Nuclear Pore Complex (NPC) is of paramount importance for cellular processes since it is the unique gateway for molecular exchange through the nucleus. Unraveling the modifications of the NPC structure in response to physiological cues, also called nuclear pore plasticity, is key to the understanding of the selectivity of this molecular machinery. As a step towards this goal, we use the optical super-resolution microscopy method called direct Stochastic Optical Reconstruction Microscopy (dSTORM), to analyze oocyte development impact on the internal structure and large-scale organization of the NPC. Staining of the FG-Nups proteins and the gp210 proteins allowed us to pinpoint a decrease of the global diameter by measuring the mean diameter of the central channel and the luminal ring of the NPC via autocorrelation image processing. Moreover, by using an angular and radial density function we show that development of the Xenopus laevis oocyte is correlated with a progressive decrease of the density of NPC and an ordering on a square lattice.
Models of emergent phenomena are designed to provide an explanation to global-scale phenomena from local-scale processes. Model validation is commonly done by verifying that the model is able to reproduce the patterns to be explained. We argue that robust validation must not only be based on corroboration, but also on attempting to falsify the model, i.e. making sure that the model behaves soundly for any reasonable input and parameter values. We propose an open-ended evolutionary method based on Novelty Search to look for the diverse patterns a model can produce. The Pattern Space Exploration method was tested on a model of collective motion and compared to three common a priori sampling experiment designs. The method successfully discovered all known qualitatively different kinds of collective motion, and performed much better than the a priori sampling methods. The method was then applied to a case study of city system dynamics to explore the model’s predicted values of city hierarchisation and population growth. This case study showed that the method can provide insights on potential predictive scenarios as well as falsifiers of the model when the simulated dynamics are highly unrealistic.
In the presented work we aimed at improving confocal imaging to obtain highest possible resolution in thick biological samples, such as the mouse oocyte. We therefore developed an image processing workflow that allows improving the lateral and axial resolution of a standard confocal microscope. Our workflow comprises refractive index matching, the optimization of microscope hardware parameters and image restoration by deconvolution. We compare two different deconvolution algorithms, evaluate the necessity of denoising and establish the optimal image restoration procedure. We validate our workflow by imaging sub resolution fluorescent beads and measuring the maximum lateral and axial resolution of the confocal system. Subsequently, we apply the parameters to the imaging and data restoration of fluorescently labelled meiotic spindles of mouse oocytes. We measure a resolution increase of approximately 2-fold in the lateral and 3-fold in the axial direction throughout a depth of 60μm. This demonstrates that with our optimized workflow we reach a resolution that is comparable to 3D-SIM-imaging, but with better depth penetration for confocal images of beads and the biological sample.
International audienceEmergence and complex systems have been the topic of many papers and are still disputed concepts in many fields. This lack of consensus hinders the use of these concepts in practice, particularly in modelling. All definitions of emergence imply the existence of a hierarchical system: a system that can be observed, 15 measured and analysed at both macroscopic and microscopic levels. We argue that such systems are well described by mathematical graphs and, using graph theory, we propose an ontology (i.e. a set of consistent, formal concept definitions) of dynamic hierarchical systems capable of displaying emergence. Using graph theory enables formal definitions of system macro-state, micro-state and dynamic structural changes. From these definitions, we identify four major families of emergence that match existing definitions from the 20 literature. All but one depend on the relation between the observer and the system, and remind us that a major feature of most supposedly complex systems is our inability to describe them in full. The fourth definition is related to causality, in particular, to the ability of the system itself to create sources of change, independent from other external or internal sources. Feedback loops play a key role in this process. We propose that their presence is a necessary condition for a hierarchical system to be qualified as complex
In this paper, we present an automatic, robust and reliable process to quantify liver steatosis. The degree of steatosis is a useful marker of steatohepatitis. This degree is routinely assessed visually by an expert and then lacks of accuracy and robustness. The process that we have developed is divided in two steps. A fuzzy classification first merges into classes pixels according to their intensity. We use a generalized objective function that allows to detect micro and blurredness vacuoles of steatosis. Then, regions with inhomogeneous texture and irregular shape were eliminated with compactness and standard deviation parameters. The obtained results are good correlated with expert graduation (in five levels). A better correlation is obtained with a more precise grading.
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