Search results for "Quantitative Biology"

showing 10 items of 1025 documents

Spatial graphs and Convolutive Models

2020

In the last two decades, many complex systems have benefited from the use of graph theory, and these approaches have shown robust applicability in the field of finance, computer circuits and in biological systems. Large scale models of brain systems make also a great use of random graph models. Graph theory can be instrumental in modeling the connectivity and spatial distribution of neurons, through a characterization of the relative topological properties. However, all approaches in studying brain function have been so far limited to use experimental constraints obtained at a macroscopic level (e.g. fMRI, EEG, MEG, DTI, DSI). In this contribution, we present a microscopic use (i.e. at the …

Random graphSettore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni0303 health sciencesQuantitative Biology::Neurons and CognitionSettore INF/01 - InformaticaReal systemsComputer scienceComplex systemGraph theoryPower law03 medical and health sciences0302 clinical medicineSettore MAT/05 - Analisi MatematicaSpatial graph random graph neural system networksMerge (version control)Scale modelAlgorithm030217 neurology & neurosurgeryBrain function030304 developmental biology
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Compartmental analysis of dynamic nuclear medicine data: Models and identifiability

2016

Compartmental models based on tracer mass balance are extensively used in clinical and pre-clinical nuclear medicine in order to obtain quantitative information on tracer metabolism in the biological tissue. This paper is the first of a series of two that deal with the problem of tracer coefficient estimation via compartmental modelling in an inverse problem framework. Specifically, here we discuss the identifiability problem for a general n-dimension compartmental system and provide uniqueness results in the case of two-compartment and three-compartment compartmental models. The second paper will utilize this framework in order to show how non-linear regularization schemes can be applied t…

Regularization (mathematics)Quantitative Biology - Quantitative Methods030218 nuclear medicine & medical imagingTheoretical Computer ScienceData modeling03 medical and health sciences0302 clinical medicinecompartmental analysis; identifiability; nuclear medicine dataTRACERFOS: Mathematicscompartmental analysisUniquenessMathematics - Numerical AnalysisMathematical PhysicsQuantitative Methods (q-bio.QM)Mathematicsbusiness.industryApplied MathematicsBiological tissueNumerical Analysis (math.NA)Inverse problemidentifiabilityComputer Science ApplicationsNonlinear systemnuclear medicine dataFOS: Biological sciencesSignal ProcessingIdentifiabilityNuclear medicinebusiness030217 neurology & neurosurgery
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A novel numerical meshless approach for electric potential estimation in transcranial stimulation

2015

In this paper, a first application of the method of fundamental solutions in estimating the electric potential and the spatial current density distribution in the brain due to transcranial stimulation, is presented. The coupled boundary value p roblems for the electric potential are solved in a meshless way, so avoiding the use of grid based numerical methods. A multi-spherical geometry is considered and numerical results are discussed.

Regularized meshless methodMathematical optimizationmethod of fundamental solutionQuantitative Biology::Neurons and CognitionNumerical analysistranscranial electrical stimulationCurrent density distributionGrid basedBoundary valuesPhysics and Astronomy (all)Settore MAT/08 - Analisi NumericaSettore ING-IND/31 - ElettrotecnicaApplied mathematicsMethod of fundamental solutionsMeshfree methodsmeshless methodElectric potentialnumerical approximationMathematics
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Poly-sarcosine and poly(ethylene-glycol) interactions with proteins investigated using molecular dynamics simulations

2018

Nanoparticles coated with hydrophilic polymers often show a reduction in unspecific interactions with the biological environment, which improves their biocompatibility. The molecular determinants of this reduction are not very well understood yet, and their knowledge may help improving nanoparticle design. Here we address, using molecular dynamics simulations, the interactions of human serum albumin, the most abundant serum protein, with two promising hydrophilic polymers used for the coating of therapeutic nanoparticles, poly(ethylene-glycol) and poly-sarcosine. By simulating the protein immersed in a polymer-water mixture, we show that the two polymers have a very similar affinity for the…

SarcosineBiocompatibilityPoly-peptoidlcsh:BiotechnologyBiophysicsFOS: Physical sciencesNanoparticle02 engineering and technologyCondensed Matter - Soft Condensed MatterProtein aggregation010402 general chemistry01 natural sciencesBiochemistryNanoparticle protein coronachemistry.chemical_compoundMolecular dynamicsAdsorptionStructural Biologylcsh:TP248.13-248.65GeneticsmedicinePhysics - Biological Physicschemistry.chemical_classificationBiomolecules (q-bio.BM)MD simulationPolymer021001 nanoscience & nanotechnologyHuman serum albuminPEG0104 chemical sciencesComputer Science ApplicationsQuantitative Biology - BiomoleculeschemistryChemical engineeringBiological Physics (physics.bio-ph)FOS: Biological sciencesSoft Condensed Matter (cond-mat.soft)Poly-sarcosine0210 nano-technologyResearch ArticleBiotechnologymedicine.drug
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Nonequilibrium thermodynamics of the RNA-RNA interaction underlying a genetic transposition program

2021

Thermodynamic descriptions are powerful tools to formally study complex gene expression programs evolved in living cells on the basis of macromolecular interactions. While transcriptional regulations are often modeled in the equilibrium, other interactions that occur in the cell follow a more complex pattern. Here, we adopt a nonequilibrium thermodynamic scheme to explain the RNA-RNA interaction underlying IS10 transposition. We determine the energy landscape associated with such an interaction at the base-pair resolution, and we present an original scaling law for expression prediction that depends on different free energies characterizing that landscape. Then, we show that massive experim…

Scaling lawNon-equilibrium thermodynamicsEnergy landscapeRNAExpression (computer science)Quantitative Biology::Genomics01 natural sciences010305 fluids & plasmasKinetics0103 physical sciencesRNAThermodynamicsFree energiesTransposition (logic)Statistical physics010306 general physicsBase PairingPhysical Review E
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Embedding Evolution in Epidemic-Style Forwarding

2007

International audience; In this work, we introduce a framework to let forwarding schemes evolve in order to adapt to changing and a priori unknown environments. The framework is inspired by genetic algorithms: at each node a genotype describes the forwarding scheme used, a selection process fosters the diffusion of the fittest genotypes in the system and new genotypes are created by combining existing ones or applying random changes. A case study implementation is presented and its performance evaluated via numerical simulations.

Scheme (programming language)Theoretical computer scienceComputer scienceSurvival of the fittestNode (networking)Quality control and genetic algorithmsProcess (computing)Quantitative Biology::Genomics[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI]EmbeddingQuantitative Biology::Populations and EvolutioncomputerSelection (genetic algorithm)computer.programming_language
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Integration of Gravitational Torques in Cerebellar Pathways Allows for the Dynamic Inverse Computation of Vertical Pointing Movements of a Robot Arm

2008

BackgroundSeveral authors suggested that gravitational forces are centrally represented in the brain for planning, control and sensorimotor predictions of movements. Furthermore, some studies proposed that the cerebellum computes the inverse dynamics (internal inverse model) whereas others suggested that it computes sensorimotor predictions (internal forward model).Methodology/principal findingsThis study proposes a model of cerebellar pathways deduced from both biological and physical constraints. The model learns the dynamic inverse computation of the effect of gravitational torques from its sensorimotor predictions without calculating an explicit inverse computation. By using supervised …

ScienceComputationComputational Biology/Computational NeuroscienceModels BiologicalInverse dynamicsComputer Science::Robotics03 medical and health sciences0302 clinical medicineNeuroscience/Motor SystemsGravitational fieldControl theoryCerebellum030304 developmental biologyPhysics0303 health sciencesNeuroscience/Behavioral NeuroscienceMultidisciplinaryQuantitative Biology::Neurons and CognitionArtificial neural networkbusiness.industryQRRoboticsRoboticsCerebellar cortexMedicineRobotArtificial intelligencebusinessRobotic arm030217 neurology & neurosurgeryGravitationResearch ArticlePLoS ONE
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Optimization of cogging force in a linear permanent magnet generator for the conversion of sea waves energy

2015

In this paper an approach to the optimization of cogging force in a linear permanent magnet generator for the conversion of sea waves energy is presented. We have optimized the cogging force of a linear permanent magnet generator by using a 3D FEM parametric simulation. Several possible design solutions have been computed. A generator has been built and the results of the minimization procedure has been experimentally validated.

Sea wavesEngineeringSettore ING-IND/11 - Fisica Tecnica Ambientalebusiness.industryLinear MachinedMechanical EngineeringCogging ForceMechanical engineeringPermanent magnet synchronous generatorSettore ING-IND/32 - Convertitori Macchine E Azionamenti ElettriciElectric MachineMagnetic fluxForgingFinite element methodGenerator (circuit theory)Quantitative Biology::Quantitative MethodsAutomotive EngineeringElectronic engineeringMinificationElectrical and Electronic EngineeringbusinessEnergy (signal processing)
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Extraction of ERP from EEG data

2007

In this article, a simple but novel technique for extracting a linear subspace related to event related potentials (ERPs) from ElectroEncephaloGraphy (EEG) data is introduced. The technique consists of a sequence of basic linear operations applied to multidimensional EEG data in a problem-specific manner. The derivation of the proposed technique is given and results with real data are described together with overall conclusions.

SequenceQuantitative Biology::Neurons and Cognitionmedicine.diagnostic_testComputer sciencebusiness.industrySpeech recognitionPattern recognitionElectroencephalographyIndependent component analysisLinear subspaceComputingMethodologies_PATTERNRECOGNITIONSignal-to-noise ratioEeg dataEvent-related potentialmedicineArtificial intelligenceNoise (video)business2007 9th International Symposium on Signal Processing and Its Applications
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Magnetic resonance image segmentation and heart motion tracking with an active mesh based system

2002

International audience; Abstract: The work presented here relates to a method fir motion tracking in sequences of medical images. The purpose is to. quantify the general motions and the local deformations of a beating heart during a cardiac cycle. In order to achieve this goal, we first tessellate the,first image of the sequence into triangular patches. A Delaunay triangulation is applied to find the optimal set of triangles describing this image, giving a mesh covering the organs. One imposes the contours of the organs to correspond to edges of triangles so that each part of the heart (left ventricle, right ventricle, myocardium) can he described as a different set of triai izles, each set…

Sequence[ INFO.INFO-IM ] Computer Science [cs]/Medical ImagingDelaunay triangulationbusiness.industryQuantitative Biology::Tissues and OrgansPhysics::Medical Physics[INFO.INFO-IM] Computer Science [cs]/Medical Imaging02 engineering and technologyImage segmentationTracking (particle physics)01 natural sciencesImage (mathematics)010309 opticsMatch movingMesh generation0103 physical sciences[INFO.INFO-IM]Computer Science [cs]/Medical Imaging0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingComputer visionSegmentationArtificial intelligencebusinessMathematics
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