Free Clustering Research Articles

Molecular dynamics simulations of the structure and dynamics in mixtures of ionic liquids and alcohols.

Molecular dynamics simulations were conducted on mixtures of ionic liquids (ILs) and alcohols, specifically methanol, ethanol, and 1-propanol. Two different ILs, [Mmim][MeSO4] and [Bmim][MeSO4], were used with varying alcohol mole fractions to investigate the impact of alkyl chain length of cations, alcohol types, and alcohol concentrations on different structural and dynamic properties. Unique characteristics of the ILs were observed due to the varying polarity of solvents and the creation of diverse local environments surrounding the ILs. The alcohols weaken the hydrogen bond network of the ILs. A better anion-cation packing was observed for [Mmim][MeSO4]-alcohol than for [Bmim][MeSO4]-alcohol mixtures. The free volume and clustering of alcohol molecules in [Mmim][MeSO4]-alcohol mixtures were stronger than those in [Bmim][MeSO4]-alcohol mixtures; these effects were stronger for mixtures containing longer-chain alcohols. Alcohols accelerate the dynamics of ILs, examined in terms of the anion/cation diffusion coefficients, dynamics of ion-pair formation/rupture, and dynamics of the hydrogen bond formed between the anion and the cation. This effect is more pronounced for mixtures of ILs with shorter-chain alcohols. These findings are consistent with the results obtained for the structural perturbations in ILs in the presence of alcohols, i.e., weakening of the hydrogen bond network in ILs upon addition of (shorter-chain) alcohols to the ILs, and reduced alcohol clustering.

Morphology of a Transmembrane Aβ42 Tetramer via REMD Simulations.

The folding/misfolding of membrane-permiable Amyloid beta (Aβ) peptides is likely associated with the advancing stage of Alzheimer's disease (AD) by disrupting Ca2+ homeostasis. In this context, the aggregation of four transmembrane Aβ17-42 peptides was investigated using temperature replica-exchange molecular dynamics (REMD) simulations. The obtained results indicated that the secondary structure of transmembrane Aβ peptides tends to have different propensities compared to those in solution. Interestingly, the residues favorably forming β-structure were interleaved by residues rigidly adopting turn-structure. A combination of β and turn regions likely forms a pore structure. Six morphologies of 4Aβ were found over the free energy landscape and clustering analyses. Among these, the morphologies include (1) Aβ binding onto the membrane surface and three transmembrane Aβ; (2) three helical and coil transmembrane Aβ; (3) four helical transmembrane Aβ; (4) three helical and one β-hairpin transmembrane Aβ; (5) two helical and two β-strand transmembrane Aβ; and (6) three β-strand and one helical transmembrane Aβ. Although the formation of the β-barrel structure was not observed during the 0.28 ms─long MD simulation, the structure is likely to form when the simulation time is further extended.

Quality of Service Improvement in Neighborhood Area Networking for AMI With ZigBee-Based Tunable Clustered Scale-Free Topology and RPL Routing

This paper deals with the unique development of tunable clustered scale-free neighbourhood area networking (TSFNAN) using ZigBee for establishment of smart grid communication. The complexity of ZigBee based multihop mesh links between gateway and smart meters (SMs) are reduced by extracting the hop reduction feature of scale free and tunable clustering schemes. Further, the routing protocol for low power and lossy networking (RPL) is also adopted to avoid the requirement of route discovery during data transmission. The different service challenges of neighbourhood area networking (NAN) in advanced metering infrastructure of smart grid can thus be met with improved quality of service. A Markov chain delay model is developed to analyse the latency and packet loss in data transmission. The gateway position and the number of SMs are made scalable by correlating the hop count with the tunable clustering coefficient. The proposed system is simulated for a given area with (i) four clusters each containing 2500 SMs and (ii) one cluster containing 10000 SMs. A prototype model with 20 ZigBee nodes is experimented with different coverage range. The system performance is discussed and compared for all such case studies and an encouraging result suggests its practical feasibility with improved QoS.

A Novel Classification Performance Approach for Remotely Sensed Multispectral Image Data by Using Data Mining Techniques

In order to combine multimedia imagery and multispectral remote sensing data to analyze information, preprocessing becomes a necessary part of it. As one of the most important branches in the field of data analysis, it is widely used in many fields such as classification, regression, missing value filling, and machine learning. As a lazy algorithm, this method requires no prior statistical knowledge and no additional data to train description rules and is easy to implement. This study compares classification algorithm performances of data mining clustering algorithms for remotely sensed multispectral image data using WEKA data mining software. Clustering algorithm selection is very important for data mining classification method-based clustering. The class attribute for remotely sensed multispectral image data is obtained from six different clustering algorithms for classification. Classification algorithm performances computed depending on the data labeling of six different clustering algorithms in terms of correctly classified instances and kappa statistics for seven different classification algorithms. A strategy is developed for selecting the best unsupervised clustering algorithm, among different clustering algorithms, giving the highest supervised classification accuracy in terms of correctly classified instances and kappa statistics for semi supervised classification of remotely-sensed multispectral image data. The performances of seven semi-supervised classification methods assessed depending on six different unsupervised clustering algorithms for supervised classification of remotely sensed multispectral image data. This study determines data free clustering algorithms for classification.

Modular-topology optimization of structures and mechanisms with free material design and clustering

Topology optimization of modular structures and mechanisms enables balancing the performance of automatically-generated individualized designs, as required by Industry 4.0, with enhanced sustainability by means of component reuse. For optimal modular design, two key questions must be answered: (i) what should the topology of individual modules be like and (ii) how should modules be arranged at the product scale? We address these challenges by proposing a bi-level sequential strategy that combines free material design, clustering techniques, and topology optimization. First, using free material optimization enhanced with checkerboard suppression, we determine the distribution of elasticity tensors at the product scale. To extract the sought-after modular arrangement, we partition the obtained elasticity tensors with a novel deterministic clustering algorithm and interpret its outputs within Wang tiling formalism. Finally, we design interiors of individual modules by solving a single-scale topology optimization problem with the design space reduced by modular mapping, conveniently starting from an initial guess provided by free material optimization. We illustrate these developments with three benchmarks first, covering compliance minimization of modular structures, and, for the first time, the design of non-periodic compliant modular mechanisms. Furthermore, we design a set of modules reusable in an inverter and in gripper mechanisms, which ultimately pave the way towards the rational design of modular architectured (meta)materials.

Where I’m from? Third Culture Kids about their cultural identity shifts and belonging

This study explored the affect of Third Culture Kids (TCKs) towards their home and host culture(s) and how this affect may indicate possible cultural identity shifts as distinguished in Sussman’s (2000) cultural identity shift model. To this end, the method of poetic inquiry was used. The poems were concerned with TCKs’ affective experiences (Prendergast, 2009). We also investigated whether TCKs described their belonging in terms of personal relationships rather than in terms of geographical locations.Twenty TCKs, ranging in age from 26 to 70 years and from five ‘home cultures’, expressed their early cross-cultural experiences through the free verse poem of “Where I’m from”. A mixed method approach of qualitative and quantitative research was applied, by combining poetic inquiry using a free verse poem format and clustering these data by means of coding in Atlas.ti. TCKs’ poems were analyzed using belonging, affect, and practices-food-nature-events as key codes.Findings revealed that TCKs expressed stronger positive affect towards their host cultures than towards their ‘home’ cultures, indicating a subtractive cultural identity shift. We also found that TCKs defined their belonging more in terms of personal relationships than in terms of geographical locations. This study shows that TCKs’ sense of belonging seems more related to the question who than where I am from.

An atomistic investigation on the interaction of distamycin A and its derivative with the telomeric G-Quadruplex as anticancer agents by molecular dynamics simulation

Human telomerase that activates within cancer cells has a telomeric sequence at the 3′ end. Each factor that stabilizes the G-quadruplex in guanine-rich telomeric sequences can inhibit the regular telomerase activity. Therefore, the telomeric G-quadruplex is known as a promising target in cancer treatment. In this work, we studied the binding of positively charged distamycin A and its uncharged derivative to the G-quadruplex in a solution environment by Molecular Dynamics (MD) simulation. The binding mechanism and subtle conformational changes were investigated as a result of the ligand attachment. Moreover, binding free energy and clustering analysis describe the stability and flexibility of G-quadruplexes upon ligand binding. Structural analyses displayed that the favorable binding of both ligands imposes significant stability and rigidity in G-quadruplex conformation compared to free G-quadruplex, especially charged distamycin. Hydration pattern and ion distribution were different for free G-quadruplex and both of the ligand complexes. Energy decomposition reveals the electrostatic effect on the stability of G-quadruplex. The radial distribution function displayed the solvent shell and ion moving away from the groove. The hydrogen bond played an essential role in the binding of both ligands, especially for the charged derivative. van der Waals interaction is the only factor that is more important in binding uncharged distamycin into G-quadruplex than the charged one. The calculated ΔGbind showed the stability of both ligands within grooves and good agreement with the experimental binding free energy data. Finally, the results suggest that ligand modification improves the binding mode toward stabilizing G-quadruplexes.

Multi Labeled Multi-Expressions to Explore Descriptive Documents

Expressive Clustering contains ordinarily filtering through information occasions into get-togethers and making a practical plan for each get-together. The depiction should inspire a client to press ahead with no more prominent assessment of the specific occasions regarding the substance regarding each social occasion, enabling a client to channel quickly for suitable classes. Once in a while, the choice of delineations relies on anxious representation of heuristic data. We model and coordinate reasonable assembly that recognises highlights from bundle assignments and from a subset of highlights seeks bundle assignments. For updated extraction of Multi Labeled Multi-Word Phrases, we present a zone free clustering based way of thought (MLMWEs). The framework incorporates true information from Wikipedia articles from an all-around obliging corpus and connexions. We lace alliance checks to package MLMWEs through pieces of server homesteads and then the organising score for each MLMWEs subject to the closest model offered to a social affair after that process. Results of the assessment, A combination of association figures, achieved for two vernaculars, shows that an improvement in the organisation of independent and vital coefficient frequency controls and ultimately undeniable steps for MLMWEs is given.

Cosmological Vlasov–Poisson Simulations of Structure Formation with Relic Neutrinos: Nonlinear Clustering and the Neutrino Mass

Abstract We present the results of cosmological simulations of large-scale structure formation with massive neutrinos. The phase-space distribution of the cosmic relic neutrinos is followed, for the first time, by directly integrating the six-dimensional Vlasov–Poisson equations. Our novel approach allows us to represent free streaming and clustering of neutrinos, and their gravitational interaction with cold dark matter accurately. We thus obtain solutions for the collisionless dynamics independent of conventional N-body methods. We perform a suite of hybrid N-body/Vlasov simulations with varying the neutrino mass, and systematically examine the dynamical effects of massive neutrinos on large-scale structure formation. Our simulations show characteristic large-scale clustering of the neutrinos and their coherent streaming motions relative to dark matter. The effective local neutrino “temperature” around massive galaxy clusters varies by several percent with respect to the cosmic mean; the neutrinos in clusters can be hotter or colder depending on the neutrino mass. We study a number of statistics of the large-scale structure and of dark matter halos in comparison with those obtained by N-body simulations and/or by perturbation theory. Our simulations mark an important milestone in numerical cosmology, and pave a new way to study cosmic structure formation with massive neutrinos.

Interference-free Clustering Protocol for Large-Scale and Dense Wireless Sensor Networks

Saving energy is a big challenge for Wireless Sensor Networks (WSNs), which becomes even more critical in large-scale WSNs. Most energy waste is communication related, such as collision, overhearing and idle listening, so the schedule-based access which can avoid these wastes is preferred for WSNs. On the other hand, clustering technique is considered as the most promising solution for topology management in WSNs. Hence, providing interference-free clustering is vital for WSNs, especially for large-scale WSNs. However, schedule management in cluster-based networks is never a trivial work, since it requires inter-cluster cooperation. In this paper, we propose a clustering method, called Interference -Free Clustering Protocol (IFCP), to partition a WSN into interference-free clusters, making timeslot management much easier to achieve. Moreover, we model the clustering problem as a multi-objective optimization issue and use non-dominated sorting genetic algorithm II to solve it. Our proposal is finally compared with two adaptive clustering methods, HEED-CSMA and HEED-BMA, demonstrating that it achieves the good performance in terms of delay, packet delivery ratio, and energy consumption.

Relaxation of bending stresses and the reversibility of residual stresses in amorphous soft magnetic alloys

The reversibility of residual bending stresses is revealed in ribbon samples of cobalt- and iron-based amorphous alloys Co69Fe3.7Cr3.8Si12.5B11 and Fe57Co31Si2.9B9.1: the ribbons that are free of applied stresses and bent under the action of residual stresses become completely or incompletely straight upon annealing at the initial temperatures. The influence of annealing on the relaxation of bending stresses is studied. Preliminary annealing is found to sharply decrease the relaxation rate of bending stresses, and the initial stage of fast relaxation of these stresses is absent. Complete straightening of preliminarily annealed ribbons is shown to occur at significantly higher temperatures than that of the initial ribbons. Incomplete straightening of the ribbons is explained by the fact that bending stresses relaxation at high annealing temperatures proceeds due to both reversible anelastic deformation and viscous flow, which is a fully irreversible process. Incomplete reversibility is also caused by irreversible processes, such as the release of excess free volume and clustering (detected by small-angle X-ray scattering). The revealed differences in the relaxation processes that occur in the cobalt- and iron-based amorphous alloys are discussed in terms of different atomic diffusion mobilities in these alloys.

Functional Role of ATP Binding to Synapsin I In Synaptic Vesicle Trafficking and Release Dynamics

Synapsins (Syns) are synaptic vesicle (SV)-associated proteins involved in the regulation of synaptic transmission and plasticity, which display a highly conserved ATP binding site in the central C-domain, whose functional role is unknown. Using molecular dynamics simulations, we demonstrated that ATP binding to SynI is mediated by a conformational transition of a flexible loop that opens to make the binding site accessible; such transition, prevented in the K269Q mutant, is not significantly affected in the absence of Ca(2+) or by the E373K mutation that abolishes Ca(2+)-binding. Indeed, the ATP binding to SynI also occurred under Ca(2+)-free conditions and increased its association with purified rat SVs regardless of the presence of Ca(2+) and promoted SynI oligomerization. However, although under Ca(2+)-free conditions, SynI dimerization and SV clustering were enhanced, Ca(2+) favored the formation of tetramers at the expense of dimers and did not affect SV clustering, indicating a role of Ca(2+)-dependent dimer/tetramer transitions in the regulation of ATP-dependent SV clustering. To elucidate the role of ATP/SynI binding in synaptic physiology, mouse SynI knock-out hippocampal neurons were transduced with either wild-type or K269Q mutant SynI and inhibitory transmission was studied by patch-clamp and electron microscopy. K269Q-SynI expressing inhibitory synapses showed increased synaptic strength due to an increase in the release probability, an increased vulnerability to synaptic depression and a dysregulation of SV trafficking, when compared with wild-type SynI-expressing terminals. The results suggest that the ATP-SynI binding plays predocking and postdocking roles in the modulation of SV clustering and plasticity of inhibitory synapses.

Free clustering optimal particle probability hypothesis density (PHD) filter

As to the fact that it is difficult to obtain analytical form of optimal sampling density and tracking performance of standard particle probability hypothesis density (P-PHD) filter would decline when clustering algorithm is used to extract target states, a free clustering optimal P-PHD (FCO-P-PHD) filter is proposed. This method can lead to obtainment of analytical form of optimal sampling density of P-PHD filter and realization of optimal P-PHD filter without use of clustering algorithms in extraction target states. Besides, as sate extraction method in FCO-P-PHD filter is coupled with the process of obtaining analytical form for optimal sampling density, through decoupling process, a new single-sensor free clustering state extraction method is proposed. By combining this method with standard P-PHD filter, FC-P-PHD filter can be obtained, which significantly improves the tracking performance of P-PHD filter. In the end, the effectiveness of proposed algorithms and their advantages over other algorithms are validated through several simulation experiments.

Integrin-free tetraspanin CD151 can inhibit tumor cell motility upon clustering and is a clinical indicator of prostate cancer progression.

Normal physiology relies on the organization of transmembrane proteins by molecular scaffolds, such as tetraspanins. Oncogenesis frequently involves changes in their organization or expression. The tetraspanin CD151 is thought to contribute to cancer progression through direct interaction with the laminin-binding integrins α3β1 and α6β1. However, this interaction cannot explain the ability of CD151 to control migration in the absence of these integrins or on non-laminin substrates. We demonstrate that CD151 can regulate tumor cell migration without direct integrin binding and that integrin-free CD151 (CD151(free)) correlates clinically with tumor progression and metastasis. Clustering CD151(free) through its integrin-binding domain promotes accumulation in areas of cell-cell contact, leading to enhanced adhesion and inhibition of tumor cell motility in vitro and in vivo. CD151(free) clustering is a strong regulator of motility even in the absence of α3 expression but requires PKCα, suggesting that CD151 can control migration independent of its integrin associations. The histologic detection of CD151(free) in prostate cancer correlates with poor patient outcome. When CD151(free) is present, patients are more likely to recur after radical prostatectomy and progression to metastatic disease is accelerated. Multivariable analysis identifies CD151(free) as an independent predictor of survival. Moreover, the detection of CD151(free) can stratify survival among patients with elevated prostate-specific antigen levels. Cumulatively, these studies demonstrate that a subpopulation of CD151 exists on the surface of tumor cells that can regulate migration independent of its integrin partner. The clinical correlation of CD151(free) with prostate cancer progression suggests that it may contribute to the disease and predict cancer progression.

PFClust: a novel parameter free clustering algorithm

BackgroundWe present the algorithm PFClust (Parameter Free Clustering), which is able automatically to cluster data and identify a suitable number of clusters to group them into without requiring any parameters to be specified by the user. The algorithm partitions a dataset into a number of clusters that share some common attributes, such as their minimum expectation value and variance of intra-cluster similarity. A set of n objects can be clustered into any number of clusters from one to n, and there are many different hierarchical and partitional, agglomerative and divisive, clustering methodologies available that can be used to do this. Nonetheless, automatically determining the number of clusters present in a dataset constitutes a significant challenge for clustering algorithms. Identifying a putative optimum number of clusters to group the objects into involves computing and evaluating a range of clusterings with different numbers of clusters. However, there is no agreed or unique definition of optimum in this context. Thus, we test PFClust on datasets for which an external gold standard of ‘correct’ cluster definitions exists, noting that this division into clusters may be suboptimal according to other reasonable criteria. PFClust is heuristic in the sense that it cannot be described in terms of optimising any single simply-expressed metric over the space of possible clusterings.ResultsWe validate PFClust firstly with reference to a number of synthetic datasets consisting of 2D vectors, showing that its clustering performance is at least equal to that of six other leading methodologies – even though five of the other methods are told in advance how many clusters to use. We also demonstrate the ability of PFClust to classify the three dimensional structures of protein domains, using a set of folds taken from the structural bioinformatics database CATH.ConclusionsWe show that PFClust is able to cluster the test datasets a little better, on average, than any of the other algorithms, and furthermore is able to do this without the need to specify any external parameters. Results on the synthetic datasets demonstrate that PFClust generates meaningful clusters, while our algorithm also shows excellent agreement with the correct assignments for a dataset extracted from the CATH part-manually curated classification of protein domain structures.

Genetic Divergence Studies in Roselle (Hibiscus sabdariffa L.)

Genetic biodiversity among sixty genetically diversified genotypes of roselle (Hibiscus sabdariffa L.) for nine quantitative characters by Multivariate analysis revealed that the factors other than geographical diversity might have been responsible for grouping of genetic divergence. The clustering pattern revealed that the genotypes originating from different geographical regions got themselves grouped into different clusters. This indicated that the geographic biodiversity is not the only factor in determining genetic biodiversity. The genotypes with same geographic origin could have undergone change for different characters under selection during the process of evolution. The free clustering of the ecotypes suggested the influence of direction of selection pressure (applied) for realizing maximum yield in different ecosystems, the nicely evolved homeostatic devices would favour constancy of the associated characters and thus show indiscriminate clustering. The comparative study of efficacy of Tocher’s method of grouping with Complete linkage dendrogram indicated the superiority of Complete linkage dendrogram over Tocher’s method with a defined uniformity of index of homogeneity and optimum number of uniform clusters.

Selective particle and cell clustering at air–liquid interfaces within ultrasonic microfluidic systems

In this study, we demonstrate particle and cell clustering in distinct patterns on the free surface of microfluidic volumes. Employing ultrasonic actuation, submersed microparticles are forced to two principal positions: nodal lines (pressure minima) of a standing wave within the liquid bulk, and distinct locations on the air–liquid interface (free surface); the latter of which has not been previously demonstrated using ultrasonic standing waves. As such, we unravel the fundamental mechanisms behind such patterns, showing that the contribution of fluid particle velocity variations on the free surface (acoustic radiation force) results in patterned particle clustering. In addition, by varying the size and density of the microparticles (3.5–31 μm polystyrene and 1–5 μm silica), acoustic streaming is found to increase the tendency for a smaller and lighter particle to cluster at the air–liquid interface. This selectivity is exploited for the isolation of multiple microparticle and cell types on the free surface from their nodally aligned counterparts. Free surface clustering is demonstrated in both an open microfluidic chamber and a sessile droplet, as well as using a range of biological species Escherichia coli, blood cells, Ragweed pollen and Paper Mulberry pollen). The ability to selectively cluster submersed microparticles and cells in distinct patterns on the free surface showcases the excellent suitability of this method to lab-on-a-chip systems.

APSCAN: A parameter free algorithm for clustering

DBSCAN is a density based clustering algorithm and its effectiveness for spatial datasets has been demonstrated in the existing literature. However, there are two distinct drawbacks for DBSCAN: (i) the performances of clustering depend on two specified parameters. One is the maximum radius of a neighborhood and the other is the minimum number of the data points contained in such neighborhood. In fact these two specified parameters define a single density. Nevertheless, without enough prior knowledge, these two parameters are difficult to be determined; (ii) with these two parameters for a single density, DBSCAN does not perform well to datasets with varying densities. The above two issues bring some difficulties in applications. To address these two problems in a systematic way, in this paper we propose a novel parameter free clustering algorithm named as APSCAN. Firstly, we utilize the Affinity Propagation (AP) algorithm to detect local densities for a dataset and generate a normalized density list. Secondly, we combine the first pair of density parameters with any other pair of density parameters in the normalized density list as input parameters for a proposed DDBSCAN (Double-Density-Based SCAN) to produce a set of clustering results. In this way, we can obtain different clustering results with varying density parameters derived from the normalized density list. Thirdly, we develop an updated rule for the results obtained by implementing the DDBSCAN with different input parameters and then synthesize these clustering results into a final result. The proposed APSCAN has two advantages: first it does not need to predefine the two parameters as required in DBSCAN and second, it not only can cluster datasets with varying densities but also preserve the nonlinear data structure for such datasets.

Aging and Semantic Cueing during Learning and Retention of Verbal Episodic Information

ABSTRACT The purpose of this study was to determine the effectiveness of semantic cues provided at encoding and during retention for older adults' memory. For the California Verbal Learning Test-II, participants received semantic or nonsemantic cues that were varied across groups at encoding and during the retention interval. Provision of a semantic cue at encoding led to greater semantic clustering at learning, but not increased recall performance. Providing a semantic cue during the retention interval led to better delayed free recall and greater semantic clustering. No group differences in recall or semantic clustering were found at delayed cued recall. The current findings suggest that semantic cues can be beneficial for recalling unstructured information when administered during the retention interval.

Cluster Analysis of Biomedical Image Time-Series

In this paper, we present neural network clustering by deterministic annealing as a powerful strategy for self-organized segmentation of biomedical image time-series data identifying groups of pixels sharing common properties of local signal dynamics. After introducing the theoretical concept of minimal free energy vector quantization and related clustering techniques, we discuss its potential to serve as a multi-purpose computer vision strategy to image time-series analysis and visualization for many fields of medicine ranging from biomedical basic research to clinical assessment of patient data. In particular, we present applications to (i) functional MRI data analysis for human brain mapping, (ii) dynamic contrast-enhanced perfusion MRI for the diagnosis of cerebrovascular disease, and (iii) magnetic resonance mammography for the analysis of suspicious lesions in patients with breast cancer. This wide scope of completely different medical applications illustrates the flexibility and conceptual power of neural network vector quantization in this context. Although there are obvious methodological similarities, each application requires specific careful consideration w.r.t. data preprocessing, postprocessing and interpretation. This challenge can only be managed by close interdisciplinary cooperation of medical doctors, engineers, and computer scientists. Hence, this field of research can serve as an example for lively cross-fertilization between computer vision and related research.