8,214 research outputs found
Relation Structure molĂ©culaire - Odeur Utilisation des RĂ©seaux de Neurones pour lâestimation de lâOdeur Balsamique
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 lâodeur. Ce travail prĂ©sente une approche de prĂ©diction de l'odeur des molĂ©cules basĂ©e sur les descripteurs molĂ©culaires. Les techniques dâanalyse en composantes principales (PCA) et de dâanalyse de colinĂ©aritĂ© permettent dâidentifier 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 lâodeur. La base de donnĂ©es dĂ©crite prĂ©cĂ©demment est utilisĂ©e pour lâapprentissage. Un ensemble de paramĂštres est modifiĂ© jusquâĂ 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. Câest la premiĂšre Ă©tape dâun 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
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
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
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:20â63years).
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
We present new parallel software, SUSY LATTICE, for lattice studies of
four-dimensional 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
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 αâBi4I4 phase in C2/m symmetry transforms to a trigonal P31c structure (ÉâBi4I4) at 8.4 GPa, then to a tetragonal P4/mmm structure (ζâBi4I4) 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 ÉâBi4I4 phase exhibits distinct ionic states of IÎŽâ and (Bi4I3)ÎŽ + (ÎŽ=0.4123 e), driven by a pressure-induced volume reduction. We show that both É- and ζâBi4I4 are superconductors, and the emergence of pressure-induced superconductivity might be intimately linked to the underlying structural phase transitions
Universality in protein residue networks
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 5Ă
, the cavities found are very large corresponding almost to the whole protein surface. On the contrary, for large cutoff value, more than 10.0 Ă
, 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-Rényi model or the preferential attachment method of Barabåsi-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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