Showing papers by "Paul Bourgine published in 2010"

Cell Lineage Reconstruction of Early Zebrafish Embryos Using Label-Free Nonlinear Microscopy

Abstract: Quantifying cell behaviors in animal early embryogenesis remains a challenging issue requiring in toto imaging and automated image analysis. We designed a framework for imaging and reconstructing unstained whole zebrafish embryos for their first 10 cell division cycles and report measurements along the cell lineage with micrometer spatial resolution and minute temporal accuracy. Point-scanning multiphoton excitation optimized to preferentially probe the innermost regions of the embryo provided intrinsic signals highlighting all mitotic spindles and cell boundaries. Automated image analysis revealed the phenomenology of cell proliferation. Blastomeres continuously drift out of synchrony. After the 32-cell stage, the cell cycle lengthens according to cell radial position, leading to apparent division waves. Progressive amplification of this process is the rule, contrasting with classical descriptions of abrupt changes in the system dynamics.

Cells Segmentation From 3-D Confocal Images of Early Zebrafish Embryogenesis

Abstract: We designed a strategy for extracting the shapes of cell membranes and nuclei from time lapse confocal images taken throughout early zebrafish embryogenesis using a partial-differential-equation-based segmentation. This segmentation step is a prerequisite for an accurate quantitative analysis of cell morphodynamics during embryogenesis and it is the basis for an integrated understanding of biological processes. The segmentation of embryonic cells requires live zebrafish embryos fluorescently labeled to highlight sub-cellular structures and designing specific algorithms by adapting classical methods to image features. Our strategy includes the following steps: the signal-to-noise ratio is first improved by an edge-preserving filtering, then the cell shape is reconstructed applying a fully automated algorithm based on a generalized version of the Subjective Surfaces technique. Finally we present a procedure for the algorithm validation either from the accuracy and the robustness perspective.

Cognitive Economics: An Interdisciplinary Approach

Abstract: 1 What is Cognitive Economics?.- 2 Rational Choice under Uncertainty.- 3 General Equilibrium.- 4 The Principles of Game Theory.- 5 Rationality and the Experimental Study of Reasoning.- 6 Supraclassical Inference without Probability.- 7 From Natural to Artificial Intelligence: Numerical Processing for Cognitive Tasks.- 8 An Introduction to Statistical Mechanics.- 9 Spontaneous Symmetry Breaking and the Transition to Disorder in Physics.- 10 Co-Evolutionist Stochastic Dynamics: Emergence of Power Laws.- 11 Topics of Cognitive Economics.- 12 What is a Collective Belief?.- 13 Conditional Statements and Directives.- 14 Choice Axioms for a Positive Value of Information.- 15 Elements of Viability Theory for the Analysis of Dynamic Economics.- 16 Stochastic Evolutionary Game Theory.- 17 The Evolutionary Analysis of Signal Games.- 18 The Structure of Economic Interaction: Individual and Collective Rationality.- 19 Experimental Markets: Empirical Data for Theorists.- 20 Social Interactions in Economic Theory: An Insight from Statistical Mechanics.- 21 Adjustment and Social Choice.- 22 From Agent-based Computational Economics Towards Cognitive Economics.- 23 Social Networks and Economic Dynamics.- 24 Coalitions and Networks in Economic Analysis.- 25 Threshold Phenomena versus Killer Clusters in Bimodal Competion for Standards.- 26 Cognitive Efficiency of Social Networks Providing Consumption Advice on Experience Goods.- The Future of Cognitive Economics.

4d embryogenesis image analysis using pde methods of image processing

Abstract: In this paper, we introduce a set of methods for processing and analyzing long time series of 3D images representing embryo evolution. The images are obtained by in vivo scanning using a confocal microscope where one of the channels represents the cell nuclei and the other one the cell membranes. Our image processing chain consists of three steps: image filtering, object counting (center detection) and segmentation. The corresponding methods are based on numerical solution of nonlinear PDEs, namely the geodesic mean curvature flow model, flux-based level set center detection and generalized subjective surface equation. All three models have a similar character and therefore can be solved using a common approach. We explain in details our semi-implicit time discretization and finite volume space discretization. This part is concluded by a short description of parallelization of the algorithms. In the part devoted to experiments, we provide the experimental order of convergence of the numerical scheme, the validation of the methods and numerous experiments with the data representing an early developmental stage of a zebrafish embryo.

Optimal viable path search for a cheese ripening process using a multi-objective EA.

Abstract: Viability theory is a very attractive theoretical approach for the modeling of complex dynamical systems. However, its scope of application is limited due to the high computational power it necessitates. Evolutionary computation is a convenient way to address some issues related to this theory. In this paper, we present a multi-objective evolutionary approach to address the optimisation problem related to the computation of optimal command profiles of a complex process. The application we address here is a real size problem from dairy industry, the modeling of a Camembert cheese ripening process. We have developed a parallel implementation of a multiobjective EA that has produced a Pareto front of optimal control profiles (or trajectories), with respect to four objectives. The Pareto front was then analysed by an expert who selected a interesting compromise, yielding a new control profile that seems promising for industrial applications.

Global Strategy of Active Machine Learning for Complex Systems: Embryogenesis Application on Cell Division Detection

Abstract: The intrinsic complexity of biological systems creates huge amounts of unlabeled experimental data. The exploitation of such data can be achieved by performing active machine learning accompanied by a high-level symbolic expert who defines categories and their best boundaries using as little data as possible. We present a global strategy for designing active machine learning methods suited for the observation and analysis of complex systems, such as embryonic development. We developed a procedure that uses all available knowledge, whether gathered manually or automatically, and is able to readjust when new data is provided. We show that it is a powerful method for the investigation of the morphogenetic features of embryogenesis and specifically mitosis detection. It will make possible to properly reconstruct the in vivo cell morphodynamics, a main challenge of the post-genomic era.