8,214 research outputs found

    Relation Structure molĂ©culaire - Odeur Utilisation des RĂ©seaux de Neurones pour l’estimation de l’Odeur Balsamique

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    Les molĂ©cules odorantes (parfums ou flaveurs) sont utilisĂ©es dans une grande variĂ©tĂ© de produits de consommation, pour inciter les consommateurs Ă  associer les impressions favorables Ă  un produit donnĂ©. La Relation Structure molĂ©culaire-Odeur (SOR) est cruciale pour la synthĂšse de ces molĂ©cules mais est trĂšs difficile Ă  Ă©tablir due Ă  la subjectivitĂ© de l’odeur. Ce travail prĂ©sente une approche de prĂ©diction de l'odeur des molĂ©cules basĂ©e sur les descripteurs molĂ©culaires. Les techniques d’analyse en composantes principales (PCA) et de d’analyse de colinĂ©aritĂ© permettent d’identifier les descripteurs les plus pertinents. un rĂ©seau de neurones supervisĂ©5 Ă  deux couches (cachĂ©e et sortie) est employĂ© pour corrĂ©ler la structure molĂ©culaire Ă  l’odeur. La base de donnĂ©es dĂ©crite prĂ©cĂ©demment est utilisĂ©e pour l’apprentissage. Un ensemble de paramĂštres est modifiĂ© jusqu’à la satisfaction de la meilleure rĂ©gression. Les rĂ©sultats obtenus sont encouragent, ainsi les descripteurs molĂ©culaires convenables corrĂšlent efficacement l'odeur des molĂ©cules. C’est la premiĂšre Ă©tape d’un modĂšle gĂ©nĂ©rique en dĂ©veloppement pour corrĂ©ler l'odeur avec les structures molĂ©culaire

    Modeling of the Acute Toxicity of Benzene Derivatives by Complementary QSAR Methods

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    A data set containing acute toxicity values (96-h LC50) of 69 substituted benzenes for fathead minnow (Pimephales promelas) was investigated with two Quantitative Structure- Activity Relationship (QSAR) models, either using or not using molecular descriptors, respectively. Recursive Neural Networks (RNN) derive a QSAR by direct treatment of the molecular structure, described through an appropriate graphical tool (variable-size labeled rooted ordered trees) by defining suitable representation rules. The input trees are encoded by an adaptive process able to learn, by tuning its free parameters, from a given set of structureactivity training examples. Owing to the use of a flexible encoding approach, the model is target invariant and does not need a priori definition of molecular descriptors. The results obtained in this study were analyzed together with those of a model based on molecular descriptors, i.e. a Multiple Linear Regression (MLR) model using CROatian MultiRegression selection of descriptors (CROMRsel). The comparison revealed interesting similarities that could lead to the development of a combined approach, exploiting the complementary characteristics of the two approaches

    `The frozen accident' as an evolutionary adaptation: A rate distortion theory perspective on the dynamics and symmetries of genetic coding mechanisms

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    We survey some interpretations and related issues concerning the frozen hypothesis due to F. Crick and how it can be explained in terms of several natural mechanisms involving error correction codes, spin glasses, symmetry breaking and the characteristic robustness of genetic networks. The approach to most of these questions involves using elements of Shannon's rate distortion theory incorporating a semantic system which is meaningful for the relevant alphabets and vocabulary implemented in transmission of the genetic code. We apply the fundamental homology between information source uncertainty with the free energy density of a thermodynamical system with respect to transcriptional regulators and the communication channels of sequence/structure in proteins. This leads to the suggestion that the frozen accident may have been a type of evolutionary adaptation

    EEG Resting-State Brain Topological Reorganization as a Function of Age

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    Resting state connectivity has been increasingly studied to investigate the effects of aging on the brain. A reduced organization in the communication between brain areas was demonstrated b y combining a variety of different imaging technologies (fMRI, EEG, and MEG) and graph theory. In this paper, we propose a methodology to get new insights into resting state connectivity and its variations with age, by combining advanced techniques of effective connectivity estimation, graph theoretical approach, and classification by SVM method. We analyzed high density EEG signal srecordedatrestfrom71healthysubjects(age:20–63years). Weighted and directed connectivity was computed by means of Partial Directed Coherence based on a General Linear Kalman filter approach. To keep the information collected by the estimator, weighted and directed graph indices were extracted from the resulting networks. A relation between brain network properties and age of the subject was found, indicating a tendency of the network to randomly organize increasing with age. This result is also confirmed dividing the whole population into two subgroups according to the age (young and middle-aged adults): significant differences exist in terms of network organization measures. Classification of the subjects by means of such indices returns an accuracy greater than 80

    Parallel software for lattice N=4 supersymmetric Yang--Mills theory

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    We present new parallel software, SUSY LATTICE, for lattice studies of four-dimensional N=4\mathcal N = 4 supersymmetric Yang--Mills theory with gauge group SU(N). The lattice action is constructed to exactly preserve a single supersymmetry charge at non-zero lattice spacing, up to additional potential terms included to stabilize numerical simulations. The software evolved from the MILC code for lattice QCD, and retains a similar large-scale framework despite the different target theory. Many routines are adapted from an existing serial code, which SUSY LATTICE supersedes. This paper provides an overview of the new parallel software, summarizing the lattice system, describing the applications that are currently provided and explaining their basic workflow for non-experts in lattice gauge theory. We discuss the parallel performance of the code, and highlight some notable aspects of the documentation for those interested in contributing to its future development.Comment: Code available at https://github.com/daschaich/sus

    First-principles Study of High-Pressure Phase Stability and Superconductivity of Bi4I4

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    Bismuth iodide Bi4I4 exhibits intricate crystal structures and topological insulating states that are highly susceptible to influence by environments, making its physical properties highly tunable by external conditions. In this work, we study the evolution of structural and electronic properties of Bi4I4 at high pressure using an advanced structure search method in conjunction with first-principles calculations. Our results indicate that the most stable ambient-pressure monoclinic α−Bi4I4 phase in C2/m symmetry transforms to a trigonal P31c structure (ɛ−Bi4I4) at 8.4 GPa, then to a tetragonal P4/mmm structure (ζ−Bi4I4) above 16.6 GPa. In contrast to the semiconducting nature of ambient-pressure Bi4I4, the two high-pressure phases are metallic, in agreement with reported electrical measurements. The ɛ−Bi4I4 phase exhibits distinct ionic states of Iή− and (Bi4I3)ÎŽ + (ÎŽ=0.4123 e), driven by a pressure-induced volume reduction. We show that both ɛ- and ζ−Bi4I4 are superconductors, and the emergence of pressure-induced superconductivity might be intimately linked to the underlying structural phase transitions

    Universality in protein residue networks

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    Residue networks representing 595 nonhomologous proteins are studied. These networks exhibit universal topological characteristics as they belong to the topological class of modular networks formed by several highly interconnected clusters separated by topological cavities. There are some networks which tend to deviate from this universality. These networks represent small-size proteins having less than 200 residues. We explain such differences in terms of the domain structure of these proteins. On the other hand, we find that the topological cavities characterizing proteins residue networks match very well with protein binding sites. We then investigate the effect of the cutoff value used in building the residue network. For small cutoff values, less than 5Å, the cavities found are very large corresponding almost to the whole protein surface. On the contrary, for large cutoff value, more than 10.0 Å, only very large cavities are detected and the networks look very homogeneous. These findings are useful for practical purposes as well as for identifying "protein-like" complex networks. Finally, we show that the main topological class of residue networks is not reproduced by random networks growing according to Erdös-RĂ©nyi model or the preferential attachment method of BarabĂĄsi-Albert. However, the Watts-Strogatz (WS) model reproduces very well the topological class as well as other topological properties of residue network. We propose here a more biologically appealing modification of the WS model to describe residue networks
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