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Patent
Ecole Normale Superieure de Paris, French National Center for Scientific Research, University Pierre and Marie Curie | Date: 2016-10-26

The present invention relates to a method for the determination of a nucleic acid sequence by physical manipulation. The method is based on the precise determination of the localization of the replicating fork on the template by measuring the physical distance between one end of the molecule and the fork. This allows the determination of the physical location of the site where a pause or a blockage of the replication occurs, and deducing therefrom information on the sequence of the nucleic acid.


Patent
Ecole Normale Superieure de Paris, French National Center for Scientific Research, University Pierre and Marie Curie | Date: 2016-10-26

The present invention relates to a method for the determination of a nucleic acid sequence by physical manipulation. In particular, the said method comprises the steps of denaturing a double-stranded nucleic acid molecule corresponding to the said nucleic acid sequence by applying a physical force to the said molecule; and detecting a blockage of the renaturation of the double-stranded nucleic acid molecule. More specifically, the method comprises the steps of denaturing a double-stranded nucleic acid molecule corresponding to the said nucleic acid sequence by applying a physical force to the said molecule; providing a single-stranded nucleic acid molecule; renaturing the said double stranded nucleic acid molecule in the presence of the said single-stranded nucleic acid molecule; and detecting a blockage of the renaturation of the double-stranded nucleic acid.


Paoletti P.,Ecole Normale Superieure de Paris | Bellone C.,University of Geneva | Zhou Q.,Genentech
Nature Reviews Neuroscience | Year: 2013

NMDA receptors (NMDARs) are glutamate-gated ion channels and are crucial for neuronal communication. NMDARs form tetrameric complexes that consist of several homologous subunits. The subunit composition of NMDARs is plastic, resulting in a large number of receptor subtypes. As each receptor subtype has distinct biophysical, pharmacological and signalling properties, there is great interest in determining whether individual subtypes carry out specific functions in the CNS in both normal and pathological conditions. Here, we review the effects of subunit composition on NMDAR properties, synaptic plasticity and cellular mechanisms implicated in neuropsychiatric disorders. Understanding the rules and roles of NMDAR diversity could provide new therapeutic strategies against dysfunctions of glutamatergic transmission. © 2013 Macmillan Publishers Limited. All rights reserved.


Giraud A.-L.,Ecole Normale Superieure de Paris | Poeppel D.,New York University
Nature Neuroscience | Year: 2012

Neuronal oscillations are ubiquitous in the brain and may contribute to cognition in several ways: for example, by segregating information and organizing spike timing. Recent data show that delta, theta and gamma oscillations are specifically engaged by the multi-timescale, quasi-rhythmic properties of speech and can track its dynamics. We argue that they are foundational in speech and language processing, 'packaging' incoming information into units of the appropriate temporal granularity. Such stimulus-brain alignment arguably results from auditory and motor tuning throughout the evolution of speech and language and constitutes a natural model system allowing auditory research to make a unique contribution to the issue of how neural oscillatory activity affects human cognition. © 2012 Nature America, Inc. All rights reserved.


Ostojic S.,Ecole Normale Superieure de Paris
Nature Neuroscience | Year: 2014

Asynchronous activity in balanced networks of excitatory and inhibitory neurons is believed to constitute the primary medium for the propagation and transformation of information in the neocortex. Here we show that an unstructured, sparsely connected network of model spiking neurons can display two fundamentally different types of asynchronous activity that imply vastly different computational properties. For weak synaptic couplings, the network at rest is in the well-studied asynchronous state, in which individual neurons fire irregularly at constant rates. In this state, an external input leads to a highly redundant response of different neurons that favors information transmission but hinders more complex computations. For strong couplings, we find that the network at rest displays rich internal dynamics, in which the firing rates of individual neurons fluctuate strongly in time and across neurons. In this regime, the internal dynamics interact with incoming stimuli to provide a substrate for complex information processing and learning. © 2014 Nature America, Inc. All rights reserved.


Corson F.,Ecole Normale Superieure de Paris
Physical Review Letters | Year: 2010

The structure of networks that provide optimal transport properties has been investigated in a variety of contexts. While many different formulations of this problem have been considered, it is recurrently found that optimal networks are trees. It is shown here that this result is contingent on the assumption of a stationary flow through the network. When time variations or fluctuations are allowed for, a different class of optimal structures is found, which share the hierarchical organization of trees yet contain loops. The transitions between different network topologies as the parameters of the problem vary are examined. These results may have strong implications for the structure and formation of natural networks, as is illustrated by the example of leaf venation networks. © 2010 The American Physical Society.


Felix M.-A.,Ecole Normale Superieure de Paris
Current Opinion in Genetics and Development | Year: 2012

Developmental systems can produce a variety of patterns and morphologies when the molecular and cellular activities within them are varied. With the advent of quantitative modeling, the range of phenotypic output of a developmental system can be assessed by exploring model parameter space. Here I review recent examples where developmental evolution is studied using quantitative models, which increasingly rely on empirically determined molecular signaling pathways and their crosstalk. Quantitative pathway evolution may result in dramatic morphological changes. Alternatively, in many developmental systems, the phenotypic output is robust to a range of parameter variation, and cryptic developmental evolution may occur without morphological change. Formalization and measurements of the relationship between genetic variation and parameter variation in developmental models remain in their infancy. © 2012 Elsevier Ltd.


Balibar S.,Ecole Normale Superieure de Paris
Nature | Year: 2010

A 'supersolid' is a quantum solid in which a fraction of the mass is superfluid. As a remarkable consequence, it is rigid, but part of its mass is able to flow owing to quantum physical processes. This paradoxical state of matter was considered as a theoretical possibility as early as 1969, but its existence was discovered only in 2004, in 4 He. Since then, intense experimental and theoretical efforts have been made to explain the origins of this exotic state of matter. It now seems that its physical interpretation is more complicated than originally thought. © 2010 Macmillan Publishers Limited. All rights reserved.


Bach F.,Ecole Normale Superieure de Paris
Foundations and Trends in Machine Learning | Year: 2013

Submodular functions are relevant to machine learning for at least two reasons: (1) some problems may be expressed directly as the optimization of submodular functions and (2) the Lovasz extension of submodular functions provides a useful set of regularization functions for supervised and unsupervised learning. In this monograph, we present the theory of submodular functions from a convex analysis perspective, presenting tight links between certain polyhedra, combinatorial optimization and convex optimization problems. In particular, we show how submodular function minimization is equivalent to solving a wide variety of convex optimization problems. This allows the derivation of new efficient algorithms for approximate and exact submodular function minimization with theoretical guarantees and good practical performance. By listing many examples of submodular functions, we review various applications to machine learning, such as clustering, experimental design, sensor placement, graphical model structure learning or subset selection, as well as a family of structured sparsity-inducing norms that can be derived and used from submodular functions. © 2013 F. Bach.


Ramus F.,Ecole Normale Superieure de Paris
Trends in Cognitive Sciences | Year: 2014

A new study reports that activations of superior temporal regions for speech are normal in dyslexia, although being less well connected to downstream frontal regions. These findings support the hypothesis of a deficit in the access to phonological representations rather than in the representations themselves. © 2014 Elsevier Ltd.

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