Quantification Of Dispersion Research Articles

Quantification of golgi dispersal and classification using machine learning models

The Golgi body is a critical organelle in eukaryotic cells responsible for processing and modifying proteins and lipids. Under certain conditions, such as stress, disease, or ageing, the Golgi structure alters. Therefore, understanding the mechanisms that regulate Golgi dispersion has significant research contributions to identifying disease. However, there is a lack of tools to quantify the Golgi dispersion datasets. In this paper, we aim to automate the process of quantification of Golgi dispersion and use extracted features to classify dispersed Golgi images from undispersed Golgi images using machine learning models. First, we collected confocal microscopy images of transiently transfected HeLa cells expressing Galactose-1-phosphate uridylyltransferase (GALT)- green fluorescent protein (GFP) to quantify Golgi dispersal and classification. For the quantification, we introduced automated image processing and segmentation by applying mean and Gaussian filters. Then we used Otsu thresholding on preprocessed images and watershed segmentation to refine the segmentation of dispersed Golgi particles. In the case of classification, we extracted features from the Golgi dispersal images and classified them into empty vector (EV) versus CARP1 ring mutant (CARP1 RM) and empty vector (EV) versus CARP1 wildtype (CARP1 WT) classes. Our approach used machine-learning models, including logistic regression, decision tree, random forest, Naive Bayes, k-Nearest Neighbor (KNN), and gradient boosting for dispersed Golgi image classification. The experiment results show that our quantification technique on Golgi dispersal images reached 65% classification accuracy when the system uses a gradient boosting classifier for EV vs. CARP1 WT classification. Furthermore, our approach achieved 65% classification accuracy using a random forest classifier for EV vs. CARP1 RM classification.

Quantified analysis of 2D dispersion of carbon nanotubes in hardened cement composite using confocal Raman microspectroscopy

Dispersion of carbon nanotubes (CNTs) is the critical factor affecting the performance of nanocomposites; however, the quality of CNT dispersion could be indirectly understood due to the limitations of evaluation methods of the dispersion. This study aimed to visualize the macroscopic spatial distribution of CNTs in an as-is hardened cement matrix for the quantification of dispersion. Using a confocal Raman microspectroscopy equipped with an optical microscope, the major phases in cement, hydrates, and CNTs were identified from their characteristic Raman signals to construct the definitive phase map. The dispersion efficiency and heterogeneity were defined to quantify the fraction of CNT-affected region and the unevenness of CNT distribution. The experimental results demonstrate the importance of a well-dispersed stable state in CNT suspension and suggest that it is possible to evaluate the effectiveness of ultrasonication with superplasticizers that have been frequently adopted as a dispersion technique in hardened cement composites.

Fluid deformation in isotropic Darcy flow

The deformation of fluid elements governs many processes in porous media, including the transport, dispersion and mixing of solutes, chemical reactions and colloidal particles. Recently, it has been shown that even in strongly heterogeneous porous media, steady three-dimensional (3-D) Darcy flow with isotropic hydraulic conductivity admits only constrained kinematics. This is due to its inherently helicity-free nature, as the velocity is everywhere orthogonal to the vorticity. This property precludes braided streamlines and instead admits a pair of coherent 3-D streamfunctions, and the streamlines cannot wander freely throughout the flow domain. In this study, we consider the impact of these kinematic constraints upon fluid deformation at the Darcy scale. We show that the helicity-free condition corresponds to an orthogonal 3-D streamline coordinate system, which we use to derive anab initiocontinuous time random walk framework for fluid deformation. We find that the helicity-free condition combined with the intermittent nature of shear events leads to fluid deformation that is limited to algebraic growth, with stretching ranging from sublinear to superlinear behaviour. Fluid deformation in 3-D isotropic Darcy flow is remarkably similar to that of two-dimensional (2-D) Darcy flow, and the structure of 3-D Darcy flow is fundamentally the same as two superposed 2-D Darcy flows. These results have implications for understanding flow and transport in heterogeneous porous media, and provide a basis for quantification of mixing and dispersion in isotropic 3-D Darcy flow.

Correlation dispersion as a measure to better estimate uncertainty in remotely sensed glacier displacements

Abstract. In recent years a vast amount of glacier surface velocity data from satellite imagery has emerged based on correlation between repeat images. Thereby, much emphasis has been put on the fast processing of large data volumes and products with complete spatial coverage. The metadata of such measurements are often highly simplified when the measurement precision is lumped into a single number for the whole dataset, although the error budget of image matching is in reality neither isotropic nor constant over the whole velocity field. The spread of the correlation peak of individual image offset measurements is dependent on the image structure and the non-uniform flow of the ice and is used here to extract a proxy for measurement uncertainty. A quantification of estimation error or dispersion for each individual velocity measurement can be important for the inversion of, for instance, rheology, ice thickness and/or bedrock friction. Errors in the velocity data can propagate into derived results in a complex and exaggerating way, making the outcomes very sensitive to velocity noise and outliers. Here, we present a computationally fast method to estimate the matching precision of individual displacement measurements from repeat imaging data, focusing on satellite data. The approach is based upon Gaussian fitting directly on the correlation peak and is formulated as a linear least-squares estimation, making its implementation into current pipelines straightforward. The methodology is demonstrated for Sermeq Kujalleq (Jakobshavn Isbræ), Greenland, a glacier with regions of strong shear flow and with clearly oriented crevasses, and Malaspina Glacier, Alaska. Directionality within an image seems to be the dominant factor influencing the correlation dispersion. In our cases these are crevasses and moraine bands, while a relation to differential flow, such as shear, is less pronounced on the correlation spread.

Mori-Tanaka-based statistical methodology to compute the effective Young modulus of polymer matrix nano-composites considering the experimental quantification of nanotubes dispersion and alignment degree

This paper presents a Mori-Tanaka-based statistical methodology to predict the effective Young modulus of carbon nanotubes (CNTs)-reinforced composites considering three variables: weight content, reinforcement dispersion and orientation. Last two variables are quantified by two parameters, namely, free-path distance between nano-reinforcements and orientation angle regarding the loading direction. To validate the present methodology, samples of multi-walled CNTs (MWCNTs)-reinforced polyvinyl alcohol (PVA)-matrix composite were manufactured by mixing solution. The MWCNT/PVA Young modulus was measured by nano-indentation, while the MWCNTs Young modulus was quantified by micro-Raman spectroscopy. Both stretched and unstretched composite specimens were fabricated. Transmission electron microscopy (TEM) and in-plane image analysis were used to obtain fitting coefficients of log-normal frequency distribution functions for the free-path distance and orientation angle. It was evidenced that numerical results fit well to measured values of effective Young modulus of MWCNTs and MWCNT/PVA, with exception of some particular cases where significant differences were found. Microstructural heterogeneities, cluster formation, polymer chains alignment, errors associated with the dispersion, orientation and mechanical characterization procedures, as well as idealization and statistical errors, were identified as possible causes of these differences. Finally, using the proposed methodology and the dispersion and orientation distribution functions experimentally obtained, the effective Young modulus is estimated for three kinds of thermoplastic matrices (polyvinyl alcohol, polyethylene ketone, and ultra-high molecular weight polyethylene) with different kinds of nanotubes (single wall, double wall, and multi-walled), at different weight contents, finding the superior mechanical performance for double-walled CNTs-reinforced composites and the lower one for multi-walled CNTs-reinforced ones.

SARS-CoV-2 in wastewater from Mexico City used for irrigation in the Mezquital Valley: quantification and modeling of geographic dispersion.

Quantification of SARS-CoV-2 in urban wastewaters has emerged as a cheap, efficient strategy to follow trends of active COVID-19 cases in populations. Moreover, mathematical models have been developed that allow the prediction of active cases following the temporal patterns of viral loads in wastewaters. In Mexico, no systematic efforts have been reported in the use of these strategies. In this work, we quantified SARS-CoV-2 in rivers and irrigation canals in the Mezquital Valley, Hidalgo, an agricultural region where wastewater from Mexico City is distributed and used for irrigation. Using quantitative RT-PCR, we detected the virus in six out of eight water samples from rivers and five out of eight water samples from irrigation canals. Notably, samples showed a general consistent trend of having the highest viral loads in the sites closer to Mexico City, indicating that this is the main source that contributes to detection. Using the data for SARS-CoV-2 concentration in the river samples, we generated a simplified transport model that describes the spatial patterns of dispersion of virus in the river. We suggest that this model can be extrapolated to other wastewater systems where knowledge of spatial patterns of viral dispersion, at a geographic scale, is required. Our work highlights the need for improved practices and policies related to the use of wastewater for irrigation in Mexico and other countries.

Quantification of biomaterial dispersion during otologic procedures and role of barrier drapes in Covid 2019 era - a laboratory model.

Aerosol generation during temporal bone surgery caries the risk of viral transmission. Steps to mitigate this problem are of particular importance during the coronavirus disease 2019 pandemic. To quantify the effect of barrier draping on particulate material dispersion during temporal bone surgery. The study involved a cadaveric model in a simulated operating theatre environment. Particle density and particle count for particles sized 1-10 μ were measured in a simulated operating theatre environment while drilling on a cadaveric temporal bone. The effect of barrier draping to decrease dispersion was recorded and analysed. Barrier draping decreased counts of particles smaller than 5 μ by a factor of 80 in the operating theatre environment. Both particle density and particle count showed a statistically significant reduction with barrier draping (p = 0.027). Simple barrier drapes were effective in decreasing particle density and particle count in the operating theatre model and can prevent infection in operating theatre personnel.

Polymer nanodielectrics—Short history and future perspective

This article provides a perspective on the development of polymer nanodielectrics for electrical insulation applications. It includes a short history of the development of the field, barriers to implementation, a chemical toolbox available for optimizing properties, some examples of potential commercial development, and the latest advances. It offers opinions on critical research needed to fully develop the models needed to predict the behavior and to develop design tools. Key findings include the need for quantification of nanofiller dispersion, investment in long term aging data research, better scale-up methods, a data resource that brings the broad data together in a format that enables faster scientific discovery, and a commercial entity willing to take the risk in implementing these promising materials.

BSE-based dispersion quantification of carbon fibres in cementitious composite through FCM-CA approach

Micro-engineered cementitious composite requires a quantitative evaluation of the phase dispersion state. In the application of the carbon fibre enhanced cementitious composite, the excellent electrical conductivity of the fibre network has been utilized for structural monitoring and internal heat generation. The dispersion state of carbon fibres significantly influences the performance of the functional application. However, it is challenging to evaluate the fibre dispersion with a quantitative consideration of the spatial arrangement. In this paper, an image-based approach is proposed to evaluate the spatial dispersion of carbon fibres in the cementitious composite. The independently programmed approach integrates fuzzy c-mean (FCM) clustering, random sequential addition simulation and originally developed cellular automata (CA). Quantification of fibre dispersion is performed with the nearest-neighbour analysis on the backscattered electron (BSE) images of the fibre-cement composite. Further investigation is conducted with the proposed approach, BSE images, electrical resistivity, compressive strength and flexural strength. Mechanical and chemical mixing techniques are applied during the sample preparation to investigate the dispersion behaviour in different systems. The evaluation results in a quantitative pattern of the dispersion coefficient within the range from 0.44 to 0.48. The dispersion and electrical resistivity results demonstrate that a well-distributed conductive network is formed with up to 6.5 min of mechanically controlled mixing. A negative correlation between the dispersion coefficient and the electrical resistivity is found in the form of the second-order power law. The microscale arrangement of fibre dispersion minorly influences compressive and flexural strength, but significantly influences the electrical function. Electrical evaluation of fibre dispersion confronts inconsistency in the chemical mixing system.

Caged fish experiment and hydrodynamic bidimensional modeling highlight the importance to consider 2D dispersion in fluvial environmental DNA studies

AbstractThe analysis of environmental DNA (eDNA) is a powerful tool to increase the efficiency of species detection and monitoring in aquatic ecosystems. Yet, several points remain to be clarified in order to estimate with better precision the distribution and abundance of targeted species, such as the dispersion and dilution of eDNA in large lotic systems. This study aimed to document the dispersion patterns of eDNA in the St. Lawrence River, the largest fluvial system in eastern North America. Caged Brown trout (Salmo trutta) were placed in two different water masses present in this part of the river, the Ottawa River, and water from the outlet of the Laurentian Great Lakes. eDNA detection of the caged fish was performed for two days following cage removal at 53 sampling stations located at 500 upstream, and 10, 100, 500, 1,000, and 5,000 m downstream from the cages. Quantitative PCR analysis using a Brown trout specific assay revealed a positive detection only at downstream stations and up to 5,000 m. To further investigate patterns of dispersion, the relative concentrations of eDNA were predicted using a bidimensional hydrodynamic model, calibrated for downstream advection and lateral mixing of particles (i.e., quantification of 2D dispersion). The detection and the quantities of eDNA obtained by qPCR analyses were compared with the model predictions. Our model which predicts a low lateral mixing and a downstream flow in direct line from the eDNA source best fits the results. We discuss how such studies can improve our capacity to produce more precise estimates of species abundance and distribution in order to better interpret eDNA signals in large lotic systems.

Quantification of Dispersion for Weakly and Strongly Correlated Nanofillers in Polymer Nanocomposites

The dispersion of nanoparticles in viscous polymers is dictated by kinetics, interaction potentials between particles, and interfacial compatibility between the matrix and dispersed phases. It was ...

Automated quantification of reinforcement dispersion in B4C/Al metal matrix composites

This study proposes an automated quantification technique for reinforcement dispersion in metal matrix composites. While a fine and homogeneous dispersion of reinforcements is necessary to achieve an optimum reinforcing effect in metal matrix composites, there have been few studies on the quantitative measurement of reinforcement dispersion. The proposed technique extracts reinforcement information from a given microscopic image through image analysis with minimized human interruption and quantifies the reinforcement dispersion automatically using a statistical approach. The feasibility of the proposed technique is shown by analyzing the effect of the hot rolling process on the reinforcement dispersion in a B4C/Al metal matrix composite and comparing it with the as-cast state.

A simple tool for two-dimensional quantification of filler dispersion: A proposal

An effective filler dispersion within a polymeric matrix is known to be a crucial requirement for obtaining the desired performance from a composite. A quantification of this parameter cannot be always easily achieved and several methodologies have been proposed so far, generally based on complex image processing algorithms. However, no standard has been established yet.In order to contribute to the quantitative evaluation of the filler dispersion in a polymeric matrix, a novel straightforward two-dimensional alternative method based on the analysis of electron microscopy micrographs is here proposed. Several case studies were considered to test the reliability of the approach, providing as resulting output, either a qualitative polar plot representative of the analyzed image and an index to measure the filler dispersion.

Quantification of nanoparticle dispersion within polymer matrix using gap statistics

This study was prompted by the inadequacy of most dispersion quantification techniques to address issues pertaining to scalability, implementation complexity, accuracy/error, uncertainty factors and versatility. Therefore, a method for quantifying dispersion based on gap statistics was developed. A dispersion quantity (D) was formulated from a Gap factor Particle spacing dispersity (PSD1) and Particle size dispersity (PSD2) factors. The summation of the factors resulted in the dispersion parameter (Dp) which must be equal to one for an ideal or uniformly distributed condition. The state of dispersion increases as D → 100%. The concept was tested with simulated models having uniform dispersion, random dispersion, small aggregate, three large aggregate and one large aggregate were successfully quantified to show 99.34%, 82.42%, 34.17%, 8.95% and 3.65% respectively. For validation of concept, the state of dispersion when samples with (scenario 1) and without (scenario 4) silane treatment were quantified as 32,02% and 7.72% respectively. The concepts were then validated using real microscopy images. This approach is robust, versatile and easy to implement.

Homogeneity quantification of nanoparticles dispersion in composite materials

As the quantitative evaluation of dispersion of nanoparticles are crucial for the research of nanocomposites, including ceramic, metal, and polymer nanocomposites, we proposed two quantifiable tools for the evaluation of dispersion in the nanocomposites by using machine‐learning algorithms, that is the coefficient of variation of K‐nearest neighboring distances (CVKD value) and the information entropy derived from the probability density function. In total 230 different type of dispersion morphologies of nanocomposites were investigated by using K‐nearest neighboring algorithms, Gaussian mixture model, expectation maximization, and Kernel density estimation algorithms. The smaller values of CVKD or information entropy represented much more homogeneous dispersion of nanoparticles. It might be expected the information exhibited in the present study could assist in the rational design and fabrication of superior nanocomposites. POLYM. COMPOS., 40:1000–1005, 2019. © 2018 Society of Plastics Engineers

Small-Scale Fading Statistics of Emerging 3-D Mobile Radio Cellular Propagation Channels

In delivering fifth generation (5G) communication networks, the fundamental advancements in the scale of antenna arrays, density of networks, mobility of communicating nodes, size of cells, and range of frequencies necessitate the derivation of an appropriate and reliable channel model. A tunable three dimensional (3-D) geometric channel model comprehending the mobility of user terminal together with high degree of flexibility in modelling the shape, orientation, and scale of the scattering region is proposed. Characterization of Doppler spectrum, quantization of multipath dispersion in angular domain, and second order fading statistics of the radio propagation channel is presented. Mathematical expressions for joint and marginal probability density function of Doppler shift and multipath power are derived for this advanced 3-D hollow geometric scattering model. Next, an analysis on the Doppler spectrum is presented, where the impact of various physical channel parameters on its statistical characteristics is analyzed. Since, the quantification of multipath dispersion in 3-D angular domain is of vital importance for designing large scale planner antenna arrays with very high directional resolution for emerging 5G communications, therefore, a thorough analysis on the multipath shape factors (SFs) of the proposed analytical 3-D channel model is conducted. Finally, the analysis on SFs is extended for characterization of second order fading statistics of multipath channels.

Improving the boundary efficiency of a compact finite difference scheme through optimising its composite template

This paper presents efforts to improve the boundary efficiency and accuracy of a compact finite difference scheme, based on its composite template. Unlike precursory attempts the current methodology is unique in its quantification of dispersion and dissipation errors, which are only evaluated after the matrix system of equations has been rearranged for the derivative. This results in a more accurate prediction of the boundary performance, since the analysis is directly based on how the derivative is represented in simulations. A genetic algorithm acts as a comprehensive method for the optimisation of the boundary coefficients, incorporating an eigenvalue constraint for the linear stability of the matrix system of equations. The performance of the optimised composite template is tested on one-dimensional linear wave convection and two-dimensional inviscid vortex convection problems, with uniform and curvilinear grids. In all cases, it yields substantial accuracy and efficiency improvements while maintaining stable solutions and fourth-order accuracy.

Bingham–NODDI: Mapping anisotropic orientation dispersion of neurites using diffusion MRI

This paper presents Bingham–NODDI, a clinically-feasible technique for estimating the anisotropic orientation dispersion of neurites. Direct quantification of neurite morphology on clinical scanners was recently realised by a diffusion MRI technique known as neurite orientation dispersion and density imaging (NODDI). However in its current form NODDI cannot estimate anisotropic orientation dispersion, which is widespread in the brain due to common fanning and bending of neurites. This work proposes Bingham–NODDI that extends the NODDI formalism to address this limitation. Bingham–NODDI characterises anisotropic orientation dispersion by utilising the Bingham distribution to model neurite orientation distribution. The new model estimates the extent of dispersion about the dominant orientation, separately along the primary and secondary dispersion orientations. These estimates are subsequently used to estimate the overall dispersion about the dominant orientation and the dispersion anisotropy. We systematically evaluate the ability of the new model to recover these key parameters of anisotropic orientation dispersion with standard NODDI protocol, both in silico and in vivo. The results demonstrate that the parameters of the proposed model can be estimated without additional acquisition requirements over the standard NODDI protocol. Thus anisotropic dispersion can be determined and has the potential to be used as a marker for normal brain development and ageing or in pathology. We additionally find that the original NODDI model is robust to the effects of anisotropic orientation dispersion, when the quantification of anisotropic dispersion is not of interest.

Assessment of Tumor Blood Flow Distribution by Dynamic Contrast-Enhanced CT

A distinct feature of the tumor vasculature is its tortuosity and irregular branching of vessels, which can translate to a wider dispersion and higher variability of blood flow in the tumor. To enable tumor blood flow variability to be assessed in vivo by imaging, a tracer kinetic model that accounts for flow dispersion is developed for use with dynamic contrast-enhanced (DCE) CT. The proposed model adopts a multiple-pathway approach and allows for the quantification of relative dispersion in the blood flow distribution, which reflects flow variability in the tumor vasculature. Monte Carlo simulation experiments were performed to study the possibility of reducing the number of model parameters based on the Akaike information criterion approach and to explore possible noise and tissue conditions in which the model might be applicable. The model was used for region-of-interest analysis and to generate perfusion parameter maps for three patient DCE CT cases with cerebral tumors, to illustrate clinical applicability.

Quantitative evaluation of the dispersion achievable using different preparation methods and DC electrical conductivity of vapor grown carbon nanofiber/epoxy composites

The aim of this work is to quantitatively analyze the dispersion achievable using different methods for the preparation of vapor grown carbon nanofiber-epoxy composites. Four different dispersion methods were used, differing in stress level intensity: blender mixing, capillary rheometry mixing, 3 roll milling and planetary centrifuge mixing. Furthermore, the relationship between dispersion and DC conductivity of the composites was evaluated. For the dispersion analysis, four nanofiber concentrations ranging from 0.1 to 3.0 wt.% were prepared for each method, while the DC measurements were performed for eight concentrations, ranging from 0.0 to 4.0 wt.%. The dispersion was analyzed by transmitted light optical microscopy and greyscale analysis, following a methodology previously established. The results show that as the VGCNF content increases the dispersion level decreases, as indicated by the increase of the variance of the corresponding greyscale histograms. The 3 roll-mill method produces the samples with the highest dispersion levels, whilst the samples from the remaining methods show large VGCNF agglomerates. The dispersion along the length of the samples was also estimated, indicating symmetric variation of dispersion from the center. The dispersion method also strongly influences the overall composite electrical response. No relationship was found between the electrical conductivity and the greyscale analysis achieved by the different methods. Thus, this method for the quantification of dispersion works well for length scales around 0.1 μm, but this is above the relevant scale that determines the electrical response.