1. |
- Fager, Cecilia, 1990, et al.
(author)
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Correlating 3D porous structure in polymer films with mass transport properties using FIB-SEM tomography
- 2021
-
In: Chemical Engineering Science: X. - : Elsevier BV. - 2590-1400. ; 12
-
Journal article (peer-reviewed)abstract
- Porous polymer coatings are used to control drug release from pharmaceutical products. The coating covers a drug core and depending on the porous structure, different drug release rates are obtained. This work presents mass transport simulations performed on porous ethyl cellulose films with different porosities. The simulations were performed on high spatial resolution 3D data obtained using a focused ion beam scanning electron microscope. The effective diffusion coefficient of water was determined using a diffusion chamber. Lattice Boltzmann simulations were used to simulate water diffusion in the 3D data. The simulated coefficient was in good agreement with the measured coefficient. From the results it was concluded that the tortuosity and constrictivity of the porous network increase with decreasing amount of added hydroxypropyl cellulose, resulting in a sharp decrease in effective diffusion. This work shows that high spatial resolution 3D data is necessary, and that 2D data is insufficient, in order to predict diffusion through the porous structure with high accuracy.
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2. |
- Andersson, Claes, 1987, et al.
(author)
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A Bayesian hierarchical point process model for epidermal nerve fiber patterns
- 2019
-
In: Mathematical Biosciences. - : Elsevier BV. - 1879-3134 .- 0025-5564. ; 313, s. 48-60
-
Journal article (peer-reviewed)abstract
- We introduce the Thomas process in a Bayesian hierarchical setting as a model for point pattern data with a nested structure. This model is applied to a nerve fiber data set which consists of several point patterns of nerve entry points from 47 subjects divided into 3 groups, where the grouping is based on the diagnosed severity of a certain nerve disorder. The modeling assumption is that each point pattern is a realization of a Thomas process, with parameter values specific to the subject. These parameter values are in turn assumed to come from distributions that depend on which group the subject belongs to. To fit the model, we construct an MCMC algorithm, which is evaluated in a simulation study. The results of the simulation study indicate that the group level mean of each parameter is well estimated, but that the estimation of the between subject variance is more challenging. When fitting the model to the nerve fiber data, we find that the structure within clusters appears to be the same in all groups, but that the number of clusters decreases with the progression of the nerve disorder.
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3. |
- Andersson, Claes, 1987, et al.
(author)
-
Discovering early diabetic neuropathy from epidermal nerve fiber patterns
- 2016
-
In: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 35:24, s. 4427-4442
-
Journal article (peer-reviewed)abstract
- Epidermal nerve fibre (ENF) density and morphology are used to study small fibre involvement in diabetic, HIV, chemotherapy induced and other neuropathies. ENF density and summed length of ENFs per epidermal surface area are reduced, and ENFs may appear more clustered within the epidermis in subjects with small fibre neuropathy than in healthy subjects. Therefore, it is important to understand the spatial structure of ENFs. In this paper, we compare the ENF patterns between healthy subjects and subjects suffering from mild diabetic neuropathy. The study is based on suction skin blister specimens from the right foot of 32 healthy subjects and eight subjects with mild diabetic neuropathy. We regard the ENF entry point (location where the trunks of a nerve enters the epidermis) and ENF end point (termination of the nerve fibres) patterns as realizations of spatial point processes, and develop tools that can be used in the analysis and modelling of ENF patterns. We use spatial summary statistics and shift plots and define a new tool, reactive territory, to study the spatial patterns and to compare the patterns of the two groups. We will also introduce a simple model for these data in order to understand the growth process of the nerve fibres.
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4. |
- Andersson, Claes, 1987, et al.
(author)
-
Hierarchical models for epidermal nerve fiber data
- 2018
-
In: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 37:3, s. 357-374
-
Journal article (peer-reviewed)abstract
- While epidermal nerve fiber (ENF) data have been used to study the effects of small fiber neuropathies through the density and the spatial patterns of the ENFs, little research has been focused on the effects on the individual nerve fibers. Studying the individual nerve fibers might give a better understanding of the effects of the neuropathy on the growth process of the individual ENFs. In this study, data from 32 healthy volunteers and 20 diabetic subjects, obtained from suction induced skin blister biopsies, are analyzed by comparing statistics for the nerve fibers as a whole and for the segments that a nerve fiber is composed of. Moreover, it is evaluated whether this type of data can be used to detect diabetic neuropathy, by using hierarchical models to perform unsupervised classification of the subjects. It is found that using the information about the individual nerve fibers in combination with the ENF counts yields a considerable improvement as compared to using the ENF counts only. Copyright © 2017 John Wiley & Sons, Ltd.
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5. |
- Antonsson, Jakob, et al.
(author)
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Modeling Colloidal Particle Aggregation Using Cluster Aggregation with Multiple Particle Interactions
- 2024
-
In: Journal of Physical Chemistry B. - 1520-6106 .- 1520-5207. ; 128:18, s. 4513-4524
-
Journal article (peer-reviewed)abstract
- In this study, we investigate the aggregation dynamics of colloidal silica by generating simulated structures and comparing them to experimental data gathered through scanning transmission electron microscopy (STEM). More specifically, diffusion-limited cluster aggregation and reaction-limited cluster aggregation models with different functions for the probability of particles sticking upon contact were used. Aside from using a constant sticking probability, the sticking probability was allowed to depend on the masses of the colliding clusters and on the number of particles close to the collision between clusters. The different models of the sticking probability were evaluated based on the goodness-of-fit of spatial summary statistics. Furthermore, the models were compared to the experimental data by calculating the structures’ fractal dimension and mass transport properties from simulations of flow and diffusion. The sticking probability, depending on the interaction with multiple particles close to the collision site, led to structures most similar to the STEM data.
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6. |
- Bolin, David, 1983, et al.
(author)
-
Statistical Prediction of Global Sea Level From Global Temperature
- 2015
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In: Statistica Sinica. - : Statistica Sinica (Institute of Statistical Science). - 1017-0405. ; 25:1, s. 351-367
-
Journal article (peer-reviewed)abstract
- Sea level rise is a threat to many coastal communities, and projection of future sea level for different climate change scenarios is an important societal task In this paper, we first construct a time series regression model to predict global sea level from global temperature. The model is fitted to two sea level data sets (with and without corrections for reservoir storage of water) and three temperature data sets. The effect of smoothing before regression is also studied. Finally, we apply a novel methodology to develop confidence bands for the projected sea level, simultaneously for 2000-2100, under different scenarios, using temperature projections from the latest climate modeling experiment. The main finding is that different methods for sea level projection, which appear to disagree, have confidence intervals that overlap, when taking into account the different sources of variability in the analyses.
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7. |
- Carmona, Pierre, 1995, et al.
(author)
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Controlling the structure of spin-coated multilayer ethylcellulose/ hydroxypropylcellulose films for drug release
- 2023
-
In: International Journal of Pharmaceutics. - 0378-5173 .- 1873-3476. ; 644
-
Journal article (peer-reviewed)abstract
- Porous phase-separated ethylcellulose/hydroxypropylcellulose (EC/HPC) films are used to control drug transport out of pharmaceutical pellets. Water-soluble HPC leaches out and forms a porous structure that controls the drug transport. Industrially, the pellets are coated using a fluidized bed spraying device, and a layered film exhibiting varying porosity and structure after leaching is obtained. A detailed understanding of the formation of the multilayered, phase-separated structure during production is lacking. Here, we have investigated multilayered EC/HPC films produced by sequential spin-coating, which was used to mimic the industrial process. The effects of EC/HPC ratio and spin speed on the multilayer film formation and structure were investigated using advanced microscopy techniques and image analysis. Cahn-Hilliard simulations were performed to analyze the mixing behavior. A gradient with larger structures close to the substrate surface and smaller structures close to the air surface was formed due to coarsening of the layers already coated during successive deposition cycles. The porosity of the multilayer film was found to vary with both EC/HPC ratio and spin speed. Simulation of the mixing behavior and in situ characterization of the structure evolution showed that the origin of the discontinuities and multilayer structure can be explained by the non-mixing of the layers.
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8. |
- Carmona, Pierre, 1995, et al.
(author)
-
Cross-sectional structure evolution of phase-separated spin-coated ethylcellulose/hydroxypropylcellulose films during solvent quenching
- 2022
-
In: RSC Advances. - : Royal Society of Chemistry. - 2046-2069. ; 12:40, s. 26078-26089
-
Journal article (peer-reviewed)abstract
- Porous phase-separated ethylcellulose/hydroxypropylcellulose (EC/HPC) films are used to control drug transport out of pharmaceutical pellets. The films are applied on the pellets using fluidized bed spraying. The drug transport rate is determined by the structure of the porous films that are formed as the water-soluble HPC leaches out. However, a detailed understanding of the evolution of the phase-separated structure during production is lacking. Here, we have investigated EC/HPC films produced by spin-coating, which mimics the industrial manufacturing process. This work aimed to understand the structure formation and film shrinkage during solvent evaporation. The cross-sectional structure evolution was characterized using confocal laser scanning microscopy (CLSM), profilometry and image analysis. The effect of the EC/HPC ratio on the cross-sectional structure evolution was investigated. During shrinkage of the film, the phase-separated structure undergoes a transition from 3D to nearly 2D structure evolution along the surface. This transition appears when the typical length scale of the phase-separated structure is on the order of the thickness of the film. This was particularly pronounced for the bicontinuous systems. The shrinkage rate was found to be independent of the EC/HPC ratio, while the initial and final film thickness increased with increasing HPC fraction. A new method to estimate part of the binodal curve in the ternary phase diagram for EC/HPC in ethanol has been developed. The findings of this work provide a good understanding of the mechanisms responsible for the morphology development and allow tailoring of thin EC/HPC films structure for controlled drug release.
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9. |
- Carmona, Pierre, 1995, et al.
(author)
-
Structure evolution during phase separation in spin-coated ethylcellulose/hydroxypropylcellulose films
- 2021
-
In: Soft Matter. - : Royal Society of Chemistry. - 1744-683X .- 1744-6848. ; 17:14, s. 3913-3922
-
Journal article (peer-reviewed)abstract
- Porous phase-separated films made of ethylcellulose (EC) and hydroxypropylcellulose (HPC) are commonly used for controlled drug release. The structure of these thin films is controlling the drug transport from the core to the surrounding liquids in the stomach or intestine. However, detailed understanding of the time evolution of these porous structures as they are formed remains elusive. In this work, spin-coating, a widely applied technique for making thin uniform polymer films, was used to mimic the industrial manufacturing process. The focus of this work was on understanding the structure evolution of phase-separated spin-coated EC/HPC films. The structure evolution was determined using confocal laser scanning microscopy (CLSM) and image analysis. In particular, we determined the influence of spin-coating parameters and EC : HPC ratio on the final phase-separated structure and the film thickness. The film thickness was determined by profilometry and it influences the ethanol solvent evaporation rate and thereby the phase separation kinetics. The spin speed was varied between 1000 and 10 000 rpm and the ratio of EC : HPC in the polymer blend was varied between 78 : 22 wt% and 40 : 60 wt%. The obtained CLSM micrographs showed phase separated structures, typical for the spinodal decomposition phase separation mechanism. By using confocal laser scanning microscopy combined with Fourier image analysis, we could extract the characteristic length scale of the phase-separated final structure. Varying spin speed and EC : HPC ratio gave us precise control over the characteristic length scale and the thickness of the film. The results showed that the characteristic length scale increases with decreasing spin speed and with increasing HPC ratio. The thickness of the spin-coated film decreases with increasing spin speed. It was found that the relation between film thickness and spin speed followed the Meyerhofer equation with an exponent close to 0.5. Furthermore, good correlations between thickness and spin speed were found for the compositions 22 wt% HPC, 30 wt% HPC and 45 wt% HPC. These findings give a good basis for understanding the mechanisms responsible for the morphology development and increase the possibilities to tailor thin EC/HPC film structures.
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10. |
- Carmona, Pierre, 1995, et al.
(author)
-
Structure formation and coarsening kinetics of phase-separated spin-coated ethylcellulose/hydroxypropylcellulose films
- 2022
-
In: Soft Matter. - : Royal Society of Chemistry. - 1744-683X .- 1744-6848. ; 18:16, s. 3206-3217
-
Journal article (peer-reviewed)abstract
- Porous phase-separated ethylcellulose/hydroxypropylcellulose (EC/HPC) films are used to control drug transport from pharmaceutical pellets. The drug transport rate is determined by the structure of the porous films that are formed as water-soluble HPC leaches out. However, a detailed understanding of the evolution of the phase-separated structure in the films is lacking. In this work, we have investigated EC/HPC films produced by spin-coating, mimicking the industrial fluidized bed spraying. The aim was to investigate film structure evolution and coarsening kinetics during solvent evaporation. The structure evolution was characterized using confocal laser scanning microscopy and image analysis. The effect of the EC:HPC ratio (15 to 85 wt% HPC) on the structure evolution was determined. Bicontinuous structures were found for 30 to 40 wt% HPC. The growth of the characteristic length scale followed a power law, L(t) ∼ t(n), with n ∼ 1 for bicontinuous structures, and n ∼ 0.45-0.75 for discontinuous structures. The characteristic length scale after kinetic trapping ranged between 3.0 and 6.0 μm for bicontinuous and between 0.6 and 1.6 μm for discontinuous structures. Two main coarsening mechanisms could be identified: interfacial tension-driven hydrodynamic growth for bicontinuous structures and diffusion-driven coalescence for discontinuous structures. The 2D in-plane interface curvature analysis showed that the mean curvature decreased as a function of time for bicontinuous structures, confirming that interfacial tension is driving the growth. The findings of this work provide a good understanding of the mechanisms responsible for morphology development and open for further tailoring of thin EC/HPC film structures for controlled drug release. © 2022 The Royal Society of Chemistry
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11. |
- Comas, Carles, et al.
(author)
-
A third order point process characteristic for multi-type point processes
- 2010
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In: Statistica Neerlandica. - : Wiley. - 0039-0402 .- 1467-9574. ; 64:1, s. 19-44
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Journal article (peer-reviewed)abstract
- The description and analysis of spatial point patterns have mainly been based on first- and second-order characteristics. However, and especially when analyzing complex and multivariate point patterns, the use of higher-order characteristics would be more informative. In this paper, we introduce a third-order characteristic for multi-type point processes, which is based on the number of r-close triples of points, where the three points are of three different types (species). This characteristic is useful, when the second-order characteristics indicate that the three point patterns are pairwise uncorrelated but there is some relationship between triples of points. Furthermore, we conjecture that the new statistic can be used to test independence between the three point processes.
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12. |
- Cronie, Ottmar, 1979, et al.
(author)
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Some edge correction methods for marked spatio-temporal point process models
- 2011
-
In: Computational Statistics & Data Analysis. - : Elsevier BV. - 0167-9473 .- 1872-7352. ; 55:7, s. 2209-2220
-
Journal article (peer-reviewed)abstract
- Three edge correction methods for (marked) spatio-temporal point processes are proposed. They are all based on the idea of placing an approximated expected behaviour of the process at hand (simulated realisations) outside the study region which interacts with the data during the estimation. These methods are applied to the so-called growth-interaction model. The specific choices of growth function and interaction function made are purely motivated by the forestry applications considered. The parameters of the growth and interaction functions, i.e. the parameters related to the development of the marks, are estimated using the least-squares approach together with the proposed edge corrections. Finally, the edge corrected estimation methods are applied to a data set of Swedish Scots pine.
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13. |
- Eckel, Stefanie, et al.
(author)
-
Modelling tree roots in mixed forest stands by inhomogeneous marked Gibbs point processes.
- 2009
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In: Biometrical Journal. - 1521-4036 .- 0323-3847. ; 51, s. 522-539
-
Journal article (peer-reviewed)abstract
- The aim of the paper is to apply point processes to root data modelling. We propose a new approach to parametric inference when data are inhomogeneous replicated marked point patterns.} We generalize Geyer's saturation point process to a model which combines inhomogeneity, marks and interaction between the marked points. Furthermore, the inhomogeneity influences the definition of the neighborhood of points. Using the maximum pseudolikelihood method, this model is then fitted to root data from mixed stands of Norway spruce (Picea abies (L.) Karst.) and European beech (Fagus sylvatica L.) to quantify the degree of root aggregation in such mixed stands. According to the analysis there is no evidence that the two root systems are dependent.
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14. |
- Fager, Cecilia, 1990, et al.
(author)
-
Optimization of FIB-SEM Tomography and Reconstruction for Soft, Porous, and Poorly Conducting Materials
- 2020
-
In: Microscopy and Microanalysis. - 1431-9276 .- 1435-8115. ; 26:4, s. 837-845
-
Journal article (peer-reviewed)abstract
- Tomography using a focused ion beam (FIB) combined with a scanning electron microscope (SEM) is well-established for a wide range of conducting materials. However, performing FIB-SEM tomography on ion- and electron-beam-sensitive materials as well as poorly conducting soft materials remains challenging. Some common challenges include cross-sectioning artifacts, shadowing effects, and charging. Fully dense materials provide a planar cross section, whereas pores also expose subsurface areas of the planar cross-section surface. The image intensity of the subsurface areas gives rise to overlap between the grayscale intensity levels of the solid and pore areas, which complicates image processing and segmentation for three-dimensional (3D) reconstruction. To avoid the introduction of artifacts, the goal is to examine porous and poorly conducting soft materials as close as possible to their original state. This work presents a protocol for the optimization of FIB-SEM tomography parameters for porous and poorly conducting soft materials. The protocol reduces cross-sectioning artifacts, charging, and eliminates shadowing effects. In addition, it handles the subsurface and grayscale intensity overlap problems in image segmentation. The protocol was evaluated on porous polymer films which have both poor conductivity and pores. 3D reconstructions, with automated data segmentation, from three films with different porosities were successfully obtained.
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15. |
- Ghorbanpour, F., et al.
(author)
-
Marked point process analysis of epidermal nerve fibres
- 2021
-
In: Journal of Microscopy. - : Wiley. - 0022-2720 .- 1365-2818. ; 283:1, s. 41-50
-
Journal article (peer-reviewed)abstract
- Epidermal nerve fibre (ENF) density and summed length of ENFs per epidermal surface area are reduced, and ENFs may appear more clustered within the epidermis in subjects suffering from diabetic neuropathy compared to healthy subjects. Therefore, it is important to understand the spatial behaviour of ENFs in healthy and neuropathy subjects. By using confocal microscopy data , we study the spatial structure of epidermal nerves by regarding the nerve tree locations as realizations of marked point processes . The termination points of the fibres of a nerve tree are used to define a reactive territory which is taken as a mark for the nerve tree location. We study the differences in the spatial pattern of ENFs between healthy subjects and subjects suffering from mild diabetic neuropathy by using Ripley's K function and the mark correlation function. In addition, we propose a marked sequential point process model for the nerve tree locations. Data are replicated point patterns, where we have several patterns from each subject and from each group.
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16. |
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17. |
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18. |
- Grabarnik, P., et al.
(author)
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Modelling the spatial and space-time structure of forest stands: How to model asymmetric interaction between neighbouring trees
- 2011
-
In: Procedia Environmental Sciences. 1st International Conference on Spatial Statistics 2011,Enschede,23-25 March 2011. - : Elsevier BV. - 1878-0296. ; 7, s. 62-67
-
Conference paper (peer-reviewed)abstract
- Spatial relationships between trees play an important role in forest ecosystem and its dynamics. These relationships determine how much of the common resources are available for an individual tree and influence the growth and mortality of the tree through a competition process. The way how plants share the available resources determines the mode of competition. In the case where a tree influences another tree but not vice versa we speak about asymmetric competition, otherwise competition is symmetric. When modelling interactions between neighbouring trees it is natural to assume that the size of a tree determines its hierarchical level: the largest trees are not influenced by any other trees than the trees in the same size class, while trees in the other size classes are influenced by the other trees in the same class as well as by all larger trees. Thus, in general there are both kind of interaction between trees: symmetric and asymmetric. We take an approach to quantify the strength of the competition process between the trees which is based on the hierarchy of trees. The space-time model considered here is based on a spatial point process with time-dependent marks where the asymmetric competition is incorporated into the model by interaction kernels.
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19. |
- Grabarnik, Pavel, et al.
(author)
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Modelling the spatial structure of forest stands by multivariate point processes with hierarchical interactions
- 2009
-
In: Ecological Modelling. - 0304-3800. ; 220, s. 1232-1240
-
Journal article (peer-reviewed)abstract
- A stochastic model is applied to describe the spatial structure of a forest stand. We aim at quantifying the strength of the competition process between the trees in terms of interaction within and between different size classes of trees using multivariate Gibbs point processes with hierarchical interactions introduced by Högmander and Särkkä (1999). The new model overcomes the main limitation of the traditional use of the Gibbs models allowing to describe systems with non-symmetric interactions between different objects. When analyzing interactions between neighbouring trees it is natural to assume that the size of a tree determines its hierarchical level: the largest trees are not influenced by any other trees than the trees in the same size class, while trees in the other size classes are influenced by the other trees in the same class as well as by all larger trees. In this paper, we describe a wide range of Gibbs models with both hierarchical and non-hierarchical interactions as well as a simulation algorithm and a parameter estimation procedure for the hierarchical models. We apply the hierarchical interaction model to the analysis of forest data consisting of locations and diameters of tree stems.
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20. |
- Guttorp, P., et al.
(author)
-
Assessing the Uncertainty in Projecting Local Mean Sea Level from Global Temperature
- 2014
-
In: Journal of Applied Meteorology and Climatology. - : American Meteorological Society. - 1558-8424 .- 1558-8432. ; 53:9, s. 2163-2170
-
Journal article (peer-reviewed)abstract
- The process of moving from an ensemble of global climate model temperature projections to local sea level projections requires several steps. Sea level was estimated in Olympia, Washington (a city that is very concerned with sea level rise because parts of downtown are barely above mean highest high tide), by relating global mean temperature to global sea level; relating global sea level to sea levels at Seattle, Washington; and finally relating Seattle to Olympia. There has long been a realization that accurate assessment of the precision of projections is needed for science-based policy decisions. When a string of statistical and/or deterministic models is connected, the uncertainty of each individual model needs to be accounted for. Here the uncertainty is quantified for each model in the described system and the total uncertainty is assessed in a cascading effect throughout the system. The projected sea level rise over time and its total estimated uncertainty are visualized simultaneously for the years 2000-2100, the increased uncertainty due to each of the component models at a particular projection year is identified, and estimates of the time at which a certain sea level rise will first be reached are made.
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21. |
- Häbel, Henrike, 1987, et al.
(author)
-
A three-dimensional anisotropic point process characterization for pharmaceutical coatings
- 2017
-
In: Spatial Statistics. - : Elsevier BV. - 2211-6753. ; 22:2, s. 306-320
-
Journal article (peer-reviewed)abstract
- © 2017 Elsevier B.V. Spatial characterization and modeling of the structure of a material may provide valuable knowledge on its properties and function. Especially, for a drug formulation coated with a polymer film, understanding the relationship between pore structure and drug release properties is important to optimize the coating film design. Here, we use methods from image analysis and spatial statistics to characterize and model the pore structure in pharmaceutical coatings. More precisely, we use and develop point process theory to characterize the branching structure of a polymer blended film with data from confocal laser scanning microscopy. Point patterns, extracted by identifying branching points of pore channels, are both inhomogeneous and anisotropic. Therefore, we introduce a directional version of the inhomogeneous K-function to study the anisotropy and then suggest two alternative ways to model the anisotropic three-dimensional structure. First, we apply a linear transformation to the data such that it appears isotropic and subsequently fit isotropic inhomogeneous Strauss or Lennard-Jones models to the transformed pattern. Second, we include the anisotropy directly in a Lennard-Jones and a more flexible step-function model with anisotropic pair-potential functions. The methods presented will be useful for anisotropic inhomogeneous point patterns in general and for characterizing porous material in particular.
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22. |
- Häbel, Henrike, 1987, et al.
(author)
-
Characterization of pore structure of polymer blended films used for controlled drug release
- 2016
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In: Journal of Controlled Release. - : Elsevier BV. - 0168-3659 .- 1873-4995. ; 222, s. 151-158
-
Journal article (peer-reviewed)abstract
- The characterization of the pore structure in pharmaceutical coatings is crucial for understanding and controlling mass transport properties and function in controlled drug release. Since the drug release rate can be associated with the film permeability, the effect of the pore structure on the permeability is important to study. In this paper, a new approach for characterizing the pore structure in polymer blended films was developed based on an image processing procedure for given two-dimensional scanning electron microscopy images of film cross-sections. The focus was on different measures for characterizing the complexity of the shape of a pore. The pore characterization developed was applied to ethyl cellulose (EC) and hydroxypropyl cellulose (HPC) blended films, often used as pharmaceutical coatings, where HPC acts as the pore former. It was studied how two different HPC viscosity grades influence the pore structure and, hence, mass transport through the respective films. The film with higher HPC viscosity grade had been observed to be more permeable than the other in a previous study; however, experiments had failed to show a difference between their pore structures. By instead characterizing the pore structures using tools from image analysis, statistically significant differences in pore area fraction and pore shape were identified. More specifically, it was found that the more permeable film with higher HPC viscosity grade seemed to have more extended and complex pore shapes than the film with lower HPC viscosity grade. This result indicates a greater degree of connectivity in the film with higher permeability and statistically confirms hypotheses on permeability from related experimental studies.
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23. |
- Häbel, Henrike, 1987, et al.
(author)
-
Colloidal particle aggregation in three dimensions
- 2019
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In: Journal of Microscopy. - : Wiley. - 0022-2720 .- 1365-2818. ; 275:3, s. 149-158
-
Journal article (peer-reviewed)abstract
- Colloidal systems are of importance not only for everyday products, but also for the development of new advanced materials. In many applications, it is crucial to understand and control colloidal interaction. In this paper, we study colloidal particle aggregation of silica nanoparticles, where the data are given in a three-dimensional micrograph obtained by high-angle annular dark field scanning transmission electron microscopy tomography. We investigate whether dynamic models for particle aggregation, namely the diffusion limited cluster aggregation and the reaction limited cluster aggregation models, can be used to construct structures present in the scanning transmission electron microscopy data. We compare the experimentally obtained silica aggregate to the simulated postaggregated structures obtained by the dynamic models. In addition, we fit static Gibbs point process models, which are commonly used models for point patterns with interactions, to the silica data. We were able to simulate structures similar to the silica structures by using Gibbs point process models. By fitting Gibbs models to the simulated cluster aggregation patterns, we saw that a smaller probability of aggregation would be needed to construct structures similar to the observed silica particle structure.
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24. |
- Häbel, Henrike, 1987, et al.
(author)
-
From static micrographs to particle aggregation dynamics in three dimensions
- 2016
-
In: Journal of Microscopy. - : Wiley. - 1365-2818 .- 0022-2720. ; 262:1, s. 102-111
-
Journal article (peer-reviewed)abstract
- Studies on colloidal aggregation have brought forth theories on stability of colloidal gels and models for aggregation dynamics. Still, a complete link between developed frameworks and obtained laboratory observations has to be found. In this work, aggregates of silica nanoparticles (20 nm) are studied using diffusion limited cluster aggregation (DLCA) and reaction limited cluster aggregation (RLCA) models. These processes are driven by the probability of particles to aggregate upon collision. This probability of aggregation is one in the DLCA and close to zero in the RLCA process. We show how to study the probability of aggregation from static micrographs on the example of a silica nanoparticle gel at 9 wt%. The analysis includes common summary functions from spatial statistics, namely the empty space function and Ripley's K-function, as well as two newly developed summary functions for cluster analysis based on graph theory. One of the new cluster analysis functions is related to the clustering coefficient in communication networks and the other to the size of a cluster. All four topological summary statistics are used to quantitatively compare in plots and in a least-square approach experimental data to cluster aggregation simulations with decreasing probabilities of aggregation. We study scanning transmission electron micrographs and utilize the intensity - mass thickness relation present in such images to create comparable micrographs from three-dimensional simulations. Finally, a characterization of colloidal silica aggregates and simulated structures is obtained, which allows for an evaluation of the cluster aggregation process for different aggregation scenarios. As a result, we find that the RLCA process fits the experimental data better than the DLCA process.
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25. |
- Konstantinou, Konstantinos, 1993, et al.
(author)
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Pairwise interaction Markov model for 3D epidermal nerve fibre endings
- 2022
-
In: Journal of Microscopy. - : Wiley. - 1365-2818 .- 0022-2720. ; 288:1, s. 54-67
-
Journal article (peer-reviewed)abstract
- In this paper, the spatial arrangement and possible interactions between epidermal nerve fibre endings are investigated and modelled by using confocal microscopy data. We are especially interested in possible differences between patterns from healthy volunteers and patients suffering from mild diabetic neuropathy. The locations of the points, where nerves enter the epidermis, the first branching points and the points where the nerve fibres terminate, are regarded as realizations of spatial point processes. We propose an anisotropic point process model for the locations of the nerve fibre endings in three dimensions, where the points interact in cylindrical regions. First, the locations of end points in (Formula presented.) are modelled as clusters around the branching points and then, the model is extended to three dimensions using a pairwise interaction Markov field model with cylindrical neighbourhood for the z-coordinates conditioned on the planar locations of the points. We fit the model to samples taken from healthy subjects and subjects suffering from diabetic neuropathy. In both groups, after a hardcore radius, there is some attraction between the end points. However, the range and strength of attraction are not the same in the two groups. Performance of the model is evaluated by using a cylindrical version of Ripley's K function due to the anisotropic nature of the data. Our findings suggest that the proposed model is able to capture the 3D spatial structure of the end points.
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26. |
- Konstantinou, Konstantinos, 1993, et al.
(author)
-
Statistical modeling of diabetic neuropathy: Exploring the dynamics of nerve mortality
- 2023
-
In: Statistics in Medicine. - 0277-6715 .- 1097-0258. ; 42:23, s. 4128-4146
-
Journal article (peer-reviewed)abstract
- Diabetic neuropathy is a disorder characterized by impaired nerve function and reduction of the number of epidermal nerve fibers per epidermal surface. Additionally, as neuropathy related nerve fiber loss and regrowth progresses over time, the two-dimensional spatial arrangement of the nerves becomes more clustered. These observations suggest that with development of neuropathy, the spatial pattern of diminished skin innervation is defined by a thinning process which remains incompletely characterized. We regard samples obtained from healthy controls and subjects suffering from diabetic neuropathy as realisations of planar point processes consisting of nerve entry points and nerve endings, and propose point process models based on spatial thinning to describe the change as neuropathy advances. Initially, the hypothesis that the nerve removal occurs completely at random is tested using independent random thinning of healthy patterns. Then, a dependent parametric thinning model that favors the removal of isolated nerve trees is proposed. Approximate Bayesian computation is used to infer the distribution of the model parameters, and the goodness-of-fit of the models is evaluated using both non-spatial and spatial summary statistics. Our findings suggest that the nerve mortality process changes as neuropathy advances.
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27. |
- Konstantionu, Konstantinos, et al.
(author)
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Spatial modeling of epidermal nerve fiber patterns
- 2021
-
In: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 40:29, s. 6479-6500
-
Journal article (peer-reviewed)abstract
- Peripheral neuropathy is a condition associated with poor nerve functionality. Epidermal nerve fiber (ENF) counts per epidermal surface are dramatically reduced and the two-dimensional (2D) spatial structure of ENFs tends to become more clustered as neuropathy progresses. Therefore, studying the spatial structure of ENFs is essential to fully understand the mechanisms that guide those morphological changes. In this article, we compare ENF patterns of healthy controls and subjects suffering from mild diabetic neuropathy by using suction skin blister specimens obtained from the right foot. Previous analysis of these data has focused on the analysis and modeling of the spatial ENF patterns consisting of the points where the nerves enter the epidermis, base points, and the points where the nerve fibers terminate, end points, projected on a 2D plane, regarding the patterns as realizations of spatial point processes. Here, we include the first branching points, the points where the nerve trees branch for the first time, and model the three-dimensional (3D) patterns consisting of these three types of points. To analyze the patterns, spatial summary statistics are used and a new epidermal active territory that measures the volume in the epidermis that is covered by the individual nerve fibers is constructed. We developed a model for both the 2D and the 3D patterns including the branching points. Also, possible competitive behavior between individual nerves is examined. Our results indicate that changes in the ENFs spatial structure can more easily be detected in the later parts of the ENFs.
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28. |
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29. |
- Kühlmann-Berenzon, Sharon, 1971, et al.
(author)
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An additive edge correction for the influence potential of trees
- 2005
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In: Biometrical Journal. - : Wiley. - 1521-4036 .- 0323-3847. ; 47:4, s. 517-526
-
Journal article (peer-reviewed)abstract
- The influence potential on a quadrat (IPQ) is an index for measuring the ecological effect that trees have on understory vegetation observed in a quadrat of a plot. IPQ is defined as the sum of the effect of every trees in the plot, where the effect depends on the size of the tree and the distance between the tree and the quadrat. Since only the trees in the plot have been observed and not the trees outside the plot, the true IPQ may be underestimated. Existing edge corrections are not appropriate for this case. We propose a correction that consists of adding the expected IPQ due to effects of trees outside the plot to the observed IPQ. The expectation is obtained by applying the Campbell theorem for stationary marked point processes. Data from the 1985-86 National Forest Inventory of Finland was used to calculate IPQ for six quadrats systematically allocated to each of 1240 plots. The implementation of the correction for this data is described. The distributions of IPQ with and without the correction proved the existence of edge effects and the effectiveness of the correction to eliminate the bias. This method has the potential to be applied to other additive functions. © 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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30. |
- Kuronen, M., et al.
(author)
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Hierarchical log Gaussian Cox process for regeneration in uneven-aged forests
- 2022
-
In: Environmental and Ecological Statistics. - : Springer Science and Business Media LLC. - 1352-8505 .- 1573-3009. ; 29:1, s. 185-205
-
Journal article (peer-reviewed)abstract
- We propose a hierarchical log Gaussian Cox process (LGCP) for point patterns, where a set of points x affects another set of points y but not vice versa. We use the model to investigate the effect of large trees on the locations of seedlings. In the model, every point in x has a parametric influence kernel or signal, which together form an influence field. Conditionally on the parameters, the influence field acts as a spatial covariate in the intensity of the model, and the intensity itself is a non-linear function of the parameters. Points outside the observationwindowmay affect the influence field inside the window. We propose an edge correction to account for this missing data. The parameters of the model are estimated in a Bayesian framework using Markov chain Monte Carlo where a Laplace approximation is used for the Gaussian field of the LGCP model. The proposed model is used to analyze the effect of large trees on the success of regeneration in uneven-aged forest stands in Finland.
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31. |
- Kuronen, M., et al.
(author)
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Point process models for sweat gland activation observed with noise
- 2021
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In: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 40:8, s. 2055-2072
-
Journal article (peer-reviewed)abstract
- The aim of this article is to construct spatial models for the activation of sweat glands for healthy subjects and subjects suffering from peripheral neuropathy by using videos of sweating recorded from the subjects. The sweat patterns are regarded as realizations of spatial point processes and two point process models for the sweat gland activation and two methods for inference are proposed. Several image analysis steps are needed to extract the point patterns from the videos and some incorrectly identified sweat gland locations may be present in the data. To take into account the errors, we either include an error term in the point process model or use an estimation procedure that is robust with respect to the errors.
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32. |
- Lautensack, Claudia, et al.
(author)
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Modeling the microstructure of sintered copper
- 2006
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In: Proceedings of the Stereology, Spatial Statistics and Stochastic Geometry 6th International Conference Prague, Czech Republic, 26-29 June 2006.
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Conference paper (peer-reviewed)
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33. |
- Lee, Adél, et al.
(author)
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Characterizing cross-subject spatial interaction patterns in functional magnetic resonance imaging studies: A two-stage point-process model
- 2017
-
In: Biometrical Journal. - : Wiley. - 0323-3847 .- 1521-4036. ; 59, s. 1352-1381
-
Journal article (peer-reviewed)abstract
- © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim We develop a two-stage spatial point process model that introduces new characterizations of activation patterns in multisubject functional Magnetic Resonance Imaging (fMRI) studies. Conventionally multisubject fMRI methods rely on combining information across subjects one voxel at a time in order to identify locations of peak activation in the brain. The two-stage model that we develop here addresses shortcomings of standard methods by explicitly modeling the spatial structure of functional signals and recognizing that corresponding cross-subject functional signals can be spatially misaligned. In our first stage analysis, we introduce a marked spatial point process model that captures the spatial features of the functional response and identifies a configuration of activation units for each subject. The locations of these activation units are used as input for the second stage model. The point process model of the second stage analysis is developed to characterize multisubject activation patterns by estimating the strength of cross-subject interactions at different spatial ranges. The model uses spatial neighborhoods to account for the cross-subject spatial misalignment in corresponding functional units. We applied our methods to an fMRI study of 21 individuals who performed an attention test. We identified four brain regions that are involved in the test and found that our model results agree well with our understanding of how these regions engage with the tasks performed during the attention test. Our results highlighted that cross-subject interactions are stronger in brain areas that have a more specific function in performing the experimental tasks than in other areas.
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34. |
- Longfils, Marco, 1990, et al.
(author)
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Raster Image Correlation Spectroscopy Performance Evaluation
- 2019
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In: Biophysical Journal. - : Elsevier BV. - 0006-3495 .- 1542-0086. ; 117:10, s. 1900-1914
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Journal article (peer-reviewed)abstract
- Raster image correlation spectroscopy (RICS) is a fluorescence image analysis method for extracting the mobility, concentration, and stoichiometry of diffusing fluorescent molecules from confocal image stacks. The method works by calculating a spatial correlation function for each image and analyzing the average of those by model fitting. Rules of thumb exist for RICS image acquisitioning, yet a rigorous theoretical approach to predict the accuracy and precision of the recovered parameters has been lacking. We outline explicit expressions to reveal the dependence of RICS results on experimental parameters. In terms of imaging settings, we observed that a twofold decrease of the pixel size, e.g., from 100 to 50 nm, decreases the error on the translational diffusion constant (D) between three- and fivefold. For D = 1 mu m(2) s(-1), a typical value for intracellular measurements, similar to 25-fold lower mean-squared relative error was obtained when the optimal scan speed was used, although more drastic improvements were observed for other values of D. We proposed a slightly modified RICS calculation that allows correcting for the significant bias of the autocorrelation function at small (<<50 x 50 pixels) sizes of the region of interest. In terms of sample properties, at molecular brightness E = 100 kHz and higher, RICS data quality was sufficient using as little as 20 images, whereas the optimal number of frames for lower E scaled pro rata. RICS data quality was constant over the nM-mM concentration range. We developed a bootstrap-based confidence interval of D that outperformed the classical leastsquares approach in terms of coverage probability of the true value of D. We validated the theory via in vitro experiments of enhanced green fluorescent protein at different buffer viscosities. Finally, we outline robust practical guidelines and provide free software to simulate the parameter effects on recovery of the diffusion coefficient.
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35. |
- Longfils, Marco, 1990, et al.
(author)
-
Single particle raster image analysis of diffusion
- 2017
-
In: Journal of Microscopy. - : Wiley. - 0022-2720 .- 1365-2818. ; 266:1, s. 3-14
-
Journal article (peer-reviewed)abstract
- As a complement to the standard RICS method of analysing Raster Image Correlation Spectroscopy images with estimation of the image correlation function, we introduce the method SPRIA, Single Particle Raster Image Analysis. Here, we start by identifying individual particles and estimate the diffusion coefficient for each particle by a maximum likelihood method. Averaging over the particles gives a diffusion coefficient estimate for the whole image. In examples both with simulated and experimental data, we show that the new method gives accurate estimates. It also gives directly standard error estimates. The method should be possible to extend to study heterogeneous materials and systems of particles with varying diffusion coefficient, as demonstrated in a simple simulation example. A requirement for applying the SPRIA method is that the particle concentration is low enough so that we can identify the individual particles. We also describe a bootstrap method for estimating the standard error of standard RICS.
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36. |
- Longfils, Marco, 1990, et al.
(author)
-
Single particle raster image analysis of diffusion for particle mixtures
- 2018
-
In: Journal of Microscopy. - : Wiley. - 0022-2720 .- 1365-2818. ; 269:3, s. 269-281
-
Journal article (peer-reviewed)abstract
- Recently we complemented the raster image correlation spectroscopy (RICS) method of analysing raster images via estimation of the image correlation function with the method single particle raster image analysis (SPRIA). In SPRIA, individual particles are identified and the diffusion coefficient of each particle is estimated by a maximum likelihood method. In this paper, we extend the SPRIA method to analyse mixtures of particles with a finite set of diffusion coefficients in a homogeneous medium. In examples with simulated and experimental data with two and three different diffusion coefficients, we show that SPRIA gives accurate estimates of the diffusion coefficients and their proportions. A simple technique for finding the number of different diffusion coefficients is also suggested. Further, we study the use of RICS for mixtures with two different diffusion coefficents and investigate, by plotting level curves of the correlation function, how large the quotient between diffusion coefficients needs to be in order to allow discrimination between models with one and two diffusion coefficients. We also describe a minor correction (compared to published papers) of the RICS autocorrelation function. Lay description Diffusion is a key mass transport mechanism for small particles. Efficient methods for estimating diffusion coefficients are crucial for analysis of microstructures, for example in soft biomaterials. The sample of interest may consist of a mixture of particles with different diffusion coefficients. Here, we extend a method called Single Particle Raster Image Analysis (SPRIA) to account for particle mixtures and estimation of the diffusion coefficients of the mixture components. SPRIA combines elements of classical single particle tracking methods with utilizing the raster scan with which images obtained by using a confocal laser scanning microscope. In particular, single particles are identified and their motion estimated by following their center of mass. Thus, an estimate of the diffusion coefficient will be obtained for each particle. Then, we analyse the distribution of the estimated diffusion coefficients of the population of particles, which allows us to extract information about the diffusion coefficients of the underlying components in the mixture. On both simulated and experimental data with mixtures consisting of two and three components with different diffusion coefficients, SPRIA provides accurate estimates and, with a simple criterion, the correct number of mixture components is selected in most cases.
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37. |
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38. |
- Myllymaki, M., et al.
(author)
-
Analysis of spatial structure of epidermal nerve entry point patterns based on replicated data
- 2012
-
In: Journal of Microscopy. - : Wiley. - 0022-2720 .- 1365-2818. ; 247:3, s. 228-239
-
Journal article (peer-reviewed)abstract
- Epidermal nerve fiber (ENF) density and morphology are used to diagnose small fiber involvement in diabetic, HIV, chemotherapy induced, and other neuropathies. ENF density and summed length of ENFs per epidermal surface area are reduced, and ENFs may appear clustered within the epidermis in subjects with small fiber neuropathy compared to healthy subjects. Therefore, it is important to understand the spatial behaviour of ENFs in healthy and diseased subjects. This work investigates the spatial structure of ENF entry points, which are locations where the nerves enter the epidermis (the outmost living layer of the skin). The study is based on suction skin blister specimens from two body locations of 25 healthy subjects. The ENF entry points are regarded as a realization of a spatial point process and a second-order characteristic, namely Ripleys K function, is used to investigate the effect of covariates (e.g. gender) on the degree of clustering of ENF entry points. First, the effects of covariates are evaluated by means of pooled K functions for groups and, secondly, the statistical significance of the effects and individual variation are characterized by a mixed model approach. Based on our results the spatial pattern of ENFs in samples taken from calf is affected by the covariates but not in samples taken from foot.
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39. |
- Myllymäki, Mari, et al.
(author)
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Hierarchical second-order analysis of replicated spatial point patterns with non-spatial covariates
- 2014
-
In: Spatial Statistics. - : Elsevier BV. - 2211-6753. ; 8, s. 104-121
-
Journal article (peer-reviewed)abstract
- In this paper we propose a method for incorporating the effect of non-spatial covariates into the spatial second-order analysis of replicated point patterns. The variance stabilizing transformation of Ripley’s K function is used to summarize the spatial arrangement of points, and the relationship between this summary function and covariates is modelled by hierarchical Gaussian process regression. In particular, we investigate how disease status and some other covariates affect the level and scale of clustering of epidermal nerve fibres. The data are point patterns with replicates extracted from skin blister samples taken from 47 subjects.
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40. |
- Olsbo, Viktor, 1972, et al.
(author)
-
Development and evaluation of spatial point process models for epidermal nerve fibers
- 2013
-
In: Mathematical Biosciences. - : Elsevier BV. - 0025-5564 .- 1879-3134. ; 243:2, s. 178-189
-
Journal article (peer-reviewed)abstract
- We propose two spatial point process models for the spatial structure of epidermal nerve fibers (ENFs) across human skin. The models derive from two point processes, Φb and Φe, describing the locations of the base and end points of the fibers. Each point of Φe (the end point process) is connected to a unique point in Φb (the base point process). In the first model, both Φe and Φb are Poisson processes, yielding a null model of uniform coverage of the skin by end points and general baseline results and reference values for moments of key physiologic indicators. The second model provides a mechanistic model to generate end points for each base, and we model the branching structure more directly by defining Φe as a cluster process conditioned on the realization of Φb as its parent points. In both cases, we derive distributional properties for observable quantities of direct interest to neurologists such as the number of fibers per base, and the direction and range of fibers on the skin. We contrast both models by fitting them to data from skin blister biopsy images of ENFs and provide inference regarding physiological properties of ENFs.
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41. |
- Pommerening, A., et al.
(author)
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What mark variograms tell about spatial plant interactions
- 2013
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In: Ecological Modelling. - : Elsevier BV. - 0304-3800. ; 251, s. 64-72
-
Journal article (peer-reviewed)abstract
- Many if not all data collected in ecology have both a spatial as well as a temporal dimension. This suggests the use of summary characteristics from spatial statistics to gain more refined insight into plant interactions. Spatial tree data can for example be considered as point patterns (formed by tree locations) with attached marks (e.g. tree sizes). If only the pattern of tree locations is of interest one can use for example the pair correlation function. If in addition, the sizes (or some other characteristics) of trees or other plants are of interest, marked summary statistics can be more suitable. In this paper, we propose the so-called mark variogram as a useful tool in ecological studies. This summary characteristic basically indicates how similar two plants within a certain distance from each other are. For example, if two plants are approximately of the same size, the mark variogram has small values, and if their sizes differ somewhat, the mark variogram has large values. Recently, there has been a lot of discussion on how to interpret the shape of mark variograms caused by pairs of plants with different sizes at close proximity. Such variogram shapes exhibiting so-called negative autocorrelation, another expression for high small-scaled size diversity, are assumed to indicate strong competition between plants. In this study, we reconstructed two spatial tree time series where negative autocorrelation has occurred and also simulated four alternative forest development paths in order to experimentally explore the causes of negative autocorrelation. Interestingly the results highlighted that man-made disturbances (e.g. thinnings) often result in a significant number of pairs of large and small trees at close proximity leading to negative autocorrelation. We could also show that whilst negative autocorrelation can be the consequence of natural forest development including competition processes, it can, however, also be the consequence of disturbances and of subsequent colonisation by small trees. Since disturbances play an important role in the development of negative autocorrelation, the mark variogram is a key summary characteristic in disturbance ecology.
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42. |
- Rajala, Tuomas, et al.
(author)
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A review on anisotropy analysis of spatial point patterns
- 2018
-
In: Spatial Statistics. - : Elsevier BV. - 2211-6753. ; 28, s. 141-168
-
Research review (peer-reviewed)abstract
- A spatial point pattern is called anisotropic if its spatial structure depends on direction. Several methods for anisotropy analysis have been introduced in the literature. In this paper, we give an overview of nonparametric methods for anisotropy analysis of (stationary) point patterns in ℝ2 andℝ3. We discuss methods based on nearest neighbour and second order summary statistics as well as spectral and wavelet analysis. All techniques are illustrated on both a clustered and a regular example. Finally, we discuss methods for testing for isotropy as well as for estimating preferred directions in a point pattern.
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43. |
- Rajala, Tuomas, 1981, et al.
(author)
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Estimating geometric anisotropy in spatial point patterns
- 2016
-
In: Spatial Statistics. - : Elsevier BV. - 2211-6753. ; 15, s. 100-114
-
Journal article (peer-reviewed)abstract
- Anisotropy in stationary spatial point patterns is investigated. We develop a two-stage non-parametric method for quantifying geometric anisotropy arising for example when the pattern is compressed or stretched. First, we fit ellipsoids to the pattern of pairwise difference vectors to estimate the direction of anisotropy. Then, we estimate the scale of anisotropy by identifying the back-transformation resulting in the most isotropic pattern. We demonstrate the applicability of the method mainly for regular patterns by numerical examples, and use it to improve the estimation of compression in 3D polar ice air bubble patterns.
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44. |
- Rajala, T., et al.
(author)
-
Tests for isotropy in spatial point patterns - A comparison of statistical indices
- 2022
-
In: Spatial Statistics. - : Elsevier BV. - 2211-6753. ; 52
-
Journal article (peer-reviewed)abstract
- Isotropy of a point process, defined as invariance of the distribu-tion under rotation, is often assumed in spatial statistics. Formal tests for the hypothesis of isotropy can be created by comparing directional summary statistics in different directions. In this pa-per, the statistical powers of tests based on a variety of summary statistics and several choices of deviance measures are compared in a simulation study. Four models for anisotropic point pro-cesses are considered covering both regular and clustered cases. We discuss the robustness of the results to changes of the tuning parameters, and highlight the strengths and limitations of the methods. (c) 2022 Elsevier B.V. All rights reserved.
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45. |
- Rajala, Tuomas, 1981, et al.
(author)
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Variational Bayes approach for classification of points in superpositions of point processes
- 2016
-
In: Spatial Statistics. - : Elsevier BV. - 2211-6753. ; 15, s. 85-99
-
Journal article (peer-reviewed)abstract
- We investigate the problem of classifying superpositions of spatial point processes. In particular, we are interested in realizations formed as a superposition of a regular point process and a Poisson point process. The aim is to decide which of the two processes each point belongs to. Recently, a Markov chain Monte Carlo (MCMC) approach was suggested by Redenbach et al. (2015), which however, is computationally heavy. In this paper, we will introduce a method based on variational Bayes approximation and compare its performance to the performance of a slightly refined version of the MCMC approach.
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46. |
- Redenbach, Claudia, et al.
(author)
-
Anisotropy analysis of pressed point processes
- 2009
-
In: Advances in Statistical Analysis. ; 93:3, s. 237-261
-
Journal article (peer-reviewed)abstract
- This paper introduces methods for the detection of anisotropies which are caused by compression of regular three-dimensional point patterns. Isotropy tests base d on directional summary statistics and estimators for the compression factor are developed. These allow not only for the detection of anisotropies but also for the estimation of their strength. Using simulated data the power of the methods and the dependence of the power on the intensity, the degree of regularity, and the compression strength are studied. The motivation of this paper is the investigation of anisotropies in the structure of polar ice. Therefore, our methods a re applied to the point patterns of centres of air pores extracted from tomographic images of ice cores. This way the presence of anisotropies in the ice caused by the compression of the ice sheet as well as an increase of their strength with increasing depth are shown.
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47. |
- Redenbach, Claudia, et al.
(author)
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Classification of Points in Superpositions of Strauss and Poisson Processes
- 2015
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In: Spatial Statistics. - : Elsevier BV. - 2211-6753. ; 12, s. 81-95
-
Journal article (peer-reviewed)abstract
- Consider a realisation of a point process which is formed as a superposition of a regular point process, here a Strauss process, and some Poisson noise. The aim of the current work is to decide which of the two processes each point belongs to. We construct an MCMC algorithm which estimates the parameters of the superposition model and obtains posterior probabilities for each point of being a Strauss point. The algorithm is evaluated in a simulation study. Finally, it is applied to our motivating data set containing the locations of air bubbles, some of which are noise, in an Antarctic ice core.
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48. |
- Redenbach, C., et al.
(author)
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Parameter estimation for growth interaction processes using spatio-temporal information
- 2013
-
In: Computational Statistics & Data Analysis. - : Elsevier BV. - 0167-9473. ; 57:1, s. 672-683
-
Journal article (peer-reviewed)abstract
- Methods for the parameter estimation for a spatio-temporal marked point process model, the so-called growth-interaction model, are investigated. Least squares estimation methods for this model found in the literature are only concerned with fitting the mark distribution observed in the data. These methods are unable to distinguish between models which have the same birth, death, interaction and growth functions and parameters but different arrival strategies for the points. Hence, they are extended such that the spatial structure of a point pattern is also taken into account. The suggested methods are evaluated in a simulation study and applied to a small data set from forestry.
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49. |
- Röding, Magnus, 1984, et al.
(author)
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Automatic Particle Detection in Microscopy Using Temporal Correlations
- 2013
-
In: Microscopy Research and Technique. - : Wiley. - 1059-910X .- 1097-0029. ; 76:10, s. 997-1006
-
Journal article (peer-reviewed)abstract
- One of the fundamental problems in the analysis of single particle tracking data is the detection of individual particle positions from microscopy images. Distinguishing true particles from noise with a minimum of false positives and false negatives is an important step that will have substantial impact on all further analysis of the data. A common approach is to obtain a plausible set of particles from a larger set of candidate particles by filtering using manually selected threshold values for intensity, size, shape, and other parameters describing a particle. This introduces subjectivity into the analysis and hinders reproducibility. In this paper, we introduce a method for automatic selection of these threshold values based on maximizing temporal correlations in particle count time series. We use Markov Chain Monte Carlo to find the threshold values corresponding to the maximum correlation, and we study several experimental data sets to assess the performance of the method in practice by comparing manually selected threshold values from several independent experts with automatically selected threshold values. We conclude that the method produces useful results, reducing subjectivity and the need for manual intervention, a great benefit being its easy integratability into many already existing particle detection algorithms.
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50. |
- Röding, Magnus, 1984, et al.
(author)
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Identifying directional persistence in intracellular particle motion using Hidden Markov Models
- 2014
-
In: Mathematical Biosciences. - : Elsevier BV. - 0025-5564 .- 1879-3134. ; 248, s. 140-145
-
Journal article (peer-reviewed)abstract
- Particle tracking is a widely used and promising technique for elucidating complex dynamics of the living cell. The cytoplasm is an active material, in which the kinetics of intracellular structures are highly heterogeneous. Tracer particles typically undergo a combination of random motion and various types of directed motion caused by the activity of molecular motors and other non-equilibrium processes. Random switching between more and less directional persistence of motion generally occurs. We present a method for identifying states of motion with different directional persistence in individual particle trajectories. Our analysis is based on a multi-scale turning angle model to characterize motion locally, together with a Hidden Markov Model with two states representing different directional persistence. We define one of the states by the motion of particles in a reference data set where some active processes have been inhibited. We illustrate the usefulness of the method by studying transport of vesicles along microtubules and transport of nanospheres activated by myosin. We study the results using mean square displacements, durations, and particle speeds within each state. We conclude that the method provides accurate identification of states of motion with different directional persistence, with very good agreement in terms of mean-squared displacement between the reference data set and one of the states in the two-state model.
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