Multi-scale Path Research Articles

Single‐Particle and Single‐Molecule Characterization of Immobilized Enzymes: A Multiscale Path toward Optimizing Heterogeneous Biocatalysts

AbstractHeterogeneous biocatalysis is highly relevant in biotechnology as it offers several benefits and practical uses. To leverage the full potential of heterogeneous biocatalysts, the establishment of well‐crafted protocols, and a deeper comprehension of enzyme immobilization on solid substrates are essential. These endeavors seek to optimize immobilized biocatalysts, ensuring maximal enzyme performance within confined spaces. For this aim, multidimensional characterization of heterogeneous biocatalysts is required. In this context, spectroscopic and microscopic methodologies conducted at different space and temporal scales can inform about the intraparticle enzyme kinetics, the enzyme spatial distribution, and the mass transport issues. In this Minireview, we identify enzyme immobilization, enzyme catalysis, and enzyme inactivation as the three main processes for which advanced characterization tools unveil fundamental information. Recent advances in operando characterization of immobilized enzymes at the single‐particle (SP) and single‐molecule (SM) levels inform about their functional properties, unlocking the full potential of heterogeneous biocatalysis toward biotechnological applications.

Single-Particle and Single-Molecule Characterization of Immobilized Enzymes: A Multiscale Path toward Optimizing Heterogeneous Biocatalysts.

Heterogeneous biocatalysis is highly relevant in biotechnology as it offers several benefits and practical uses. To leverage the full potential of heterogeneous biocatalysts, the establishment of well-crafted protocols, and a deeper comprehension of enzyme immobilization on solid substrates are essential. These endeavors seek to optimize immobilized biocatalysts, ensuring maximal enzyme performance within confined spaces. For this aim, multidimensional characterization of heterogeneous biocatalysts is required. In this context, spectroscopic and microscopic methodologies conducted at different space and temporal scales can inform about the intraparticle enzyme kinetics, the enzyme spatial distribution, and the mass transport issues. In this Minireview, we identify enzyme immobilization, enzyme catalysis, and enzyme inactivation as the three main processes for which advanced characterization tools unveil fundamental information. Recent advances in operando characterization of immobilized enzymes at the single-particle (SP) and single-molecule (SM) levels inform about their functional properties, unlocking the full potential of heterogeneous biocatalysis toward biotechnological applications.

Multilayer network analysis of Alzheimer disease links multiscale paths to the phenotype

AbstractBackgroundAlzheimer Disease (AD) cover a wide range of biological scales, from genes and proteins to cells and tissues, up to behavior. However, how these scales, the endotype, interact with one another to define the clinical phenotype remains poorly understood.MethodsHere we conducted a systems biology study of AD, based on multilayer network analysis and deep phenotyping that integrates genomics, CSF biomarkers, tau and amyloid‐beta PET, brain MRI, risk factors and clinical data, obtained from the ADNI collaboration. Multilayer networks were constructed using mutual information, a nonlinear measure of correlation, at each layer and between layers. Boolean simulations identified paths within and among all layers through which dynamic information is transmitted. Multiple linear regression was then used to predict the phenotype (clinical or imaging variables) from paths within the genetic, molecular and imaging layers.Resultsthe most prominent paths that significantly predicted the phenotype included hippocampus atrophy and tau levels from CSF and PET. Our results thus show that integrating the flow of information across biological scales reveals relevant paths for AD.Conclusionpaths identified by multilayer networks can be pursued as biomarkers or new therapeutic targets.

MPARN: multi-scale path attention residual network for fault diagnosis of rotating machines

Abstract Multi-scale convolutional neural network structures consisting of parallel convolution paths with different kernel sizes have been developed to extract features from multiple temporal scales and applied for fault diagnosis of rotating machines. However, when the extracted features are used to the same extent regardless of the temporal scale inside the network, good diagnostic performance may not be guaranteed due to the influence of the features of certain temporal scale less related to faults. Considering this issue, this paper presents a novel architecture called a multi-scale path attention residual network to further enhance the feature representational ability of a multi-scale structure. Multi-scale path attention residual network adopts a path attention module after a multi-scale dilated convolution layer, assigning different weights to features from different convolution paths. In addition, the network is composed of a stacked multi-scale attention residual block structure to continuously extract meaningful multi-scale characteristics and relationships between scales. The effectiveness of the proposed method is verified by examining its application to a helical gearbox vibration dataset and a permanent magnet synchronous motor current dataset. The results show that the proposed multi-scale path attention residual network can improve the feature learning ability of the multi-scale structure and achieve better fault diagnosis performance.

Multi-Scale tailoring of lithium ion diffusion from densification to coarsening

The performance of lithium-ion batteries is highly dependent on the morphology of the grain and structure of the electrode, which can be optimized for either high power or high energy. Despite the progress in all-solid-state batteries, the behavior of densification and coarsening has not yet been investigated on cathode. Herein, we investigated the strategies how to manipulate Li-ion diffusion at the different dimensions of micro- to macro level, facilitating the diffusion as tailoring the multi-scale path from densification to coarsening. First, the influence of microstructure on tortuosity and diffusivity was studied to understand the effect of sintering. Increased sintering time was expected to deteriorate electrochemical properties owing to lower diffusivity, and higher tortuosity. Second, the grain growth and densification behavior were studied in conjunction with the critical temperature for multi-scale tailoring without significant thermal degradation. At longer sintering times, densification was sluggish and had a monotonous increment of 2.5 %/h. However, the grain sizes greatly increased to 8.37 μm, leading isolated pores breaking. Finally, the tailored diffusivity and tortuosity were compared with the calculated results to understand intra- and inter-diffusion. Our finding provides clues to facilitate the diffusion of Li-ion by controlled densification and coarsening behavior in highly densified electrode.

Seeing Traffic Paths: Encrypted Traffic Classification With Path Signature Features

Although many network traffic protection methods have been developed to protect user privacy, encrypted traffic can still reveal sensitive user information with sophisticated analysis. In this paper, we propose ETC-PS, a novel encrypted traffic classification method with path signature. We first construct the traffic path with a session packet length sequence to represent the interactions between the client and the server. Then, path transformations are conducted to exhibit its structure and obtain different information. A multiscale path signature is finally computed as a kind of distinctive feature to train the traditional machine learning classifier, which achieves highly robust accuracy and low training overhead. Six publicly available datasets with different traffic types of HTTPS/1, HTTPS/2, QUIC, VPN, non-VPN, Tor, and non-Tor are used to conduct closed-world and open-world evaluations to verify the effectiveness of ETC-PS. The experimental results demonstrate that ETC-PS is superior to the state-of-the-art methods in terms of accuracy, f1 score, time complexity, and stability.

Multiscale Control of Generic Second Order Traffic Models by Driver-Assist Vehicles

We study the derivation of generic high order macroscopic traffic models from a follow-the-leader particle description via a kinetic approach. First, we recover a third order traffic model as the hydrodynamic limit of an Enskog-type kinetic equation. Next, we introduce in the vehicle interactions a binary control modelling the automatic feedback provided by driver-assist vehicles and we upscale such a new particle description by means of another Enskog-based hydrodynamic limit. The resulting macroscopic model is now a Generic Second Order Model (GSOM), which contains in turn a control term inherited from the microscopic interactions. We show that such a control may be chosen so as to optimise global traffic trends, such as the vehicle flux or the road congestion, constrained by the GSOM dynamics. By means of numerical simulations, we investigate the effect of this control hierarchy in some specific case studies, which exemplify the multiscale path from the vehicle-wise implementation of a driver-assist control to its optimal hydrodynamic design.

Multi-scale path-level analysis of jaguar habitat use in the Pantanal ecosystem

Jaguars (Panthera onca), like other apex predators, are highly susceptible to habitat loss and fragmentation given their low demographic potential and large habitat area requirements. Across their range, the Pantanal is considered critical for the jaguar's long-term conservation. Here we provide the first multi-scale path selection function model for jaguars, and the first empirically-based movement model covering the entire Pantanal ecosystem. Out of eight investigated variables, six were related to jaguar habitat use in the Pantanal: terrain roughness, human population density, grassland, percentage of tree cover, flooded habitats and shrubland. The results of scale optimization revealed that jaguars responded primarily to landscape variables at broad scales (32 km) of habitat availability, with only one variable (grassland) influencing jaguar path selection at a finer scale (4 km). Jaguar habitat use was positively associated with flooded habitats and densely forested areas and negatively associated with grassland, terrain roughness, and human population density, with the latter having the strongest negative effect on jaguar movement. The prediction map suggested that only 9.3% of the total suitable jaguar habitat in Pantanal is protected by Conservation Units. Among the most suitable areas, the largest continuous habitats were located in the northwestern portions of the Pantanal, which corresponds to the interfluvial areas between Corixo Grande and Cuiaba rivers. Our results suggested that the implementation of already proposed North Pantanal Conservation Unit Mosaic in this area would be highly valuable for jaguar conservation. This study provides a foundation for future research to delineate and prioritize core areas and corridors for jaguars in the region.

A Fast and Efficient CAD System for Improving the Performance of Malignancy Level Classification on Lung Nodules

Accurate malignancy level classification of lung nodules can reduce lung cancer mortality rate effectively. In this study, we present a fast and efficient CAD system to improve the performance of nodules malignancy level classification. Firstly, to reduce false positives (FPs), we propose a novel vessel segmentation method which measures vessel likelihood by tubular-like structures discriminating from multiple views. The method can recognize irregular vascular structures robustly and sensitively, and achieve fast vessel segmentation. In addition, a mathematical description for 3D pulmonary entities using neighbor centroids clustering is provided as a fundamental condition for spatial feature extraction. To optimize features extraction, we formulate a gray values cumulative function and a patches selection function based on the mathematical description, to generate axial spatial outline and spatial density distribution samples of the entities, respectively. Then, we use Edge Orientation Histogram (EOH) to extract edge features from the spatial outline and propose a multi-scale path LBP (MSPLBP) to extract the texture feature of the density distribution samples. Finally, the fused EOH and MSPLBP are classified into 6 malignancy levels by three state-of-the-art classifiers. The experimental results show that the vessel segmentation method achieves an average $F1\_Score$ of 78.14% and AUC value under $PR$ curves of 0.8149. Moreover, our system reaches an average $accuracy$ of 95.88% and consumes average 176.26 seconds for evaluating a CT set on malignancy level classification. These results indicate that the system can segment vessels exactly, and classify the malignancy level of nodules efficiently. Our system is the potential to be a powerful tool for early diagnosis of lung cancer.

Pavement Crack Detection Using Progressive Curvilinear Structure Anisotropy Filtering and Adaptive Graph-Cuts

Delineation of pavement cracks is essential for the damage assessment and maintenance of pavements. Existing methods are not sufficiently robust to interferences including varied illumination, non-uniform intensity, and complex texture noise. An integrated system for the automatic extraction of pavement cracks based on progressive curvilinear structure filtering and optimized segmentation techniques is proposed in this paper. Considering phase congruency and path morphological transformation, a phase congruency guided multi-scale path anisotropy filtering (PCmPA) method is first developed to generate a crack saliency map, significantly enhancing crack structures and eliminating isotropic texture noise. Phase congruency guided multi-scale free-form anisotropic filter (PCmFFA) is then presented as an extended curvilinear structure filter considering context information to enhance PCmPA. Finally, to accurately identify crack pixels and background, the two independent global filtering responses are incorporated with the phase congruency map and integrated into the graph-cuts based global optimization model with an adaptive regularization parameter. Experiments are conducted on two public pavement datasets and a self-captured laser-scanned pavement dataset, with results demonstrating that the proposed method can achieve superior performance compared to six existing algorithms.

Subpixel Land Cover Mapping Based on Dual Processing Paths for Hyperspectral Image

The subpixel mapping (SPM) technique can handle coarse fractional images derived by unmixing coarse original hyperspectral (HS) image to produce a fine land cover map at the subpixel scale. A popular SPM approach is a two-step model. It first increases the spatial resolution of coarse fractional images by subpixel sharpening to produce fine fractional images and then assigns class labels to each subpixel by the class allocation method. However, there is only a single processing path of the current SPM algorithm, and the information type of the fine fractional images is not rich. To enrich the information type, SPM based on dual processing paths (DPP) is proposed. DPP contains two processing paths, namely spatial–spectral path and multiscale path. First, the coarse original HS image and the high spatial resolution multispectral image are fused by component substitution to produce the fine fractional images with more spatial–spectral information in the spatial–spectral path. At the same time, deep Laplacian pyramid networks are used to obtain the fine fractional images with multiscale information in the multiscale path. The fine fractional images from the two paths are then integrated to generate the improved fraction images with multiscale spatial–spectral information. Finally, the multiscale spatial–spectral information is utilized to allocate class labels by the class allocation method. Experimental results on three real HS remote sensing data show that the proposed DPP outperforms the other SPM methods, demonstrating the effectiveness of the use of DPP in enriching the information type of the fine fractional images.

Multi-Scale Modeling for Redox Flow Battery Electrode

For redox flow batteries, the reaction distribution in macro or device-scale has been studied, however the reaction distribution in pore-scale is not well understood. Moreover, the understanding on how the reaction distribution in pore-scale impact the overall flow battery performance is lacking. This study introduces a framework that can provide the multi-scale path that help researchers understand the relationship between pore-scale electrode structure reaction and the device-scale electrochemical reaction uniformity for the redox flow batteries. A reduced model is build based on the 128 pore-scale simulations, which provide a quantitative relationship between the battery operation conditions (inlet velocity, current density, inlet concentration) and the surface reaction uniformity for the pore-scale sample. The multi-scale framework upscale the pore-scale surface reaction uniformity to device-scale combine uniformity. The inlet velocity can be optimized based on this multi-scale framework, and a time-varying inlet velocity can be derived for an example flow battery setup. It shows that keeping a constant inlet flow rate for the entire charging period waste much pump power comparing to the optimized time-vary inlet flow rate. Figure 1 shows the optimized flow rate and inlet concentration evolution with color lines represent various flow rate for different target normalized standard deviation of surface reaction rate. Figure 1

Weighted time-based global hierarchical path planning in dynamic environment

A weighted time-based global hierarchical path planning method is proposed to obtain the global optimal path from the starting point to the destination with time optimal control. First, the grid- or graph-based modeling is performed and the environment is divided into a set of grids or nodes. Then two time-based features of time interval and time cost are presented. The time intervals for each grid are built, during each interval the condition of the grid remains stable, and a time cost of passing through the grid is defined and assigned to each interval. Furthermore, the weight is introduced for taking both time and distance into consideration, and thus a sequence of multiscale paths with total time cost can be achieved. Experimental results show that the proposed method can handle the complex dynamic environment, obtain the global time optimal path and has the potential to be applied to the autonomous robot navigation and traffic environment.

Multiscale Path Optimization for the Reduced Environmental Impact of Air Transportation

Recent research has stressed the importance of understanding the impact of air transportation on the environment. The environmental impact of aviation comes from many different sources, which occur on different spatial and temporal scales. The objective of this work is to develop a path-planning strategy that generates an optimal trajectory in a multiscale environment such that fuel consumption and persistent contrail formation are minimized. A receding-horizon controller is constructed to adapt to diverse and evolving obstacles while maintaining tractability in an onboard operation. The multiscale control strategy is shown to perform better than a controller designed with respect to a single scale.

Quantitative comparison of primitive chain network simulations with literature data of linear viscoelasticity for polymer melts

The primitive chain network model of entangled polymers, where primitive chains and entanglements are assigned in real 3-D space, is a possible step in the multi-scale path from atomistic simulations to macroscopic constitutive equations. In any coarse-grained model of a polymeric system, however, determination of the basic parameters is a crucial step. By fitting the linear viscoelastic response of several melts of monodisperse linear polymers taken from the literature, we here determine the corresponding basic parameters of the primitive chain network model. We concentrate in particular on the value of the mean molar mass between entanglements, and on how such a mass is related to plateau modulus from our simulations. We consistently find values that fall around 60% of the entanglement molecular weight as obtained from Ferry's definition, and we envisage that such a result is due to Brownian fluctuations of the entanglement nodes.

Evaluation of an improved technique for lumen path definition and lumen segmentation of atherosclerotic vessels in CT angiography

Vessel image analysis is crucial when considering therapeutical options for (cardio-) vascular diseases. Our method, VAMPIRE (Vascular Analysis using Multiscale Paths Inferred from Ridges and Edges), involves two parts: a user defines a start- and endpoint upon which a lumen path is automatically defined, and which is used for initialization; the automatic segmentation of the vessel lumen on computed tomographic angiography (CTA) images. Both parts are based on the detection of vessel-like structures by analyzing intensity, edge, and ridge information. A multi-observer evaluation study was performed to compare VAMPIRE with a conventional method on the CTA data of 15 patients with carotid artery stenosis. In addition to the start- and endpoint, the two radiologists required on average 2.5 (SD: 1.9) additional points to define a lumen path when using the conventional method, and 0.1 (SD: 0.3) when using VAMPIRE. The segmentation results were quantitatively evaluated using Similarity Indices, which were slightly lower between VAMPIRE and the two radiologists (respectively 0.90 and 0.88) compared with the Similarity Index between the radiologists (0.92). The evaluation shows that the improved definition of a lumen path requires minimal user interaction, and that using this path as initialization leads to good automatic lumen segmentation results.