Search results for " ensemble"

showing 10 items of 93 documents

Inverse Conformational Selection in Lipid–Protein Binding

2021

International audience; Interest in lipid interactions with proteins and other biomolecules is emerging not only in fundamental biochemistry but also in the field of nanobiotechnology where lipids are commonly used, for example, in carriers of mRNA vaccines. The outward-facing components of cellular membranes and lipid nanoparticles, the lipid headgroups, regulate membrane interactions with approaching substances, such as proteins, drugs, RNA, or viruses. Because lipid headgroup conformational ensembles have not been experimentally determined in physiologically relevant conditions, an essential question about their interactions with other biomolecules remains unanswered: Do headgroups excha…

DYNAMICSELECTRIC CHARGEBILAYERSPHOSPHATIDYLCHOLINE HEADGROUPMembrane lipidsDEUTERIUMPlasma protein bindingMolecular Dynamics Simulationlipidit010402 general chemistry01 natural sciencesBiochemistrybiomolekyylitCatalysis03 medical and health sciencesMolecular dynamicskemialliset sidoksetColloid and Surface ChemistryProtein structurePHOSPHOLIPID-BINDINGMAGNETIC-RESONANCE[SDV.BBM] Life Sciences [q-bio]/Biochemistry Molecular BiologySEGMENTAL ORDER[SDV.BBM]Life Sciences [q-bio]/Biochemistry Molecular BiologyConformational ensemblesNuclear Magnetic Resonance Biomolecular030304 developmental biologychemistry.chemical_classification0303 health sciencesChemistryBiomoleculeMEMBRANE-LIPIDSProteinsPhosphatidylglycerolsGeneral Chemistrycomputer.file_formatProtein Data BankLipids0104 chemical sciencesBiophysicsPhospholipid BindingPhosphatidylcholinesMAS NMR1182 Biochemistry cell and molecular biologylipids (amino acids peptides and proteins)proteiinitcomputerProtein Binding
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An Integrated fuzzy Cells-classifier

2006

The term soft-computing has been introduced by Zadeh in 1994. Soft-computing provides an appropriate paradigm to program malleable and smooth concepts. In this paper a genetic algorithm is proposed to fuse the classification results due to different distance functions. The combination is based on the optimization of a vote strategy and it is applied to cells classification.

Evolutionary algorithms Classifier ensembleSettore INF/01 - Informaticabusiness.industryComputer scienceArtificial intelligencebusinessFuzzy logicClassifier (UML)Global optimization problem
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An ensemble approach to short-term forecast of COVID-19 intensive care occupancy in Italian Regions

2020

Abstract The availability of intensive care beds during the COVID‐19 epidemic is crucial to guarantee the best possible treatment to severely affected patients. In this work we show a simple strategy for short‐term prediction of COVID‐19 intensive care unit (ICU) beds, that has proved very effective during the Italian outbreak in February to May 2020. Our approach is based on an optimal ensemble of two simple methods: a generalized linear mixed regression model, which pools information over different areas, and an area‐specific nonstationary integer autoregressive methodology. Optimal weights are estimated using a leave‐last‐out rationale. The approach has been set up and validated during t…

FOS: Computer and information sciencesStatistics and ProbabilityTime FactorsOccupancyCoronavirus disease 2019 (COVID-19)Computer science01 natural sciencesGeneralized linear mixed modelSARS‐CoV‐2law.inventionclustered data; COVID-19; generalized linear mixed model; integer autoregressive; integer autoregressive model; panel data; SARS-CoV-2; weighted ensembleMethodology (stat.ME)panel data010104 statistics & probability03 medical and health sciences0302 clinical medicinelawCOVID‐19Intensive careEconometricsHumansclustered data030212 general & internal medicine0101 mathematicsPandemicsStatistics - MethodologySARS-CoV-2Reproducibility of ResultsCOVID-19General Medicineweighted ensembleIntensive care unitResearch PapersTerm (time)integer autoregressiveIntensive Care UnitsAutoregressive modelItalyNonlinear Dynamicsgeneralized linear mixed modelinteger autoregressive modelclustered data; COVID-19; generalized linear mixed model; integer autoregressive; integer autoregressive model; panel data; SARS-CoV-2; weighted ensemble; COVID-19; Humans; Intensive Care Units; Italy; Nonlinear Dynamics; Pandemics; Reproducibility of Results; Time Factors; ForecastingStatistics Probability and UncertaintySettore SECS-S/01Settore SECS-S/01 - StatisticaPanel dataResearch PaperForecasting
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Topological effects of a rigid chiral spacer on the electronic interactions in donor-acceptor ensembles

2005

Two triads (donor-spacer-acceptor), etTTF-BN-C 6 0 (6) and ZnP-BN-C 6 0 (7), in which electron donors (i.e., exTTF or ZnP) are covalently linked to C 6 0 through a chiral binaphthyl bridge (BN), have been prepared in a multistep synthetic procedure starting from a highly soluble enantiomerically pure binaphthyl building block (1). Unlike other oligomeric bridges, with hinaphthyl bridges, the conjugation between the donor and the acceptor units is broken and geometric conformational changes are facilitated. Consequently, distances and electronic interactions between the donor and C 6 0 are drastically changed. Both donor-spacer-acceptor (D-s-A) systems (i.e., 6 and 7) exhibit redox processes…

FullereneStereochemistryOrganic ChemistryTriad (anatomy)Donor-Acceptor EnsemblesGeneral ChemistrySettore CHIM/06 - Chimica OrganicaFluorescenceAcceptorRedoxCatalysischemistry.chemical_compoundmedicine.anatomical_structurePhotophysicschemistryFullereneCovalent bondUltrafast laser spectroscopymedicineTetrathiafulvalene
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A genetic integrated fuzzy classifier

2005

This paper introduces a new classifier, that is based on fuzzy-integration schemes controlled by a genetic optimisation procedure. Two different types of integration are proposed here, and are validated by experiments on real data sets of biological cells. The performance of our classifier is tested against a feed-forward neural network and a Support Vector Machine. Results show the good performance and robustness of the integrated classifier strategies.

Fuzzy classificationNeuro-fuzzyComputer scienceFuzzy setMachine learningcomputer.software_genreClassification Classifier Ensemble Evolutionary Algorithms.Artificial IntelligenceRobustness (computer science)Genetic algorithmCluster analysisAdaptive neuro fuzzy inference systemLearning classifier systemSettore INF/01 - InformaticaArtificial neural networkStructured support vector machinebusiness.industryPattern recognitionQuadratic classifierSupport vector machineComputingMethodologies_PATTERNRECOGNITIONSignal ProcessingMargin classifierFuzzy set operationsComputer Vision and Pattern RecognitionArtificial intelligencebusinesscomputerClassifier (UML)SoftwarePattern Recognition Letters
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Phase behaviour of heteronuclear dimers in three-dimensional systems—a Monte Carlo study

2008

Monte Carlo simulation in the grand canonical ensemble, the histogram reweighting technique and finite size scaling are used to study the phase behaviour of dimers in three-dimensional systems. A single molecule is composed of two segments A and B, and the bond between them cannot be broken. The phase diagrams have been estimated for a set of model systems. Different structures formed by heteronuclear dimers have been found. The results show a great variety of vapour–liquid coexistence behaviour depending on the strength of the interactions between segments.

Grand canonical ensembleHeteronuclear moleculeChemistryHistogramPhase (matter)Monte Carlo methodMoleculeGeneral Materials ScienceStatistical physicsCondensed Matter PhysicsScalingPhase diagramJournal of Physics: Condensed Matter
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Monte Carlo Methods for the Sampling of Free Energy Landscapes

2019

In this chapter, we return to classical statistical mechanics, wherein the canonical ensemble averages of an observable \(A(\overrightarrow{x})\), where \(\overrightarrow{x} \) stands symbolically for the “microstate” coordinate in the configurational part of the phase space of the system, are given by (cf. Sect. 2.1.1)

Hybrid Monte CarloCanonical ensemblePhysicsPhase spaceMonte Carlo methodObservableMonte Carlo method in statistical physicsStatistical physicsStatistical mechanicsMicrostate (statistical mechanics)
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From determination of the fugacity coefficients to estimation of hydrogen storage capacity: A convenient theoretical method

2015

Abstract The equation of state (EOS) from virial expansion (VE) is used in this work to pave the way for determining the fugacity coefficients of the hydrogen fluid at arbitrary temperature and pressure. The fugacity coefficients from our VE method have more physical meanings than the empirical values. In this way, the hydrogen storage capacity of a novel material model can be estimated by using few density functional theory (DFT) calculations with the aid of a continuum model. The efficient continuum model can provide a more accurate estimation of the hydrogen storage capacity than the pure DFT calculations. Furthermore, the expensive grand canonical ensemble (μNT) simulations combining wi…

HydrogenRenewable Energy Sustainability and the EnvironmentChemistryEnergy Engineering and Power TechnologyThermodynamicschemistry.chemical_elementCondensed Matter PhysicsHydrogen storageGrand canonical ensembleFuel TechnologyTemperature and pressureVirial expansionDensity functional theoryFugacityBilayer grapheneInternational Journal of Hydrogen Energy
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Hippocampo-cerebellar theta band phase synchrony in rabbits.

2009

Hippocampal functioning, in the form of theta band oscillation, has been shown to modulate and predict cerebellar learning of which rabbit eyeblink conditioning is perhaps the most well-known example. The contribution of hippocampal neural activity to cerebellar learning is only possible if there is a functional connection between the two structures. Here, in the context of trace eyeblink conditioning, we show (1) that, in addition to the hippocampus, prominent theta oscillation also occurs in the cerebellum, and (2) that cerebellar theta oscillation is synchronized with that in the hippocampus. Further, the degree of phase synchrony (PS) increased both as a response to the conditioning sti…

MaleCerebellumPeriodicityHippocampusContext (language use)Hippocampal formationHippocampus03 medical and health sciencesRandom Allocation0302 clinical medicineNeural ensembleCerebellummedicineOscillation (cell signaling)AnimalsLearningCortical SynchronizationTheta Rhythm030304 developmental biology0303 health sciencesAnalysis of VarianceGeneral NeuroscienceConditioning EyelidElectrodes Implantedmedicine.anatomical_structurenervous systemEyeblink conditioningPractice PsychologicalRabbitsPsychologyNeuroscience030217 neurology & neurosurgeryCortical SynchronizationNeuroscience
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Modeling the Interaction of Carbon Monoxide with Flexible Graphene: From Coupled Cluster Calculations to Molecular-Dynamics Simulations

2018

The interaction of CO with graphene was studied at different theoretical levels. Quantum-mechanical calculations on finite graphene models with the use of coronene for coupled cluster calculations and circumcoronene for B97D calculations showed that there was no preferential site for adsorption and that the most important factor was the orientation of CO relative to graphene. The parallel orientation was preferred, with binding energies around 9 kJ mol-1 at the CCSD(T) and B97D levels, which was in good agreement with experimental findings. From a large number of CO-circumcoronene and CO-CO interactions, computed at different distances and randomly generated orientations, parameters were fi…

Materials scienceBinding energy02 engineering and technologyMolecular dynamics010402 general chemistry01 natural scienceslaw.inventionMolecular dynamicschemistry.chemical_compoundAdsorptionlawAtomic and Molecular PhysicsAdsorption; Density functional calculations; Graphene; Interaction energies; Molecular dynamics; Atomic and Molecular Physics and Optics; Physical and Theoretical ChemistryInteraction energiesPhysical and Theoretical ChemistryCanonical ensembleGraphene021001 nanoscience & nanotechnologyAtomic and Molecular Physics and OpticsCoronene0104 chemical sciencesDensity functional calculationsCoupled clusterchemistryChemical physicsIntramolecular forceAdsorptionGrapheneand Optics0210 nano-technologyChemPhysChem
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