Background: Autism Spectrum Disorders (ASD) are a heterogeneous group of neurodevelopmental disorders featuring early impairments in social domain, with autonomic nervous system (ANS) unbalance possibly representing a useful marker for such disturbances. Impairments in joint attention (JA) are one of the earliest markers of social deficits in ASD. In this study, we assessed the feasibility of using wearable technologies for characterizing the ANS response in ASD toddlers during the presentation of JA stimuli.Methods: Twenty ASD toddlers and 20 age- and gender-matched typically developed (TD) children were recorded at baseline and during a JA task through an unobtrusive chest strap for electrocardiography (ECG). Specific algorithms for feature extraction, including Heart Rate (HR), Standard Deviation of the Normal-to-Normal Intervals (SDNN), Coefficient of Variation (CV), pNN10 as well as low frequency (LF) and high frequency (HF), were applied to the ECG signal and a statistical comparison between the two groups was performed.Results: As regards the single phases, SDNN (p = 0.04) and CV (p = 0.021) were increased in ASD at baseline together with increased LF absolute power (p = 0.034). Moreover, CV remained higher in ASD during the task (p = 0.03). Considering the phase and group interaction, LF increased from baseline to task in TD group (p = 0.04) while it decreased in the ASD group (p = 0.04).Conclusions: The results of this study indicate the feasibility of characterizing the ANS response in ASD toddlers through a minimally obtrusive tool. Our analysis showed an increased SDNN and CV in toddlers with ASD particularly at baseline compared to TD and lower LF during the task. These findings could suggest the possibility of using the proposed approach for evaluating physiological correlates of JA response in young children with ASD.

The development of accurate replicas of the circulatory and cardiac system is fundamental for a deeper understanding of cardiovascular diseases and the testing of new devices. Although numerous works concerning mock circulatory loops are present in the current state of the art, still some limitations are present. In particular, a pumping system able to reproduce the left ventricle motion and completely compatible with the magnetic resonance environment to permit the four-dimensional flow monitoring is still missing. The aim of this work was to evaluate the feasibility of an actuator suitable for cardiovascular mock circuits. Particular attention was given to the ability to mimic the left ventricle dynamics including both compression and twisting with the magnetic resonance compatibility. In our study, a left ventricle model to be actuated through vacuum was designed. The realization of the system was evaluated with finite element analysis of different design solutions. After the in silico evaluation phase, the most suitable design in terms of physiological values reproduction was fabricated through three-dimensional printing for in vitro validation. A pneumatic experimental setup was developed to evaluate the pump performances in terms of actuation, in particular ventricle radial and longitudinal displacement, twist rotation, and ejection fraction. The study demonstrated the feasibility of a custom pneumatic pump for mock circulatory loops able to reproduce the physiological ventricle movement and completely suitable for the magnetic resonance environment.

Medium-density fiberboard (MDF), a wood-based material that consists of a tight random network of wood fibers, deforms more than wood when exposed to water. For the first time, the microscopic deformations of MDF were tracked during swelling. A hygroscopic swelling setup imposing the material to deform throughout tomographic acquisition was used coupled to X-ray microtomography. An advanced reconstruction algorithm enabled reconstruction of images free of motion artefacts, and state-of-the-art digital volume correlation was applied to determine the mechanical strain fields at high resolution. Wood fiber bundles were then segmented from single fibers with deep learning using the UNet3D architecture. Combined with the strain fields, this segmentation showed that wood fiber bundles were the drivers of MDF swelling. This contrasts with the hygroscopic behavior of wood, where structured wood swells less than single fibers, which might be caused by a difference in penetration and distribution of the adhesive, in and on the wood fiber cell wall. The unique characterization of MDF’s dynamic behavior can already be used to develop manufacturing strategies to improve water resistance, therefore widening the uses of natural fiber-based materials.

Lab‐based high‐resolution Computed Tomography (μCT) is an important tool to investigate multiphase fluid flow through porous media. Rapid and continuous μCT acquisition can be used to resolve the dynamics of the 3D fluid distribution in the pores over time while the flow processes occur. However, the temporal resolution of this technique remains limited due to a trade‐off between acquisition time and image quality. This work presents a method to improve the temporal resolution of multiphase flow imaging experiments by limiting the angular range of radiographs used to reconstruct each time step, while compensating for this loss of information by including a temporal total variation term in the reconstruction algorithm. This addition penalizes improper temporal fluctuations in the reconstructed images, but not those dynamic events that are consistent with the radiographs. We perform a thorough evaluation of the resulting gain in temporal resolution at the single‐pore level. The method is validated on both simulated and experimental data representing multiphase flow in porous media. We find that this method improved the temporal resolution up to a factor 3 compared to reconstructions that use full 360° rotations for each time step.

Digital volume correlation (DVC) is a 3D image-based technique for displacement and strain computation. Traditionally, both (digital image correlation) DIC and DVC are methods based on two individual time frames; the estimation of the displacement and strain field is done using one reference and one moving frame as input. However, dynamic experiments generate more than two temporal frames. Therefore, with classical DVC techniques, only a subset of the available data is used. In this study, we propose a novel DVC method that can rely on more than two frames for the displacement and strain computation. The proposed method aims to be as general as possible; there is no constraint regarding the nature or the rate of the displacement (e.g., cyclic or linear). The aim of this method is to impose a temporal regularization that improves the self-consistency of the algorithm. The multi-frame DVC improves the quality of the registration in challenging situations. As an example, we investigate the dissolution of a pharmaceutical tablet in water, which undergoes three processes: swelling, gel formation, and material erosion. The accuracy of the registration—quantified by the sum of square differences (SSD)—has improved by 23% on an average with respect to the classical two-frame method. Classical DVC methods fail in registering images with structures that change appearance through time, such as the tablet that, in contact with water, reacts chemically, changing phase and becoming a gel. Moreover, we proved that multi-frame DVC is more robust in registering images with severe but realistic motion artefacts. As an example for this case, we apply the method to a series of μ-CT datasets of aluminum foam during a compression experiment. As seen with the tablets, we are in a situation where the appearance of the structures in the images changes through time, but in this case it is because of motion artefacts. Finally, the use of more than two frames makes the method more robust against noisy images, with an average improvement of 35% in registration accuracy obtained using the three-frame DVC method compared to the classical two-frame DVC method.