Multi-scale Level Research Articles

Ethical Issues of 4D Printed Medical Devices.

Since the dawn of additive manufacturing technologies in the 1980s and 90s, now commonly named 3D printing, the possibility of processing raw materials into freeform designed objects with unprecedented shape complexity opened new avenues for the development of medical devices. Indeed, the geometries of nature and the human body are extremely multifaceted, with even fractal- like or multiscale levels of detail, counting with functional gradients of properties, including topology and topography optimizations, to cite some interesting features. In consequence, classical subtracting manufacturing technologies, shape forming tools, and mass production chains are suboptimal for personalizing medical devices and adequately emulating life.

Towards Digital Twinning on the Web: Heterogeneous 3D Data Fusion Based on Open-Source Structure

Recent advances in Computer Science and the spread of internet connection have allowed specialists to virtualize complex environments on the web and offer further information with realistic exploration experiences. At the same time, the fruition of complex geospatial datasets (point clouds, Building Information Modelling (BIM) models, 2D and 3D models) on the web is still a challenge, because usually it involves the usage of different proprietary software solutions, and the input data need further simplification for computational effort reduction. Moreover, integrating geospatial datasets acquired in different ways with various sensors remains a challenge. An interesting question, in that respect, is how to integrate 3D information in a 3D GIS (Geographic Information System) environment and manage different scales of information in the same application. Integrating a multiscale level of information is currently the first step when it comes to digital twinning. It is needed to properly manage complex urban datasets in digital twins related to the management of the buildings (cadastral management, prevention of natural and anthropogenic hazards, structure monitoring, etc.). Therefore, the current research shows the development of a freely accessible 3D Web navigation model based on open-source technology that allows the visualization of heterogeneous complex geospatial datasets in the same virtual environment. This solution employs JavaScript libraries based on WebGL technology. The model is accessible through web browsers and does not need software installation from the user side. The case study is the new building of the University of Twente—Faculty of Geo-Information (ITC), located in Enschede (the Netherlands). The developed solution allows switching between heterogeneous datasets (point clouds, BIM, 2D and 3D models) at different scales and visualization (indoor first-person navigation, outdoor navigation, urban navigation). This solution could be employed by governmental stakeholders or the private sector to remotely visualize complex datasets on the web in a unique visualization, and take decisions only based on open-source solutions. Furthermore, this system can incorporate underground data or real-time sensor data from the IoT (Internet of Things) for digital twinning tasks.

Multiscale spectroscopic analysis of lipids in dimorphic and oleaginous Mucor circinelloides accommodate sustainable targeted lipid production

BackgroundOleaginous fungi have versatile metabolism and able to transform a wide range of substrates into lipids, accounting up to 20–70% of their total cell mass. Therefore, oleaginous fungi are considered as an alternative source of lipids. Oleaginous fungi can accumulate mainly acyl glycerides and free fatty acids which are localized in lipid droplets. Some of the oleaginous fungi possessing promising lipid productivity are dimorphic and can exhibit three cell forms, flat hyphae, swollen hyphae and yeast-like cells. To develop sustainable targeted fungal lipid production, deep understanding of lipogenesis and lipid droplet chemistry in these cell forms is needed at multiscale level. In this study, we explored the potential of infrared spectroscopy techniques for examining lipid droplet formation and accumulation in different cell forms of the dimorphic and oleaginous fungus Mucor circinelloides.ResultsBoth transmission- and reflectance-based spectroscopy techniques are shown to be well suited for studying bulk fungal biomass. Exploring single cells with infrared microspectroscopy reveals differences in chemical profiles and, consequently, lipogenesis process, for different cell forms. Yeast-like cells of M. circinelloides exhibited the highest absorbance intensities for lipid-associated peaks in comparison to hyphae-like cell forms. Lipid-to-protein ratio, which is commonly used in IR spectroscopy to estimate lipid yield was the lowest in flat hyphae. Swollen hyphae are mainly composed of lipids and characterized by more uniform distribution of lipid-to-protein concentration. Yeast-like cells seem to be comprised mostly of lipids having the largest lipid-to-protein ratio among all studied cell forms. With infrared nanospectroscopy, variations in the ratios between lipid fractions triglycerides and free fatty acids and clear evidence of heterogeneity within and between lipid droplets are illustrated for the first time.ConclusionsVibrational spectroscopy techniques can provide comprehensive information on lipogenesis in dimorphic and oleaginous fungi at the levels of the bulk of cells, single cells and single lipid droplets. Unicellular spectra showed that various cell forms of M. circinelloides differs in the total lipid content and profile of the accumulated lipids, where yeast-like cells are the fatty ones and, therefore, could be considered as preferable cell form for producing lipid-rich biomass. Spectra of single lipid droplets showed an indication of possible droplet-to-droplet and within-droplet heterogeneity.

Regulating electrochemistry kinetics and discharge product selectivity with near-free cobalt single-atom catalyst in Li−O2 batteries

Recognization of the dynamic evolution of the catalytic active site and battery reaction intermediates under working conditions is essential for optimal design of advanced electrocatalysts for lithium–oxygen batteries, yet remaining formidable challenges because of size, carrier, and surface interface effect on traditional supported catalysts. Herein, based on the designed single-Co-atom catalyst model with uniform and isolated active centers, the structural dynamic evolution of near-free active centers and the complicated reaction pathways of lithium–oxygen electrochemistry is firstly in-depth identified at the atomic level by virtue of structural and ingredient measurements at multiscale levels. We discover that the near-free Co site (Co1-N3) tends to be dynamically released from the nitrogen-carbon substrate, and then forms a freer O*-Co1-N2 site, facilitating the surface adsorption and activation of the key *O intermediate for oxygen reduction reaction during discharge. More interestingly, near-free Co exists a better lattice match with the (100) crystal plane of Li2O2, forming an easily decomposed single-oriented sheet-like Li2O2 with higher electron transport capacity and weaker *LiO2 combination, thus improving the kinetics of oxygen evolution reaction during recharge. The integration of multiple test techniques on single-atom catalyst model in this study may pave the way for revealing important dynamic evolution steps and reaction mechanisms at three-phase boundaries in metal–air batteries.

Loss of differentiation and complexity in the sleeping human brain: a multi-scale analysis

Abstract Complexity, defined as the coexistence of functional differentiation and functional integration is a general property of thalamo-cortical circuits that can be characterized at a multiscale level. Previous studies suggest that local dynamics occurring at the micro/meso-scale, such as sleep-like neuronal bistability, can be responsible for the collapse/emergence of global patterns of complex interactions among brain areas at the macro-scale. Here, we link the two scales by combining, for the first time, intracerebral single pulse electrical stimulation (SPES) with simultaneous invasive recordings (stereo-EEG, SEEG) and scalp high-density EEG (hdEEG) during both wakefulness and sleep. This work includes data collected during presurgical evaluation from 30 epileptic subjects. SEEG was recorded trough ∼150 intracerebral contacts connected to a 192-channel amplifier together with simultaneous scalp recordings using a 256 channels hdEEG. Recordings of simultaneous SEEG and hd-EEG activity were combined with SPES (5 mA, 1/0.5ms, 0.5Hz).100 stimulation sessions were performed by delivering SPES during wakefulness at rest in different areas. 66 stimulation sessions were repeated during NREM sleep (stages N2 or N3). We show that (1) the overall complexity, as assessed by the Perturbational Complexity Index, was significantly higher in wakefulness with respect to NREM (p<0.05) and that (2) the differentiation of the response to SPES, evaluated by performing the Principal Component Analysis across all sessions and comparing the ensuing number of components across states, was significantly higher in wakefulness with respect to NREM over the parietal areas (p<0.05). These results allow linking bistable dynamics (neuronal downstates in the Local Field Potential) and their effects on local cortico-cortical interactions with the overall connectivity and complexity assessed at the scalp level. Importantly, combining multiple stimulation sites across different states strongly suggests that during NREM sleep the reduction of complexity is mainly due to loss of differentiation. Research Category and Technology and Methods Basic Research: 15. Electroencephalography (EEG) Keywords: SEEG, Bistability, Sleep, hd-EEG

Multiscaled Multi-Head Attention-Based Video Transformer Network for Hand Gesture Recognition

Dynamic gesture recognition is one of the challenging research areas due to variations in pose, size, and shape of the signer's hand. In this letter, Multiscaled Multi-Head Attention Video Transformer Network (MsMHA-VTN) for dynamic hand gesture recognition is proposed. A pyramidal hierarchy of multiscale features is extracted using the transformer multiscaled head attention model. The proposed model employs different attention dimensions for each head of the transformer which enables it to provide attention at the multiscale level. Further, in addition to single modality, recognition performance using multiple modalities is examined. Extensive experiments demonstrate the superior performance of the proposed MsMHA-VTN with an overall accuracy of 88.22% and 99.10% on NVGesture and Briareo datasets, respectively.

Imaging Techniques in Pharmacological Precision Medicine.

Biomedical imaging is a powerful tool for medical diagnostics and personalized medicines. Examples of commonly used imaging modalities include Positron Emission Tomography (PET), Ultrasound (US), Single Photon Emission Computed Tomography (SPECT), and hybrid imaging. By combining these modalities, scientists can gain a comprehensive view and better understand physiology and pathology at the preclinical, clinical, and multiscale levels. This can aid in the accuracy of medical diagnoses and treatment decisions. Moreover, biomedical imaging allows for evaluating the metabolic, functional, and structural details of living tissues. This can be particularly useful for the early diagnosis of diseases such as cancer and for the application of personalized medicines. In the case of hybrid imaging, two or more modalities are combined to produce a high-resolution image with enhanced sensitivity and specificity. This can significantly improve the accuracy of diagnosis and offer more detailed treatment plans. In this book chapter, we showcase how continued advancements in biomedical imaging technology can potentially revolutionize medical diagnostics and personalized medicine.

A Novel Arcing Detection Model of Pantograph–Catenary for High-Speed Train in Complex Scenes

The Pantograph-catenary system is crucial for transferring electrical power from catenary lines to electrified train and the occurrence of arcing could damage railway operations, thus, it is important to detect arcing. Detecting arcing in complex scenes and detecting different size and shape of arcing is still a challenge. To overcome these issues, a robust image-based semantic segmentation model named ArcMSF is proposed for arcing detection of pantograph-catenary, which designs a novel hybrid multi-scale feature fusion model that aggregates Transformer with CNN to realize arcing pixel segmentation. A down-top decoder for combining low-level features with high-level features is designed to achieve multi-scale level arcing feature detecting in complex scenes. Inspired by the arcing image properties that arcing is always the brightest, the global max features and global threshold features are designed to augment the arcing features. Experiments on dataset IVAIS-PCA2021 and comparative experiments are conducted to demonstrate the effectiveness of the ArcMSF, which can achieve 89.13% segmentation accuracy and the fast inference speed of 23.84ms. Moreover, the detection results have a clear depiction of edge details.

Evaluation of the efflorescence resistance of calcium sulfoaluminate cement mortar: from indoor accelerated testing to outdoor exposure

Efflorescence is an inevitable phenomenon in all cementitious materials, particularly for Portland cement (PC) - based materials. Considering the relations between ion availability and crystallization, the factors influencing efflorescence involve the compositions of binders, pore structure of the hardened matrix, and so on. This study investigated the efflorescence of calcium sulfoaluminate cement (SAC) mortar at 5–40 °C from perspectives of carbonation potential and pore structure, while PC mortar was chosen as a reference. The microstructure of the efflorescence substances, such as phase assemblage and distribution, was systematically unraveled using optical microscope, in-situ Raman spectroscopy, SEM and EDS at multi scale-level. Water absorption test and XCT were employed for the pore structure characterization. Additionally, an outdoor exposure was carried out to compare with the results from the indoor accelerated experiments. Results reveal that a lower temperature has a limited effect on the efflorescence potential. The dominant efflorescence ingredient for SAC and PC mortars cured at 5–40 °C is CaCO3, with varied polymorphs. However, both primary and secondary efflorescence of SAC mortar is much slighter than that of PC mortar. The volume of fine pores (less than 0.1 mm3) in SAC mortar is higher than that of PC, accompanied by higher capillary absorption coefficient and water absorption rate. However, the low carbonation potential of ettringite tends to be more effective in reducing efflorescence risk for SAC mortar in both lab tests and outdoor exposure. The understanding of the relationship between efflorescence and carbonation provides important insights into the inhibition of efflorescence in cementitious materials.

Photocurable 3D-Printable Systems with Controlled Porosity towards CO2 Air Filtering Applications

Porous organic polymers are versatile platforms, easily adaptable to a wide range of applications, from air filtering to energy devices. Their fabrication via vat photopolymerization enables them to control the geometry on a multiscale level, obtaining hierarchical porosity with enhanced surface-to-volume ratio. In this work, a photocurable ink based on 1,6 Hexanediol diacrylate and containing a high internal phase emulsion (HIPE) is presented, employing PLURONIC F-127 as a surfactant to generate stable micelles. Different parameters were studied to assess the effects on the morphology of the pores, the printability and the mechanical properties. The tests performed demonstrates that only water-in-oil emulsions were suitable for 3D printing. Afterwards, 3D complex porous objects were printed with a Digital Light Processing (DLP) system. Structures with large, interconnected, homogeneous porosity were fabricated with high printing precision (300 µm) and shape fidelity, due to the addition of a Radical Scavenger and a UV Absorber that improved the 3D printing process. The formulations were then used to build scaffolds with complex architecture to test its application as a filter for CO2 absorption and trapping from environmental air. This was obtained by surface decoration with NaOH nanoparticles. Depending on the surface coverage, tested specimens demonstrated long-lasting absorption efficiency.

A Fractional Integral and Fractal Dimension-Based Deep Learning Approach for Pavement Crack Detection in Transportation Service Management

With artificial intelligence prevailing in intelligent transportation system, pavement crack detection with deep learning has aroused wide attentions in both academia and transportation sector. Nevertheless, it still remains a challenge to accomplish crack detection due to the complexity in pavement background. Motivated by latest advents in computer vision research, a fractional integral-based filtering method is advocated to remove pavement noise, and a fractal dimension estimation method has also emerged to present shape feature at pixel level, with the multi-scale feature architecture. Therefore, we try to propose a deep learning method, integrating fractional integral with fractal dimension, for crack detection in transportation service management. Firstly, the crack image is taken as input in the bottom-up architecture to extract fractal dimension on multi-scale levels, and a per-level feature unit is built to incorporate maps to make context information flow. Secondly, after fed into a convolutional filter for dimension resizing, all the resized feature maps are next fused at each level to comprise a group network. Finally, extensive experiments are executed on different crack datasets, exhibiting that the proposed method surpasses existing cutting-edge ones in terms of both generalizability and accuracy, with the benefits from not only fractional integral filtering, but also multi-scale fractal dimension features.

An improved U-Net model for concrete crack detection

Crack detection plays an important role in disease assessment of concrete buildings. However, factors such as complex background, irregular edge, and the real-time and accuracy requirement also make crack detection a challenging task. Aiming at the above challenges, an improved U-Net model for concrete crack detection is proposed, which has strong capability to extract the linear object, improving the performance in crack detection. The model is named Residual Linear Attention U-Net (RLAU-Net). There are three key measures in this paper. First, mirror padding the source image before convolution. Second, the multi-level features are obtained by aggregating the multi-scale features level by level. Third, strip pooling kernels are used to extract global contextual information, reducing information interference from the background. We tested the performance of RLAU-Net on our crack dataset, and the experimental results exhibited that it can improve the quantitative results of mean Intersection Over Union to 81.69%. In addition, F1 score has increased to, 78.21%, the Intersection Over Union of crack increased to 64.47%. We also compared the detect time-consuming of RLAU-Net and that of the original U-Net. Results demonstrate that the proposed model has a short processing time while maintaining a high detection accuracy for crack detection.

Multi-Scale Analysis of the Evolution of Jiangsu’s Ecological Footprint Depth and Its Factor Decomposition

The ecological footprint (EF), as a set of land-based ecological indicators, plays an important role in land ecology and evaluations of ecological pressure. Multi-scale levels of Jiangsu’s three-dimensional EF were analyzed, and 3D maps were presented to demonstrate the geographical distribution of the ecological footprint depth (EFD) of Jiangsu’s counties in 1995–2015 at the geographic scales of prefecture-level cities and counties. The results show that the overall EFD of Jiangsu gradually increased during the study period. The county-scale results show that the distribution of EFDs was high in the south and low in the north, and EFDs were mainly concentrated in urban areas of prefecture-level cities. The logarithmic mean Divisia index (LMDI) was used to decompose the factors in explaining the change in EFD. The LMDI analysis shows that the changes in factors every year differ among geographical units on different scales. Affluence is the main factor that promotes EFD, and the change in the ratio between EFD and scientific and technological level is the main factor that suppresses EFD. Countermeasures and suggestions for balancing ecological pressure in specific regions and reducing the depth of the EF from various factors with multi-scale heterogeneity are suggested.

The structural motifs of mineralized hard tissues from nano- to mesoscale: A future perspective for material science.

Biological tissues have developed structures that fulfil their various specific requirements. Mineralized tissues, such as tooth and bone, are often of mechanical competence for load bearing. Tooth enamel is the hardest and toughest mineralized tissue. Despite a few millimeters thick and with minimal regenerative capacity, human tooth enamel maintains its functions throughout a lifetime. Bone provides skeletal support and essential metabolism to our body. Degenerative diseases and ageing induce the loss of mechanical integrity of the bone, increasing the susceptibility to fractures. Tooth and bone share certain commonalities in chemical components and material characteristics, both consisting of nanocrystalline apatite and matrix proteins as their basic foundational structural units. Although the mechanical properties of such mineralized hard tissues remain unclear, it is plausible that they have an inherent toughening mechanism. Nanoindentation is able to characterize the mechanical properties of tooth enamel and bone at multiscale levels, and the results suggest that such toughening mechanisms of enamel and bone may be mainly associated with the smallest-scale structure-function relationships. These findings will benefit the development of advanced biomaterials in the field of material science and will further our understanding of degenerative bone disease in the clinical community.

Indirect 3D Bioprinting of a Robust Trilobular Hepatic Construct with Decellularized Liver Matrix Hydrogel.

The liver exhibits complex geometrical morphologies of hepatic cells arranged in a hexagonal lobule with an extracellular matrix (ECM) organized in a specific pattern on a multi-scale level. Previous studies have utilized 3D bioprinting and microfluidic perfusion systems with various biomaterials to develop lobule-like constructs. However, they all lack anatomical relevance with weak control over the size and shape of the fabricated structures. Moreover, most biomaterials lack liver-specific ECM components partially or entirely, which might limit their biomimetic mechanical properties and biological functions. Here, we report 3D bioprinting of a sacrificial PVA framework to impart its trilobular hepatic structure to the decellularized liver extracellular matrix (dLM) hydrogel with polyethylene glycol-based crosslinker and tyrosinase to fabricate a robust multi-scale 3D liver construct. The 3D trilobular construct exhibits higher crosslinking, viscosity (182.7 ± 1.6 Pa·s), and storage modulus (2554 ± 82.1 Pa) than non-crosslinked dLM. The co-culture of HepG2 liver cells and NIH 3T3 fibroblast cells exhibited the influence of fibroblasts on liver-specific activity over time (7 days) to show higher viability (90-91.5%), albumin secretion, and increasing activity of four liver-specific genes as compared to the HepG2 monoculture. This technique offers high lumen patency for the perfusion of media to fabricate a densely populated scaled-up liver model, which can also be extended to other tissue types with different biomaterials and multiple cells to support the creation of a large functional complex tissue.

Multiscale Characteristics and Drivers of the Bundles of Ecosystem Service Budgets in the Su-Xi-Chang Region, China.

Managing ecosystem services (ESs) to meet human needs is critical to achieving sustainable development in rapidly urbanizing regions. Identifying ES budget bundles and analyzing their drivers at a multiscale level can facilitate management decision-making; however, further research is required in areas undergoing rapid urbanization. This study quantified the supply, demand, and budgets of six typical ESs at the county, township, and village scales in the Su-Xi-Chang region in 2020. Additionally, the influence of natural environmental and socioeconomic factors on ES budget bundles was investigated based on K-means cluster analysis and the Geodetector model. The results showed that ESs on all three scales showed a mismatch between supply and demand. The similarity in the spatial pattern of supply, demand, and budgets of ESs at the township and village scales was higher than that at the township and county scales. The location and area of surplus, balance, and deficit varied with scale. We found that population density and the proportion of impervious surfaces are the main factors influencing the formation of the ES budget bundles at different scales. In addition, the diversity and degree of interpretation of drivers varied with scale. We believe that focusing on the overall situation on a large scale and implementing precise management on a small scale can make management decisions more effective. This study can provide a scientific basis for the sustainable utilization of ESs in the Su-Xi-Chang region, and the research results and methods can provide a reference for similar studies in other rapidly urbanizing areas in the world.

Multiscale depthwise separable convolution based network for high-resolution image segmentation

ABSTRACT Deep learning-based segmentation methods have demonstrated significant performance over their traditional counterparts. However, striving for better accuracy with such networks usually leads to the deterioration of the network’s computational efficiency, thereby rendering them inefficient for deployment on resource constraint devices. Establishing the required tradeoff between the accuracy of pixel prediction and computational efficiency remains challenging. In this article, a lightweight multiscale segmentation framework is proposed. We leverage the representation power of different receptive fields to attain optimal accuracy while maintaining computational efficiency by embedding the sparse network architecture with the depthwise separable convolution at the multiscale level. Experimental results from two challenging remote sensing segmentation datasets show that the proposed network can achieve substantial pixel prediction accuracy at relatively low computational overhead compared to state-of-the-art networks.

Longitudinal alterations of cortical structural-functional coupling in temporal lobe epilepsy.

To investigate the longitudinal alterations of cortical structural-functional coupling (SF coupling) in patients with temporal lobe epilepsy (TLE) over a 2-year follow-up, thereby exploring the neuropathophysiological mechanisms of TLE. Twenty-eight TLE patients and 42 age- and gender-matched healthy controls (HCs) were recruited. We used resting-state functional MRI and diffusion-weighted imaging to estimate and compare SF coupling at the multiscale network level (whole-brain, modular, and regional levels). Then, we analyzed the relationships between the spatial patterns of SF coupling, the principal functional connectivity (FC) gradient, and the functional participation coefficient (PC). Finally, we related regional SF coupling changes between baseline and follow-up to the expression of regional TLE-specific genes. Compared with HCs, TLE patients showed higher baseline SF couplings within the whole-brain, limbic, and default-mode modules. SF couplings within visual and dorsal attention modules were increased at follow-up compared to baseline. In all three groups, the spatial patterns of SF coupling aligned with the principal FC gradient and the functional PC. The longitudinal change in regional SF coupling in TLE patients was significantly positively correlated with the expression of the CUX2 gene. Aberrant SF coupling was revealed in TLE and related to macroscale cortical hierarchies, functional segregation, and TLE-specific gene expression; these data help increase our understanding of the neuropathophysiological mechanisms underlying TLE.

Smart E-Textiles: Overview of Components and Outlook

Smart textiles have gained great interest from academia and industries alike, spanning interdisciplinary efforts from materials science, electrical engineering, art, design, and computer science. While recent innovation has been promising, unmet needs between the commercial and academic sectors are pronounced in this field, especially for electronic-based textiles, or e-textiles. In this review, we aim to address the gap by (i) holistically investigating e-textiles’ constituents and their evolution, (ii) identifying the needs and roles of each discipline and sector, and (iii) addressing the gaps between them. The components of e-textiles—base fabrics, interconnects, sensors, actuators, computers, and power storage/generation—can be made at multiscale levels of textile, e.g., fiber, yarn, fabric, coatings, and embellishments. The applications, current state, and sustainable future directions for e-textile fields are discussed, which encompasses health monitoring, soft robotics, education, and fashion applications.

Defining the ultrastructure of the hematopoietic stem cell niche by correlative light and electron microscopy.

The blood system is supported by hematopoietic stem and progenitor cells (HSPCs) found in a specialized microenvironment called the niche. Many different niche cell types support HSPCs, however how they interact and their ultrastructure has been difficult to define. Here, we show that single endogenous HSPCs can be tracked by light microscopy, then identified by serial block-face scanning electron microscopy (SBEM) at multiscale levels. Using the zebrafish larval kidney marrow (KM) niche as a model, we followed single fluorescently labeled HSPCs by light sheet microscopy, then confirmed their exact location in a 3D SBEM dataset. We found a variety of different configurations of HSPCs and surrounding niche cells, suggesting there could be functional heterogeneity in sites of HSPC lodgement. Our approach also allowed us to identify dopamine beta-hydroxylase (dbh) positive ganglion cells as a previously uncharacterized functional cell type in the HSPC niche. By integrating multiple imaging modalities, we could resolve the ultrastructure of single rare cells deep in live tissue and define all contacts between an HSPC and its surrounding niche cell types.