An Open Resource for Non-human Primate Imaging.

Non-human primate neuroimaging is a rapidly growing area of research that promises to transform and scale translational and cross-species comparative neuroscience. Unfortunately, the technological and methodological advances of the past two decades have outpaced the accrual of data, which is particularly challenging given the relatively few centers that have the necessary facilities and capabilities. The PRIMatE Data Exchange (PRIME-DE) addresses this challenge by aggregating independently acquired non-human primate magnetic resonance imaging (MRI) datasets and openly sharing them via the International Neuroimaging Data-sharing Initiative (INDI). Here, we present the rationale, design, and procedures for the PRIME-DE consortium, as well as the initial release, consisting of 25 independent data collections aggregated across 22 sites (total = 217 non-human primates). We also outline the unique pitfalls and challenges that should be considered in the analysis of non-human primate MRI datasets, including providing automated quality assessment of the contributed datasets

Identification of Rubber-like Behaviour from Non Homogeneous Multiaxial Testing

International audienc

A three-dimensional network model for rubber elasticity: The effect of local entanglements constraints

International audienceWe present a micro-mechanical model based on the network theory for the description of the elastic response of rubber-like materials at large strains. The material microstructure is characterized by chain-like macromolecules linked together at certain points; therefore an irregular three-dimensional network is formed. The material behaviour at the micro-level is usually described by means of statistical mechanics. Using certain assumptions for the certain distributions, one arrives at a continuum mechanical model of finite elasticity. However, the macromolecules interactions are neglected usually in these approaches. In the present contribution, we propose to add the effect of the interactions between chains of the cross-linked network. Following Arruda and Boyce (1993, 2000) [31,2], a cubic unit cell is defined where the entanglements fluctuations are localised in the corners of the cubic sub unit cell. These entanglements are linked by chains which ensure the interactions between the chains of idealized network (without interactions). These interactions can be represented by chains which are located in the principal directions of the cubic sub unit cell in undeformed state. We assume the probability densities which describe the free chain response of idealized network, and, the chain of constraints networks are independent. Then, the free-energy of the entire network is obtained by adding the free-energies of the free idealized (without interactions) and constraints (due to the chains interactions) networks. The constraint network reduces to four of the three-chain model of James and Guth (1943) [4] in undeformed state. Therefore, the free-energy of constraint network is obtained using the standard three-chain model, and, the free-energy of the free idealized network is constructed by means of the eight-chain model. The constitutive model involves five physical material parameters, namely, the shear modulus at small strains (μ0), the numbers of links that form the macromolecular chain of the eight-chain, and three-chain models (N8,N3) respectively, a micro-macro variable Ki, and, non-dimensional parameters (η,ρ). In order to determine the material parameters, the Langevin function in the single chain configuration is replaced by its first order Padé approximant [see, Cohen (1991) [5]; Perrin (2000) [6]], and, the material parameters are identified. The excellent predictive performance of the proposed model is shown by comparative to various available experimental data of homogeneous tests. However, the present model requires a validation because the relationship between the micro and macro levels needs to be clarified. Indeed, the identification of the physical parameters (μf,μc,N8,N3) from experimental results data at micro is hoped in order to simulate the macroscopic (i.e. bulk) behaviour of the material

Assessment of heat transfer and the consequences of iron oxide (Fe3O4) nanoparticles on flow of blood in an abdominal aortic aneurysm

The present study is established on a simulation using CFD analysis in COMSOL. Blood acted as the base fluid with this simulation. The taken flow is been modeled as incompressible, unsteady, laminar and Newtonian fluid, which is appropriate at high rates of shear. The characteristic of flow of blood is been studied in order to determine pressure, velocity and temperature impact caused by an abdominal aortic aneurysm (AAA). This work employs nanoparticles of the Iron Oxide (Fe3O4) type. The CFD technique is utilized to evaluate the equations of mass, momentum, and energy. The COMSOL software is utilized to generate a normal element sized mesh. The findings of this study demonstrate that velocity alters through aneurysmal part of the aorta, that velocity is higher in a diseased segment, and that velocity increases before and after the aneurysmal region. For the heat transfer feature, the reference temperature and general inward heat flux is taken as 293.15K and 800W/m2. The nanoparticles altered blood's physical properties, including conductivity, dynamic viscosity, specific heat, and density. The inclusion of Iron Oxide (Fe3O4) nanoparticles managed to prevent overheating because taken nanoparticles have significant thermal conductivity. These findings will be extremely beneficial in the treatment of abdominal aortic aneurysm