Effect of fMRI acoustic noise on non-auditory working memory task: comparison between continuous and pulsed sound emitting EPI

Conventional blood oxygenation level-dependent (BOLD) based functional magnetic resonance imaging (fMRI) is accompanied by substantial acoustic gradient noise. This noise can influence the performance as well as neuronal activations. Conventional fMRI typically has a pulsed noise component, which is a particularly efficient auditory stimulus. We investigated whether the elimination of this pulsed noise component in a recent modification of continuous-sound fMRI modifies neuronal activations in a cognitively demanding non-auditory working memory task. Sixteen normal subjects performed a letter variant n-back task. Brain activity and psychomotor performance was examined during fMRI with continuous-sound fMRI and conventional fMRI. We found greater BOLD responses in bilateral medial frontal gyrus, left middle frontal gyrus, left middle temporal gyrus, left hippocampus, right superior frontal gyrus, right precuneus and right cingulate gyrus with continuous-sound compared to conventional fMRI. Conversely, BOLD responses were greater in bilateral cingulate gyrus, left middle and superior frontal gyrus and right lingual gyrus with conventional compared to continuous-sound fMRI. There were no differences in psychomotor performance between both scanning protocols. Although behavioral performance was not affected, acoustic gradient noise interferes with neuronal activations in non-auditory cognitive tasks and represents a putative systematic confoun

Improving the fatigue life of printed structures using stochastic variations

Additive manufacturing allows designers to create geometries that would not be possible or economical to manufacture using traditional manufacturing processes. Production with these technologies does, however, introduce a large amount of variation and additional unknowns. These random variations from idealized geometry or material properties can harm the performance of the design. The current work presents an approach to improve the fatigue life of such structures, and simultaneously reduce its influence from random variations in local thickness. Following an initial numerical study, the results are experimentally validated. Experimental results show a significant improvement in fatigue life in the redesigned sample with a tailored thickness distribution

Extrapolation to complete basis-set limit in density-functional theory by quantile random-forest models

The numerical precision of density-functional-theory (DFT) calculations depends on a variety of computational parameters, one of the most critical being the basis-set size. The ultimate precision is reached with an infinitely large basis set, i.e., in the limit of a complete basis set (CBS). Our aim in this work is to find a machine-learning model that extrapolates finite basis-size calculations to the CBS limit. We start with a data set of 63 binary solids investigated with two all-electron DFT codes, exciting and FHI-aims, which employ very different types of basis sets. A quantile-random-forest model is used to estimate the total-energy correction with respect to a fully converged calculation as a function of the basis-set size. The random-forest model achieves a symmetric mean absolute percentage error of lower than 25% for both codes and outperforms previous approaches in the literature. Our approach also provides prediction intervals, which quantify the uncertainty of the models' predictions

Time-resolved 3D contrast-enhanced MRA with GRAPPA on a 1.5-T system for imaging of craniocervical vascular disease: initial experience

Introduction: For three-dimensional (3D) imaging with magnetic resonance angiography (MRA) of the cerebral and cervical circulation, both a high temporal and a high spatial resolution with isovolumetric datasets are of interest. In an initial evaluation, we analyzed the potential of contrast-enhanced (CE) time-resolved 3D-MRA as an adjunct for neurovascular MR imaging. Methods: In ten patients with various cerebrovascular disorders and vascularized tumors in the cervical circulation, high-speed MR acquisition using parallel imaging with the GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) algorithm on a 1.5-T system with a temporal resolution of 1.5s per dataset and a nearly isovolumetric spatial resolution was applied. The results were assessed and compared with those from conventional MRA and digital subtraction angiography (DSA). Results: CE time-resolved 3D-MRA enabled the visualization and characterization of high-flow arteriovenous shunts in cases of vascular malformations or hypervascularized tumors. In steno-occlusive disease, the method provided valuable additional information about altered vessel perfusion compared to standard MRA techniques such as time-of-flight (TOF) MRA. The use of a nearly isovolumetric voxel size allowed a free-form interrogation of 3D datasets. Its comparatively low spatial resolution was found to be the major limitation. Conclusion: In this preliminary analysis, CE time-resolved 3D-MRA was revealed to be a promising complementary MRA sequence that enabled the visualization of contrast flow dynamics in various types of neurovascular disorders and vascularized cervical tumor

an In Vitro Study

The poor healing potential of tendons is still a clinical problem, and the use of Platelet Rich Plasma (PRP) was hypothesized to stimulate healing. As the efficacy of PRPs remains unproven, platelet lysate (PL) could be an alternative with its main advantages of storage and characterization before use. Five different blood products were prepared from 16 male donors: human serum, two PRPs (Arthrex, (PRP-ACP); RegenLab (PRP-BCT)), platelet concentrate (apheresis, PC), and PL (freezing-thawing destruction of PC). Additionally, ten commercial allogenic PLs (AlloPL) from pooled donors were tested. The highest concentration of most growth factors was found in AlloPL, whereas the release of growth factors lasted longer in the other products. PRP-ACP, PRP- BCT, and PC significantly increased cell viability of human tenocyte-like cells, whereas PC and AlloPL increased Col1A1 expression and PRP-BCT increased Col3A1 expression. MMP-1, IL-1β, and HGF expression was significantly increased and Scleraxis expression decreased by most blood products. COX1 expression significantly decreased by PC and AlloPL. No clear positive effects on tendon cell biology could be shown, which might partially explain the weak outcome results in clinical practice. Pooled PL seemed to have the most beneficial effects and might be the future in using blood products for tendon tissue regeneration

A machine learning-based viscoelastic–viscoplastic model for epoxy nanocomposites with moisture content

In this work, we propose a deep learning (DL)-based constitutive model for investigating the cyclic viscoelastic-viscoplastic-damage behavior of nanoparticle/epoxy nanocomposites with moisture content. For this, a long short-term memory network is trained using a combined framework of a sampling technique and a perturbation method. The training framework, along with the training data generated by an experimentally validated viscoelastic-viscoplastic model, enables the DL model to accurately capture the rate-dependent stress–strain relationship and consistent tangent moduli. In addition, the DL-based constitutive model is implemented into finite element analysis. Finite element simulations are performed to study the effect of load rate and moisture content on the force–displacement response of nanoparticle/epoxy samples. Numerical examples show that the computational efficiency of the DL model depends on the loading condition and is significantly higher than the conventional constitutive model. Furthermore, comparing numerical results and experimental data demonstrates good agreement with different nanoparticle and moisture contents

Numerical Quality Control for DFT-based Materials Databases

Electronic-structure theory is a strong pillar of materials science. Many different computer codes that employ different approaches are used by the community to solve various scientific problems. Still, the precision of different packages has only recently been scrutinized thoroughly, focusing on a specific task, namely selecting a popular density functional, and using unusually high, extremely precise numerical settings for investigating 71 monoatomic crystals. Little is known, however, about method- and code-specific uncertainties that arise under numerical settings that are commonly used in practice. We shed light on this issue by investigating the deviations in total and relative energies as a function of computational parameters. Using typical settings for basis sets and k-grids, we compare results for 71 elemental and 63 binary solids obtained by three different electronic-structure codes that employ fundamentally different strategies. On the basis of the observed trends, we propose a simple, analytical model for the estimation of the errors associated with the basis-set incompleteness. We cross-validate this model using ternary systems obtained from the NOMAD Repository and discuss how our approach enables the comparison of the heterogeneous data present in computational materials databases.Comment: 7 pages, 4 figure