Search results for "Life Sciences–Physics interface"

showing 2 items of 2 documents

Anomalous water dynamics in brain: a combined diffusion magnetic resonance imaging and neutron scattering investigation

2019

International audience; Water diffusion is an optimal tool for investigating the architecture of brain tissue on which modern medical diagnostic imaging techniques rely. However, intrinsic tissue heterogeneity causes systematic deviations from pure free-water diffusion behaviour. To date, numerous theoretical and empirical approaches have been proposed to explain the non-Gaussian profile of this process. The aim of this work is to shed light on the physics piloting water diffusion in brain tissue at the micrometre-to-atomic scale. Combined diffusion magnetic resonance imaging and first pioneering neutron scattering experiments on bovine brain tissue have been performed in order to probe dif…

Medical diagnosticMaterials science[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/ImagingQuantitative Biology::Tissues and OrgansPhysics::Medical PhysicsBiomedical EngineeringBiophysicsproton dynamicsBioengineeringbrain imagingNeutron scatteringBiochemistryAtomic unitsBiomaterials03 medical and health sciences0302 clinical medicineTissue heterogeneityWater dynamicsNuclear magnetic resonancemedicineAnimalsDiffusion (business)030304 developmental biologydiffusion magnetic resonance imaging0303 health sciencesProton dynamicmedicine.diagnostic_testneutron scatteringBrainWaterMagnetic resonance imagingwater diffusionLife Sciences–Physics interfaceMagnetic Resonance ImagingSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)Neutron Diffraction[SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/ImagingBovine brainBrain imaging; Diffusion magnetic resonance imaging; Neutron scattering; Proton dynamics; Water diffusionCattle030217 neurology & neurosurgeryBiotechnology
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Revealing the unique features of each individual's muscle activation signatures

2021

International audience; There is growing evidence that each individual has unique movement patterns, or signatures. The exact origin of these movement signatures, however, remains unknown. We developed an approach that can identify individual muscle activation signatures during two locomotor tasks (walking and pedalling). A linear support vector machine was used to classify 78 participants based on their electromyographic (EMG) patterns measured on eight lower limb muscles. To provide insight into decision-making by the machine learning classification model, a layer-wise relevance propagation (LRP) approach was implemented. This enabled the model predictions to be decomposed into relevance …

Movement patternsComputer science[SDV]Life Sciences [q-bio]MovementBiomedical EngineeringBiophysicsBioengineeringWalkingElectromyographyBiochemistryLower limbMachine LearningBiomaterials03 medical and health sciences0302 clinical medicine[SDV.MHEP.PHY]Life Sciences [q-bio]/Human health and pathology/Tissues and Organs [q-bio.TO]medicineHumansRelevance (information retrieval)Muscle SkeletalElectromyographic (EMG)030304 developmental biology0303 health sciencesmedicine.diagnostic_testElectromyographybusiness.industryMusclesMotor controlLife Sciences–Physics interfacePattern recognitionMuscle activationSignature (logic)Support vector machineStatistical classificationArtificial intelligencebusiness030217 neurology & neurosurgeryBiotechnologyJournal of The Royal Society Interface
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