Description Of Clusters Research Articles

Thermodynamics of mixtures of patchy and spherical colloids of different sizes: A multi-body association theory with complete reference fluid information.

We present a theory to predict the structure and thermodynamics of mixtures of colloids of different diameters, building on our earlier work [A. Bansal et al., J. Chem. Phys. 145, 074904 (2016)] that considered mixtures with all particles constrained to have the same size. The patchy, solvent particles have short-range directional interactions, while the solute particles have short-range isotropic interactions. The hard-sphere mixture without any association site forms the reference fluid. An important ingredient within the multi-body association theory is the description of clustering of the reference solvent around the reference solute. Here we account for the physical, multi-body clusters of the reference solvent around the reference solute in terms of occupancy statistics in a defined observation volume. These occupancy probabilities are obtained from enhanced sampling simulations, but we also present statistical mechanical models to estimate these probabilities with limited simulation data. Relative to an approach that describes only up to three-body correlations in the reference, incorporating the complete reference information better predicts the bonding state and thermodynamics of the physical solute for a wide range of system conditions. Importantly, analysis of the residual chemical potential of the infinitely dilute solute from molecular simulation and theory shows that whereas the chemical potential is somewhat insensitive to the description of the structure of the reference fluid, the energetic and entropic contributions are not, with the results from the complete reference approach being in better agreement with particle simulations.

α clustering in Si28 probed through the identification of high-lying 0+ states

Background: Aspects of the nuclear structure of light $\ensuremath{\alpha}$-conjugate nuclei have long been associated with nuclear clustering based on $\ensuremath{\alpha}$ particles and heavier $\ensuremath{\alpha}$-conjugate systems such as $^{12}\mathrm{C}$ and $^{16}\mathrm{O}$. Such structures are associated with strong deformation corresponding to superdeformed or even hyperdeformed bands. Superdeformed bands have been identified in $^{40}\mathrm{Ca}$ and neighboring nuclei and find good description within shell model, mean-field, and $\ensuremath{\alpha}$-cluster models. The utility of the $\ensuremath{\alpha}$-cluster description may be probed further by extending such studies to more challenging cases comprising lighter $\ensuremath{\alpha}$-conjugate nuclei such as $^{24}\mathrm{Mg}, ^{28}\mathrm{Si}$, and $^{32}\mathrm{S}$.Purpose: The purpose of this study is to look for the number and energy of isoscalar ${0}^{+}$ states in $^{28}\mathrm{Si}$. These states are the potential bandheads for superdeformed bands in $^{28}\mathrm{Si}$ corresponding to the exotic structures of $^{28}\mathrm{Si}$. Of particular interest is locating the ${0}^{+}$ bandhead of the previously identified superdeformed band in $^{28}\mathrm{Si}$.Methods: $\ensuremath{\alpha}$-particle inelastic scattering from a $^{\mathrm{nat}}\mathrm{Si}$ target at very forward angles including ${0}^{\ensuremath{\circ}}$ has been performed at the iThemba Laboratory for Accelerator-Based Sciences in South Africa. Scattered particles corresponding to the excitation energy region of 6 to 14 MeV were momentum-analysed in the K600 magnetic spectrometer and detected at the focal plane using two multiwire drift chambers and two plastic scintillators.Results: Several ${0}^{+}$ states have been identified above 9 MeV in $^{28}\mathrm{Si}$. A newly identified 9.71 MeV ${0}^{+}$ state is a strong candidate for the bandhead of the previously discussed superdeformed band. The multichannel dynamical symmetry of the semimicroscopic algebraic model predicts the spectrum of the excited ${0}^{+}$ states. The theoretical prediction is in good agreement with the experimental finding, supporting the assignment of the 9.71-MeV state as the bandhead of a superdeformed band.Conclusion: Excited isoscalar ${0}^{+}$ states in $^{28}\mathrm{Si}$ have been identified. The number of states observed in the present experiment shows good agreement with the prediction of the multichannel dynamical symmetry.

Population-based description of familial clustering of Chiari malformation Type I.

OBJECTIVE A population-based genealogical resource with linked medical data was used to define the observed familial clustering of Chiari malformation Type I (CM-I). METHODS All patients with CM-I were identified from the 2 largest health care providers in Utah; those patients with linked genealogical data were used to test hypotheses regarding familial clustering. Relative risks (RRs) in first-, second-, and third-degree relatives were estimated using internal cohort-specific CM-I rates; the Genealogical Index of Familiality (GIF) test was used to test for an excess of relationships between all patients with CM-I compared with the expected distribution of relationships for matched control sets randomly selected from the resource. Pedigrees with significantly more patients with CM-I than expected (p < 0.05) based on internal rates were identified. RESULTS A total of 2871 patients with CM-I with at least 3 generations of genealogical data were identified. Significantly increased RRs were observed for first- and third-degree relatives (RR 4.54, p < 0.001, and RR 1.36, p < 0.001, respectively); the RR for second-degree relatives was elevated, but not significantly (RR 1.20, p = 0.13). Significant excess pairwise relatedness was observed among the patients with CM-I (p < 0.001), and borderline significant excess pairwise relatedness was observed when all relationships closer than first cousins were ignored (p = 0.051). Multiple extended high-risk CM-I pedigrees with closely and distantly related members were identified. CONCLUSIONS This population-based description of the familial clustering of 2871 patients with CM-I provided strong evidence for a genetic contribution to a predisposition to CM-I.

The dawn of amphioxus molecular biology - a personal perspective.

The cloning and embryonic expression analysis of an amphioxus Hox gene in 1992 marked the start of molecular analysis of cephalochordate development. Other papers quickly followed, including a description of the amphioxus Hox gene cluster in 1994, fuelling a resurgence of interest in a long-forgotten animal. I describe the academic background, laboratory experiments and field work leading to the earliest publications in amphioxus molecular developmental biology and explain their scientific impact. The story of amphioxus biology in the 1990s involved collaboration, team work, opportunistic meetings, serendipity, incredulous journal editors, stingrays, airport departure lounges and popcorn.

Cu13 {S2 CNn Bu2 }6 (acetylide)4 ]+ : A Two-Electron Superatom.

The first structurally characterized copper cluster with a Cu13 centered cuboctahedral arrangement, a model of the bulk copper fcc structure, was observed in [Cu13 (S2 CNn Bu2 )6 (C≡CR)4 ](PF6 ) (R=C(O)OMe, C6 H4 F) nanoclusters. Four of the eight triangular faces of the cuboctahedron are capped by acetylide groups in μ3 fashion, and each of the six square faces is bridged by a dithiolate ligand in μ2 ,μ2 fashion, which leads to a truncated tetrahedron of twelve sulfur atoms. DFT calculations are fully consistent with the description of these Cu13 clusters as two-electron superatoms, that is, a [Cu13 ]11+ core passivated by ten monoanionic ligands, with an a1 HOMO containing two 1S jellium electrons.

Cu13{S2CNnBu2}6(acetylide)4]+: A Two‐Electron Superatom

AbstractThe first structurally characterized copper cluster with a Cu13 centered cuboctahedral arrangement, a model of the bulk copper fcc structure, was observed in [Cu13(S2CNnBu2)6(C≡CR)4](PF6) (R=C(O)OMe, C6H4F) nanoclusters. Four of the eight triangular faces of the cuboctahedron are capped by acetylide groups in μ3 fashion, and each of the six square faces is bridged by a dithiolate ligand in μ2,μ2 fashion, which leads to a truncated tetrahedron of twelve sulfur atoms. DFT calculations are fully consistent with the description of these Cu13 clusters as two‐electron superatoms, that is, a [Cu13]11+ core passivated by ten monoanionic ligands, with an a1 HOMO containing two 1S jellium electrons.

Secondary metabolite gene clusters in the entomopathogen fungus Metarhizium anisopliae: genome identification and patterns of expression in a cuticle infection model.

BackgroundThe described species from the Metarhizium genus are cosmopolitan fungi that infect arthropod hosts. Interestingly, while some species infect a wide range of hosts (host-generalists), other species infect only a few arthropods (host-specialists). This singular evolutionary trait permits unique comparisons to determine how pathogens and virulence determinants emerge. Among the several virulence determinants that have been described, secondary metabolites (SMs) are suggested to play essential roles during fungal infection. Despite progress in the study of pathogen-host relationships, the majority of genes related to SM production in Metarhizium spp. are uncharacterized, and little is known about their genomic organization, expression and regulation. To better understand how infection conditions may affect SM production in Metarhizium anisopliae, we have performed a deep survey and description of SM biosynthetic gene clusters (BGCs) in M. anisopliae, analyzed RNA-seq data from fungi grown on cattle-tick cuticles, evaluated the differential expression of BGCs, and assessed conservation among the Metarhizium genus. Furthermore, our analysis extended to the construction of a phylogeny for the following three BGCs: a tropolone/citrinin-related compound (MaPKS1), a pseurotin-related compound (MaNRPS-PKS2), and a putative helvolic acid (MaTERP1).ResultsAmong 73 BGCs identified in M. anisopliae, 20 % were up-regulated during initial tick cuticle infection and presumably possess virulence-related roles. These up-regulated BGCs include known clusters, such as destruxin, NG39x and ferricrocin, together with putative helvolic acid and, pseurotin and tropolone/citrinin-related compound clusters as well as uncharacterized clusters. Furthermore, several previously characterized and putative BGCs were silent or down-regulated in initial infection conditions, indicating minor participation over the course of infection.Interestingly, several up-regulated BGCs were not conserved in host-specialist species from the Metarhizium genus, indicating differences in the metabolic strategies employed by generalist and specialist species to overcome and kill their host. These differences in metabolic potential may have been partially shaped by horizontal gene transfer (HGT) events, as our phylogenetic analysis provided evidence that the putative helvolic acid cluster in Metarhizium spp. originated from an HGT event.ConclusionsSeveral unknown BGCs are described, and aspects of their organization, regulation and origin are discussed, providing further support for the impact of SM on the Metarhizium genus lifestyle and infection process.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3067-6) contains supplementary material, which is available to authorized users.

Specifics of cluster policy in Russia

The article presents the results of management quality survey in Russian clusters that reveals specifics of cluster support policy in Russia. We compare 22 Russian clusters, supported by the Government, using series of indicators measuring cooperation intensity of cluster participants and activity of cluster management teams. We introduce a description of the typical Russian innovative territorial cluster, based on the average values of the indicators. Our analysis revealed that international communications, information about funding and training courses are highly useful tools to improve collaborations among cluster participants. This paper proposes a methodology for measuring cluster performance by the cluster scale index, cluster development index and cluster management efficiency index. In conclusion, we formulate recommendations for cluster policy improvement in Russia, based on our analysis of indicators’ correlations and comparison between the results of our research and the similar researches in other countries. This analysis will be useful for researchers and policymakers from countries, where cluster policy has recently become a popular topic.

Improving the modelling of redshift-space distortions– II. A pairwise velocity model covering large and small scales

We develop a model for the redshift-space correlation function, valid for both dark matter particles and halos on scales $>5\,h^{-1}$Mpc. In its simplest formulation, the model requires the knowledge of the first three moments of the line-of-sight pairwise velocity distribution plus two well-defined dimensionless parameters. The model is obtained by extending the Gaussian-Gaussianity prescription for the velocity distribution, developed in a previous paper, to a more general concept allowing for local skewness, which is required to match simulations. We compare the model with the well known Gaussian streaming model and the more recent Edgeworth streaming model. Using N-body simulations as a reference, we show that our model gives a precise description of the redshift-space clustering over a wider range of scales. We do not discuss the theoretical prescription for the evaluation of the velocity moments, leaving this topic to further investigation.

A new design knowledge retrieval model based on granularity and clustering theories

Former research has proved that the design knowledge involved in the conceptual design stage has multiple attributes/perspectives, which can be further abstracted into the concepts with different abstract/granularity levels, from coarse to fine. Based on this, we believe that it is necessary to use multiple attributes to describe design knowledge during the knowledge retrieval process. Firstly, an attribute ontology with multi-perspective and multi-granularity is built in the new retrieval model. Therefore, the knowledge documents can be abstracted by the concepts of the ontology. Based on the correlation of the concepts, the clustering theory is introduced into the new model to cluster knowledge documents, and the combined keyword concepts of the clusters are generated as well. The keyword concepts of the clusters, rather than the keyword concepts of documents, are used as the index of the retrieval. During the knowledge matching process, the keywords and their semantic extension of design problems are extracted, and the similarity between the abstract descriptions of the knowledge clusters and design problems is calculated, so the best cluster can be found by the calculated results. Based on the granularity levels of the keywords (concepts) the design problems, finally the documents in the selected cluster are ranked in the order of granularity levels. The selected document by the model is that with the highest relevance and most suitable granularity level about the design problem. In the last section of the paper, we used a simple real case to evaluate the new model, and also develop the scheme of the whole design knowledge retrieval system for the future work.

Quantifying cluster dynamics to improve EMMS drag law and radial heterogeneity description in coupling with gas-solid two-fluid method

Quantification of multiscale heterogeneity is the key to the simulation of gas-solid two-phase flow. The energy-minimization multiscale (EMMS) model can be used at different scales to describe local and global heterogeneities as well as steady-state fluid dynamics of gas-solid fluidization. However, the same cluster diameter correlation based on energy dissipation was used in all cases, which may give a smaller cluster diameter than single particle diameter at low solids fluxes. In this paper, a description of the solids mass transfer between the dilute and dense phases in heterogeneous gas-solid flow is proposed to replace the correlation of cluster diameter to avoid some problems associated with the empirical calculation of the cluster diameter. This cluster description can facilitate the prediction of the drag coefficient at the scale of numerical cells in computational fluid dynamics (CFD) at extremely low solids fluxes and the radial heterogeneity in large-scale circulating fluidized bed (CFB) risers with downward particle velocities near the wall. It is integrated with two-fluid model (TFM) to simulate the fluid dynamics in an industrial-scale CFB riser accurately, which is also accelerated significantly by using the predicted radial voidage distribution as the initial condition of CFD.

A class of spherical, truncated, anisotropic models for application to globular clusters

Recently, a class of non-truncated radially-anisotropic models (the so-called $f^{(\nu)}$-models), originally constructed in the context of violent relaxation and modeling of elliptical galaxies, has been found to possess interesting qualities in relation to observed and simulated globular clusters. In view of new applications to globular clusters, we improve this class of models along two directions. To make them more suitable for the description of small stellar systems hosted by galaxies, we introduce a 'tidal' truncation (by means of a procedure that guarantees full continuity of the distribution function). The new $f_T^{(\nu)}$-models are shown to provide a better fit to the observed photometric and spectroscopic profiles for a sample of 13 globular clusters studied earlier by means of non-truncated models; interestingly, the best-fit models also perform better with respect to the radial-orbit instability. Then we design a flexible but simple two-component family of truncated models, to study the separate issues of mass segregation and of multiple populations. We do not aim at a fully realistic description of globular clusters, to compete with the description currently obtained by means of dedicated simulations. The goal here is to try to identify the simplest models, that is, those with the smallest number of free parameters, but still able to provide a reasonable description for clusters that are evidently beyond the reach of one-component models: with this tool we aim at identifying the key factors that characterize mass segregation or the presence of multiple populations. To reduce the relevant parameter space, we formulate a few physical arguments (based on recent observations and simulations). A first application to two well-studied globular clusters is briefly described and discussed.

“Strangles-like” disease and outbreaks caused by Streptococcus equi subspecies zooepidemicus: Case cluster description and diagnostics by a real-time PCR strangles screen

“Strangles-like” disease and outbreaks caused by Streptococcus equi subspecies zooepidemicus: Case cluster description and diagnostics by a real-time PCR strangles screen

Cosmology and astrophysics from relaxed galaxy clusters – III. Thermodynamic profiles and scaling relations

This is the third in a series of papers studying the astrophysics and cosmology of massive, dynamically relaxed galaxy clusters. Our sample comprises 40 clusters identified as being dynamically relaxed and hot (i.e., massive) in Papers I and II of this series. Here we consider the thermodynamics of the intracluster medium, in particular the profiles of density, temperature and related quantities, as well as integrated measurements of gas mass, average temperature, total luminosity and center-excluded luminosity. We fit power-law scaling relations of each of these quantities as a function of redshift and cluster mass, which can be measured precisely and with minimal bias for these relaxed clusters using hydrostatic arguments. For the thermodynamic profiles, we jointly model the density and temperature and their intrinsic scatter as a function of radius, thus also capturing the behavior of the gas pressure and entropy. For the integrated quantities, we also jointly fit a multidimensional intrinsic covariance. Our results reinforce the view that simple hydrodynamical models provide a good description of relaxed clusters outside their centers, but that additional heating and cooling processes are important in the inner regions (radii $r < 0.5r_{2500} \approx 0.15r_{500}$). The thermodynamic profiles remain regular, with small intrinsic scatter, down to the smallest radii where deprojection is straightforward ($\sim 20$ kpc); within this radius, even the most relaxed systems show clear departures from spherical symmetry. Our results suggest that heating and cooling are continuously regulated in a tight feedback loop, allowing the cluster atmosphere to remain stratified on these scales.

A Graph-Based Digital Pathology Approach To Describe Lymphocyte Clustering Patterns After Renal Transplantation

Introduction/ Background Renal transplantation (rTx) induces an adaptive immune response against foreign donor antigens mediated by lymphocytes of the recipient. Local accumulation of B- and T-cells is an important component of this response enabling and controlling immune cell interactions [1]. Combining digital microscopic images with network analysis [2][3] opens new perspectives to study the spa- tial dimension of lymphocyte clustering and to model their potential interactions. Aims The aim of this study is to characterize the range of B- and T-lymphocytic infiltrates below the threshold of rejection defined by theBanffclassification [4][5] and to propose a mathematical description of immune cell clustering for use in systems medicine approaches. Methods We established a workflow to comprehensively characterize lymphocyte clusters and compare their morphological features with organized structures such as secondary or tertiary lymphoid organs (TLO/SLO) [6]. 51 renal protocol and indication biopsies from 13 patients without evidence for severe rejection over 10 years were stained by CD3/CD20 duplex immunohisto- chemistry. Whole slide images (WSIs) were acquired to automatically detect biologically relevant regions of in- terest (ROIs) by means of density maps for lymphocytes (image analysis workflow illustrated in Fig. 1a). They are generated from single nuclei identification using an au- to-adaptive random forest pixelwise classifier (“nucleus container” module [7],Definiens,Germany). We imple- mented a graph-based tool in Java using individual cell coordinates to identify cell compartments (Fig. 1b) and applied it to each selected ROI. For this, a neighborhood graph is built by Delaunay triangulation and Euclidean distances. This analysis allows describing their specific clustering behavior based on features as described in [8]. The convex hull of the neighborhood graph allows a visualization of B- and T-cell compartments. Results We identified B-cell rich compartments in about 55% of 150 ROIs in kidney tissue after successful transplantation (examples in Fig. 2). The B-cell compartments in rTx tended towards smaller overall size with on average about 90 cells in a B-cell cluster compared to more than 600 B-cells observed in mature TLOs and SLOs and they showed less prominent spatial organization (average degree on average 3.92 instead of 4.97; degree shows generally Poisson distribution as illustrated in Fig. 3A). Further, the graph analysis confirmed lower B-cell density (Fig. 3B displays the exponential character of the spatial B-cell distribution in a selected ROI), a different ratio between T- and B-cell compartments, and more frequent overlap between both regions than in mature lymphoid structures. We conclude that the graph-based approach is feasible to distinguish relevant immune cell patterns in rTx and provides a useful mathematical description of neighborhood relationships between immune cells and their spatial organization. The workflow has the potential to improve throughput and robustness of immune cell evaluation for use in translational science.

Population-balance description of shear-induced clustering, gelation and suspension viscosity in sheared DLVO colloids.

Application of shear flow to charge-stabilized aqueous colloidal suspensions is ubiquitous in industrial applications and as a means to achieve controlled field-induced assembly of nanoparticles. Yet, applying shear flow to a charge-stabilized colloidal suspension, which is initially monodisperse and in quasi-equilibrium leads to non-trivial clustering phenomena (and sometimes to a gelation transition), dominated by the complex interplay between DLVO interactions and shear flow. The quantitative understanding of these strongly nonequilibrium phenomena is still far from being complete. By taking advantage of a recent shear-induced aggregation rate theory developed in our group, we present here a systematic numerical study, based on the governing master kinetic equation (population-balance) for the shear-induced clustering and breakup of colloids exposed to shear flow. In the presence of sufficiently stable particles, the clustering kinetics is characterized by an initial very slow growth, controlled by repulsion. During this regime, particles are slowly aggregating to form clusters, the reactivity of which increases along with their size growth. When their size reaches a critical threshold, a very rapid, explosive-like growth follows, where shear forces are able to overcome the energy barrier between particles. This stage terminates when a dynamic balance between shear-induced aggregation and cluster breakage is reached. It is also observed that these systems are characterized by a cluster mass distribution that for a long time presents a well-defined bimodality. The model predictions are quantitatively in excellent agreement with available experimental data, showing how the theoretical picture is able to quantitatively account for the underlying nonequilibrum physics.

Ampelographic description of cluster, berry and seed of merlot cultivar (Vitis vinifera L.) and its selected clones

During a four-year period, ampelographic experiments focusing on the berry cluster (average length of grape cluster, number of grape clusters per shoot, number of berries per grape cluster and length of peduncle), berry (length of berry and berry juice yield), length of pedicel and seed (length of berry seed) of Merlot cultivar (used as a relevant standard) and 11 clones (Nos. 022, 023, 025, 026, 027, 028, 029, 030, 031, 033 and 034) were performed in order to establish the differences among them. These experiments were actually conducted in the third phase of individual clonal selection of Merlot cultivar carried out in Serbia. The lengths of grape cluster and pedicel as well as berry must yields differed significantly among the examined clones. The cluster and principal component analyses classified 12 samples into three divergent clusters/groups, respectively. The clones belonging to the cluster II /the second group/ had significantly higher values of numbers of grape clusters per shoot and berries per grape cluster; lengths of peduncle and berry; berry must yield and length of pedicel, compared both to standard Merlot /the cluster I, the first group/ and the clones of the cluster III /the third group/. The phenological observations showed no significant differences in the beginnings and durations of phenological stages and vegetation period of the examined clones. The obtained results indicate the real need for further research work focused both on the agrobiological and technological properties of the grapes and wines aiming to better describe the selected clones.

Developing alternative regression models for describing water quality using a self-organizing map

Statistical models play an important role in elucidating the dynamic behaviors of surface water quality, given limited data on a large scale. In this study, we examine alternative approaches to develop regression models that predict fecal coliform (FC) concentrations in a river using different methods for selecting important variables provided by a self-organizing map (SOM). The raw data used as input to the SOM included 11 water quality, 6 meteorological, and 7 land use parameters that were monitored along the Yeongsan River in Korea on various time scales (from daily to half a decade) during 1996–2008. In both test and validation data sets, (multiple) regressions using backward elimination were compared against regression models via forced entry, which included a set of ranked variables simultaneously based on four indices in the SOM (i.e. structuring index, relative importance, cluster description, and Spearman’s rank correlation). Results showed that the SOM effectively illustrated the complex relationship between FC and the remaining variables in the entire data set. This relationship was seen more clearly in homogeneous clusters, indicating that the regression models became more robust in each subdivided group. While the original backward elimination model (R2 = 0.66) had much better performance than the models with four indices (R2 = 0.40–0.45) in the test data set, its performance (R2 = 0.42) was quite comparable to the relative importance model (R2 = 0.38) in the validation data set. Based on this preliminary study, we recommend further investigation of these indices for a reliable regression analysis, as the t values currently used for the variable selection in regressions provide only a locally optimal solution for the final model. The proposed methodology, if verified successfully, would be useful in developing early warning models that control mortality or disease rates of fishes in high-density aquafarms via water quality.

Research Update: Plentiful magnetic moments in oxygen deficient SrTiO3

Correlated band theory is employed to investigate the magnetic and electronic properties of different arrangements of oxygen di- and tri-vacancy clusters in SrTiO3. Hole and electron doping of oxygen deficient SrTiO3 yields various degrees of magnetization as a result of the interaction between localized magnetic moments at the defect sites. Different kinds of Ti atomic orbital hybridization are described as a function of the doping level and defect geometry. We find that magnetism in SrTiO3−δ is sensitive to the arrangement of neighbouring vacancy sites, charge carrier density, and vacancy-vacancy interaction. Permanent magnetic moments in the absence of vacancy doping electrons are observed. Our description of the charged clusters of oxygen vacancies widens the previous descriptions of mono- and multi-vacancies and points out the importance of the controlled formation at the atomic level of defects for the realization of transition metal oxide based devices with a desirable magnetic performance.

A Hybrid Kernel-Based Change Detection Method for Remotely Sensed Data in a Similarity Space

Detection of damages caused by natural disasters is a delicate and difficult task due to the time constraints imposed by emergency situations. Therefore, an automatic Change Detection (CD) algorithm, with less user interaction, is always very interesting and helpful. So far, there is no existing CD approach that is optimal and applicable in the case of (a) labeled samples not existing in the study area; (b) multi-temporal images being corrupted by either noise or non-normalized radiometric differences; (c) difference images having overlapped change and no-change classes that are non-linearly separable from each other. Also, a low degree of automation is not optimal for real-time CD applications and also one-dimensional representations of classical CD methods hide the useful information in multi-temporal images. In order to resolve these problems, two automatic kernel-based CD algorithms (KCD) were proposed based on kernel clustering and support vector data description (SVDD) algorithms in high dimensional Hilbert space. In this paper (a( a new similarity space was proposed in order to increase the separation between change and no-change classes, and also to decrease the processing time, (b) three kernel-based approaches were proposed for transferring the multi-temporal images from spectral space into high dimensional Hilbert space, (c) automatic approach was proposed to extract the precise labeled samples; (d) kernel parameter was selected automatically by optimizing an improved cost function and (e) initial value of the kernel parameter was estimated by a statistical method based on the L2-norm distance. Two different datasets including Quickbird and Landsat TM/ETM+ imageries were used for the accuracy of analysis of proposed methods. The comparative analysis showed the accuracy improvements of kernel clustering based CD and SVDD based CD methods with respect to the conventional CD techniques such as Minimum Noise Fraction, Independent Component Analysis, Spectral Angle Mapper, Simple Image differencing and Image Rationing, and also the computational cost analysis showed that implementation of the proposed CD method in similarity space decreases the processing runtime.