultrasound Technology, business.industry, Phantoms, Imaging, Ultrasound, Image-Guided Neurosurgery, Image guided neurosurgery, Reproducibility of Results, Tracking system, Image Enhancement, Computer Graphics and Computer-Aided Design, Magnetic Resonance Imaging, Computer Vision and Pattern Recognition, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithms
Abstract

Three-dimensional (3D) Freehand ultrasound uses the acquisition of non parallel B-scans localized in 3D by a tracking system (optic, mechanical or magnetic). Using the positions of the irregularly spaced B-scans, a regular 3D lattice volume can be reconstructed, to which conventional 3D computer vision algorithms (registration and segmentation) can be applied. This paper presents a new 3D reconstruction method which explicitly accounts for the probe trajectory. Experiments were conducted on phantom and intra-operative datasets using various probe motion types and varied slice-to-slice B-scan distances. Results suggest that this technique improves on classical methods at the expense of computational time.

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