Multilayered Clusters Research Articles

Identification of clusters of rapid and slow decliners among subjects at risk for Alzheimer\u2019s disease

The heterogeneity of Alzheimer’s disease contributes to the high failure rate of prior clinical trials. We analyzed 5-year longitudinal outcomes and biomarker data from 562 subjects with mild cognitive impairment (MCI) from two national studies (ADNI) using a novel multilayer clustering algorithm. The algorithm identified homogenous clusters of MCI subjects with markedly different prognostic cognitive trajectories. A cluster of 240 rapid decliners had 2-fold greater atrophy and progressed to dementia at almost 5 times the rate of a cluster of 184 slow decliners. A classifier for identifying rapid decliners in one study showed high sensitivity and specificity in the second study. Characterizing subgroups of at risk subjects, with diverse prognostic outcomes, may provide novel mechanistic insights and facilitate clinical trials of drugs to delay the onset of AD.

Fuzzy infrared image segmentation based on multilayer immune clustering neural network

Image segmentation of fuzzy mid-wave infrared image is a challenging task according to the fuzziness of the image. Inspired by coordination network mechanism of biological immunity, a multilayer immune clustering neural network method is proposed, which based on imaging mechanism and clustering network statistical property of infrared image. First, by minimizing the between-class variance, fuzzy infrared image is segmented into three areas by preliminary immune clustering neural network, a bright area, a dark area and an unclear not dark area, then computes clustering network features of samples. Then, proceeds immune neural network clustering for samples in bright area and dark area based on clustering network features, thus each sample in unclear not dark area is divided into bright area or dark area. Experimental results show that the proposed algorithm can segment fuzzy infrared image efficiently. Compared with classical edge template and conventional region template methods, the multilayer immune clustering neural network method has better segmentation efficiency.

Approach for smart meter load profiling in Monte Carlo simulation applications

Smart grids introduce new technological elements into power systems which take prevalent challenges to a new level by shaping parameters of power systems towards a complex regime of uncertainties. Rapid proliferation of advanced metering infrastructure (AMI) and integration of renewable energy sources in smart grids increase system-wide complexities. This study proposes an innovative approach to classify the energy consumptions of smart meter customers with typical profiles by processing with multi-layered clustering of energy consumption data of smart consumers extracted from the AMI. There are two stages for the approach of which the first stage analyses the data for intra-cluster similarity of energy consumption patterns and in case the patterns do not have a high intra-cluster similarity, they are fed back for re-clustering with multi-layered clustering process until the clearly identifiable energy patterns with high intra-cluster similarity is obtained. The second stage linearises the complex energy patterns using interpolant and curve fitting techniques until stabilised profiles are obtained. This study also proposes a methodology for smart meter load modelling for Monte Carlo (MC) simulation applications to reduce the computing time compared with traditional alternatives. This study validates the robustness of the approach and provides the corroboration of the method for MC simulation applications in a smart grid environment.

Multi-Layered Clustering for Power Consumption Profiling in Smart Grids

Smart Grids (SGs) have many advantages over traditional power grids as they enhance the way electricity is generated, distributed, and consumed by adopting advanced sensing, communication, and control functionalities that depend on power consumption profiles of consumers. Clustering algorithms ( e.g. , centralized clustering) are used for profiling individuals’ power consumption. Due to the distributed nature and ever growing size of SGs, it is predicted that massive amounts of data will be created. However, conventional clustering algorithms are neither efficient enough nor scalable enough to deal with such amount of data. In addition, the cost for transferring and analyzing large amounts of data is high both computationally and communicationally. This paper thus proposes a power consumption profiling model based on two levels of clustering. At the first level, local power consumption profiles are derived, which are then used by the second level in order to create a global power consumption profile. The followed approach reduces the communication and computation complexity of the proposed two level model and improves the privacy of consumers. We point out that having a good knowledge of the local power profiles leads to more effective prediction model and cost-effective power pricing scheme, especially in a heterogeneous grid topology. In addition, the correlations between the local and global profiles can be used to localize/identify power consumption outliers. Simulation results illustrate that the proposed model is effective in reducing the computational complexity without much affecting its accuracy. The reduction in computational complexity is about 52% and the reduction in the communicational complexity is about 95% when compared with the centralized clustering approach.

Performance Analysis of Energy Efficient Dynamic Multilayer Cluster Designing Routing Protocol in Wireless Sensor Network

Objective: Reliability and Energy conservation are two main factors to be considered in wireless sensor networks. Wireless Sensor Networks (WSN) is widely used in time-critical applications. WSN are resource constrained, so developing energy efficient protocols is a major concern in sensor networks. Multilayer clusters are preferred for sensors used in Remote Monitoring applications. When the position of sensor changes then it becomes very difficult to conserve energy as the cluster head is mainly responsible for delivery of the data. Protocols like EADUC, TLPER follows Multilayer clustering approach. The main aim of this paper is to develop dynamic clusters with less energy consumption. Methods/Statistical Analysis: The proposed method D-MCDA is efficient when the location of nodes varies dynamically and the clusters are formed based on their locations which proves efficient when compared to other protocols. First all the nodes are involved in the formation of the cluster and then the Cluster head is selected. The actual data packet is sent from the source to destination and the consumption of energy is calculated for various protocols and their results are compared. Findings: As energy consumption was the main motivation, use of multilayer and additional cluster heads have reduced the energy consumption by 0.019. The Proposed D-MCDA algorithms have performed better in terms of average energy consumption per node and in the process of designing a cluster. The proposed algorithm is compared with the existing algorithm like TLPER and MCDA. Applications/Improvements: Energy Consumption is very essential in applications like Remote monitoring, process control and automations. Reliability is another issue to be considered as it is another major reason for energy depletion. Reliability on the basis of congestion avoidance scheme, acknowledgement mechanisms can be considered which can reduce the energy consumption to a major level.

Rigidity of polymer micelles affects interactions with tumor cells

Controlling the interaction of drug delivery systems (DDS) with tissues is critical for the success of therapies. Specifically in cancer, due to the high density of the tumors, tissue penetration of DDS is critical and may be challenging. In previous work we have shown that Solidified Polymer Micelles (SPMs) rapidly internalize into cells and tissues. Using AFM analysis, in the present work we measured differences in rigidity of SPM compared with Wet Polymer Micelles (WPM). We further examined whether the semi-solid form of hydrated SPMs has an effect on the interaction with tumor cells both in mono-layer systems and in multi-layer clusters of cells as spheroids. For that we have performed detailed characterization of SPM compared to WPM, including examinations of particle size, stability, drug release kinetics and cell transcytosis, in melanoma A-375 cells. Cell uptake measurements were done using fluorescent signal analysis, FACS and microscopy imaging, showing enhanced abilities of SPMs to penetrate cells and tissues. A simple physical model is presented that well agrees with the experiments and provides insight about the role of particle rigidity in the engulfment mechanism. We conclude that particle rigidity enhances cellular uptake and tissue penetration and that SPMs have a promising potential as an effective and highly permeable DDS. Our findings can be important in future rational design of DDS for particle adjustment to specific tissues and pathologies.

Main findings and advances in biomedical engineering and bioinformatics from IWBBIO 2015.

Main findings and advances in biomedical engineering and bioinformatics from IWBBIO 2015.

Modeling of adsorption phenomena in porous materials of carbonaceous origin by employing multilayer clustering based adsorption model (uniBET) with various variants of geometrical and energetical heterogeneities

Multilayer clustering based adsorption model (uniBET) is aimed at adsorption phenomena modeling with having regard to surface geometrical and energetical heterogeneities. Facilitation of the uniBET model formulas leads to the LBET class models. A review on uniBET model and the analysis of the multilayer heterogeneous adsorption models for characterization of porous materials of carbonaceous origin are presented.

Cellular Dynamics of Mouse Trophoblast Stem Cells: Identification of a Persistent Stem Cell Type.

Mouse trophoblast stem cells (TSCs) proliferate indefinitely in vitro, despite their highly heterogeneous nature. In this study, we sought to characterize TSC colony types by using methods based on cell biology and biochemistry for a better understanding of how TSCs are maintained over multiple passages. Colonies of TSCs could be classified into four major types: type 1 is compact and dome-shaped, type 4 is flattened but with a large multilayered cell cluster, and types 2 and 3 are their intermediates. A time-lapse analysis indicated that type 1 colonies predominantly appeared after passaging, and a single type 1 colony gave rise to all other types. These colony transitions were irreversible, but at least some type 1 colonies persisted throughout culture. The typical cells comprising type 1 colonies were small and highly motile, and they aggregated together to form primary colonies. A hierarchical clustering based on global gene expression profiles suggested that a TSC line containing more type 1 colony cells was similar to in vivo extraembryonic tissues. Among the known TSC genes examined, Elf5 showed a differential expression pattern according to colony type, indicating that this gene might be a reliable marker of undifferentiated TSCs. When aggregated with fertilized embryos, cells from types 1 and 2, but not from type 4, distributed to the polar trophectoderm in blastocysts. These findings indicate that cells typically found in type 1 colonies can persist indefinitely as stem cells and are responsible for the maintenance of TSC lines. They may provide key information for future improvements in the quality of TSC lines.

Multilayer Graphene Nanoclusters: Structure, Electronic and Magnetic Properties

The data on atomic and electronic structure and some magnetic properties of graphite domains in activated carbon fibers (ACF) are presented. It has been established that graphite domains have nanometer sizes in all three dimensions, and consist of about three turbostratically stacked nanographenes. Such multilayer nanographene clusters (nanographites) are structural blocks of ACF and they are separated from each other by the micropores and/or sp3-amorphous carbon phase. From the data of magnetic methods of investigations, it follows that the density of states at the Fermi energy in nanographites is significantly larger than the value of that parameter in macroscopic graphite, which testifies the existence of edge π-electron states in them.

Geographical multi‐layered energy‐efficient clustering scheme for ad hoc distributed wireless sensor networks

This study introduces a geographical multi-layered clustering protocol for ad hoc wireless sensor networks (WSNs), where the size of clusters is variable so that the closer clusters to the base station (BS) have a smaller size than farther ones. Moreover, in each cluster, using some intelligent fuzzy rules and in a decentralised way, a novel sub-tree strategy is determined. In this way, some parent nodes are chosen that are responsible for collecting and aggregating data from their adjacent ordinary nodes and sending them to its cluster head, directly or via other parent nodes, which substantially decreases intra-cluster communication energy costs. Furthermore, these two compatible techniques can fairly mitigate the hot spot problem resulting from multi-hop communication with the BS. The simulation results demonstrate that the proposed protocol outperforms two energy-efficient protocols named DSBCA and LEACH in terms of functional network longevity for both small-scale and large-scale sensor networks.

On characterizing population commonalities and subject variations in brain networks.

Brain networks based on resting state connectivity as well as inter-regional anatomical pathways obtained using diffusion imaging have provided insight into pathology and development. Such work has underscored the need for methods that can extract sub-networks that can accurately capture the connectivity patterns of the underlying population while simultaneously describing the variation of sub-networks at the subject level. We have designed a multi-layer graph clustering method that extracts clusters of nodes, called 'network hubs', which display higher levels of connectivity within the cluster than to the rest of the brain. The method determines an atlas of network hubs that describes the population, as well as weights that characterize subject-wise variation in terms of within- and between-hub connectivity. This lowers the dimensionality of brain networks, thereby providing a representation amenable to statistical analyses. The applicability of the proposed technique is demonstrated by extracting an atlas of network hubs for a population of typically developing controls (TDCs) as well as children with autism spectrum disorder (ASD), and using the structural and functional networks of a population to determine the subject-level variation of these hubs and their inter-connectivity. These hubs are then used to compare ASD and TDCs. Our method is generalizable to any population whose connectivity (structural or functional) can be captured via non-negative network graphs.

Enhanced Multilayer Clustering Protocol for Wireless Sensor Network using Hierarchical and Hybrid Multilayer Architecture

Background/Objectives: Sensor nodes will be in fixed/mobile locations. The exchanges can also be huge that it may result in overlapping or loss of packets. This will lead to heavy overhead and less packet forwarding ratio. A layer level model will be a good solution. Methods/Statistical Analysis: In this research work, Hierarchical Hybrid Model based clustering is proposed to combines the merits of both hierarchical and hybrid approaches in routing for improving the lifetime of the network. A mathematical model is being proposed for a wireless sensor network. It consists of following phases. Layering Design, Clustering, Multilayer clustering, Region categorization. The model is improved as it has facilities for clustering and routing based on combined layered approach. Findings: For simulation, the network simulator tool (NS 2) is used. The performance of the proposed scheme has been shown in graphs for the metrics namely, Total consumed energy, control overhead, good put, throughput, delay, packet delivery ratio and lifetime. The paper provides conclusions how the longetivity of the network is improved by combining hierarchical and hybrid approaches in routing. This approach can be applied for users in indoor environment.

Distribution of Industrial Growth in Nagoya Metropolitan Area, Japan: An Exploratory Analysis Using Geographical and Technological Proximities

Yamada E. and Kawakami T. Distribution of industrial growth in Nagoya Metropolitan Area, Japan: an exploratory analysis using geographical and technological proximities, Regional Studies. This study explores the spatial pattern of industry dynamics in Japan's motor metropolis of Nagoya. Exploratory spatial data analysis methodologies that reflect aspects of both geographical and technological proximities within regional industries are proposed and applied to the long-term, sustained manufacturing and service sector growth rates. The descriptive results identify the presence of positive multilayered growth clusters, driven mainly by the automobile and associated industries. These growth clusters differ in sectoral composition and geographical scale; the larger specialized growth cluster of transportation equipment encompasses the smaller one composed of diverse manufacturing and service sectors.

E-MCDA: Extended-Multilayer Cluster Designing Algorithm for Network Lifetime Improvement of Homogenous Wireless Sensor Networks

The proposed idea in this paper is the constructive ramification of cluster designing mechanism floated in recently published work, multilayer cluster designing algorithm (MCDA) to ameliorate the performance in network lifetime. Novel algorithms for time slot allocation, minimizing the cluster head completion candidates, and cluster member selection∖node affiliation to cluster head play underpinning roles to achieve the target. These incorporations in MCDA result in minimizing transmissions, suppressing unfavorable response of transmissions and near-equal size and equal load clusters. We have done extensive simulations in NS2 and evaluate the performance of E-MCDA in energy consumption at various aspects of energy, packets transmission, number of designed clusters, number of nodes per cluster, and unclustered nodes. It is found that the proposed mechanism optimistically outperforms the competition with MCD and EADUC.

Discovering social influencers with network visualization: evidence from the tourism domain

Identifying influential users and predicting their network impact on social networks have attracted tremendous interest from both academia and industry. Social networks follow a power-law degree distribution of nodes, with a few hub nodes and a long tail of peripheral nodes. While there exist consolidated approaches supporting the identification and characterization of hub nodes, research on the analysis of the multi-layered distribution of peripheral nodes is limited. In social media, hub nodes represent social influencers. However, the literature provides evidence of the multi-layered structure of influence networks, emphasizing the distinction between influencers and influence. Information seems to spread following multi-hop paths across nodes in peripheral network layers. This paper proposes a visual approach to the graphical representation and exploration of peripheral layers and clusters by exploiting the theory of k-shell decomposition analysis. The core concept of the proposed approach is to partition the node set of a graph into pre-defined hub and peripheral nodes. Then, a power-law based modified force-directed method is applied to clearly display local multi-layered neighborhood clusters around hub nodes based on a characterization of the content of message that we refer to as content specificity. We put forward three hypotheses that allow the graphical identification of the peripheral nodes that are more likely to be influential and contribute to the spread of information. Hypotheses are tested on a large sample of tweets from the tourism domain.

Facile Attachment of TAT Peptide on Gold Monolayer Protected Clusters: Synthesis and Characterization.

High affinity thiolate-based polymeric capping ligands are known to impart stability onto nanosized gold nanoparticles. Due to the stable gold-sulfur bond, the ligand forms a protective layer around the gold core and subsequently controls the physicochemical properties of the resultant nanogold mononuclear protected clusters (AuMPCs). The choice of ligands to use as surfactants for AuMPCs largely depends on the desired degree of hydrophilicity and biocompatibility of the MPCs, normally dictated by the intended application. Subsequent surface modification of AuMPCs allows further conjugation of additional biomolecules yielding bilayer or multilayered clusters suitable for bioanalytical applications ranging from targeted drug delivery to diagnostics. In this study, we discuss our recent laboratory findings on a simple route for the introduction of Trans-Activator of Transcription (TAT) peptide onto the surface of biotin-derivatised gold MPCs via the biotin-strepavidin interaction. By changing the surface loading of biotin, controlled amounts of TAT could be attached. This bioconjugate system is very attractive as a carrier in intercellular delivery of various delivery cargoes such as antibodies, proteins and oligonucleotides.

Development of a model for forecasting of PM10 concentrations in Salamanca, Mexico

Salamanca has been ranked as one of the most polluted cities in Mexico. The industry in the area led to a major economic development and rapid population growth in the second half of the twentieth century. The concerning registered pollutants are sulfur dioxide (SO2) and particles in the order of 10 micrometers or less (PM10). The prediction of concentrations of those pollutants can be a powerful tool in order to take preventive measures such as the reduction of emissions and alerting the affected population. This work presents a forecasting model to predict average concentration of PM10 for the next 24 hours. The proposed model uses a combination of Multilayer Perceptron Neural Network and clustering algorithm. The source database used contains historical time series of meteorological variables and concentrations of PM10 collected in three different stations in Salamanca. The clustering algorithms have been implemented in order to find relationships between PM10 and meteorological variables. These relationships will help us to get additional information that will be used in the prediction model. The proposed model was compared, for accuracy and validation purposes, with a simple Multilayer Perceptron and a multiple Linear Regression. The performance estimation is determined using the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). The obtained results show the importance of this set of meteorological variables in the prediction of pollutant concentrations.

Synthesis and characterization of highly organized crystalline rutile nanoparticles by low-temperature dissolution-reprecipitation process

Abstract

Energy-Efficient Data Reporting Strategy (EDRS) for Multilayer Clustering WSN

Energy consumption is one of the key issues in data reporting in wireless sensor networks (WSNs). Unbalanced energy consumption that characterized by many-to-one traffic pattern results in uneven energy dissipation among sensors which consequently reduces network lifetime significantly. In event tracking and monitoring system with direct constant data reporting of the ongoing event, energy depletion of sensors in specific region in the network where the event takes place is unavoidable. However, energy depletion can be balanced if sensors are clustered into groups and sensed data are transmitted through multi-hop towards the Based Station (BS) over Cluster Heads (CHs) with different transmission ranges capabilities. CHs should have the knowledge that enables them to know how many packets should be transmitted either in multi-hop or directly in order to avoid the fast energy drain of sensors near the BS. The knowledge can be provided by the BS based on the region where CHs are located. In this paper, we propose an energy-efficient strategy for reporting the sensed data in multilayer clustered WSN. The proposed strategy maximizes network lifetime through balancing energy consumption by using dual transmission range for forwarding pre-defined number of data packets according to the network condition. Experimental simulations have been performed to validate the proposed strategy