Differential network analysis in human cancer research.
Gill, Ryan; Datta, Somnath; Datta, Susmita
2014-01-01
A complex disease like cancer is hardly caused by one gene or one protein singly. It is usually caused by the perturbation of the network formed by several genes or proteins. In the last decade several research teams have attempted to construct interaction maps of genes and proteins either experimentally or reverse engineer interaction maps using computational techniques. These networks were usually created under a certain condition such as an environmental condition, a particular disease, or a specific tissue type. Lately, however, there has been greater emphasis on finding the differential structure of the existing network topology under a novel condition or disease status to elucidate the perturbation in a biological system. In this review/tutorial article we briefly mention some of the research done in this area; we mainly illustrate the computational/statistical methods developed by our team in recent years for differential network analysis using publicly available gene expression data collected from a well known cancer study. This data includes a group of patients with acute lymphoblastic leukemia and a group with acute myeloid leukemia. In particular, we describe the statistical tests to detect the change in the network topology based on connectivity scores which measure the association or interaction between pairs of genes. The tests under various scores are applied to this data set to perform a differential network analysis on gene expression for human leukemia. We believe that, in the future, differential network analysis will be a standard way to view the changes in gene expression and protein expression data globally and these types of tests could be useful in analyzing the complex differential signatures.
Differential network analysis reveals dysfunctional regulatory networks in gastric carcinogenesis.
Cao, Mu-Shui; Liu, Bing-Ya; Dai, Wen-Tao; Zhou, Wei-Xin; Li, Yi-Xue; Li, Yuan-Yuan
2015-01-01
Gastric Carcinoma is one of the most common cancers in the world. A large number of differentially expressed genes have been identified as being associated with gastric cancer progression, however, little is known about the underlying regulatory mechanisms. To address this problem, we developed a differential networking approach that is characterized by including a nascent methodology, differential coexpression analysis (DCEA), and two novel quantitative methods for differential regulation analysis. We first applied DCEA to a gene expression dataset of gastric normal mucosa, adenoma and carcinoma samples to identify gene interconnection changes during cancer progression, based on which we inferred normal, adenoma, and carcinoma-specific gene regulation networks by using linear regression model. It was observed that cancer genes and drug targets were enriched in each network. To investigate the dynamic changes of gene regulation during carcinogenesis, we then designed two quantitative methods to prioritize differentially regulated genes (DRGs) and gene pairs or links (DRLs) between adjacent stages. It was found that known cancer genes and drug targets are significantly higher ranked. The top 4% normal vs. adenoma DRGs (36 genes) and top 6% adenoma vs. carcinoma DRGs (56 genes) proved to be worthy of further investigation to explore their association with gastric cancer. Out of the 16 DRGs involved in two top-10 DRG lists of normal vs. adenoma and adenoma vs. carcinoma comparisons, 15 have been reported to be gastric cancer or cancer related. Based on our inferred differential networking information and known signaling pathways, we generated testable hypotheses on the roles of GATA6, ESRRG and their signaling pathways in gastric carcinogenesis. Compared with established approaches which build genome-scale GRNs, or sub-networks around differentially expressed genes, the present one proved to be better at enriching cancer genes and drug targets, and prioritizing
Differential Regulatory Analysis Based on Coexpression Network in Cancer Research
Junyi Li
2016-01-01
Full Text Available With rapid development of high-throughput techniques and accumulation of big transcriptomic data, plenty of computational methods and algorithms such as differential analysis and network analysis have been proposed to explore genome-wide gene expression characteristics. These efforts are aiming to transform underlying genomic information into valuable knowledges in biological and medical research fields. Recently, tremendous integrative research methods are dedicated to interpret the development and progress of neoplastic diseases, whereas differential regulatory analysis (DRA based on gene coexpression network (GCN increasingly plays a robust complement to regular differential expression analysis in revealing regulatory functions of cancer related genes such as evading growth suppressors and resisting cell death. Differential regulatory analysis based on GCN is prospective and shows its essential role in discovering the system properties of carcinogenesis features. Here we briefly review the paradigm of differential regulatory analysis based on GCN. We also focus on the applications of differential regulatory analysis based on GCN in cancer research and point out that DRA is necessary and extraordinary to reveal underlying molecular mechanism in large-scale carcinogenesis studies.
A statistical framework for differential network analysis from microarray data
Datta Somnath
2010-02-01
Full Text Available Abstract Background It has been long well known that genes do not act alone; rather groups of genes act in consort during a biological process. Consequently, the expression levels of genes are dependent on each other. Experimental techniques to detect such interacting pairs of genes have been in place for quite some time. With the advent of microarray technology, newer computational techniques to detect such interaction or association between gene expressions are being proposed which lead to an association network. While most microarray analyses look for genes that are differentially expressed, it is of potentially greater significance to identify how entire association network structures change between two or more biological settings, say normal versus diseased cell types. Results We provide a recipe for conducting a differential analysis of networks constructed from microarray data under two experimental settings. At the core of our approach lies a connectivity score that represents the strength of genetic association or interaction between two genes. We use this score to propose formal statistical tests for each of following queries: (i whether the overall modular structures of the two networks are different, (ii whether the connectivity of a particular set of "interesting genes" has changed between the two networks, and (iii whether the connectivity of a given single gene has changed between the two networks. A number of examples of this score is provided. We carried out our method on two types of simulated data: Gaussian networks and networks based on differential equations. We show that, for appropriate choices of the connectivity scores and tuning parameters, our method works well on simulated data. We also analyze a real data set involving normal versus heavy mice and identify an interesting set of genes that may play key roles in obesity. Conclusions Examining changes in network structure can provide valuable information about the
Differential network analysis with multiply imputed lipidomic data.
Maiju Kujala
Full Text Available The importance of lipids for cell function and health has been widely recognized, e.g., a disorder in the lipid composition of cells has been related to atherosclerosis caused cardiovascular disease (CVD. Lipidomics analyses are characterized by large yet not a huge number of mutually correlated variables measured and their associations to outcomes are potentially of a complex nature. Differential network analysis provides a formal statistical method capable of inferential analysis to examine differences in network structures of the lipids under two biological conditions. It also guides us to identify potential relationships requiring further biological investigation. We provide a recipe to conduct permutation test on association scores resulted from partial least square regression with multiple imputed lipidomic data from the LUdwigshafen RIsk and Cardiovascular Health (LURIC study, particularly paying attention to the left-censored missing values typical for a wide range of data sets in life sciences. Left-censored missing values are low-level concentrations that are known to exist somewhere between zero and a lower limit of quantification. To make full use of the LURIC data with the missing values, we utilize state of the art multiple imputation techniques and propose solutions to the challenges that incomplete data sets bring to differential network analysis. The customized network analysis helps us to understand the complexities of the underlying biological processes by identifying lipids and lipid classes that interact with each other, and by recognizing the most important differentially expressed lipids between two subgroups of coronary artery disease (CAD patients, the patients that had a fatal CVD event and the ones who remained stable during two year follow-up.
Structure analysis of growing network based on partial differential equations
Junbo JIA
2016-04-01
Full Text Available The topological structure is one of the most important contents in the complex network research. Therein the node degree and the degree distribution are the most basic characteristic quantities to describe topological structure. In order to calculate the degree distribution, first of all, the node degree is considered as a continuous variable. Then, according to the Markov Property of growing network, the cumulative distribution function's evolution equation with time can be obtained. Finally, the partial differential equation (PDE model can be established through distortion processing. Taking the growing network with preferential and random attachment mechanism as an example, the PDE model is obtained. The analytic expression of degree distribution is obtained when this model is solved. Besides, the degree function over time is the same as the characteristic line of PDE. At last, the model is simulated. This PDE method of changing the degree distribution calculation into problem of solving PDE makes the structure analysis more accurate.
Differential Protein Network Analysis of the Immune Cell Lineage
Trevor Clancy
2014-01-01
Full Text Available Recently, the Immunological Genome Project (ImmGen completed the first phase of the goal to understand the molecular circuitry underlying the immune cell lineage in mice. That milestone resulted in the creation of the most comprehensive collection of gene expression profiles in the immune cell lineage in any model organism of human disease. There is now a requisite to examine this resource using bioinformatics integration with other molecular information, with the aim of gaining deeper insights into the underlying processes that characterize this immune cell lineage. We present here a bioinformatics approach to study differential protein interaction mechanisms across the entire immune cell lineage, achieved using affinity propagation applied to a protein interaction network similarity matrix. We demonstrate that the integration of protein interaction networks with the most comprehensive database of gene expression profiles of the immune cells can be used to generate hypotheses into the underlying mechanisms governing the differentiation and the differential functional activity across the immune cell lineage. This approach may not only serve as a hypothesis engine to derive understanding of differentiation and mechanisms across the immune cell lineage, but also help identify possible immune lineage specific and common lineage mechanism in the cells protein networks.
Differentially Private Data Analysis of Social Networks via Restricted Sensitivity
Blocki, Jeremiah; Datta, Anupam; Sheffet, Or
2012-01-01
We introduce the notion of restricted sensitivity as an alternative to global and smooth sensitivity to improve accuracy in differentially private data analysis. The definition of restricted sensitivity is similar to that of global sensitivity except that instead of quantifying over all possible datasets, we take advantage of any beliefs about the dataset that a querier may have, to quantify over a restricted class of datasets. Specifically, given a query f and a hypothesis H about the structure of a dataset D, we show generically how to transform f into a new query f_H whose global sensitivity (over all datasets including those that do not satisfy H) matches the restricted sensitivity of the query f. Moreover, if the belief of the querier is correct (i.e., D is in H) then f_H(D) = f(D). If the belief is incorrect, then f_H(D) may be inaccurate. We demonstrate the usefulness of this notion by considering the task of answering queries regarding social-networks, which we model as a combination of a graph and a ...
Structure analysis of growing network based on partial differential equations
Junbo JIA; Jin, Zhen
2016-01-01
The topological structure is one of the most important contents in the complex network research. Therein the node degree and the degree distribution are the most basic characteristic quantities to describe topological structure. In order to calculate the degree distribution, first of all, the node degree is considered as a continuous variable. Then, according to the Markov Property of growing network, the cumulative distribution function's evolution equation with time can be obtained. Finally...
Ma, Chuang; Xin, Mingming; Feldmann, Kenneth A; Wang, Xiangfeng
2014-02-01
Machine learning (ML) is an intelligent data mining technique that builds a prediction model based on the learning of prior knowledge to recognize patterns in large-scale data sets. We present an ML-based methodology for transcriptome analysis via comparison of gene coexpression networks, implemented as an R package called machine learning-based differential network analysis (mlDNA) and apply this method to reanalyze a set of abiotic stress expression data in Arabidopsis thaliana. The mlDNA first used a ML-based filtering process to remove nonexpressed, constitutively expressed, or non-stress-responsive "noninformative" genes prior to network construction, through learning the patterns of 32 expression characteristics of known stress-related genes. The retained "informative" genes were subsequently analyzed by ML-based network comparison to predict candidate stress-related genes showing expression and network differences between control and stress networks, based on 33 network topological characteristics. Comparative evaluation of the network-centric and gene-centric analytic methods showed that mlDNA substantially outperformed traditional statistical testing-based differential expression analysis at identifying stress-related genes, with markedly improved prediction accuracy. To experimentally validate the mlDNA predictions, we selected 89 candidates out of the 1784 predicted salt stress-related genes with available SALK T-DNA mutagenesis lines for phenotypic screening and identified two previously unreported genes, mutants of which showed salt-sensitive phenotypes.
Manoj Tripathy
2012-01-01
Full Text Available This paper describes a new approach for power transformer differential protection which is based on the wave-shape recognition technique. An algorithm based on neural network principal component analysis (NNPCA with back-propagation learning is proposed for digital differential protection of power transformer. The principal component analysis is used to preprocess the data from power system in order to eliminate redundant information and enhance hidden pattern of differential current to discriminate between internal faults from inrush and overexcitation conditions. This algorithm has been developed by considering optimal number of neurons in hidden layer and optimal number of neurons at output layer. The proposed algorithm makes use of ratio of voltage to frequency and amplitude of differential current for transformer operating condition detection. This paper presents a comparative study of power transformer differential protection algorithms based on harmonic restraint method, NNPCA, feed forward back propagation neural network (FFBPNN, space vector analysis of the differential signal, and their time characteristic shapes in Park’s plane. The algorithms are compared as to their speed of response, computational burden, and the capability to distinguish between a magnetizing inrush and power transformer internal fault. The mathematical basis for each algorithm is briefly described. All the algorithms are evaluated using simulation performed with PSCAD/EMTDC and MATLAB.
Xinan Zhang
Full Text Available Embryonic stem cells are conventionally differentiated by modulating specific growth factors in the cell culture media. Recently the effect of cellular mechanical microenvironment in inducing phenotype specific differentiation has attracted considerable attention. We have shown the possibility of inducing endoderm differentiation by culturing the stem cells on fibrin substrates of specific stiffness. Here, we analyze the regulatory network involved in such mechanically induced endoderm differentiation under two different experimental configurations of 2-dimensional and 3-dimensional culture, respectively. Mouse embryonic stem cells are differentiated on an array of substrates of varying mechanical properties and analyzed for relevant endoderm markers. The experimental data set is further analyzed for identification of co-regulated transcription factors across different substrate conditions using the technique of bi-clustering. Overlapped bi-clusters are identified following an optimization formulation, which is solved using an evolutionary algorithm. While typically such analysis is performed at the mean value of expression data across experimental repeats, the variability of stem cell systems reduces the confidence on such analysis of mean data. Bootstrapping technique is thus integrated with the bi-clustering algorithm to determine sets of robust bi-clusters, which is found to differ significantly from corresponding bi-clusters at the mean data value. Analysis of robust bi-clusters reveals an overall similar network interaction as has been reported for chemically induced endoderm or endodermal organs but with differences in patterning between 2-dimensional and 3-dimensional culture. Such analysis sheds light on the pathway of stem cell differentiation indicating the prospect of the two culture configurations for further maturation.
Differential forms on electromagnetic networks
Balasubramanian, N V; Sen Gupta, D P
2013-01-01
Differential Forms on Electromagnetic Networks deals with the use of combinatorial techniques in electrical circuit, machine analysis, and the relationship between circuit quantities and electromagnetic fields. The monograph is also an introduction to the organization of field equations by the methods of differential forms. The book covers topics such as algebraic structural relations in an electric circuit; mesh and node-pair analysis; exterior differential structures; generalized Stoke's theorem and tensor analysis; and Maxwell's electromagnetic equation. Also covered in the book are the app
Ciucci, Sara; Ge, Yan; Durán, Claudio; Palladini, Alessandra; Jiménez-Jiménez, Víctor; Martínez-Sánchez, Luisa María; Wang, Yuting; Sales, Susanne; Shevchenko, Andrej; Poser, Steven W.; Herbig, Maik; Otto, Oliver; Androutsellis-Theotokis, Andreas; Guck, Jochen; Gerl, Mathias J.; Cannistraci, Carlo Vittorio
2017-01-01
Omic science is rapidly growing and one of the most employed techniques to explore differential patterns in omic datasets is principal component analysis (PCA). However, a method to enlighten the network of omic features that mostly contribute to the sample separation obtained by PCA is missing. An alternative is to build correlation networks between univariately-selected significant omic features, but this neglects the multivariate unsupervised feature compression responsible for the PCA sample segregation. Biologists and medical researchers often prefer effective methods that offer an immediate interpretation to complicated algorithms that in principle promise an improvement but in practice are difficult to be applied and interpreted. Here we present PC-corr: a simple algorithm that associates to any PCA segregation a discriminative network of features. Such network can be inspected in search of functional modules useful in the definition of combinatorial and multiscale biomarkers from multifaceted omic data in systems and precision biomedicine. We offer proofs of PC-corr efficacy on lipidomic, metagenomic, developmental genomic, population genetic, cancer promoteromic and cancer stem-cell mechanomic data. Finally, PC-corr is a general functional network inference approach that can be easily adopted for big data exploration in computer science and analysis of complex systems in physics. PMID:28287094
Prediction of disease-gene-drug relationships following a differential network analysis.
Zickenrott, S; Angarica, V E; Upadhyaya, B B; del Sol, A
2016-01-01
Great efforts are being devoted to get a deeper understanding of disease-related dysregulations, which is central for introducing novel and more effective therapeutics in the clinics. However, most human diseases are highly multifactorial at the molecular level, involving dysregulation of multiple genes and interactions in gene regulatory networks. This issue hinders the elucidation of disease mechanism, including the identification of disease-causing genes and regulatory interactions. Most of current network-based approaches for the study of disease mechanisms do not take into account significant differences in gene regulatory network topology between healthy and disease phenotypes. Moreover, these approaches are not able to efficiently guide database search for connections between drugs, genes and diseases. We propose a differential network-based methodology for identifying candidate target genes and chemical compounds for reverting disease phenotypes. Our method relies on transcriptomics data to reconstruct gene regulatory networks corresponding to healthy and disease states separately. Further, it identifies candidate genes essential for triggering the reversion of the disease phenotype based on network stability determinants underlying differential gene expression. In addition, our method selects and ranks chemical compounds targeting these genes, which could be used as therapeutic interventions for complex diseases.
Network analysis of differential expression for the identification of disease-causing genes.
Daniela Nitsch
Full Text Available Genetic studies (in particular linkage and association studies identify chromosomal regions involved in a disease or phenotype of interest, but those regions often contain many candidate genes, only a few of which can be followed-up for biological validation. Recently, computational methods to identify (prioritize the most promising candidates within a region have been proposed, but they are usually not applicable to cases where little is known about the phenotype (no or few confirmed disease genes, fragmentary understanding of the biological cascades involved. We seek to overcome this limitation by replacing knowledge about the biological process by experimental data on differential gene expression between affected and healthy individuals. Considering the problem from the perspective of a gene/protein network, we assess a candidate gene by considering the level of differential expression in its neighborhood under the assumption that strong candidates will tend to be surrounded by differentially expressed neighbors. We define a notion of soft neighborhood where each gene is given a contributing weight, which decreases with the distance from the candidate gene on the protein network. To account for multiple paths between genes, we define the distance using the Laplacian exponential diffusion kernel. We score candidates by aggregating the differential expression of neighbors weighted as a function of distance. Through a randomization procedure, we rank candidates by p-values. We illustrate our approach on four monogenic diseases and successfully prioritize the known disease causing genes.
Kaushik, Abhinav; Ali, Shakir; Gupta, Dinesh
2017-01-01
Gene connection rewiring is an essential feature of gene network dynamics. Apart from its normal functional role, it may also lead to dysregulated functional states by disturbing pathway homeostasis. Very few computational tools measure rewiring within gene co-expression and its corresponding regulatory networks in order to identify and prioritize altered pathways which may or may not be differentially regulated. We have developed Altered Pathway Analyzer (APA), a microarray dataset analysis tool for identification and prioritization of altered pathways, including those which are differentially regulated by TFs, by quantifying rewired sub-network topology. Moreover, APA also helps in re-prioritization of APA shortlisted altered pathways enriched with context-specific genes. We performed APA analysis of simulated datasets and p53 status NCI-60 cell line microarray data to demonstrate potential of APA for identification of several case-specific altered pathways. APA analysis reveals several altered pathways not detected by other tools evaluated by us. APA analysis of unrelated prostate cancer datasets identifies sample-specific as well as conserved altered biological processes, mainly associated with lipid metabolism, cellular differentiation and proliferation. APA is designed as a cross platform tool which may be transparently customized to perform pathway analysis in different gene expression datasets. APA is freely available at http://bioinfo.icgeb.res.in/APA. PMID:28084397
Nitsch Daniela
2010-09-01
Full Text Available Abstract Background Discovering novel disease genes is still challenging for diseases for which no prior knowledge - such as known disease genes or disease-related pathways - is available. Performing genetic studies frequently results in large lists of candidate genes of which only few can be followed up for further investigation. We have recently developed a computational method for constitutional genetic disorders that identifies the most promising candidate genes by replacing prior knowledge by experimental data of differential gene expression between affected and healthy individuals. To improve the performance of our prioritization strategy, we have extended our previous work by applying different machine learning approaches that identify promising candidate genes by determining whether a gene is surrounded by highly differentially expressed genes in a functional association or protein-protein interaction network. Results We have proposed three strategies scoring disease candidate genes relying on network-based machine learning approaches, such as kernel ridge regression, heat kernel, and Arnoldi kernel approximation. For comparison purposes, a local measure based on the expression of the direct neighbors is also computed. We have benchmarked these strategies on 40 publicly available knockout experiments in mice, and performance was assessed against results obtained using a standard procedure in genetics that ranks candidate genes based solely on their differential expression levels (Simple Expression Ranking. Our results showed that our four strategies could outperform this standard procedure and that the best results were obtained using the Heat Kernel Diffusion Ranking leading to an average ranking position of 8 out of 100 genes, an AUC value of 92.3% and an error reduction of 52.8% relative to the standard procedure approach which ranked the knockout gene on average at position 17 with an AUC value of 83.7%. Conclusion In this study we
Rebuffo-Scheer, Cecilia A; Schmitt, Jürgen; Scherer, Siegfried
2007-02-01
A classification system based on Fourier transform infrared (FTIR) spectroscopy combined with artificial neural network analysis was designed to differentiate 12 serovars of Listeria monocytogenes using a reference database of 106 well-defined strains. External validation was performed using a test set of another 166 L. monocytogenes strains. The O antigens (serogroup) of 164 strains (98.8%) could be identified correctly, and H antigens were correctly determined in 152 (91.6%) of the test strains. Importantly, 40 out of 41 potentially epidemic serovar 4b strains were unambiguously identified. FTIR analysis is superior to PCR-based systems for serovar differentiation and has potential for the rapid, simultaneous identification of both species and serovar of an unknown Listeria isolate by simply measuring a whole-cell infrared spectrum.
Sriram Devanathan
Full Text Available Estrogen exerts diverse biological effects in multiple tissues in both animals and humans. Much of the accumulated knowledge on the role of estrogen receptor (ER in the heart has been obtained from studies using ovariectomized mice, whole body ER gene knock-out animal models, ex vivo heart studies, or from isolated cardiac myocytes. In light of the wide systemic influence of ER signaling in regulating a host of biological functions in multiple tissues, it is difficult to infer the direct role of ER on the heart. Therefore, we developed a mouse model with a cardiomyocyte-specific deletion of the ERα allele (cs-ERα-/-. Male and female cs-ERα-/- mice with age/sex-matched wild type controls were examined for differences in cardiac structure and function by echocardiogram and differential gene expression microarray analysis. Our study revealed sex-differences in structural parameters in the hearts of cs-ERα-/- mice, with minimal functional differences. Analysis of microarray data revealed differential variations in the expression of 208 genes affecting multiple transcriptional networks. Furthermore, we report sex-specific differences in the expression of 56 genes. Overall, we developed a mouse model with cardiac-specific deletion of ERα to characterize the role of ERα in the heart independent of systemic effects. Our results suggest that ERα is involved in controlling the expression of diverse genes and networks in the cardiomyocyte in a sex-dependent manner.
Bonde, Bhushan K; Beste, Dany J V; Laing, Emma; Kierzek, Andrzej M; McFadden, Johnjoe
2011-06-01
A general paucity of knowledge about the metabolic state of Mycobacterium tuberculosis within the host environment is a major factor impeding development of novel drugs against tuberculosis. Current experimental methods do not allow direct determination of the global metabolic state of a bacterial pathogen in vivo, but the transcriptional activity of all encoded genes has been investigated in numerous microarray studies. We describe a novel algorithm, Differential Producibility Analysis (DPA) that uses a metabolic network to extract metabolic signals from transcriptome data. The method utilizes Flux Balance Analysis (FBA) to identify the set of genes that affect the ability to produce each metabolite in the network. Subsequently, Rank Product Analysis is used to identify those metabolites predicted to be most affected by a transcriptional signal. We first apply DPA to investigate the metabolic response of E. coli to both anaerobic growth and inactivation of the FNR global regulator. DPA successfully extracts metabolic signals that correspond to experimental data and provides novel metabolic insights. We next apply DPA to investigate the metabolic response of M. tuberculosis to the macrophage environment, human sputum and a range of in vitro environmental perturbations. The analysis revealed a previously unrecognized feature of the response of M. tuberculosis to the macrophage environment: a down-regulation of genes influencing metabolites in central metabolism and concomitant up-regulation of genes that influence synthesis of cell wall components and virulence factors. DPA suggests that a significant feature of the response of the tubercle bacillus to the intracellular environment is a channeling of resources towards remodeling of its cell envelope, possibly in preparation for attack by host defenses. DPA may be used to unravel the mechanisms of virulence and persistence of M. tuberculosis and other pathogens and may have general application for extracting
Chao Ma
Full Text Available BACKGROUND: Salvianolic acid B (SB is an active component isolated from Danshen, a traditional Chinese medicine widely used for the treatment of cardiovascular disorders. Previous study suggested that SB might inhibit adhesion as well as aggregation of platelets by a mechanism involving the integrin α2β1. But, the signal cascades in platelets after SB binding are still not clear. METHODOLOGY/PRINCIPAL FINDINGS: In the present study, a differential proteomic analysis (two-dimensional electrophoresis was conducted to check the protein expression profiles of rat platelets with or without treatment of SB. Proteins altered in level after SB exposure were identified by MALDI-TOF MS/MS. Treatment of SB caused regulation of 20 proteins such as heat shock-related 70 kDa protein 2 (hsp70, LIM domain protein CLP-36, copine I, peroxiredoxin-2, coronin-1 B and cytoplasmic dynein intermediate chain 2C. The regulation of SB on protein levels was confirmed by Western blotting. The signal cascades network induced by SB after its binding with integrin α2β1 was predicted. To certify the predicted network, binding affinity of SB to integrin α2β1 was checked in vitro and ex vivo in platelets. Furthermore, the effects of SB on protein levels of hsp70, coronin-1B and intracellular levels of Ca²+ and reactive oxygen species (ROS were checked with or without pre-treatment of platelets using antibody against integrin α2β1. Electron microscopy study confirmed that SB affected cytoskeleton structure of platelets. CONCLUSIONS/SIGNIFICANCE: Integrin α2β1 might be one of the direct target proteins of SB in platelets. The signal cascades network of SB after binding with integrin α2β1 might include regulation of intracellular Ca²+ level, cytoskeleton-related proteins such as coronin-1B and cytoskeleton structure of platelets.
Ma, Chao; Yao, Yan; Yue, Qing-Xi; Zhou, Xin-Wen; Yang, Peng-Yuan; Wu, Wan-Ying; Guan, Shu-Hong; Jiang, Bao-Hong; Yang, Min; Liu, Xuan; Guo, De-An
2011-01-01
Background Salvianolic acid B (SB) is an active component isolated from Danshen, a traditional Chinese medicine widely used for the treatment of cardiovascular disorders. Previous study suggested that SB might inhibit adhesion as well as aggregation of platelets by a mechanism involving the integrin α2β1. But, the signal cascades in platelets after SB binding are still not clear. Methodology/Principal Findings In the present study, a differential proteomic analysis (two-dimensional electrophoresis) was conducted to check the protein expression profiles of rat platelets with or without treatment of SB. Proteins altered in level after SB exposure were identified by MALDI-TOF MS/MS. Treatment of SB caused regulation of 20 proteins such as heat shock-related 70 kDa protein 2 (hsp70), LIM domain protein CLP-36, copine I, peroxiredoxin-2, coronin-1 B and cytoplasmic dynein intermediate chain 2C. The regulation of SB on protein levels was confirmed by Western blotting. The signal cascades network induced by SB after its binding with integrin α2β1 was predicted. To certify the predicted network, binding affinity of SB to integrin α2β1 was checked in vitro and ex vivo in platelets. Furthermore, the effects of SB on protein levels of hsp70, coronin-1B and intracellular levels of Ca(2+) and reactive oxygen species (ROS) were checked with or without pre-treatment of platelets using antibody against integrin α2β1. Electron microscopy study confirmed that SB affected cytoskeleton structure of platelets. Conclusions/Significance Integrin α2β1 might be one of the direct target proteins of SB in platelets. The signal cascades network of SB after binding with integrin α2β1 might include regulation of intracellular Ca(2+) level, cytoskeleton-related proteins such as coronin-1B and cytoskeleton structure of platelets. PMID:21379382
Zhao, Kun; Zhang, Bangning; Pan, Kegang; Liu, Aijun; Guo, Daoxing
2014-07-01
Due to the distributed nature, cooperative networks are generally subject to multiple carrier frequency offsets (MCFOs), which make the channels time-varying and drastically degrade the system performance. In this paper, to address the MCFOs problem in detect-andforward (DetF) multi-relay cooperative networks, a robust relay selection (RS) based double-differential (DD) transmission scheme, termed RSDDT, is proposed, where the best relay is selected to forward the source's double-differentially modulated signals to the destination with the DetF protocol. The proposed RSDDT scheme can achieve excellent performance over fading channels in the presence of unknown MCFOs. Considering double-differential multiple phase-shift keying (DDMPSK) is applied, we first derive exact expressions for the outage probability and average bit error rate (BER) of the RSDDT scheme. Then, we look into the high signal-to-noise ratio (SNR) regime and present simple and informative asymptotic outage probability and average BER expressions, which reveal that the proposed scheme can achieve full diversity. Moreover, to further improve the BER performance of the RSDDT scheme, we investigate the optimum power allocation strategy among the source and the relay nodes, and simple analytical solutions are obtained. Numerical results are provided to corroborate the derived analytical expressions and it is demonstrated that the proposed optimum power allocation strategy offers substantial BER performance improvement over the equal power allocation strategy.
Shibin Mathew
2015-05-01
Full Text Available The fate choice of human embryonic stem cells (hESCs is controlled by complex signaling milieu synthesized by diverse chemical factors in the growth media. Prevalence of crosstalks and interactions between parallel pathways renders any analysis probing the process of fate transition of hESCs elusive. This work presents an important step in the evaluation of network level interactions between signaling molecules controlling endoderm lineage specification from hESCs using a statistical network identification algorithm. Network analysis was performed on detailed signaling dynamics of key molecules from TGF-β/SMAD, PI3K/AKT and MAPK/ERK pathways under two common endoderm induction conditions. The results show the existence of significant crosstalk interactions during endoderm signaling and they identify differences in network connectivity between the induction conditions in the early and late phases of signaling dynamics. Predicted networks elucidate the significant effect of modulation of AKT mediated crosstalk leading to the success of PI3K inhibition in inducing efficient endoderm from hESCs in combination with TGF-β/SMAD signaling.
Service Differentiation in Residential Broadband Networks
Sigurdsson, Halldór Matthias
2004-01-01
As broadband gains widespread adoption with residential users, revenue generating voice- and video-services have not yet taken off. This slow uptake is often attributed to lack of Quality of Service management in residential broadband networks. To resolve this and induce service variety, network...... access providers are implementing service differentiation in their networks where voice and video gets prioritised before data. This paper discusses the role of network access providers in multipurpose packet based networks and the available migration strategies for supporting multimedia services...... in digital subscriber line (DSL) based residential broadband networks. Four possible implementation scenarios and their technical characteristics and effects are described. To conclude, the paper discusses how network access providers can be induced to open their networks for third party service providers....
Andrea eVega
2015-11-01
Full Text Available Nitrogen (N is one of the main limiting nutrients for plant growth and crop yield. It is well documented that changes in nitrate availability, the main N source found in agricultural soils, influences a myriad of developmental programs and processes including the plant defense response. Indeed, many agronomical reports indicate that the plant N nutritional status influences their ability to respond effectively when challenged by different pathogens. However, the molecular mechanisms involved in N-modulation of plant susceptibility to pathogens are poorly characterized. In this work, we show that Solanum lycopersicum defense response to the necrotrophic fungus Botrytis cinerea is affected by plant N availability, with higher susceptibility in nitrate-limiting conditions. Global gene expression responses of tomato against B. cinerea under contrasting nitrate conditions reveals that plant primary metabolism is affected by the fungal infection regardless of N regimes. This result suggests that differential susceptibility to pathogen attack under contrasting N conditions is not only explained by a metabolic alteration. We used a systems biology approach to identify the transcriptional regulatory network implicated in plant response to the fungus infection under contrasting nitrate conditions. Interestingly, hub genes in this network are known key transcription factors involved in ethylene and jasmonic acid signaling. This result positions these hormones as key integrators of nitrate and defense against B. cinerea in tomato plants. Our results provide insights into potential crosstalk mechanisms between necrotrophic defense response and N status in plants.
Transcriptional networks controlling adipocyte differentiation
Siersbæk, R; Mandrup, Susanne
2011-01-01
Adipocyte differentiation is regulated by a complex cascade of signals that drive the transcriptional reprogramming of the fibroblastic precursors. Genome-wide analyses of chromatin accessibility and binding of adipogenic transcription factors make it possible to generate "snapshots" of the trans...
Location-based reliability differentiated service for wireless sensor networks
Yong ZENG; Jianfeng MA
2009-01-01
Designing reliability differentiated services for missions with different reliability requirements has become a hot topic in wireless sensor networks. Combined with a location-based routing mechanism, a quantified model without full network topology is proposed to evaluate reliability. By introducing a virtual reference point, the data transfer is limited in a specified area. The reliability function of the area is given. A detailed analysis shows that the function increases quadratically with the distance between the source node and the reference node. A reliability differentiated service mechanism is then proposed. The simulation results show the efficiency of the proposed mechanism.
Neural network approach for differential diagnosis of interstitial lung diseases
Asada, Naoki; Doi, Kunio; MacMahon, Heber; Montner, Steven M.; Giger, Maryellen L.; Abe, Chihiro; Wu, Chris Y.
1990-07-01
A neural network approach was applied for the differential diagnosis of interstitial lung diseases. The neural network was designed for distinguishing between 9 types of interstitial lung diseases based on 20 items of clinical and radiographic information. A database for training and testing the neural network was created with 10 hypothetical cases for each of the 9 diseases. The performance of the neural network was evaluated by ROC analysis. The optimal parameters for the current neural network were determined by selecting those yielding the highest ROC curves. In this case the neural network consisted of one hidden layer including 6 units and was trained with 200 learning iterations. When the decision performances of the neural network chest radiologists and senior radiology residents were compared the neural network indicated high performance comparable to that of chest radiologists and superior to that of senior radiology residents. Our preliminary results suggested strongly that the neural network approach had potential utility in the computer-aided differential diagnosis of interstitial lung diseases. 1_
Mathematical analysis differentiation and integration
Aramanovich, IG; Lyusternik, LA; Sneddon, I N
1965-01-01
Mathematical Analysis: Differentiation and Integration is devoted to two basic operations of mathematical analysis, differentiation and integration. The problems directly connected with the operations of differentiation and integration of functions of one or several variables are discussed, together with elementary generalizations of these operations. This volume is comprised of seven chapters and begins by considering the differentiation of functions of one variable and of n variables, paying particular attention to derivatives and differentials as well as their properties. The next chapter d
2014-01-01
Background Our current knowledge of tooth development derives mainly from studies in mice, which have only one set of non-replaced teeth, compared with the diphyodont dentition in humans. The miniature pig is also diphyodont, making it a valuable alternative model for understanding human tooth development and replacement. However, little is known about gene expression and function during swine odontogenesis. The goal of this study is to undertake the survey of differential gene expression profiling and functional network analysis during morphogenesis of diphyodont dentition in miniature pigs. The identification of genes related to diphyodont development should lead to a better understanding of morphogenetic patterns and the mechanisms of diphyodont replacement in large animal models and humans. Results The temporal gene expression profiles during early diphyodont development in miniature pigs were detected with the Affymetrix Porcine GeneChip. The gene expression data were further evaluated by ANOVA as well as pathway and STC analyses. A total of 2,053 genes were detected with differential expression. Several signal pathways and 151 genes were then identified through the construction of pathway and signal networks. Conclusions The gene expression profiles indicated that spatio-temporal down-regulation patterns of gene expression were predominant; while, both dynamic activation and inhibition of pathways occurred during the morphogenesis of diphyodont dentition. Our study offers a mechanistic framework for understanding dynamic gene regulation of early diphyodont development and provides a molecular basis for studying teeth development, replacement, and regeneration in miniature pigs. PMID:24498892
Constructing general partial differential equations using polynomial and neural networks.
Zjavka, Ladislav; Pedrycz, Witold
2016-01-01
Sum fraction terms can approximate multi-variable functions on the basis of discrete observations, replacing a partial differential equation definition with polynomial elementary data relation descriptions. Artificial neural networks commonly transform the weighted sum of inputs to describe overall similarity relationships of trained and new testing input patterns. Differential polynomial neural networks form a new class of neural networks, which construct and solve an unknown general partial differential equation of a function of interest with selected substitution relative terms using non-linear multi-variable composite polynomials. The layers of the network generate simple and composite relative substitution terms whose convergent series combinations can describe partial dependent derivative changes of the input variables. This regression is based on trained generalized partial derivative data relations, decomposed into a multi-layer polynomial network structure. The sigmoidal function, commonly used as a nonlinear activation of artificial neurons, may transform some polynomial items together with the parameters with the aim to improve the polynomial derivative term series ability to approximate complicated periodic functions, as simple low order polynomials are not able to fully make up for the complete cycles. The similarity analysis facilitates substitutions for differential equations or can form dimensional units from data samples to describe real-world problems.
Chauss, Daniel; Basu, Subhasree; Rajakaruna, Suren; Ma, Zhiwei; Gau, Victoria; Anastas, Sara; Brennan, Lisa A; Hejtmancik, J Fielding; Menko, A Sue; Kantorow, Marc
2014-06-13
The mature eye lens contains a surface layer of epithelial cells called the lens epithelium that requires a functional mitochondrial population to maintain the homeostasis and transparency of the entire lens. The lens epithelium overlies a core of terminally differentiated fiber cells that must degrade their mitochondria to achieve lens transparency. These distinct mitochondrial populations make the lens a useful model system to identify those genes that regulate the balance between mitochondrial homeostasis and elimination. Here we used an RNA sequencing and bioinformatics approach to identify the transcript levels of all genes expressed by distinct regions of the lens epithelium and maturing fiber cells of the embryonic Gallus gallus (chicken) lens. Our analysis detected more than 15,000 unique transcripts expressed by the embryonic chicken lens. Of these, more than 3000 transcripts exhibited significant differences in expression between lens epithelial cells and fiber cells. Multiple transcripts coding for separate mitochondrial homeostatic and degradation mechanisms were identified to exhibit preferred patterns of expression in lens epithelial cells that require mitochondria relative to lens fiber cells that require mitochondrial elimination. These included differences in the expression levels of metabolic (DUT, PDK1, SNPH), autophagy (ATG3, ATG4B, BECN1, FYCO1, WIPI1), and mitophagy (BNIP3L/NIX, BNIP3, PARK2, p62/SQSTM1) transcripts between lens epithelial cells and lens fiber cells. These data provide a comprehensive window into all genes transcribed by the lens and those mitochondrial regulatory and degradation pathways that function to maintain mitochondrial populations in the lens epithelium and to eliminate mitochondria in maturing lens fiber cells.
Skeberis, Christos; Zaharis, Zaharias; Xenos, Thomas; Spatalas, Spyridon; Stratakis, Dimitrios; Maggipinto, Tommaso; Biagi, Pier francesco
2016-04-01
This study provides an evaluation of the application of high-order differential analysis on VLF signals on a multiple-receiver multiple-transmitter network. This application provides a method for near-real-time detection of disturbances that can be attributed to seismic-ionospheric precursor phenomena and can discriminate disturbances that could be classified as false positives and thus should be attributed to other geomagnetic influences. VLF data acquired in Thessaloniki, Greece (40.59N, 22,78E) Herakleion, Greece (35.31N, 25.10E), Nicosia, Cyprus (35.17N, 33.35E), Italy (42.42N, 13.08E) and transmitted by the VLF station in Tavolara, Italy (ICV station 40.923N, 9.731E) and the station in Keflavik, Iceland (ICE 64.02N, 22.57W) from January 2015 to January 2016 were used for the purpose of this paper. The receivers have been developed by Elettronika Srl and are part of the International Network for Frontier Research on Earthquake Precursors (INFREP). The process applied for this study has been further developed and is based on differential analysis. The signals undergo transformation using an enhanced version of the Hilbert Huang Transform, and relevant spectra are produced. On the product of this process, differential analysis is applied. Finally, the method produces the correlation coefficient of signals that are on the same path over an earthquake epicenter in order to highlight disturbances, and on the opposite can make comparisons with unrelated transmitted signals of different paths to eliminate disturbances that are not localized to the area of interest. This improvement provides a simple method of noise cancellation to signals that would otherwise be considered as false positives. A further evaluation of the method is provided with the presentation and discussion of sample results. The method seems to be a robust tool of analysis of VLF signals and also an automatic detection tool with built-in noise cancellation of outside disturbances.
Mathias, Carlos Leonardo Kelmer
2014-01-01
In general, the paper develops a historiographical debate about the methodology of social network analysis. More than responding questions using such methodology, this article tries to introduce the historian to the founder bibliography of social network analysis. Since the publication of the famous article by John Barnes in 1954, sociologists linked to sociometric studies have usually employed the social network analysis in their studies. On the other hand, this methodology is not widespread...
Antagonistic Neural Networks Underlying Differentiated Leadership Roles
Richard Eleftherios Boyatzis
2014-03-01
Full Text Available The emergence of two distinct leadership roles, the task leader and the socio-emotional leader, has been documented in the leadership literature since the 1950’s. Recent research in neuroscience suggests that the division between task oriented and socio-emotional oriented roles derives from a fundamental feature of our neurobiology: an antagonistic relationship between two large-scale cortical networks -- the Task Positive Network (TPN and the Default Mode Network (DMN. Neural activity in TPN tends to inhibit activity in the DMN, and vice versa. The TPN is important for problem solving, focusing of attention, making decisions, and control of action. The DMN plays a central role in emotional self-awareness, social cognition, and ethical decision making. It is also strongly linked to creativity and openness to new ideas. Because activation of the TPN tends to suppress activity in the DMN, an over-emphasis on task oriented leadership may prove deleterious to social and emotional aspects of leadership. Similarly, an overemphasis on the DMN would result in difficulty focusing attention, making decisions and solving known problems. In this paper, we will review major streams of theory and research on leadership roles in the context of recent findings from neuroscience and psychology. We conclude by suggesting that emerging research challenges the assumption that role differentiation is both natural and necessary, in particular when openness to new ideas, people, emotions, and ethical concerns are important to success.
A stochastic differential equation model for transcriptional regulatory networks
Quirk Michelle D
2007-05-01
Full Text Available Abstract Background This work explores the quantitative characteristics of the local transcriptional regulatory network based on the availability of time dependent gene expression data sets. The dynamics of the gene expression level are fitted via a stochastic differential equation model, yielding a set of specific regulators and their contribution. Results We show that a beta sigmoid function that keeps track of temporal parameters is a novel prototype of a regulatory function, with the effect of improving the performance of the profile prediction. The stochastic differential equation model follows well the dynamic of the gene expression levels. Conclusion When adapted to biological hypotheses and combined with a promoter analysis, the method proposed here leads to improved models of the transcriptional regulatory networks.
Sakata, Katsumi; Ohyanagi, Hajime; Sato, Shinji; Nobori, Hiroya; Hayashi, Akiko; Ishii, Hideshi; Daub, Carsten O.; Kawai, Jun; Suzuki, Harukazu; Saito, Toshiyuki
2015-02-01
We present a system-wide transcriptional network structure that controls cell types in the context of expression pattern transitions that correspond to cell type transitions. Co-expression based analyses uncovered a system-wide, ladder-like transcription factor cluster structure composed of nearly 1,600 transcription factors in a human transcriptional network. Computer simulations based on a transcriptional regulatory model deduced from the system-wide, ladder-like transcription factor cluster structure reproduced expression pattern transitions when human THP-1 myelomonocytic leukaemia cells cease proliferation and differentiate under phorbol myristate acetate stimulation. The behaviour of MYC, a reprogramming Yamanaka factor that was suggested to be essential for induced pluripotent stem cells during dedifferentiation, could be interpreted based on the transcriptional regulation predicted by the system-wide, ladder-like transcription factor cluster structure. This study introduces a novel system-wide structure to transcriptional networks that provides new insights into network topology.
Artificial Neural Networks, Symmetries and Differential Evolution
Urfalioglu, Onay
2010-01-01
Neuroevolution is an active and growing research field, especially in times of increasingly parallel computing architectures. Learning methods for Artificial Neural Networks (ANN) can be divided into two groups. Neuroevolution is mainly based on Monte-Carlo techniques and belongs to the group of global search methods, whereas other methods such as backpropagation belong to the group of local search methods. ANN's comprise important symmetry properties, which can influence Monte-Carlo methods. On the other hand, local search methods are generally unaffected by these symmetries. In the literature, dealing with the symmetries is generally reported as being not effective or even yielding inferior results. In this paper, we introduce the so called Minimum Global Optimum Proximity principle derived from theoretical considerations for effective symmetry breaking, applied to offline supervised learning. Using Differential Evolution (DE), which is a popular and robust evolutionary global optimization method, we experi...
Du Toit W P Schabort
Full Text Available We investigated the transcriptomic response of a new strain of the yeast Kluyveromyces marxianus, in glucose and xylose media using RNA-seq. The data were explored in a number of innovative ways using a variety of networks types, pathway maps, enrichment statistics, reporter metabolites and a flux simulation model, revealing different aspects of the genome-scale response in an integrative systems biology manner. The importance of the subcellular localisation in the transcriptomic response is emphasised here, revealing new insights. As was previously reported by others using a rich medium, we show that peroxisomal fatty acid catabolism was dramatically up-regulated in a defined xylose mineral medium without fatty acids, along with mechanisms to activate fatty acids and transfer products of β-oxidation to the mitochondria. Notably, we observed a strong up-regulation of the 2-methylcitrate pathway, supporting capacity for odd-chain fatty acid catabolism. Next we asked which pathways would respond to the additional requirement for NADPH for xylose utilisation, and rationalised the unexpected results using simulations with Flux Balance Analysis. On a fundamental level, we investigated the contribution of the hierarchical and metabolic regulation levels to the regulation of metabolic fluxes. Metabolic regulation analysis suggested that genetic level regulation plays a major role in regulating metabolic fluxes in adaptation to xylose, even for the high capacity reactions, which is unexpected. In addition, isozyme switching may play an important role in re-routing of metabolic fluxes in subcellular compartments in K. marxianus.
Schabort, Du Toit W P; Letebele, Precious K; Steyn, Laurinda; Kilian, Stephanus G; du Preez, James C
2016-01-01
We investigated the transcriptomic response of a new strain of the yeast Kluyveromyces marxianus, in glucose and xylose media using RNA-seq. The data were explored in a number of innovative ways using a variety of networks types, pathway maps, enrichment statistics, reporter metabolites and a flux simulation model, revealing different aspects of the genome-scale response in an integrative systems biology manner. The importance of the subcellular localisation in the transcriptomic response is emphasised here, revealing new insights. As was previously reported by others using a rich medium, we show that peroxisomal fatty acid catabolism was dramatically up-regulated in a defined xylose mineral medium without fatty acids, along with mechanisms to activate fatty acids and transfer products of β-oxidation to the mitochondria. Notably, we observed a strong up-regulation of the 2-methylcitrate pathway, supporting capacity for odd-chain fatty acid catabolism. Next we asked which pathways would respond to the additional requirement for NADPH for xylose utilisation, and rationalised the unexpected results using simulations with Flux Balance Analysis. On a fundamental level, we investigated the contribution of the hierarchical and metabolic regulation levels to the regulation of metabolic fluxes. Metabolic regulation analysis suggested that genetic level regulation plays a major role in regulating metabolic fluxes in adaptation to xylose, even for the high capacity reactions, which is unexpected. In addition, isozyme switching may play an important role in re-routing of metabolic fluxes in subcellular compartments in K. marxianus.
An introduction to neural network methods for differential equations
Yadav, Neha; Kumar, Manoj
2015-01-01
This book introduces a variety of neural network methods for solving differential equations arising in science and engineering. The emphasis is placed on a deep understanding of the neural network techniques, which has been presented in a mostly heuristic and intuitive manner. This approach will enable the reader to understand the working, efficiency and shortcomings of each neural network technique for solving differential equations. The objective of this book is to provide the reader with a sound understanding of the foundations of neural networks, and a comprehensive introduction to neural network methods for solving differential equations together with recent developments in the techniques and their applications. The book comprises four major sections. Section I consists of a brief overview of differential equations and the relevant physical problems arising in science and engineering. Section II illustrates the history of neural networks starting from their beginnings in the 1940s through to the renewed...
W. de Nooy
2009-01-01
Social network analysis (SNA) focuses on the structure of ties within a set of social actors, e.g., persons, groups, organizations, and nations, or the products of human activity or cognition such as web sites, semantic concepts, and so on. It is linked to structuralism in sociology stressing the si
Group analysis of differential equations
Ovsiannikov, L V
1982-01-01
Group Analysis of Differential Equations provides a systematic exposition of the theory of Lie groups and Lie algebras and its application to creating algorithms for solving the problems of the group analysis of differential equations.This text is organized into eight chapters. Chapters I to III describe the one-parameter group with its tangential field of vectors. The nonstandard treatment of the Banach Lie groups is reviewed in Chapter IV, including a discussion of the complete theory of Lie group transformations. Chapters V and VI cover the construction of partial solution classes for the g
Bonald, Thomas
2013-01-01
The book presents some key mathematical tools for the performance analysis of communication networks and computer systems.Communication networks and computer systems have become extremely complex. The statistical resource sharing induced by the random behavior of users and the underlying protocols and algorithms may affect Quality of Service.This book introduces the main results of queuing theory that are useful for analyzing the performance of these systems. These mathematical tools are key to the development of robust dimensioning rules and engineering methods. A number of examples i
Social Network Analysis Based on Network Motifs
2014-01-01
Based on the community structure characteristics, theory, and methods of frequent subgraph mining, network motifs findings are firstly introduced into social network analysis; the tendentiousness evaluation function and the importance evaluation function are proposed for effectiveness assessment. Compared with the traditional way based on nodes centrality degree, the new approach can be used to analyze the properties of social network more fully and judge the roles of the nodes effectively. I...
Network systems security analysis
Yilmaz, Ä.°smail
2015-05-01
Network Systems Security Analysis has utmost importance in today's world. Many companies, like banks which give priority to data management, test their own data security systems with "Penetration Tests" by time to time. In this context, companies must also test their own network/server systems and take precautions, as the data security draws attention. Based on this idea, the study cyber-attacks are researched throughoutly and Penetration Test technics are examined. With these information on, classification is made for the cyber-attacks and later network systems' security is tested systematically. After the testing period, all data is reported and filed for future reference. Consequently, it is found out that human beings are the weakest circle of the chain and simple mistakes may unintentionally cause huge problems. Thus, it is clear that some precautions must be taken to avoid such threats like updating the security software.
Gebali, Fayez
2015-01-01
This textbook presents the mathematical theory and techniques necessary for analyzing and modeling high-performance global networks, such as the Internet. The three main building blocks of high-performance networks are links, switching equipment connecting the links together, and software employed at the end nodes and intermediate switches. This book provides the basic techniques for modeling and analyzing these last two components. Topics covered include, but are not limited to: Markov chains and queuing analysis, traffic modeling, interconnection networks and switch architectures and buffering strategies. · Provides techniques for modeling and analysis of network software and switching equipment; · Discusses design options used to build efficient switching equipment; · Includes many worked examples of the application of discrete-time Markov chains to communication systems; · Covers the mathematical theory and techniques necessary for ana...
Chris eGaiteri
2011-08-01
Full Text Available The structure of gene coexpression networks reflects the activation and interaction of multiple cellular systems. Since the pathology of neuropsychiatric disorders is influenced by diverse cellular systems and pathways, we investigated gene coexpression networks in major depression, and searched for putative unifying themes in network connectivity across neuropsychiatric disorders. Specifically, based on the prevalence of the lethality-centrality relationship in disease-related networks, we hypothesized that network changes between control and major depression-related networks would be centered around coexpression hubs, and secondly, that differentially expressed (DE genes would have a characteristic position and connectivity level in those networks. Mathematically, the first hypothesis tests the relationship of differential coexpression to network connectivity, while the second hybrid expression-and-network hypothesis tests the relationship of differential expression to network connectivity. To answer these questions about the potential interaction of coexpression network structure with differential expression, we utilized all available human post-mortem depression-related datasets appropriate for coexpression analysis, which spanned different microarray platforms, cohorts, and brain regions. Similar studies were also performed in an animal model of depression and in schizophrenia and bipolar disorder microarray datasets. We now provide results which consistently support (1 that genes assemble into small-world and scale-free networks in control subjects, (2 that this efficient network topology is largely resilient to changes in depressed subjects, and (3 that DE genes are positioned on the periphery of coexpression networks. Similar results were observed in a mouse model of depression, and in selected bipolar- and schizophrenia-related networks. Finally, we show that baseline expression variability contributes to the propensity of genes to be
Learning Networks of Stochastic Differential Equations
Bento, José; Montanari, Andrea
2010-01-01
We consider linear models for stochastic dynamics. To any such model can be associated a network (namely a directed graph) describing which degrees of freedom interact under the dynamics. We tackle the problem of learning such a network from observation of the system trajectory over a time interval $T$. We analyze the $\\ell_1$-regularized least squares algorithm and, in the setting in which the underlying network is sparse, we prove performance guarantees that are \\emph{uniform in the sampling rate} as long as this is sufficiently high. This result substantiates the notion of a well defined `time complexity' for the network inference problem.
The Analysis of Social Networks.
O'Malley, A James; Marsden, Peter V
2008-12-01
Many questions about the social organization of medicine and health services involve interdependencies among social actors that may be depicted by networks of relationships. Social network studies have been pursued for some time in social science disciplines, where numerous descriptive methods for analyzing them have been proposed. More recently, interest in the analysis of social network data has grown among statisticians, who have developed more elaborate models and methods for fitting them to network data. This article reviews fundamentals of, and recent innovations in, social network analysis using a physician influence network as an example. After introducing forms of network data, basic network statistics, and common descriptive measures, it describes two distinct types of statistical models for network data: individual-outcome models in which networks enter the construction of explanatory variables, and relational models in which the network itself is a multivariate dependent variable. Complexities in estimating both types of models arise due to the complex correlation structures among outcome measures.
Optimal control of complex networks based on matrix differentiation
Li, Guoqi; Ding, Jie; Wen, Changyun; Pei, Jing
2016-09-01
Finding the key node set to be connected to external control sources so as to minimize the energy for controlling a complex network, known as the minimum-energy control problem, is of critical importance but remains open. We address this critical problem where matrix differentiation is involved. To this end, the differentiation of energy/cost function with respect to the input matrix is obtained based on tensor analysis, and the Hessian matrix is compressed from a fourth-order tensor. Normalized projected gradient method (NPGM) normalized projected trust-region method (NPTM) are proposed with established convergence property. We show that NPGM is more computationally efficient than NPTM. Simulation results demonstrate satisfactory performance of the algorithms, and reveal important insights as well. Two interesting phenomena are observed. One is that the key node set tends to divide elementary paths equally. The other is that the low-degree nodes may be more important than hubs from a control point of view, indicating that controlling hub nodes does not help to lower the control energy. These results suggest a way of achieving optimal control of complex networks, and provide meaningful insights for future researches.
Quantum Key Distribution Network Based on Differential Phase Shift
WANG Wan-Ying; WANG Chuan; WEN Kai; LONG Gui-Lu
2007-01-01
Using a series of quantum correlated photon pairs, we propose a theoretical scheme for any-to-any multi-user quantum key distribution network based on differential phase shift. The differential phase shift and the different detection time slots ensure the security of our scheme against eavesdropping. We discuss the security under the intercept-resend attack and the source replacement attack.
Multidimensional real analysis I differentiation
Duistermaat, J J; van Braam Houckgeest, J P
2004-01-01
Part one of the authors' comprehensive and innovative work on multidimensional real analysis. This book is based on extensive teaching experience at Utrecht University and gives a thorough account of differential analysis in multidimensional Euclidean space. It is an ideal preparation for students who wish to go on to more advanced study. The notation is carefully organized and all proofs are clean, complete and rigorous. The authors have taken care to pay proper attention to all aspects of the theory. In many respects this book presents an original treatment of the subject and it contains man
Applied analysis and differential equations
Cârj, Ovidiu
2007-01-01
This volume contains refereed research articles written by experts in the field of applied analysis, differential equations and related topics. Well-known leading mathematicians worldwide and prominent young scientists cover a diverse range of topics, including the most exciting recent developments. A broad range of topics of recent interest are treated: existence, uniqueness, viability, asymptotic stability, viscosity solutions, controllability and numerical analysis for ODE, PDE and stochastic equations. The scope of the book is wide, ranging from pure mathematics to various applied fields such as classical mechanics, biomedicine, and population dynamics.
Identifying gene regulatory network rewiring using latent differential graphical models.
Tian, Dechao; Gu, Quanquan; Ma, Jian
2016-09-30
Gene regulatory networks (GRNs) are highly dynamic among different tissue types. Identifying tissue-specific gene regulation is critically important to understand gene function in a particular cellular context. Graphical models have been used to estimate GRN from gene expression data to distinguish direct interactions from indirect associations. However, most existing methods estimate GRN for a specific cell/tissue type or in a tissue-naive way, or do not specifically focus on network rewiring between different tissues. Here, we describe a new method called Latent Differential Graphical Model (LDGM). The motivation of our method is to estimate the differential network between two tissue types directly without inferring the network for individual tissues, which has the advantage of utilizing much smaller sample size to achieve reliable differential network estimation. Our simulation results demonstrated that LDGM consistently outperforms other Gaussian graphical model based methods. We further evaluated LDGM by applying to the brain and blood gene expression data from the GTEx consortium. We also applied LDGM to identify network rewiring between cancer subtypes using the TCGA breast cancer samples. Our results suggest that LDGM is an effective method to infer differential network using high-throughput gene expression data to identify GRN dynamics among different cellular conditions.
System Identification Using Multilayer Differential Neural Networks: A New Result
J. Humberto Pérez-Cruz
2012-01-01
Full Text Available In previous works, a learning law with a dead zone function was developed for multilayer differential neural networks. This scheme requires strictly a priori knowledge of an upper bound for the unmodeled dynamics. In this paper, the learning law is modified in such a way that this condition is relaxed. By this modification, the tuning process is simpler and the dead-zone function is not required anymore. On the basis of this modification and by using a Lyapunov-like analysis, a stronger result is here demonstrated: the exponential convergence of the identification error to a bounded zone. Besides, a value for upper bound of such zone is provided. The workability of this approach is tested by a simulation example.
On service differentiation in mobile Ad Hoc networks
张顺亮; 叶澄清
2004-01-01
A network model is proposed to support service differentiation for mobile Ad Hoc networks by combining a fully distributed admission control approach and the DIFS based differentiation mechanism of IEEE802.11. It can provide different kinds of QoS (Quality of Service) for various applications. Admission controllers determine a committed bandwidth based on the reserved bandwidth of flows and the source utilization of networks. Packets are marked when entering into networks by markers according to the committed rate. By the mark in the packet header, intermediate nodes handle the Received packets in different manners to provide applications with the QoS corresponding to the pre-negotiated profile.Extensive simulation experiments showed that the proposed mechanism can provide QoS guarantee to assured service traffic and increase the channel utilization of networks.
On service differentiation in mobile Ad Hoc networks
张顺亮; 叶澄清
2004-01-01
A network model is proposed to support service differentiation for mobile Ad Hoc networks by combining a fully distributed admission control approach and the DIFS based differentiation mechanism of IEEE802.11. It can provide different kinds of QoS (Quality of Service) for various applications. Admission controllers determine a committed bandwidth based on the reserved bandwidth of flows and the source utilization of networks. Packets are marked when entering into networks by markers according to the committed rate. By the mark in the packet header, intermediate nodes handle the received packets in different manners to provide applications with the QoS corresponding to the pre-negotiated profile. Extensive simulation experiments showed that the proposed mechanism can provide QoS guarantee to assured service traffic and increase the channel utilization of networks.
Carrying Network Accessing Architecture and Strategy Based on Business Differentiation
Yanyan Han
2013-07-01
Full Text Available Due to the abilities of real-time sensing and information sharing, Wireless Sensor Network (WSN has been applied in more and more fields. Basing on the emergence of Internet of Things (IoT, the issue about heterogeneous network integration is becoming more important. We first analyze the new businesses that arise recently for cell phone users as well as the potential effect on carrying network. After that we mainly discuss the influence on traditional carrying network for WSN accessing and taking concurrent businesses as the study case, common access architecture from WSN to carrying network is constructed, which makes use of business differentiation. Furthermore, we propose the idea of tortuous access from WSN to the gateway in the carrying network to avoid congested paths with simulation and verification. Finally, we conclude the possible impacts for the integration of these two networks and present possible solutions.
Multifractal analysis of complex networks
Wang Dan-Ling; Yu Zu-Guo; Anh V
2012-01-01
Complex networks have recently attracted much attention in diverse areas of science and technology.Many networks such as the WWW and biological networks are known to display spatial heterogeneity which can be characterized by their fractal dimensions.Multifractal analysis is a useful way to systematically describe the spatial heterogeneity of both theoretical and experimental fractal patterns.In this paper,we introduce a new box-covering algorithm for muttifractal analysis of complex networks.This algorithm is used to calculate the generalized fractal dimensions Dq of some theoretical networks,namely scale-free networks,small world networks,and random networks,and one kind of real network,namely protein-protein interaction networks of different species.Our numerical results indicate the existence of multifractality in scale-free networks and protein-protein interaction networks,while the multifractal behavior is not clear-cut for small world networks and random networks.The possible variation of Dq due to changes in the parameters of the theoretical network models is also discussed.
Structural brain network characteristics can differentiate CIS from early RRMS
Muthuraman eMuthuraman
2016-02-01
Full Text Available Focal demyelinated lesions, diffuse white matter (WM damage and grey matter (GM atrophy influence directly the disease progression in patients with multiple sclerosis. The aim of this study was to identify specific characteristics of GM and WM structural networks in subjects with clinically isolated syndrome (CIS in comparison to patients with early relapsing-remitting multiple sclerosis (RRMS.Twenty patients with CIS, thirty three with RRMS and forty healthy subjects were investigated using 3 T-MRI. Diffusion tensor imaging was applied, together with probabilistic tractography and fractional anisotropy (FA maps for WM and cortical thickness correlation analysis for GM, to determine the structural connectivity patterns. A network topology analysis with the aid of graph theoretical approaches was used to characterize the network at different community levels (modularity, clustering coefficient, global and local efficiencies. Finally, we applied support vector machines (SVM to automatically discriminate the two groups. .In comparison to CIS subjects, patients with RRMS were found to have increased modular connectivity and higher local clustering, highlighting increased local processing in both GM and WM. Both groups presented increased modularity and clustering coefficients in comparison to healthy controls. SVM algorithms achieved 97% accuracy using the clustering coefficient as classifier derived from GM and 65% using WM from probabilistic tractography and 67 % from modularity of FA maps to differentiate between CIS and RRMS patients. We demonstrate a clear increase of modular and local connectivity in patients with early RRMS in comparison to CIS and healthy subjects. Based only on a single anatomic scan and without a priori information, we developed an automated and investigator-independent paradigm that can accurately discriminate between patients with these clinically similar disease entities, and could thus complement the current
Differential Evolution Algorithm for Route Optimization Problems of Engineering Networks
O. G. Monahov
2015-01-01
Full Text Available The paper considers problems of structure optimization of engineering networks to provide a minimum total cost of engineering networks in construction and operation. The mathematical statement of the problem in terms of the hyper-network theory takes into account the interdependence of indicators of hyper-network elements, a layout area and a projected network. A digital model of terrain presents the placement area of engineering networks (a territory. In our case, it will be a weighted mesh (graph of primary network of dedicated vertices-consumers and a vertex-source for the utilities. The edges weights will be determined by the costs of construction and operation of the route between the given vertices of the network. The initial solution of the problem of minimizing the total cost will be using the minimum spanning tree, obtained on a weighted complete graph the vertices of which are defined by vertices-consumers and the vertexsource for the utilities, and the weights of edges are the distance between the vertices on the given weighted graph of the primary network. The work offers a method of differential evolution to solve the problem in hyper-network formulation that improves the initial solution by the mapping the edges of the secondary network in the primary network using additional Steiner points. As numerical experiments have shown, a differential evolution algorithm allows us to reduce the average total cost for a given engineering network compared to the initial solution by 5% - 15%, depending on the configuration, parameters, and layout area.
Quantitative Adaptive RED in Differentiated Service Networks
LONG KePing(隆克平); WANG Qian(王茜); CHENG ShiDuan(程时端); CHEN JunLiang(陈俊亮)
2003-01-01
This paper derives a quantitative model between RED (Random Early Detection)maxp and committed traffic rate for token-based marking schemes in DiffServ IP networks. Then,a DiffServ Quantitative RED (DQRED) is presented, which can adapt its dropping probabilityto marking probability of the edge router to reflect not only the sharing bandwidth but also therequirement of performance of these services. Hence, DQRED can cooperate with marking schemesto guarantee fairness between different DiffServ AF class services. A new marking probabilitymetering algorithm is also proposed to cooperate with DQRED. Simulation results verify thatDQRED mechanism can not only control congestion of DiffServ network very well, but also satisfydifferent quality requirements of AF class service. The performance of DQRED is better than thatof WRED.
Artificial Neural Networks for Solving Ordinary and Partial Differential Equations
Lagaris, I E; Fotiadis, D I
1997-01-01
We present a method to solve initial and boundary value problems using artificial neural networks. A trial solution of the differential equation is written as a sum of two parts. The first part satisfies the boundary (or initial) conditions and contains no adjustable parameters. The second part is constructed so as not to affect the boundary conditions. This part involves a feedforward neural network, containing adjustable parameters (the weights). Hence by construction the boundary conditions are satisfied and the network is trained to satisfy the differential equation. The applicability of this approach ranges from single ODE's, to systems of coupled ODE's and also to PDE's. In this article we illustrate the method by solving a variety of model problems and present comparisons with finite elements for several cases of partial differential equations.
SLA-aware differentiated QoS in elastic optical networks
Agrawal, Anuj; Vyas, Upama; Bhatia, Vimal; Prakash, Shashi
2017-07-01
differentiation capability of EON and WDM networks under such differentiated service environment. The proposed SADQ is then compared with two existing benchmark routing and spectrum allocation (RSA) schemes that are also designed under EONs. Simulations indicate that the performance of SADQ is distinctly better in EON than in WDM network under differentiated QoS scenario. The comparative analysis of the proposed SADQ with the considered benchmark RSA strategies designed under EON shows the improved performance of SADQ in EON paradigm for offering differentiated services as per the SLA.
Lowes, Damon A; Galley, Helen F; Moura, Alessandro P S; Webster, Nigel R
2017-01-15
Much is still unknown about the mechanisms of effects of even brief anaesthesia on the brain and previous studies have simply compared differential expression profiles with and without anaesthesia. We hypothesised that network analysis, in addition to the traditional differential gene expression and ontology analysis, would enable identification of the effects of anaesthesia on interactions between genes. Rats (n=10 per group) were randomised to anaesthesia with isoflurane in oxygen or oxygen only for 15min, and 6h later brains were removed. Differential gene expression and gene ontology analysis of microarray data was performed. Standard clustering techniques and principal component analysis with Bayesian rules were used along with social network analysis methods, to quantitatively model and describe the gene networks. Anaesthesia had marked effects on genes in the brain with differential regulation of 416 probe sets by at least 2 fold. Gene ontology analysis showed 23 genes were functionally related to the anaesthesia and of these, 12 were involved with neurotransmitter release, transport and secretion. Gene network analysis revealed much greater connectivity in genes from brains from anaesthetised rats compared to controls. Other importance measures were also altered after anaesthesia; median [range] closeness centrality (shortest path) was lower in anaesthetized animals (0.07 [0-0.30]) than controls (0.39 [0.30-0.53], pgenes after anaesthesia and suggests future targets for investigation. Copyright © 2016 Elsevier B.V. All rights reserved.
Network Analysis, Architecture, and Design
McCabe, James D
2007-01-01
Traditionally, networking has had little or no basis in analysis or architectural development, with designers relying on technologies they are most familiar with or being influenced by vendors or consultants. However, the landscape of networking has changed so that network services have now become one of the most important factors to the success of many third generation networks. It has become an important feature of the designer's job to define the problems that exist in his network, choose and analyze several optimization parameters during the analysis process, and then prioritize and evalua
Integrated Differentiated Survivability in IP over WDM Networks
Wei Wei; Qing-Ji Zeng
2004-01-01
The problem of differentiated Multi-Layer Integrated Survivability (MLIS) in IP over WDM networks is studied, which is decomposed into three sub-problems: survivable strategies design (SSD), spare capacity dimensioning (SCD), and dynamic survivable routing (DSR). A related work of network survivability in IP over WDM networks is firstly provided, and adaptive survivable strategies are also designed. A new Integrated Shared Pool (ISP) approach for SCD is then proposed, which is formulated by using integer-programming theory. Moreover, a novel survivable routing scheme called Differentiated Integrated Survivability Algorithm (DISA) for DSR is developed. Simulation results show that the proposed integrated survivability scheme performs much better than other solutions (e.g., "highest layer recovery" and "lowest layer recovery" schemes) in terms of traffic blocking ratio, spare resource requirement, and average traffic recovery ratio in IP over WDM networks.
The Fractional Differential Polynomial Neural Network for Approximation of Functions
Rabha W. Ibrahim
2013-09-01
Full Text Available In this work, we introduce a generalization of the differential polynomial neural network utilizing fractional calculus. Fractional calculus is taken in the sense of the Caputo differential operator. It approximates a multi-parametric function with particular polynomials characterizing its functional output as a generalization of input patterns. This method can be employed on data to describe modelling of complex systems. Furthermore, the total information is calculated by using the fractional Poisson process.
NetDiff - Bayesian model selection for differential gene regulatory network inference.
Thorne, Thomas
2016-12-16
Differential networks allow us to better understand the changes in cellular processes that are exhibited in conditions of interest, identifying variations in gene regulation or protein interaction between, for example, cases and controls, or in response to external stimuli. Here we present a novel methodology for the inference of differential gene regulatory networks from gene expression microarray data. Specifically we apply a Bayesian model selection approach to compare models of conserved and varying network structure, and use Gaussian graphical models to represent the network structures. We apply a variational inference approach to the learning of Gaussian graphical models of gene regulatory networks, that enables us to perform Bayesian model selection that is significantly more computationally efficient than Markov Chain Monte Carlo approaches. Our method is demonstrated to be more robust than independent analysis of data from multiple conditions when applied to synthetic network data, generating fewer false positive predictions of differential edges. We demonstrate the utility of our approach on real world gene expression microarray data by applying it to existing data from amyotrophic lateral sclerosis cases with and without mutations in C9orf72, and controls, where we are able to identify differential network interactions for further investigation.
Guebel, Daniel V.; Torres, Néstor V.
2016-01-01
Motivation: In the brain of elderly-healthy individuals, the effects of sexual dimorphism and those due to normal aging appear overlapped. Discrimination of these two dimensions would powerfully contribute to a better understanding of the etiology of some neurodegenerative diseases, such as “sporadic” Alzheimer. Methods: Following a system biology approach, top-down and bottom-up strategies were combined. First, public transcriptome data corresponding to the transition from adulthood to the aging stage in normal, human hippocampus were analyzed through an optimized microarray post-processing (Q-GDEMAR method) together with a proper experimental design (full factorial analysis). Second, the identified genes were placed in context by building compatible networks. The subsequent ontology analyses carried out on these networks clarify the main functionalities involved. Results: Noticeably we could identify large sets of genes according to three groups: those that exclusively depend on the sex, those that exclusively depend on the age, and those that depend on the particular combinations of sex and age (interaction). The genes identified were validated against three independent sources (a proteomic study of aging, a senescence database, and a mitochondrial genetic database). We arrived to several new inferences about the biological functions compromised during aging in two ways: by taking into account the sex-independent effects of aging, and considering the interaction between age and sex where pertinent. In particular, we discuss the impact of our findings on the functions of mitochondria, autophagy, mitophagia, and microRNAs. Conclusions: The evidence obtained herein supports the occurrence of significant neurobiological differences in the hippocampus, not only between adult and elderly individuals, but between old-healthy women and old-healthy men. Hence, to obtain realistic results in further analysis of the transition from the normal aging to incipient
Analysis of FOXO transcriptional networks
van der Vos, K.E.
2010-01-01
The PI3K-PKB-FOXO signalling module plays a pivotal role in a wide variety of cellular processes, including proliferation, survival, differentiation and metabolism. Inappropriate activation of this network is frequently observed in human cancer and causes uncontrolled proliferation and survival. In
Biomass Rapid Analysis Network (BRAN)
2003-10-01
Helping the emerging biotechnology industry develop new tools and methods for real-time analysis of biomass feedstocks, process intermediates and The Biomass Rapid Analysis Network is designed to fast track the development of modern tools and methods for biomass analysis to accelerate the development of the emerging industry. The network will be led by industry and organized and coordinated through the National Renewable Energy Lab. The network will provide training and other activities of interest to BRAN members. BRAN members will share the cost and work of rapid analysis method development, validate the new methods, and work together to develop the training for the future biomass conversion workforce.
Zheng, Zane Z.; Vicente-Grabovetsky, Alejandro; MacDonald, Ewen N.
2013-01-01
within a multivoxel pattern analysis framework, that this sensorimotor process is supported by functionally differentiated brain networks. During scanning, a real-time speech-tracking system was used to deliver two acoustically different types of distorted auditory feedback or unaltered feedback while...
Mingzhou Li; Xuewei Li; Li Zhu; Xiaokun Teng; Huasheng Xiao; Surong Shuai; Lei Chen; Qiang Li; Yujiao Guo
2008-01-01
During the growth and development of skeletal muscle cells and adipose cells, the regulatory mechanism of micro-effect polygenes determines porcine meat quality, carcass characteristics and other relative quantitative traits. Obese and lean type pig breeds show obvious differences in muscle growth and adipose deposition; however, the molecular mechanism underlying this phenotypic variation remains unknown. We used pathway-focused oligo microarray studies to examine the expression changes of 140 genes associated with muscle growth and adipose deposition in longissimus dorsi muscle at six growth stages (birth, 1, 2, 3, 4 and 5 months) of Landrace (a leaner, Western breed) and Taihu pigs (a fatty, indigenous, Chinese breed). Variance analysis (ANOVA) revealed that differences in the expression of 18 genes in Landrace pigs and three genes in Taihu pigs were very significant (FDR-adjusted permutation, P<0.01) and differences for 22 genes in Landrace pigs and seven genes in Taihu pigs were significant (FDR-adjusted permutation, P<0.05) among six growth stages. Clustering analysis revealed a high level of significance (FDR-adjusted, P<0.01) for four gene expression patterns, in which genes that strongly up-regulated were mainly associated with the positive regulation of myofiber formation and fatty acid biogenesis and genes that strongly down-regulated were mainly associated with the inhibition of cell proliferation and positive regulation of fatty acid P-oxidation. Based on a dynamic Bayesian network (DBN) model, gene regulatory networks (GRNs) were reconstructed from time-series data for each pig breed. These two GRNs initially revealed the distinct differences in physiological and biochemical aspects of muscle growth and adipose deposition between the two pig breeds; from these results, some potential key genes could be identified. Quantitative real-time RT-PCR (QRT-PCR) was used to verify the microarray data for five modulated genes, and a good correlation between the
Introduction to Social Network Analysis
Zaphiris, Panayiotis; Ang, Chee Siang
Social Network analysis focuses on patterns of relations between and among people, organizations, states, etc. It aims to describe networks of relations as fully as possible, identify prominent patterns in such networks, trace the flow of information through them, and discover what effects these relations and networks have on people and organizations. Social network analysis offers a very promising potential for analyzing human-human interactions in online communities (discussion boards, newsgroups, virtual organizations). This Tutorial provides an overview of this analytic technique and demonstrates how it can be used in Human Computer Interaction (HCI) research and practice, focusing especially on Computer Mediated Communication (CMC). This topic acquires particular importance these days, with the increasing popularity of social networking websites (e.g., youtube, myspace, MMORPGs etc.) and the research interest in studying them.
The Analysis of Social Networks
O’Malley, A James; Marsden, Peter V.
2008-01-01
Many questions about the social organization of medicine and health services involve interdependencies among social actors that may be depicted by networks of relationships. Social network studies have been pursued for some time in social science disciplines, where numerous descriptive methods for analyzing them have been proposed. More recently, interest in the analysis of social network data has grown among statisticians, who have developed more elaborate models and methods for fitting them...
Numerical solution of differential equations by artificial neural networks
Meade, Andrew J., Jr.
1995-01-01
Conventionally programmed digital computers can process numbers with great speed and precision, but do not easily recognize patterns or imprecise or contradictory data. Instead of being programmed in the conventional sense, artificial neural networks (ANN's) are capable of self-learning through exposure to repeated examples. However, the training of an ANN can be a time consuming and unpredictable process. A general method is being developed by the author to mate the adaptability of the ANN with the speed and precision of the digital computer. This method has been successful in building feedforward networks that can approximate functions and their partial derivatives from examples in a single iteration. The general method also allows the formation of feedforward networks that can approximate the solution to nonlinear ordinary and partial differential equations to desired accuracy without the need of examples. It is believed that continued research will produce artificial neural networks that can be used with confidence in practical scientific computing and engineering applications.
Artificial Neural Network Analysis System
2007-11-02
Contract No. DASG60-00-M-0201 Purchase request no.: Foot in the Door-01 Title Name: Artificial Neural Network Analysis System Company: Atlantic... Artificial Neural Network Analysis System 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Powell, Bruce C 5d. PROJECT NUMBER 5e. TASK NUMBER...34) 27-02-2001 Report Type N/A Dates Covered (from... to) ("DD MON YYYY") 28-10-2000 27-02-2001 Title and Subtitle Artificial Neural Network Analysis
Transmission analysis in WDM networks
Rasmussen, Christian Jørgen
1999-01-01
This thesis describes the development of a computer-based simulator for transmission analysis in optical wavelength division multiplexing networks. A great part of the work concerns fundamental optical network simulator issues. Among these issues are identification of the versatility and user...... with a view of reducing the required number of bits....
Melissa A Metzler
Full Text Available The transcription factor networks that drive parotid salivary gland progenitor cells to terminally differentiate, remain largely unknown and are vital to understanding the regeneration process.A systems biology approach was taken to measure mRNA and microRNA expression in vivo across acinar cell terminal differentiation in the rat parotid salivary gland. Laser capture microdissection (LCM was used to specifically isolate acinar cell RNA at times spanning the month-long period of parotid differentiation.Clustering of microarray measurements suggests that expression occurs in four stages. mRNA expression patterns suggest a novel role for Pparg which is transiently increased during mid postnatal differentiation in concert with several target gene mRNAs. 79 microRNAs are significantly differentially expressed across time. Profiles of statistically significant changes of mRNA expression, combined with reciprocal correlations of microRNAs and their target mRNAs, suggest a putative network involving Klf4, a differentiation inhibiting transcription factor, which decreases as several targeting microRNAs increase late in differentiation. The network suggests a molecular switch (involving Prdm1, Sox11, Pax5, miR-200a, and miR-30a progressively decreases repression of Xbp1 gene transcription, in concert with decreased translational repression by miR-214. The transcription factor Xbp1 mRNA is initially low, increases progressively, and may be maintained by a positive feedback loop with Atf6. Transfection studies show that Xbp1 activates the Mist1 promoter [corrected]. In addition, Xbp1 and Mist1 each activate the parotid secretory protein (Psp gene, which encodes an abundant salivary protein, and is a marker of terminal differentiation.This study identifies novel expression patterns of Pparg, Klf4, and Sox11 during parotid acinar cell differentiation, as well as numerous differentially expressed microRNAs. Network analysis identifies a novel stemness arm, a
Network analysis reveals multiscale controls on streamwater chemistry
Kevin J. McGuire; Christian E. Torgersen; Gene E. Likens; Donald C. Buso; Winsor H. Lowe; Scott W. Bailey
2014-01-01
By coupling synoptic data from a basin-wide assessment of streamwater chemistry with network-based geostatistical analysis, we show that spatial processes differentially affect biogeochemical condition and pattern across a headwater stream network. We analyzed a high-resolution dataset consisting of 664 water samples collected every 100 m throughout 32 tributaries in...
Ahmed, Faraz; Liu, Alex X
2013-01-01
Online social networks are being increasingly used for analyzing various societal phenomena such as epidemiology, information dissemination, marketing and sentiment flow. Popular analysis techniques such as clustering and influential node analysis, require the computation of eigenvectors of the real graph's adjacency matrix. Recent de-anonymization attacks on Netflix and AOL datasets show that an open access to such graphs pose privacy threats. Among the various privacy preserving models, Differential privacy provides the strongest privacy guarantees. In this paper we propose a privacy preserving mechanism for publishing social network graph data, which satisfies differential privacy guarantees by utilizing a combination of theory of random matrix and that of differential privacy. The key idea is to project each row of an adjacency matrix to a low dimensional space using the random projection approach and then perturb the projected matrix with random noise. We show that as compared to existing approaches for ...
Network analysis applications in hydrology
Price, Katie
2017-04-01
Applied network theory has seen pronounced expansion in recent years, in fields such as epidemiology, computer science, and sociology. Concurrent development of analytical methods and frameworks has increased possibilities and tools available to researchers seeking to apply network theory to a variety of problems. While water and nutrient fluxes through stream systems clearly demonstrate a directional network structure, the hydrological applications of network theory remain underexplored. This presentation covers a review of network applications in hydrology, followed by an overview of promising network analytical tools that potentially offer new insights into conceptual modeling of hydrologic systems, identifying behavioral transition zones in stream networks and thresholds of dynamical system response. Network applications were tested along an urbanization gradient in Atlanta, Georgia, USA. Peachtree Creek and Proctor Creek. Peachtree Creek contains a nest of five longterm USGS streamflow and water quality gages, allowing network application of longterm flow statistics. The watershed spans a range of suburban and heavily urbanized conditions. Summary flow statistics and water quality metrics were analyzed using a suite of network analysis techniques, to test the conceptual modeling and predictive potential of the methodologies. Storm events and low flow dynamics during Summer 2016 were analyzed using multiple network approaches, with an emphasis on tomogravity methods. Results indicate that network theory approaches offer novel perspectives for understanding long term and eventbased hydrological data. Key future directions for network applications include 1) optimizing data collection, 2) identifying "hotspots" of contaminant and overland flow influx to stream systems, 3) defining process domains, and 4) analyzing dynamic connectivity of various system components, including groundwatersurface water interactions.
Selected papers on analysis and differential equations
Society, American Mathematical
2010-01-01
This volume contains translations of papers that originally appeared in the Japanese journal Sūgaku. These papers range over a variety of topics in ordinary and partial differential equations, and in analysis. Many of them are survey papers presenting new results obtained in the last few years. This volume is suitable for graduate students and research mathematicians interested in analysis and differential equations.
Differential analysis of matrix convex functions II
Hansen, Frank; Tomiyama, Jun
2009-01-01
We continue the analysis in [F. Hansen, and J. Tomiyama, Differential analysis of matrix convex functions. Linear Algebra Appl., 420:102--116, 2007] of matrix convex functions of a fixed order defined in a real interval by differential methods as opposed to the characterization in terms of divide...
Computational Social Network Analysis
Hassanien, Aboul-Ella
2010-01-01
Presents insight into the social behaviour of animals (including the study of animal tracks and learning by members of the same species). This book provides web-based evidence of social interaction, perceptual learning, information granulation and the behaviour of humans and affinities between web-based social networks
Nodal centrality of functional network in the differentiation of schizophrenia.
Cheng, Hu; Newman, Sharlene; Goñi, Joaquín; Kent, Jerillyn S; Howell, Josselyn; Bolbecker, Amanda; Puce, Aina; O'Donnell, Brian F; Hetrick, William P
2015-10-01
A disturbance in the integration of information during mental processing has been implicated in schizophrenia, possibly due to faulty communication within and between brain regions. Graph theoretic measures allow quantification of functional brain networks. Functional networks are derived from correlations between time courses of brain regions. Group differences between SZ and control groups have been reported for functional network properties, but the potential of such measures to classify individual cases has been little explored. We tested whether the network measure of betweenness centrality could classify persons with schizophrenia and normal controls. Functional networks were constructed for 19 schizophrenic patients and 29 non-psychiatric controls based on resting state functional MRI scans. The betweenness centrality of each node, or fraction of shortest-paths that pass through it, was calculated in order to characterize the centrality of the different regions. The nodes with high betweenness centrality agreed well with hub nodes reported in previous studies of structural and functional networks. Using a linear support vector machine algorithm, the schizophrenia group was differentiated from non-psychiatric controls using the ten nodes with the highest betweenness centrality. The classification accuracy was around 80%, and stable against connectivity thresholding. Better performance was achieved when using the ranks as feature space as opposed to the actual values of betweenness centrality. Overall, our findings suggest that changes in functional hubs are associated with schizophrenia, reflecting a variation of the underlying functional network and neuronal communications. In addition, a specific network property, betweenness centrality, can classify persons with SZ with a high level of accuracy.
Heiden, Uwe
1980-01-01
The purpose of this work is a unified and general treatment of activity in neural networks from a mathematical pOint of view. Possible applications of the theory presented are indica ted throughout the text. However, they are not explored in de tail for two reasons : first, the universal character of n- ral activity in nearly all animals requires some type of a general approach~ secondly, the mathematical perspicuity would suffer if too many experimental details and empirical peculiarities were interspersed among the mathematical investigation. A guide to many applications is supplied by the references concerning a variety of specific issues. Of course the theory does not aim at covering all individual problems. Moreover there are other approaches to neural network theory (see e.g. Poggio-Torre, 1978) based on the different lev els at which the nervous system may be viewed. The theory is a deterministic one reflecting the average be havior of neurons or neuron pools. In this respect the essay is writt...
Antenna analysis using neural networks
Smith, William T.
1992-01-01
Conventional computing schemes have long been used to analyze problems in electromagnetics (EM). The vast majority of EM applications require computationally intensive algorithms involving numerical integration and solutions to large systems of equations. The feasibility of using neural network computing algorithms for antenna analysis is investigated. The ultimate goal is to use a trained neural network algorithm to reduce the computational demands of existing reflector surface error compensation techniques. Neural networks are computational algorithms based on neurobiological systems. Neural nets consist of massively parallel interconnected nonlinear computational elements. They are often employed in pattern recognition and image processing problems. Recently, neural network analysis has been applied in the electromagnetics area for the design of frequency selective surfaces and beam forming networks. The backpropagation training algorithm was employed to simulate classical antenna array synthesis techniques. The Woodward-Lawson (W-L) and Dolph-Chebyshev (D-C) array pattern synthesis techniques were used to train the neural network. The inputs to the network were samples of the desired synthesis pattern. The outputs are the array element excitations required to synthesize the desired pattern. Once trained, the network is used to simulate the W-L or D-C techniques. Various sector patterns and cosecant-type patterns (27 total) generated using W-L synthesis were used to train the network. Desired pattern samples were then fed to the neural network. The outputs of the network were the simulated W-L excitations. A 20 element linear array was used. There were 41 input pattern samples with 40 output excitations (20 real parts, 20 imaginary). A comparison between the simulated and actual W-L techniques is shown for a triangular-shaped pattern. Dolph-Chebyshev is a different class of synthesis technique in that D-C is used for side lobe control as opposed to pattern
Differentiation, ageing, and terminal differentiation: a semantic analysis.
Reiner, J M
1983-12-21
The largely unsolved problems in the theoretical analysis of differentiation and ageing involve a substantial component of linguistic (semantic) difficulties. Some of these are simple traps of ambiguity, resulting from metaphorical or analogical employment of established terms--for example, "terminal differentiation" (loss of division potential in vitro) as a borrowing from "differentiation" as used by developmental biologists, or "commitment" by analogy with "determination". Some difficulties represent a failure to adopt (at least provisionally) an operational (empirical) view--for example, failure to ask what is the nature of the evidence for the view that a fertilized ovum is totipotential, or to scrutinize the evidence for the view that cells "terminally differentiated" in vitro in a conventional medium are in fact moribund under all conditions, or to examine more closely the view that the differentiated state and the cycling state are mutually exclusive. With respect to the problem of ageing, we review some of the critical experiments on "terminal differentiation" or "clonal senescence". We then proceed to consider some of the models that have been proposed, including a molecular model proposed by the author which appears to overcome some of the objections to other models. Some of the models exemplify the results of what are ultimately semantic vices. The problems with which these remarks began should indeed yield to the immense and novel resources of molecular biology. But the development of complete analyses demands not only good luck and delicate technique but also critical semantic clarity and severity. Given the best tools, we shall solve major theoretical problems only if we understand quite fully what problem it is that we are trying to solve--and the history of science illustrates that this is not as elementary a matter as it sounds.(ABSTRACT TRUNCATED AT 250 WORDS)
Dynamic Gene Regulatory Networks Drive Hematopoietic Specification and Differentiation
Goode, Debbie K.; Obier, Nadine; Vijayabaskar, M.S.; Lie-A-Ling, Michael; Lilly, Andrew J.; Hannah, Rebecca; Lichtinger, Monika; Batta, Kiran; Florkowska, Magdalena; Patel, Rahima; Challinor, Mairi; Wallace, Kirstie; Gilmour, Jane; Assi, Salam A.; Cauchy, Pierre; Hoogenkamp, Maarten; Westhead, David R.; Lacaud, Georges; Kouskoff, Valerie; Göttgens, Berthold; Bonifer, Constanze
2016-01-01
Summary Metazoan development involves the successive activation and silencing of specific gene expression programs and is driven by tissue-specific transcription factors programming the chromatin landscape. To understand how this process executes an entire developmental pathway, we generated global gene expression, chromatin accessibility, histone modification, and transcription factor binding data from purified embryonic stem cell-derived cells representing six sequential stages of hematopoietic specification and differentiation. Our data reveal the nature of regulatory elements driving differential gene expression and inform how transcription factor binding impacts on promoter activity. We present a dynamic core regulatory network model for hematopoietic specification and demonstrate its utility for the design of reprogramming experiments. Functional studies motivated by our genome-wide data uncovered a stage-specific role for TEAD/YAP factors in mammalian hematopoietic specification. Our study presents a powerful resource for studying hematopoiesis and demonstrates how such data advance our understanding of mammalian development. PMID:26923725
NEAT: an efficient network enrichment analysis test
Signorelli, Mirko; Vinciotti, Veronica; Wit, Ernst C
2016-01-01
Background Network enrichment analysis is a powerful method, which allows to integrate gene enrichment analysis with the information on relationships between genes that is provided by gene networks. Existing tests for network enrichment analysis deal only with undirected networks, they can be computationally slow and are based on normality assumptions. Results We propose NEAT, a test for network enrichment analysis. The test is based on the hypergeometric distribution, which naturally arises ...
Numerical Analysis of Partial Differential Equations
Lui, S H
2011-01-01
A balanced guide to the essential techniques for solving elliptic partial differential equations Numerical Analysis of Partial Differential Equations provides a comprehensive, self-contained treatment of the quantitative methods used to solve elliptic partial differential equations (PDEs), with a focus on the efficiency as well as the error of the presented methods. The author utilizes coverage of theoretical PDEs, along with the nu merical solution of linear systems and various examples and exercises, to supply readers with an introduction to the essential concepts in the numerical analysis
Analysis of Anther Cell Differentiation
Ma, Hong [Pennsylvania State Univ., University Park, PA (United States)
2015-01-19
This grant supports research on genes that regulate Arabidopsis anther development. The proposed research largely concerns that functions of two key regulatory genes: SPL and DYT1, which encode two putative transcription factors, as well as genes that interact with these genes. Last year, we reported progress in preparation for ChIP analysis with SPL and DYT1, in dyt1 and ams microarray experiments and initial data analysis, in functional analysis of one of the DYT1 target gene, MYB35.
Statistical analysis of network data with R
Kolaczyk, Eric D
2014-01-01
Networks have permeated everyday life through everyday realities like the Internet, social networks, and viral marketing. As such, network analysis is an important growth area in the quantitative sciences, with roots in social network analysis going back to the 1930s and graph theory going back centuries. Measurement and analysis are integral components of network research. As a result, statistical methods play a critical role in network analysis. This book is the first of its kind in network research. It can be used as a stand-alone resource in which multiple R packages are used to illustrate how to conduct a wide range of network analyses, from basic manipulation and visualization, to summary and characterization, to modeling of network data. The central package is igraph, which provides extensive capabilities for studying network graphs in R. This text builds on Eric D. Kolaczyk’s book Statistical Analysis of Network Data (Springer, 2009).
Statistical network analysis for analyzing policy networks
Robins, Garry; Lewis, Jenny; Wang, Peng
2012-01-01
and policy network methodology is the development of statistical modeling approaches that can accommodate such dependent data. In this article, we review three network statistical methods commonly used in the current literature: quadratic assignment procedures, exponential random graph models (ERGMs...... has much to offer in analyzing the policy process....
Identifying module biomarkers from gastric cancer by differential correlation network
Liu X
2016-09-01
Full Text Available Xiaoping Liu,1–3,* Xiao Chang1,3,* 1College of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, Anhui Province, People’s Republic of China; 2Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People’s Republic of China; 3Collaborative Research Center for Innovative Mathematical Modeling, Institute of Industrial Science, University of Tokyo, Tokyo, Japan *These authors contributed equally to this work Abstract: Gastric cancer (stomach cancer is a severe disease caused by dysregulation of many functionally correlated genes or pathways instead of the mutation of individual genes. Systematic identification of gastric cancer biomarkers can provide insights into the mechanisms underlying this deadly disease and help in the development of new drugs. In this paper, we present a novel network-based approach to predict module biomarkers of gastric cancer that can effectively distinguish the disease from normal samples. Specifically, by assuming that gastric cancer has mainly resulted from dysfunction of biomolecular networks rather than individual genes in an organism, the genes in the module biomarkers are potentially related to gastric cancer. Finally, we identified a module biomarker with 27 genes, and by comparing the module biomarker with known gastric cancer biomarkers, we found that our module biomarker exhibited a greater ability to diagnose the samples with gastric cancer. Keywords: biomarkers, gastric cancer, stomach cancer, differential network
Optimization and Performance Analysis of High Speed Mobile Access Networks
Weerawardane, Thushara
2012-01-01
The design and development of cost-effective mobile broadband wireless access networks is a key challenge for many mobile network operators. The over-dimensioning or under-dimensioning of an access network results in both additional costs and customer dissatisfaction. Thushara Weerawardane introduces new transport technologies and features for High Speed Packet Access (HSPA) and Long-Term Evolution (LTE) networks. Using advanced scientific methods, he proposes new adaptive flow control and enhanced congestion control algorithms, then defends them with highly-developed analytical models derived from Markov chains. For faster analysis, compared to long-lasting detailed simulations, these models provide optimum network performance and ensure reliable quality standards for end users during transport network congestion. Further, the author investigates and analyzes LTE transport network performance by introducing novel traffic differentiation models and buffer management techniques during intra-LTE handovers.
Mahesh Wickramasinghe
Full Text Available Dynamical processes in many engineered and living systems take place on complex networks of discrete dynamical units. We present laboratory experiments with a networked chemical system of nickel electrodissolution in which synchronization patterns are recorded in systems with smooth periodic, relaxation periodic, and chaotic oscillators organized in networks composed of up to twenty dynamical units and 140 connections. The reaction system formed domains of synchronization patterns that are strongly affected by the architecture of the network. Spatially organized partial synchronization could be observed either due to densely connected network nodes or through the 'chimera' symmetry breaking mechanism. Relaxation periodic and chaotic oscillators formed structures by dynamical differentiation. We have identified effects of network structure on pattern selection (through permutation symmetry and coupling directness and on formation of hierarchical and 'fuzzy' clusters. With chaotic oscillators we provide experimental evidence that critical coupling strengths at which transition to identical synchronization occurs can be interpreted by experiments with a pair of oscillators and analysis of the eigenvalues of the Laplacian connectivity matrix. The experiments thus provide an insight into the extent of the impact of the architecture of a network on self-organized synchronization patterns.
Constructing, conducting and interpreting animal social network analysis.
Farine, Damien R; Whitehead, Hal
2015-09-01
1. Animal social networks are descriptions of social structure which, aside from their intrinsic interest for understanding sociality, can have significant bearing across many fields of biology. 2. Network analysis provides a flexible toolbox for testing a broad range of hypotheses, and for describing the social system of species or populations in a quantitative and comparable manner. However, it requires careful consideration of underlying assumptions, in particular differentiating real from observed networks and controlling for inherent biases that are common in social data. 3. We provide a practical guide for using this framework to analyse animal social systems and test hypotheses. First, we discuss key considerations when defining nodes and edges, and when designing methods for collecting data. We discuss different approaches for inferring social networks from these data and displaying them. We then provide an overview of methods for quantifying properties of nodes and networks, as well as for testing hypotheses concerning network structure and network processes. Finally, we provide information about assessing the power and accuracy of an observed network. 4. Alongside this manuscript, we provide appendices containing background information on common programming routines and worked examples of how to perform network analysis using the r programming language. 5. We conclude by discussing some of the major current challenges in social network analysis and interesting future directions. In particular, we highlight the under-exploited potential of experimental manipulations on social networks to address research questions.
Analysis of Semantic Networks using Complex Networks Concepts
Ortiz-Arroyo, Daniel
2013-01-01
In this paper we perform a preliminary analysis of semantic networks to determine the most important terms that could be used to optimize a summarization task. In our experiments, we measure how the properties of a semantic network change, when the terms in the network are removed. Our preliminar...
Spectral Analysis of Rich Network Topology in Social Networks
Wu, Leting
2013-01-01
Social networks have received much attention these days. Researchers have developed different methods to study the structure and characteristics of the network topology. Our focus is on spectral analysis of the adjacency matrix of the underlying network. Recent work showed good properties in the adjacency spectral space but there are few…
Spectral Analysis of Rich Network Topology in Social Networks
Wu, Leting
2013-01-01
Social networks have received much attention these days. Researchers have developed different methods to study the structure and characteristics of the network topology. Our focus is on spectral analysis of the adjacency matrix of the underlying network. Recent work showed good properties in the adjacency spectral space but there are few…
A gene regulatory network for root epidermis cell differentiation in Arabidopsis.
Angela Bruex
2012-01-01
Full Text Available The root epidermis of Arabidopsis provides an exceptional model for studying the molecular basis of cell fate and differentiation. To obtain a systems-level view of root epidermal cell differentiation, we used a genome-wide transcriptome approach to define and organize a large set of genes into a transcriptional regulatory network. Using cell fate mutants that produce only one of the two epidermal cell types, together with fluorescence-activated cell-sorting to preferentially analyze the root epidermis transcriptome, we identified 1,582 genes differentially expressed in the root-hair or non-hair cell types, including a set of 208 "core" root epidermal genes. The organization of the core genes into a network was accomplished by using 17 distinct root epidermis mutants and 2 hormone treatments to perturb the system and assess the effects on each gene's transcript accumulation. In addition, temporal gene expression information from a developmental time series dataset and predicted gene associations derived from a Bayesian modeling approach were used to aid the positioning of genes within the network. Further, a detailed functional analysis of likely bHLH regulatory genes within the network, including MYC1, bHLH54, bHLH66, and bHLH82, showed that three distinct subfamilies of bHLH proteins participate in root epidermis development in a stage-specific manner. The integration of genetic, genomic, and computational analyses provides a new view of the composition, architecture, and logic of the root epidermal transcriptional network, and it demonstrates the utility of a comprehensive systems approach for dissecting a complex regulatory network.
Sie, Rory
2012-01-01
Sie, R. L. L. (2012). COalitions in COOperation Networks (COCOON): Social Network Analysis and Game Theory to Enhance Cooperation Networks (Unpublished doctoral dissertation). September, 28, 2012, Open Universiteit in the Netherlands (CELSTEC), Heerlen, The Netherlands.
Sie, Rory
2012-01-01
Sie, R. L. L. (2012). COalitions in COOperation Networks (COCOON): Social Network Analysis and Game Theory to Enhance Cooperation Networks (Unpublished doctoral dissertation). September, 28, 2012, Open Universiteit in the Netherlands (CELSTEC), Heerlen, The Netherlands.
Resting-State Network Topology Differentiates Task Signals across the Adult Life Span.
Chan, Micaela Y; Alhazmi, Fahd H; Park, Denise C; Savalia, Neil K; Wig, Gagan S
2017-03-08
Brain network connectivity differs across individuals. For example, older adults exhibit less segregated resting-state subnetworks relative to younger adults (Chan et al., 2014). It has been hypothesized that individual differences in network connectivity impact the recruitment of brain areas during task execution. While recent studies have described the spatial overlap between resting-state functional correlation (RSFC) subnetworks and task-evoked activity, it is unclear whether individual variations in the connectivity pattern of a brain area (topology) relates to its activity during task execution. We report data from 238 cognitively normal participants (humans), sampled across the adult life span (20-89 years), to reveal that RSFC-based network organization systematically relates to the recruitment of brain areas across two functionally distinct tasks (visual and semantic). The functional activity of brain areas (network nodes) were characterized according to their patterns of RSFC: nodes with relatively greater connections to nodes in their own functional system ("non-connector" nodes) exhibited greater activity than nodes with relatively greater connections to nodes in other systems ("connector" nodes). This "activation selectivity" was specific to those brain systems that were central to each of the tasks. Increasing age was accompanied by less differentiated network topology and a corresponding reduction in activation selectivity (or differentiation) across relevant network nodes. The results provide evidence that connectional topology of brain areas quantified at rest relates to the functional activity of those areas during task. Based on these findings, we propose a novel network-based theory for previous reports of the "dedifferentiation" in brain activity observed in aging.SIGNIFICANCE STATEMENT Similar to other real-world networks, the organization of brain networks impacts their function. As brain network connectivity patterns differ across
Networks and network analysis for defence and security
Masys, Anthony J
2014-01-01
Networks and Network Analysis for Defence and Security discusses relevant theoretical frameworks and applications of network analysis in support of the defence and security domains. This book details real world applications of network analysis to support defence and security. Shocks to regional, national and global systems stemming from natural hazards, acts of armed violence, terrorism and serious and organized crime have significant defence and security implications. Today, nations face an uncertain and complex security landscape in which threats impact/target the physical, social, economic
DGCA: A comprehensive R package for Differential Gene Correlation Analysis.
McKenzie, Andrew T; Katsyv, Igor; Song, Won-Min; Wang, Minghui; Zhang, Bin
2016-11-15
Dissecting the regulatory relationships between genes is a critical step towards building accurate predictive models of biological systems. A powerful approach towards this end is to systematically study the differences in correlation between gene pairs in more than one distinct condition. In this study we develop an R package, DGCA (for Differential Gene Correlation Analysis), which offers a suite of tools for computing and analyzing differential correlations between gene pairs across multiple conditions. To minimize parametric assumptions, DGCA computes empirical p-values via permutation testing. To understand differential correlations at a systems level, DGCA performs higher-order analyses such as measuring the average difference in correlation and multiscale clustering analysis of differential correlation networks. Through a simulation study, we show that the straightforward z-score based method that DGCA employs significantly outperforms the existing alternative methods for calculating differential correlation. Application of DGCA to the TCGA RNA-seq data in breast cancer not only identifies key changes in the regulatory relationships between TP53 and PTEN and their target genes in the presence of inactivating mutations, but also reveals an immune-related differential correlation module that is specific to triple negative breast cancer (TNBC). DGCA is an R package for systematically assessing the difference in gene-gene regulatory relationships under different conditions. This user-friendly, effective, and comprehensive software tool will greatly facilitate the application of differential correlation analysis in many biological studies and thus will help identification of novel signaling pathways, biomarkers, and targets in complex biological systems and diseases.
Reliability Analysis of Sensor Networks
JIN Yan; YANG Xiao-zong; WANG Ling
2005-01-01
To Integrate the capacity of sensing, communication, computing, and actuating, one of the compelling technological advances of these years has been the appearance of distributed wireless sensor network (DSN) for information gathering tasks. In order to save the energy, multi-hop routing between the sensor nodes and the sink node is necessary because of limited resource. In addition, the unpredictable conditional factors make the sensor nodes unreliable. In this paper, the reliability of routing designed for sensor network and some dependability issues of DSN, such as MTTF(mean time to failure) and the probability of connectivity between the sensor nodes and the sink node are analyzed.Unfortunately, we could not obtain the accurate result for the arbitrary network topology, which is # P-hard problem.And the reliability analysis of restricted topologies clustering-based is given. The method proposed in this paper will show us a constructive idea about how to place energyconstrained sensor nodes in the network efficiently from the prospective of reliability.
Automated drawing of network plots in network meta-analysis.
Rücker, Gerta; Schwarzer, Guido
2016-03-01
In systematic reviews based on network meta-analysis, the network structure should be visualized. Network plots often have been drawn by hand using generic graphical software. A typical way of drawing networks, also implemented in statistical software for network meta-analysis, is a circular representation, often with many crossing lines. We use methods from graph theory in order to generate network plots in an automated way. We give a number of requirements for graph drawing and present an algorithm that fits prespecified ideal distances between the nodes representing the treatments. The method was implemented in the function netgraph of the R package netmeta and applied to a number of networks from the literature. We show that graph representations with a small number of crossing lines are often preferable to circular representations.
John A Haley
2014-12-01
Full Text Available BackgroundThe capacity of cancer cells to undergo epithelial mesenchymal trans-differentiation has been implicated as a factor driving metastasis, through the acquisition of enhanced migratory/invasive cell programs and the engagement of anti-apoptotic mechanisms promoting drug and radiation resistance. Our aim was to define molecular signaling changes associated with mesenchymal trans-differentiation in two KRas mutant NSCLC models. We focused on central transcription and epigenetic regulators predicted to be important for mesenchymal cell survival.Experimental designWe have modeled trans-differentiation and cancer stemness in inducible isogenic mutant KRas H358 and A549 non-small cell lung cell backgrounds. We employed large-scale quantitative phospho-proteomic, proteomic, protein-protein interaction, RNA-Seq and network function prediction approaches to dissect the molecular events associated with the establishment and maintenance of the mesenchymal state.ResultsGene set enrichment and pathway prediction indicated BMI1, KDM5B, RUNX2, MYC/MAX, NFkB, LEF1, and HIF1 target networks were significantly enriched in the trans-differentiation of H358 and A549 NSCLC models. Physical overlaps between multiple networks implicate NR4A1 as an overlapping control between TCF and NFkB pathways. Enrichment correlations also indicated marked decrease in cell cycling, which occurred early in the EMT process. RNA abundance time course studies also indicated early expression of epigenetic and chromatin regulators, including CITED4, RUNX3, CMBX1 and SIRT4. ConclusionsMultiple transcription and epigenetic pathways where altered between epithelial and mesenchymal tumor cell states, notably the polycomb repressive complex-1, HP1g and BAF/Swi-Snf. Network analysis suggests redundancy in the activation and inhibition of pathway regulators, notably factors controlling epithelial cell state.
Li, Chunhe; Wang, Jin
2013-01-01
Cellular reprogramming has been recently intensively studied experimentally. We developed a global potential landscape and kinetic path framework to explore a human stem cell developmental network composed of 52 genes. We uncovered the underlying landscape for the stem cell network with two basins of attractions representing stem and differentiated cell states, quantified and exhibited the high dimensional biological paths for the differentiation and reprogramming process, connecting the stem cell state and differentiated cell state. Both the landscape and non-equilibrium curl flux determine the dynamics of cell differentiation jointly. Flux leads the kinetic paths to be deviated from the steepest descent gradient path, and the corresponding differentiation and reprogramming paths are irreversible. Quantification of paths allows us to find out how the differentiation and reprogramming occur and which important states they go through. We show the developmental process proceeds as moving from the stem cell basin of attraction to the differentiation basin of attraction. The landscape topography characterized by the barrier heights and transition rates quantitatively determine the global stability and kinetic speed of cell fate decision process for development. Through the global sensitivity analysis, we provided some specific predictions for the effects of key genes and regulation connections on the cellular differentiation or reprogramming process. Key links from sensitivity analysis and biological paths can be used to guide the differentiation designs or reprogramming tactics. PMID:23935477
Web Page Design and Network Analysis.
Wan, Hakman A.; Chung, Chi-wai
1998-01-01
Examines problems in Web-site design from the perspective of network analysis. In view of the similarity between the hypertext structure of Web pages and a generic network, network analysis presents concepts and theories that provide insight for Web-site design. Describes the problem of home-page location and control of number of Web pages and…
Social Network Analysis and informal trade
Walther, Olivier
networks can be applied to better understand informal trade in developing countries, with a particular focus on Africa. The paper starts by discussing some of the fundamental concepts developed by social network analysis. Through a number of case studies, we show how social network analysis can...... illuminate the relevant causes of social patterns, the impact of social ties on economic performance, the diffusion of resources and information, and the exercise of power. The paper then examines some of the methodological challenges of social network analysis and how it can be combined with other...... approaches. The paper finally highlights some of the applications of social network analysis and their implications for trade policies....
Topological Analysis of Urban Drainage Networks
Yang, Soohyun; Paik, Kyungrock; McGrath, Gavan; Rao, Suresh
2016-04-01
Urban drainage networks are an essential component of infrastructure, and comprise the aggregation of underground pipe networks carrying storm water and domestic waste water for eventual discharge to natural stream networks. Growing urbanization has contributed to rapid expansion of sewer networks, vastly increasing their complexity and scale. Importance of sewer networks has been well studied from an engineering perspective, including resilient management, optimal design, and malfunctioning impact. Yet, analysis of the urban drainage networks using complex networks approach are lacking. Urban drainage networks consist of manholes and conduits, which correspond to nodes and edges, analogous to junctions and streams in river networks. Converging water flows in these two networks are driven by elevation gradient. In this sense, engineered urban drainage networks share several attributes of flows in river networks. These similarities between the two directed, converging flow networks serve the basis for us to hypothesize that the functional topology of sewer networks, like river networks, is scale-invariant. We analyzed the exceedance probability distribution of upstream area for practical sewer networks in South Korea. We found that the exceedance probability distributions of upstream area follow power-law, implying that the sewer networks exhibit topological self-similarity. The power-law exponents for the sewer networks were similar, and within the range reported from analysis of natural river networks. Thus, in line with our hypothesis, these results suggest that engineered urban drainage networks share functional topological attributes regardless of their structural dissimilarity or different underlying network evolution processes (natural vs. engineered). Implications of these findings for optimal design of sewer networks and for modeling sewer flows will be discussed.
Structural Analysis of Complex Networks
Dehmer, Matthias
2011-01-01
Filling a gap in literature, this self-contained book presents theoretical and application-oriented results that allow for a structural exploration of complex networks. The work focuses not only on classical graph-theoretic methods, but also demonstrates the usefulness of structural graph theory as a tool for solving interdisciplinary problems. Applications to biology, chemistry, linguistics, and data analysis are emphasized. The book is suitable for a broad, interdisciplinary readership of researchers, practitioners, and graduate students in discrete mathematics, statistics, computer science,
Analysis of Network Parameters Influencing Performance of Hybrid Multimedia Networks
Dominik Kovac
2013-10-01
Full Text Available Multimedia networks is an emerging subject that currently attracts the attention of research and industrial communities. This environment provides new entertainment services and business opportunities merged with all well-known network services like VoIP calls or file transfers. Such a heterogeneous system has to be able satisfy all network and end-user requirements which are increasing constantly. Therefore the simulation tools enabling deep analysis in order to find the key performance indicators and factors which influence the overall quality for specific network service the most are highly needed. This paper provides a study on the network parameters like communication technology, routing protocol, QoS mechanism, etc. and their effect on the performance of hybrid multimedia network. The analysis was performed in OPNET Modeler environment and the most interesting results are discussed at the end of this paper
Review Essay: Does Qualitative Network Analysis Exist?
Rainer Diaz-Bone
2007-01-01
Full Text Available Social network analysis was formed and established in the 1970s as a way of analyzing systems of social relations. In this review the theoretical-methodological standpoint of social network analysis ("structural analysis" is introduced and the different forms of social network analysis are presented. Structural analysis argues that social actors and social relations are embedded in social networks, meaning that action and perception of actors as well as the performance of social relations are influenced by the network structure. Since the 1990s structural analysis has integrated concepts such as agency, discourse and symbolic orientation and in this way structural analysis has opened itself. Since then there has been increasing use of qualitative methods in network analysis. They are used to include the perspective of the analyzed actors, to explore networks, and to understand network dynamics. In the reviewed book, edited by Betina HOLLSTEIN and Florian STRAUS, the twenty predominantly empirically orientated contributions demonstrate the possibilities of combining quantitative and qualitative methods in network analyses in different research fields. In this review we examine how the contributions succeed in applying and developing the structural analysis perspective, and the self-positioning of "qualitative network analysis" is evaluated. URN: urn:nbn:de:0114-fqs0701287
Sahu, Mousumi; Mallick, Bibekanand
2016-10-01
The differentiation of human Embryonic Stem Cells (hESCs) is accompanied by the formation of different intermediary cells, gradually losing its stemness and acquiring differentiation. The precise mechanisms underlying hESCs integrity and its differentiation into fibroblast (Fib) are still elusive. Here, we aimed to assess important genes and co-expression sub-networks responsible for stemness, early differentiation of hESCs into embryoid bodies (EBs) and its lineage specification into Fibs. To achieve this, we compared transcriptional profiles of hESCs-EBs and EBs-Fibs and obtained differentially expressed genes (DEGs) exclusive to hESCs-EBs (early differentiation), EBs-Fibs (late differentiation) and common DEGs in hESCs-EBs and EBs-Fibs. Then, we performed gene set enrichment analysis (GSEA) followed by overrepresentation study and identified key genes for each gene category. The regulations of these genes were studied by integrating ChIP-Seq data of core transcription factors (TFs) and histone methylation marks in hESCs. Finally, we identified co-expression sub-networks from key genes of each gene category using k-clique sub-network extraction method. Our study predicted seven genes edicting core stemness properties forming a co-expression network. From the pathway analysis of sub-networks of hESCs-EBs, we hypothesize that FGF2 is contributing to pluripotent transcription network of hESCs in association with DNMT3B and JARID2 thereby facilitating cell proliferation. On the contrary, FGF2 is found to promote cell migration in Fibs along with DDR2, CAV1, DAB2, and PARVA. Moreover, our study identified three k-clique sub-networks regulating TGF-β signaling pathway thereby promoting EBs to Fibs differentiation by: (i) modulating extracellular matrix involving ITGB1, TGFB1I1 and GBP1, (ii) regulating cell cycle remodeling involving CDKN1A, JUNB and DUSP1 and (iii) helping in epithelial to mesenchymal transition (EMT) involving THBS1, INHBA and LOX. This study put
DNA microarray analysis of genes differentially expressed in adipocyte differentiation
Chunyan Yin; Yanfeng Xiao; Wei Zhang; Erdi Xu; Weihua Liu; Xiaoqing Yi; Ming Chang
2014-06-01
In the present study, the human liposarcoma cell line SW872 was used to identify global changes in gene expression profiles occurring during adipogenesis. We further explored some of the genes expressed during the late phase of adipocyte differentiation. These genes may play a major role in promoting excessive proliferation and accumulation of lipid droplets, which contribute to the development of obesity. By using microarray-based technology, we examined differential gene expression in early differentiated adipocytes and late differentiated adipocytes. Validated genes exhibited a ≥ 10-fold increase in the late phase of adipocyte differentiation by polymerase chain reaction (RT-PCR). Compared with undifferentiated preadipocytes, we found that 763 genes were increased in early differentiated adipocytes, and 667 genes were increased in later differentiated adipocytes. Furthermore, 21 genes were found being expressed 10-fold higher in the late phase of adipocyte differentiation. The results were in accordance with the RT-PCR test, which validated 11 genes, namely, CIDEC, PID1, LYRM1, ADD1, PPAR2, ANGPTL4, ADIPOQ, ACOX1, FIP1L1, MAP3K2 and PEX14. Most of these genes were found being expressed in the later phase of adipocyte differentiation involved in obesity-related diseases. The findings may help to better understand the mechanism of obesity and related diseases.
DNA microarray analysis of genes differentially expressed in adipocyte differentiation.
Yin, Chunyan; Xiao, Yanfeng; Zhang, Wei; Xu, Erdi; Liu, Weihua; Yi, Xiaoqing; Chang, Ming
2014-06-01
In the present study, the human liposarcoma cell line SW872 was used to identify global changes in gene expression profiles occurring during adipogenesis. We further explored some of the genes expressed during the late phase of adipocyte differentiation. These genes may play a major role in promoting excessive proliferation and accumulation of lipid droplets, which contribute to the development of obesity. By using microarray-based technology, we examined differential gene expression in early differentiated adipocytes and late differentiated adipocytes. Validated genes exhibited a greater than or equal to 10-fold increase in the late phase of adipocyte differentiation by polymerase chain reaction (RT-PCR). Compared with undifferentiated preadipocytes, we found that 763 genes were increased in early differentiated adipocytes, and 667 genes were increased in later differentiated adipocytes. Furthermore, 21 genes were found being expressed 10-fold higher in the late phase of adipocyte differentiation. The results were in accordance with the RTPCR test, which validated 11 genes, namely, CIDEC, PID1, LYRM1, ADD1, PPAR?2, ANGPTL4, ADIPOQ, ACOX1, FIP1L1, MAP3K2 and PEX14. Most of these genes were found being expressed in the later phase of adipocyte differentiation involved in obesity-related diseases. The findings may help to better understand the mechanism of obesity and related diseases.
Google matrix analysis of directed networks
Ermann, Leonardo; Frahm, Klaus M.; Shepelyansky, Dima L.
2015-10-01
In the past decade modern societies have developed enormous communication and social networks. Their classification and information retrieval processing has become a formidable task for the society. Because of the rapid growth of the World Wide Web, and social and communication networks, new mathematical methods have been invented to characterize the properties of these networks in a more detailed and precise way. Various search engines extensively use such methods. It is highly important to develop new tools to classify and rank a massive amount of network information in a way that is adapted to internal network structures and characteristics. This review describes the Google matrix analysis of directed complex networks demonstrating its efficiency using various examples including the World Wide Web, Wikipedia, software architectures, world trade, social and citation networks, brain neural networks, DNA sequences, and Ulam networks. The analytical and numerical matrix methods used in this analysis originate from the fields of Markov chains, quantum chaos, and random matrix theory.
Google matrix analysis of directed networks
Ermann, Leonardo; Shepelyansky, Dima L
2014-01-01
In past ten years, modern societies developed enormous communication and social networks. Their classification and information retrieval processing become a formidable task for the society. Due to the rapid growth of World Wide Web, social and communication networks, new mathematical methods have been invented to characterize the properties of these networks on a more detailed and precise level. Various search engines are essentially using such methods. It is highly important to develop new tools to classify and rank enormous amount of network information in a way adapted to internal network structures and characteristics. This review describes the Google matrix analysis of directed complex networks demonstrating its efficiency on various examples including World Wide Web, Wikipedia, software architecture, world trade, social and citation networks, brain neural networks, DNA sequences and Ulam networks. The analytical and numerical matrix methods used in this analysis originate from the fields of Markov chain...
Network analysis literacy a practical approach to the analysis of networks
Zweig, Katharina A
2014-01-01
Network Analysis Literacy focuses on design principles for network analytics projects. The text enables readers to: pose a defined network analytic question; build a network to answer the question; choose or design the right network analytic methods for a particular purpose, and more.
Social network analysis community detection and evolution
Missaoui, Rokia
2015-01-01
This book is devoted to recent progress in social network analysis with a high focus on community detection and evolution. The eleven chapters cover the identification of cohesive groups, core components and key players either in static or dynamic networks of different kinds and levels of heterogeneity. Other important topics in social network analysis such as influential detection and maximization, information propagation, user behavior analysis, as well as network modeling and visualization are also presented. Many studies are validated through real social networks such as Twitter. This edit
Social network analysis and dual rover communications
Litaker, Harry L.; Howard, Robert L.
2013-10-01
Social network analysis (SNA) refers to the collection of techniques, tools, and methods used in sociometry aiming at the analysis of social networks to investigate decision making, group communication, and the distribution of information. Human factors engineers at the National Aeronautics and Space Administration (NASA) conducted a social network analysis on communication data collected during a 14-day field study operating a dual rover exploration mission to better understand the relationships between certain network groups such as ground control, flight teams, and planetary science. The analysis identified two communication network structures for the continuous communication and Twice-a-Day Communication scenarios as a split network and negotiated network respectfully. The major nodes or groups for the networks' architecture, transmittal status, and information were identified using graphical network mapping, quantitative analysis of subjective impressions, and quantified statistical analysis using Sociometric Statue and Centrality. Post-questionnaire analysis along with interviews revealed advantages and disadvantages of each network structure with team members identifying the need for a more stable continuous communication network, improved robustness of voice loops, and better systems training/capabilities for scientific imagery data and operational data during Twice-a-Day Communications.
ONLINE SOCIAL NETWORK INTERNETWORKING ANALYSIS
Bassant E.Youssef
2014-10-01
Full Text Available Online social networks (OSNs contain data about users, their relations, interests and daily activities andthe great value of this data results in ever growing popularity of OSNs. There are two types of OSNs data,semantic and topological. Both can be used to support decision making processes in many applicationssuch as in information diffusion, viral marketing and epidemiology. Online Social network analysis (OSNAresearch is used to maximize the benefits gained from OSNs’ data. This paper provides a comprehensive study of OSNs and OSNA to provide analysts with the knowledge needed to analyse OSNs. OSNs’internetworking was found to increase the wealth of the analysed data by depending on more than one OSNas the source of the analysed data. Paper proposes a generic model of OSNs’ internetworking system that an analyst can rely on. Twodifferent data sources in OSNs were identified in our efforts to provide a thorough study of OSNs, whichare the OSN User data and the OSN platform data. Additionally, we propose a classification of the OSNUser data according to its analysis models for different data types to shed some light into the current usedOSNA methodologies. We also highlight the different metrics and parameters that analysts can use toevaluate semantic or topologic OSN user data. Further, we present a classification of the other data typesand OSN platform data that can be used to compare the capabilities of different OSNs whether separate orin a OSNs’ internetworking system. To increase analysts’ awareness about the available tools they can use,we overview some of the currently publically available OSNs’ datasets and simulation tools and identifywhether they are capable of being used in semantic, topological OSNA, or both. The overview identifiesthat only few datasets includes both data types (semantic and topological and there are few analysis toolsthat can perform analysis on both data types. Finally paper present a scenario that
Network Reconstruction and Systems Analysis of Cardiac Myocyte Hypertrophy Signaling*
Ryall, Karen A.; Holland, David O.; Delaney, Kyle A.; Kraeutler, Matthew J.; Parker, Audrey J.; Saucerman, Jeffrey J.
2012-01-01
Cardiac hypertrophy is managed by a dense web of signaling pathways with many pathways influencing myocyte growth. A quantitative understanding of the contributions of individual pathways and their interactions is needed to better understand hypertrophy signaling and to develop more effective therapies for heart failure. We developed a computational model of the cardiac myocyte hypertrophy signaling network to determine how the components and network topology lead to differential regulation of transcription factors, gene expression, and myocyte size. Our computational model of the hypertrophy signaling network contains 106 species and 193 reactions, integrating 14 established pathways regulating cardiac myocyte growth. 109 of 114 model predictions were validated using published experimental data testing the effects of receptor activation on transcription factors and myocyte phenotypic outputs. Network motif analysis revealed an enrichment of bifan and biparallel cross-talk motifs. Sensitivity analysis was used to inform clustering of the network into modules and to identify species with the greatest effects on cell growth. Many species influenced hypertrophy, but only a few nodes had large positive or negative influences. Ras, a network hub, had the greatest effect on cell area and influenced more species than any other protein in the network. We validated this model prediction in cultured cardiac myocytes. With this integrative computational model, we identified the most influential species in the cardiac hypertrophy signaling network and demonstrate how different levels of network organization affect myocyte size, transcription factors, and gene expression. PMID:23091058
Hierarchical Parallelization of Gene Differential Association Analysis
Dwarkadas Sandhya
2011-09-01
Full Text Available Abstract Background Microarray gene differential expression analysis is a widely used technique that deals with high dimensional data and is computationally intensive for permutation-based procedures. Microarray gene differential association analysis is even more computationally demanding and must take advantage of multicore computing technology, which is the driving force behind increasing compute power in recent years. In this paper, we present a two-layer hierarchical parallel implementation of gene differential association analysis. It takes advantage of both fine- and coarse-grain (with granularity defined by the frequency of communication parallelism in order to effectively leverage the non-uniform nature of parallel processing available in the cutting-edge systems of today. Results Our results show that this hierarchical strategy matches data sharing behavior to the properties of the underlying hardware, thereby reducing the memory and bandwidth needs of the application. The resulting improved efficiency reduces computation time and allows the gene differential association analysis code to scale its execution with the number of processors. The code and biological data used in this study are downloadable from http://www.urmc.rochester.edu/biostat/people/faculty/hu.cfm. Conclusions The performance sweet spot occurs when using a number of threads per MPI process that allows the working sets of the corresponding MPI processes running on the multicore to fit within the machine cache. Hence, we suggest that practitioners follow this principle in selecting the appropriate number of MPI processes and threads within each MPI process for their cluster configurations. We believe that the principles of this hierarchical approach to parallelization can be utilized in the parallelization of other computationally demanding kernels.
Dynamic analysis of biochemical network using complex network method
Wang Shuqiang
2015-01-01
Full Text Available In this study, the stochastic biochemical reaction model is proposed based on the law of mass action and complex network theory. The dynamics of biochemical reaction system is presented as a set of non-linear differential equations and analyzed at the molecular-scale. Given the initial state and the evolution rules of the biochemical reaction system, the system can achieve homeostasis. Compared with random graph, the biochemical reaction network has larger information capacity and is more efficient in information transmission. This is consistent with theory of evolution.
Networks and Bargaining in Policy Analysis
Bogason, Peter
2006-01-01
A duscussion of the fight between proponents of rationalistic policy analysis and more political interaction models for policy analysis. The latter group is the foundation for the many network models of policy analysis of today....
Differential evolutionary conservation of motif modes in the yeast protein interaction network
Yu Chang-Yung
2006-04-01
Full Text Available Abstract Background The importance of a network motif (a recurring interconnected pattern of special topology which is over-represented in a biological network lies in its position in the hierarchy between the protein molecule and the module in a protein-protein interaction network. Until now, however, the methods available have greatly restricted the scope of research. While they have focused on the analysis in the resolution of a motif topology, they have not been able to distinguish particular motifs of the same topology in a protein-protein interaction network. Results We have been able to assign the molecular function annotations of Gene Ontology to each protein in the protein-protein interactions of Saccharomyces cerevisiae. For various motif topologies, we have developed an algorithm, enabling us to unveil one million "motif modes", each of which features a unique topological combination of molecular functions. To our surprise, the conservation ratio, i.e., the extent of the evolutionary constraints upon the motif modes of the same motif topology, varies significantly, clearly indicative of distinct differences in the evolutionary constraints upon motifs of the same motif topology. Equally important, for all motif modes, we have found a power-law distribution of the motif counts on each motif mode. We postulate that motif modes may very well represent the evolutionary-conserved topological units of a protein interaction network. Conclusion For the first time, the motifs of a protein interaction network have been investigated beyond the scope of motif topology. The motif modes determined in this study have not only enabled us to differentiate among different evolutionary constraints on motifs of the same topology but have also opened up new avenues through which protein interaction networks can be analyzed.
Yang, Hui; Zhu, Xiaoxu; Bai, Wei; Zhao, Yongli; Zhang, Jie; Liu, Zhu; Zhou, Ziguan; Ou, Qinghai
2016-09-01
Virtualization is considered to be a promising solution to support various emerging applications. This paper illustrates the problem of virtual mapping from a new perspective, and mainly focuses on survivable mapping of virtual networks and the potential trade-off between spectral resource usage effectiveness and failure resilience level. We design an optimum shared protection mapping (OSPM) scheme in elastic optical networks. A differentiable maximum shared capacity of each frequency slot is defined to more efficiently shared protection resource. In order to satisfy various assessment standards, a metric called ambiguity similitude is defined for the first time to give insight on the optimizing difficulty. Simulation results are presented to compare the outcome of the novel OSPM algorithm with traditional dedicated link protection and maximum shared protection mapping. By synthetic analysis, OSPM outperforms the other two schemes in terms of striking a perfect balance among blocking probability, resources utilization, protective success rate, and spectrum redundancy.
Genetic analysis of population differentiation and adaptation in Leuciscus waleckii.
Chang, Yumei; Tang, Ran; Sun, Xiaowen; Liang, Liqun; Chen, Jinping; Huang, Jinfeng; Dou, Xinjie; Tao, Ran
2013-12-01
Demographic events and natural selection both influence animal phenotypic and genetic variation; exploring the effects of demography and selection on population divergence is of great significance in evolutionary biology. To uncover the causes behind the patterns of genetic differentiation and adaptation among six populations of Leuciscus waleckii from Dali Basin (two populations, alkaline vs. freshwater) and Amur Basin (four populations, freshwater rivers vs. alkaline lake), a set of 21 unlinked polymorphic microsatellite markers and two mitochondrial DNA sequences (Cytb and D-loop) were applied to examine whether populations from different environments or habitats have distinct genetic differentiation and whether alkalinity is the major factor that caused population divergence. Bayesian analysis and principal component analysis as well as haplotype network analysis showed that these populations are primarily divided into two groups, which are congruent with geographic separation but not inconsistent with the habitat environment (alkalinity). Using three different approaches, outlier detection indicated that one locus, HLJYL017, may be under directional selection and involved in local adaptation processes. Overall, this study suggested that demographic events and selection of local environmental conditions including of alkalinity are jointly responsible for population divergence. These findings constitute an important step towards the understanding of the genetic basis of differentiation and adaptation, as well as towards the conservation of L. waleckii.
Networks and Bargaining in Policy Analysis
Bogason, Peter
2006-01-01
A duscussion of the fight between proponents of rationalistic policy analysis and more political interaction models for policy analysis. The latter group is the foundation for the many network models of policy analysis of today.......A duscussion of the fight between proponents of rationalistic policy analysis and more political interaction models for policy analysis. The latter group is the foundation for the many network models of policy analysis of today....
Applications of Social Network Analysis
Thilagam, P. Santhi
A social network [2] is a description of the social structure between actors, mostly persons, groups or organizations. It indicates the ways in which they are connected with each other by some relationship such as friendship, kinship, finance exchange etc. In a nutshell, when the person uses already known/unknown people to create new contacts, it forms social networking. The social network is not a new concept rather it can be formed when similar people interact with each other directly or indirectly to perform particular task. Examples of social networks include a friendship networks, collaboration networks, co-authorship networks, and co-employees networks which depict the direct interaction among the people. There are also other forms of social networks, such as entertainment networks, business Networks, citation networks, and hyperlink networks, in which interaction among the people is indirect. Generally, social networks operate on many levels, from families up to the level of nations and assists in improving interactive knowledge sharing, interoperability and collaboration.
Dissecting neural differentiation regulatory networks through epigenetic footprinting.
Ziller, Michael J; Edri, Reuven; Yaffe, Yakey; Donaghey, Julie; Pop, Ramona; Mallard, William; Issner, Robbyn; Gifford, Casey A; Goren, Alon; Xing, Jeffrey; Gu, Hongcang; Cacchiarelli, Davide; Tsankov, Alexander M; Epstein, Charles; Rinn, John L; Mikkelsen, Tarjei S; Kohlbacher, Oliver; Gnirke, Andreas; Bernstein, Bradley E; Elkabetz, Yechiel; Meissner, Alexander
2015-02-19
Models derived from human pluripotent stem cells that accurately recapitulate neural development in vitro and allow for the generation of specific neuronal subtypes are of major interest to the stem cell and biomedical community. Notch signalling, particularly through the Notch effector HES5, is a major pathway critical for the onset and maintenance of neural progenitor cells in the embryonic and adult nervous system. Here we report the transcriptional and epigenomic analysis of six consecutive neural progenitor cell stages derived from a HES5::eGFP reporter human embryonic stem cell line. Using this system, we aimed to model cell-fate decisions including specification, expansion and patterning during the ontogeny of cortical neural stem and progenitor cells. In order to dissect regulatory mechanisms that orchestrate the stage-specific differentiation process, we developed a computational framework to infer key regulators of each cell-state transition based on the progressive remodelling of the epigenetic landscape and then validated these through a pooled short hairpin RNA screen. We were also able to refine our previous observations on epigenetic priming at transcription factor binding sites and suggest here that they are mediated by combinations of core and stage-specific factors. Taken together, we demonstrate the utility of our system and outline a general framework, not limited to the context of the neural lineage, to dissect regulatory circuits of differentiation.
Analysis of Recurrent Analog Neural Networks
Z. Raida
1998-06-01
Full Text Available In this paper, an original rigorous analysis of recurrent analog neural networks, which are built from opamp neurons, is presented. The analysis, which comes from the approximate model of the operational amplifier, reveals causes of possible non-stable states and enables to determine convergence properties of the network. Results of the analysis are discussed in order to enable development of original robust and fast analog networks. In the analysis, the special attention is turned to the examination of the influence of real circuit elements and of the statistical parameters of processed signals to the parameters of the network.
Statistical Analysis of Bus Networks in India
Chatterjee, Atanu; Ramadurai, Gitakrishnan
2015-01-01
Through the past decade the field of network science has established itself as a common ground for the cross-fertilization of exciting inter-disciplinary studies which has motivated researchers to model almost every physical system as an interacting network consisting of nodes and links. Although public transport networks such as airline and railway networks have been extensively studied, the status of bus networks still remains in obscurity. In developing countries like India, where bus networks play an important role in day-to-day commutation, it is of significant interest to analyze its topological structure and answer some of the basic questions on its evolution, growth, robustness and resiliency. In this paper, we model the bus networks of major Indian cities as graphs in \\textit{L}-space, and evaluate their various statistical properties using concepts from network science. Our analysis reveals a wide spectrum of network topology with the common underlying feature of small-world property. We observe tha...
Earth slope reliability analysis under seismic loadings using neural network
PENG Huai-sheng; DENG Jian; GU De-sheng
2005-01-01
A new method was proposed to cope with the earth slope reliability problem under seismic loadings. The algorithm integrates the concepts of artificial neural network, the first order second moment reliability method and the deterministic stability analysis method of earth slope. The performance function and its derivatives in slope stability analysis under seismic loadings were approximated by a trained multi-layer feed-forward neural network with differentiable transfer functions. The statistical moments calculated from the performance function values and the corresponding gradients using neural network were then used in the first order second moment method for the calculation of the reliability index in slope safety analysis. Two earth slope examples were presented for illustrating the applicability of the proposed approach. The new method is effective in slope reliability analysis. And it has potential application to other reliability problems of complicated engineering structure with a considerably large number of random variables.
Analysis of the Features of Network Words
阳艳萍
2015-01-01
The information society makes people’s lives gradually enter a digital state for living. And the popularity of the Inter⁃net has led to the unique phenomenon of network words. What impact will network and the combination of language bring about? This article will explore the relation between the phenomenon of network words and social context from the angle of so⁃cial linguistic through the analysis of network words and grammatical features.
Neural Networks for Rapid Design and Analysis
Sparks, Dean W., Jr.; Maghami, Peiman G.
1998-01-01
Artificial neural networks have been employed for rapid and efficient dynamics and control analysis of flexible systems. Specifically, feedforward neural networks are designed to approximate nonlinear dynamic components over prescribed input ranges, and are used in simulations as a means to speed up the overall time response analysis process. To capture the recursive nature of dynamic components with artificial neural networks, recurrent networks, which use state feedback with the appropriate number of time delays, as inputs to the networks, are employed. Once properly trained, neural networks can give very good approximations to nonlinear dynamic components, and by their judicious use in simulations, allow the analyst the potential to speed up the analysis process considerably. To illustrate this potential speed up, an existing simulation model of a spacecraft reaction wheel system is executed, first conventionally, and then with an artificial neural network in place.
Overby, H.; Stol, N.; Nord, Martin
2006-01-01
Existing quality of service differentiation schemes for today's IP over point-to-point optical WDM networks take advantage of electronic RAM to implement traffic management algorithms in order to isolate the service classes. Since practical optical RAM is not available, these techniques...... are not suitable for a future all-optical network. Hence, new schemes are needed to support QoS differentiation in optical packet-switched (OPS) networks. In this article we first present an overview of existing QoS differentiation mechanisms suitable for asynchronous bufferless OPS. We then compare...
Resolving epidemic network failures through differentiated repair times
Fagertun, Anna Manolova; Ruepp, Sarah Renée; Manzano, Marc
2015-01-01
In this study, the authors investigate epidemic failure spreading in large-scale transport networks under generalisedmulti-protocol label switching control plane. By evaluating the effect of the epidemic failure spreading on the network,they design several strategies for cost-effective network...
Differential ligand-signaling network of CCL19/CCL21-CCR7 system.
Raju, Rajesh; Gadakh, Sachin; Gopal, Priyanka; George, Bijesh; Advani, Jayshree; Soman, Sowmya; Prasad, T S K; Girijadevi, Reshmi
2015-01-01
Chemokine (C-C motif) receptor 7 (CCR7), a class A subtype G-Protein Coupled Receptor (GPCR), is involved in the migration, activation and survival of multiple cell types including dendritic cells, T cells, eosinophils, B cells, endothelial cells and different cancer cells. Together, CCR7 signaling system has been implicated in diverse biological processes such as lymph node homeostasis, T cell activation, immune tolerance, inflammatory response and cancer metastasis. CCL19 and CCL21, the two well-characterized CCR7 ligands, have been established to be differential in their signaling through CCR7 in multiple cell types. Although the differential ligand signaling through single receptor have been suggested for many receptors including GPCRs, there exists no resource or platform to analyse them globally. Here, first of its kind, we present the cell-type-specific differential signaling network of CCL19/CCL21-CCR7 system for effective visualization and differential analysis of chemokine/GPCR signaling. Database URL: http:// www. netpath. org/ pathways? path_ id= NetPath_ 46.
Social network analysis and supply chain management
Raúl Rodríguez Rodríguez
2016-01-01
Full Text Available This paper deals with social network analysis and how it could be integrated within supply chain management from a decision-making point of view. Even though the benefits of using social analysis have are widely accepted at both academic and industry/services context, there is still a lack of solid frameworks that allow decision-makers to connect the usage and obtained results of social network analysis – mainly both information and knowledge flows and derived results- with supply chain management objectives and goals. This paper gives an overview of social network analysis, the main social network analysis metrics, supply chain performance and, finally, it identifies how future frameworks could close the gap and link the results of social network analysis with the supply chain management decision-making processes.
The Network Protocol Analysis Technique in Snort
Wu, Qing-Xiu
Network protocol analysis is a network sniffer to capture data for further analysis and understanding of the technical means necessary packets. Network sniffing is intercepted by packet assembly binary format of the original message content. In order to obtain the information contained. Required based on TCP / IP protocol stack protocol specification. Again to restore the data packets at protocol format and content in each protocol layer. Actual data transferred, as well as the application tier.
4th International Conference in Network Analysis
Koldanov, Petr; Pardalos, Panos
2016-01-01
The contributions in this volume cover a broad range of topics including maximum cliques, graph coloring, data mining, brain networks, Steiner forest, logistic and supply chain networks. Network algorithms and their applications to market graphs, manufacturing problems, internet networks and social networks are highlighted. The "Fourth International Conference in Network Analysis," held at the Higher School of Economics, Nizhny Novgorod in May 2014, initiated joint research between scientists, engineers and researchers from academia, industry and government; the major results of conference participants have been reviewed and collected in this Work. Researchers and students in mathematics, economics, statistics, computer science and engineering will find this collection a valuable resource filled with the latest research in network analysis.
Cai, Zuowei; Huang, Lihong; Guo, Zhenyuan; Chen, Xiaoyan
2012-09-01
This paper investigates the periodic dynamics of a general class of time-varying delayed neural networks with discontinuous right-hand sides. By employing the topological degree theory in set-valued analysis, differential inclusions theory and Lyapunov-like approach, we perform a thorough analysis of the existence, uniqueness and global exponential stability of the periodic solution for the neural networks. Especially, some sufficient conditions are derived to guarantee the existence, uniqueness and global exponential stability of the equilibrium point for the autonomous systems corresponding to the non-autonomous neural networks. Furthermore, the global convergence of the output and the convergence in finite time of the state are also discussed. Without assuming the boundedness or monotonicity of the discontinuous neuron activation functions, the obtained results improve and extend previous works on discontinuous or continuous neural network dynamical systems. Finally, two numerical examples are provided to show the applicability and effectiveness of our main results.
Differential thermal analysis microsystem for explosive detection
Olsen, Jesper Kenneth; Greve, Anders; Senesac, L.
2011-01-01
A micro differential thermal analysis (DTA) system is used for detection of trace explosive particles. The DTA system consists of two silicon micro chips with integrated heaters and temperature sensors. One chip is used for reference and one for the measurement sample. The sensor is constructed...... as a small silicon nitride membrane incorporating heater elements and a temperature measurement resistor. In this manuscript the DTA system is described and tested by measuring calorimetric response of 3 different kinds of explosives (TNT, RDX and PETN). This project is carried out under the framework...
Babiarz, Joshua E; Ravon, Morgane; Sridhar, Sriram; Ravindran, Palanikumar; Swanson, Brad; Bitter, Hans; Weiser, Thomas; Chiao, Eric; Certa, Ulrich; Kolaja, Kyle L
2012-07-20
To gain insight into the molecular regulation of human heart development, a detailed comparison of the mRNA and miRNA transcriptomes across differentiating human-induced pluripotent stem cell (hiPSC)-derived cardiomyocytes and biopsies from fetal, adult, and hypertensive human hearts was performed. Gene ontology analysis of the mRNA expression levels of the hiPSCs differentiating into cardiomyocytes revealed 3 distinct groups of genes: pluripotent specific, transitional cardiac specification, and mature cardiomyocyte specific. Hierarchical clustering of the mRNA data revealed that the transcriptome of hiPSC cardiomyocytes largely stabilizes 20 days after initiation of differentiation. Nevertheless, analysis of cells continuously cultured for 120 days indicated that the cardiomyocytes continued to mature toward a more adult-like gene expression pattern. Analysis of cardiomyocyte-specific miRNAs (miR-1, miR-133a/b, and miR-208a/b) revealed an miRNA pattern indicative of stem cell to cardiomyocyte specification. A biostatistitical approach integrated the miRNA and mRNA expression profiles revealing a cardiomyocyte differentiation miRNA network and identified putative mRNAs targeted by multiple miRNAs. Together, these data reveal the miRNA network in human heart development and support the notion that overlapping miRNA networks re-enforce transcriptional control during developmental specification.
Hierarchical differentiation of myeloid progenitors is encoded in the transcription factor network.
Krumsiek, Jan; Marr, Carsten; Schroeder, Timm; Theis, Fabian J
2011-01-01
Hematopoiesis is an ideal model system for stem cell biology with advanced experimental access. A systems view on the interactions of core transcription factors is important for understanding differentiation mechanisms and dynamics. In this manuscript, we construct a Boolean network to model myeloid differentiation, specifically from common myeloid progenitors to megakaryocytes, erythrocytes, granulocytes and monocytes. By interpreting the hematopoietic literature and translating experimental evidence into Boolean rules, we implement binary dynamics on the resulting 11-factor regulatory network. Our network contains interesting functional modules and a concatenation of mutual antagonistic pairs. The state space of our model is a hierarchical, acyclic graph, typifying the principles of myeloid differentiation. We observe excellent agreement between the steady states of our model and microarray expression profiles of two different studies. Moreover, perturbations of the network topology correctly reproduce reported knockout phenotypes in silico. We predict previously uncharacterized regulatory interactions and alterations of the differentiation process, and line out reprogramming strategies.
Delay differential analysis of time series.
Lainscsek, Claudia; Sejnowski, Terrence J
2015-03-01
Nonlinear dynamical system analysis based on embedding theory has been used for modeling and prediction, but it also has applications to signal detection and classification of time series. An embedding creates a multidimensional geometrical object from a single time series. Traditionally either delay or derivative embeddings have been used. The delay embedding is composed of delayed versions of the signal, and the derivative embedding is composed of successive derivatives of the signal. The delay embedding has been extended to nonuniform embeddings to take multiple timescales into account. Both embeddings provide information on the underlying dynamical system without having direct access to all the system variables. Delay differential analysis is based on functional embeddings, a combination of the derivative embedding with nonuniform delay embeddings. Small delay differential equation (DDE) models that best represent relevant dynamic features of time series data are selected from a pool of candidate models for detection or classification. We show that the properties of DDEs support spectral analysis in the time domain where nonlinear correlation functions are used to detect frequencies, frequency and phase couplings, and bispectra. These can be efficiently computed with short time windows and are robust to noise. For frequency analysis, this framework is a multivariate extension of discrete Fourier transform (DFT), and for higher-order spectra, it is a linear and multivariate alternative to multidimensional fast Fourier transform of multidimensional correlations. This method can be applied to short or sparse time series and can be extended to cross-trial and cross-channel spectra if multiple short data segments of the same experiment are available. Together, this time-domain toolbox provides higher temporal resolution, increased frequency and phase coupling information, and it allows an easy and straightforward implementation of higher-order spectra across time
Delay Differential Analysis of Time Series
Lainscsek, Claudia; Sejnowski, Terrence J.
2015-01-01
Nonlinear dynamical system analysis based on embedding theory has been used for modeling and prediction, but it also has applications to signal detection and classification of time series. An embedding creates a multidimensional geometrical object from a single time series. Traditionally either delay or derivative embeddings have been used. The delay embedding is composed of delayed versions of the signal, and the derivative embedding is composed of successive derivatives of the signal. The delay embedding has been extended to nonuniform embeddings to take multiple timescales into account. Both embeddings provide information on the underlying dynamical system without having direct access to all the system variables. Delay differential analysis is based on functional embeddings, a combination of the derivative embedding with nonuniform delay embeddings. Small delay differential equation (DDE) models that best represent relevant dynamic features of time series data are selected from a pool of candidate models for detection or classification. We show that the properties of DDEs support spectral analysis in the time domain where nonlinear correlation functions are used to detect frequencies, frequency and phase couplings, and bispectra. These can be efficiently computed with short time windows and are robust to noise. For frequency analysis, this framework is a multivariate extension of discrete Fourier transform (DFT), and for higher-order spectra, it is a linear and multivariate alternative to multidimensional fast Fourier transform of multidimensional correlations. This method can be applied to short or sparse time series and can be extended to cross-trial and cross-channel spectra if multiple short data segments of the same experiment are available. Together, this time-domain toolbox provides higher temporal resolution, increased frequency and phase coupling information, and it allows an easy and straightforward implementation of higher-order spectra across time
Universality in complex networks: random matrix analysis.
Bandyopadhyay, Jayendra N; Jalan, Sarika
2007-08-01
We apply random matrix theory to complex networks. We show that nearest neighbor spacing distribution of the eigenvalues of the adjacency matrices of various model networks, namely scale-free, small-world, and random networks follow universal Gaussian orthogonal ensemble statistics of random matrix theory. Second, we show an analogy between the onset of small-world behavior, quantified by the structural properties of networks, and the transition from Poisson to Gaussian orthogonal ensemble statistics, quantified by Brody parameter characterizing a spectral property. We also present our analysis for a protein-protein interaction network in budding yeast.
Dynamic Analysis of Structures Using Neural Networks
N. Ahmadi
2008-01-01
Full Text Available In the recent years, neural networks are considered as the best candidate for fast approximation with arbitrary accuracy in the time consuming problems. Dynamic analysis of structures against earthquake has the time consuming process. We employed two kinds of neural networks: Generalized Regression neural network (GR and Back-Propagation Wavenet neural network (BPW, for approximating of dynamic time history response of frame structures. GR is a traditional radial basis function neural network while BPW categorized as a wavelet neural network. In BPW, sigmoid activation functions of hidden layer neurons are substituted with wavelets and weights training are achieved using Scaled Conjugate Gradient (SCG algorithm. Comparison the results of BPW with those of GR in the dynamic analysis of eight story steel frame indicates that accuracy of the properly trained BPW was better than that of GR and therefore, BPW can be efficiently used for approximate dynamic analysis of structures.
Measuring Road Network Vulnerability with Sensitivity Analysis
Jun-qiang, Leng; Long-hai, Yang; Liu, Wei-yi; Zhao, Lin
2017-01-01
This paper focuses on the development of a method for road network vulnerability analysis, from the perspective of capacity degradation, which seeks to identify the critical infrastructures in the road network and the operational performance of the whole traffic system. This research involves defining the traffic utility index and modeling vulnerability of road segment, route, OD (Origin Destination) pair and road network. Meanwhile, sensitivity analysis method is utilized to calculate the change of traffic utility index due to capacity degradation. This method, compared to traditional traffic assignment, can improve calculation efficiency and make the application of vulnerability analysis to large actual road network possible. Finally, all the above models and calculation method is applied to actual road network evaluation to verify its efficiency and utility. This approach can be used as a decision-supporting tool for evaluating the performance of road network and identifying critical infrastructures in transportation planning and management, especially in the resource allocation for mitigation and recovery. PMID:28125706
Cellular network entropy as the energy potential in Waddington's differentiation landscape.
Banerji, Christopher R S; Miranda-Saavedra, Diego; Severini, Simone; Widschwendter, Martin; Enver, Tariq; Zhou, Joseph X; Teschendorff, Andrew E
2013-10-24
Differentiation is a key cellular process in normal tissue development that is significantly altered in cancer. Although molecular signatures characterising pluripotency and multipotency exist, there is, as yet, no single quantitative mark of a cellular sample's position in the global differentiation hierarchy. Here we adopt a systems view and consider the sample's network entropy, a measure of signaling pathway promiscuity, computable from a sample's genome-wide expression profile. We demonstrate that network entropy provides a quantitative, in-silico, readout of the average undifferentiated state of the profiled cells, recapitulating the known hierarchy of pluripotent, multipotent and differentiated cell types. Network entropy further exhibits dynamic changes in time course differentiation data, and in line with a sample's differentiation stage. In disease, network entropy predicts a higher level of cellular plasticity in cancer stem cell populations compared to ordinary cancer cells. Importantly, network entropy also allows identification of key differentiation pathways. Our results are consistent with the view that pluripotency is a statistical property defined at the cellular population level, correlating with intra-sample heterogeneity, and driven by the degree of signaling promiscuity in cells. In summary, network entropy provides a quantitative measure of a cell's undifferentiated state, defining its elevation in Waddington's landscape.
Cellular network entropy as the energy potential in Waddington's differentiation landscape
Banerji, Christopher R. S.; Miranda-Saavedra, Diego; Severini, Simone; Widschwendter, Martin; Enver, Tariq; Zhou, Joseph X.; Teschendorff, Andrew E.
2013-10-01
Differentiation is a key cellular process in normal tissue development that is significantly altered in cancer. Although molecular signatures characterising pluripotency and multipotency exist, there is, as yet, no single quantitative mark of a cellular sample's position in the global differentiation hierarchy. Here we adopt a systems view and consider the sample's network entropy, a measure of signaling pathway promiscuity, computable from a sample's genome-wide expression profile. We demonstrate that network entropy provides a quantitative, in-silico, readout of the average undifferentiated state of the profiled cells, recapitulating the known hierarchy of pluripotent, multipotent and differentiated cell types. Network entropy further exhibits dynamic changes in time course differentiation data, and in line with a sample's differentiation stage. In disease, network entropy predicts a higher level of cellular plasticity in cancer stem cell populations compared to ordinary cancer cells. Importantly, network entropy also allows identification of key differentiation pathways. Our results are consistent with the view that pluripotency is a statistical property defined at the cellular population level, correlating with intra-sample heterogeneity, and driven by the degree of signaling promiscuity in cells. In summary, network entropy provides a quantitative measure of a cell's undifferentiated state, defining its elevation in Waddington's landscape.
Enhanced Differentiated Surveillance for Static and Random Mobile Sensor Networks
Xue-Qin Zhu
2011-10-01
Full Text Available Wireless integrated sensor networks, which include collecting, processing data and communication, are used more and more widely for its low cost and convenient deployment. Nowadays the researches of sensor networks are fairly active. The security is one of the key questions in sensor networks. Intrusion detection is a kind of network security technologies used to detect any behavior that will damage or attempt to damage system confidentiality, integrality or availability, and it can provide the reasonable supplement to intrusion prevention mechanism, and construct a second wall of defense for network and system. This paper mainly focuses on the energy efficient intrusion detection technology. According to the characteristics of sensor network and the specialty of the invasions in sensor network, this paper presents an intrusion detection model based on statistics anomaly in sensor networks. The algorithm establishes models for the normal state of the nodes, and makes decisions through the deviation degree of observed value. The algorithm is fault-tolerant for non-invasion anomaly when the communication between nodes break down or the accident wrongly create anomaly.
Filtering Genes for Cluster and Network Analysis
Parkhomenko Elena
2009-06-01
Full Text Available Abstract Background Prior to cluster analysis or genetic network analysis it is customary to filter, or remove genes considered to be irrelevant from the set of genes to be analyzed. Often genes whose variation across samples is less than an arbitrary threshold value are deleted. This can improve interpretability and reduce bias. Results This paper introduces modular models for representing network structure in order to study the relative effects of different filtering methods. We show that cluster analysis and principal components are strongly affected by filtering. Filtering methods intended specifically for cluster and network analysis are introduced and compared by simulating modular networks with known statistical properties. To study more realistic situations, we analyze simulated "real" data based on well-characterized E. coli and S. cerevisiae regulatory networks. Conclusion The methods introduced apply very generally, to any similarity matrix describing gene expression. One of the proposed methods, SUMCOV, performed well for all models simulated.
Chen, Shuo; Kang, Jian; Xing, Yishi; Wang, Guoqing
2015-12-01
Group-level functional connectivity analyses often aim to detect the altered connectivity patterns between subgroups with different clinical or psychological experimental conditions, for example, comparing cases and healthy controls. We present a new statistical method to detect differentially expressed connectivity networks with significantly improved power and lower false-positive rates. The goal of our method was to capture most differentially expressed connections within networks of constrained numbers of brain regions (by the rule of parsimony). By virtue of parsimony, the false-positive individual connectivity edges within a network are effectively reduced, whereas the informative (differentially expressed) edges are allowed to borrow strength from each other to increase the overall power of the network. We develop a test statistic for each network in light of combinatorics graph theory, and provide p-values for the networks (in the weak sense) by using permutation test with multiple-testing adjustment. We validate and compare this new approach with existing methods, including false discovery rate and network-based statistic, via simulation studies and a resting-state functional magnetic resonance imaging case-control study. The results indicate that our method can identify differentially expressed connectivity networks, whereas existing methods are limited.
Penalized differential pathway analysis of integrative oncogenomics studies.
van Wieringen, Wessel N; van de Wiel, Mark A
2014-04-01
Through integration of genomic data from multiple sources, we may obtain a more accurate and complete picture of the molecular mechanisms underlying tumorigenesis. We discuss the integration of DNA copy number and mRNA gene expression data from an observational integrative genomics study involving cancer patients. The two molecular levels involved are linked through the central dogma of molecular biology. DNA copy number aberrations abound in the cancer cell. Here we investigate how these aberrations affect gene expression levels within a pathway using observational integrative genomics data of cancer patients. In particular, we aim to identify differential edges between regulatory networks of two groups involving these molecular levels. Motivated by the rate equations, the regulatory mechanism between DNA copy number aberrations and gene expression levels within a pathway is modeled by a simultaneous-equations model, for the one- and two-group case. The latter facilitates the identification of differential interactions between the two groups. Model parameters are estimated by penalized least squares using the lasso (L1) penalty to obtain a sparse pathway topology. Simulations show that the inclusion of DNA copy number data benefits the discovery of gene-gene interactions. In addition, the simulations reveal that cis-effects tend to be over-estimated in a univariate (single gene) analysis. In the application to real data from integrative oncogenomic studies we show that inclusion of prior information on the regulatory network architecture benefits the reproducibility of all edges. Furthermore, analyses of the TP53 and TGFb signaling pathways between ER+ and ER- samples from an integrative genomics breast cancer study identify reproducible differential regulatory patterns that corroborate with existing literature.
Sensitivity Analysis of Differential-Algebraic Equations and Partial Differential Equations
Petzold, L; Cao, Y; Li, S; Serban, R
2005-08-09
Sensitivity analysis generates essential information for model development, design optimization, parameter estimation, optimal control, model reduction and experimental design. In this paper we describe the forward and adjoint methods for sensitivity analysis, and outline some of our recent work on theory, algorithms and software for sensitivity analysis of differential-algebraic equation (DAE) and time-dependent partial differential equation (PDE) systems.
Lin, Y J; Wan, T T
2001-02-01
During the past decade, the missions/goals of medical providers of healthcare services in the United States have shifted--from emphasizing individual, independent illness treatments to focusing on the continuum of care, population-based wellness, and providing the appropriate care in the most efficient way. Integrated healthcare networks (IHNs)--or integrated healthcare delivery systems--have been focusing heavily on their level of various partnership integration (i.e. service differentiation strategy) in order to offer a full continuum of care. The aim of this study, using the individual IHN as the unit of analysis, was to identify organizational and environmental factors that influence IHN administrators to focus on their service differentiation of market lines, including the establishment of third-party payers' contracts, the affiliation of managed-care organizations, and the alliances of various nonhospital medical providers, to provide a continuum of care. The study findings show that tax status of an IHN, its age, and market competition affect its service differentiation strategy in the provision of a full continuum of care.
Numerical analysis of systems of ordinary and stochastic differential equations
Artemiev, S S
1997-01-01
This text deals with numerical analysis of systems of both ordinary and stochastic differential equations. It covers numerical solution problems of the Cauchy problem for stiff ordinary differential equations (ODE) systems by Rosenbrock-type methods (RTMs).
Industrial entrepreneurial network: Structural and functional analysis
Medvedeva, M. A.; Davletbaev, R. H.; Berg, D. B.; Nazarova, J. J.; Parusheva, S. S.
2016-12-01
Structure and functioning of two model industrial entrepreneurial networks are investigated in the present paper. One of these networks is forming when implementing an integrated project and consists of eight agents, which interact with each other and external environment. The other one is obtained from the municipal economy and is based on the set of the 12 real business entities. Analysis of the networks is carried out on the basis of the matrix of mutual payments aggregated over the certain time period. The matrix is created by the methods of experimental economics. Social Network Analysis (SNA) methods and instruments were used in the present research. The set of basic structural characteristics was investigated: set of quantitative parameters such as density, diameter, clustering coefficient, different kinds of centrality, and etc. They were compared with the random Bernoulli graphs of the corresponding size and density. Discovered variations of random and entrepreneurial networks structure are explained by the peculiarities of agents functioning in production network. Separately, were identified the closed exchange circuits (cyclically closed contours of graph) forming an autopoietic (self-replicating) network pattern. The purpose of the functional analysis was to identify the contribution of the autopoietic network pattern in its gross product. It was found that the magnitude of this contribution is more than 20%. Such value allows using of the complementary currency in order to stimulate economic activity of network agents.
Multilayer motif analysis of brain networks
Battiston, Federico; Chavez, Mario; Latora, Vito
2016-01-01
In the last decade network science has shed new light on the anatomical connectivity and on correlations in the activity of different areas of the human brain. The study of brain networks has made possible in fact to detect the central areas of a neural system, and to identify its building blocks by looking at overabundant small subgraphs, known as motifs. However, network analysis of the brain has so far mainly focused on structural and functional networks as separate entities. The recently developed mathematical framework of multi-layer networks allows to perform a multiplex analysis of the human brain where the structural and functional layers are considered at the same time. In this work we describe how to classify subgraphs in multiplex networks, and we extend motif analysis to networks with many layers. We then extract multi-layer motifs in brain networks of healthy subjects by considering networks with two layers, respectively obtained from diffusion and functional magnetic resonance imaging. Results i...
SOCIAL NETWORK ANALYSIS IN AN ONLINE BLOGOSPHERE
DR. M. MOHAMED SATHIK
2011-01-01
Full Text Available Social network is a social structure that exists among the similar interest of individuals, organizations or even on relations like friendship. Social network analysis is the measure of relationship between people, organizations and processing entities. In today’s scenario, Internet is acting as an interface for the people who spread across the globe to exchange their ideas. Social network analysis is a Web 2.0 application, which facilitates the users tointeract, response and express their views. Social network analysis has greatest attention as a research area in computer science in the recent past. Blogs or weblogs are like catalogs that are maintained by individuals related to a particular topic of interest. In this problem, we analyze the blog responses as social networks that are posted by AIDS patients over a period of time.
3rd International Conference on Network Analysis
Kalyagin, Valery; Pardalos, Panos
2014-01-01
This volume compiles the major results of conference participants from the "Third International Conference in Network Analysis" held at the Higher School of Economics, Nizhny Novgorod in May 2013, with the aim to initiate further joint research among different groups. The contributions in this book cover a broad range of topics relevant to the theory and practice of network analysis, including the reliability of complex networks, software, theory, methodology, and applications. Network analysis has become a major research topic over the last several years. The broad range of applications that can be described and analyzed by means of a network has brought together researchers, practitioners from numerous fields such as operations research, computer science, transportation, energy, biomedicine, computational neuroscience and social sciences. In addition, new approaches and computer environments such as parallel computing, grid computing, cloud computing, and quantum computing have helped to solve large scale...
Characterization of differentially expressed genes using high-dimensional co-expression networks
Coelho Goncalves de Abreu, Gabriel; Labouriau, Rodrigo S.
2010-01-01
of spurious information along the network are avoided. The proposed inference procedure is based on the minimization of the Bayesian Information Criterion (BIC) in the class of decomposable graphical models. This class of models can be used to represent complex relationships and has suitable properties...... construct a compact representation of the co-expression network that allows to identify the regions with high concentration of differentially expressed genes. It is argued that differentially expressed genes located in highly interconnected regions of the co-expression network are less informative than...
A Teletraffic Model for Service Differentiation in OPS Networks
H. Overby; N. Stol
2003-01-01
This paper present a formal teletraffic model for service diferentiation in optical packet switched networks by utilizing the wavelength domain. Expressions for the time congestion are derived. Simulation results are also reported.
Harish Babu
2009-09-01
Full Text Available Adult hippocampal neurogenesis is regulated by activity. But how do neural precursor cells in the hippocampus respond to surrounding network activity and translate increased neural activity into a developmental program? Here we show that long-term potential (LTP-like synaptic activity within a cellular network of mature hippocampal neurons promotes neuronal differentiation of newly generated cells. In co-cultures of precursor cells with primary hippocampal neurons, LTP-like synaptic plasticity induced by addition of glycine in Mg2+-free media for 5 min, produced synchronous network activity and subsequently increased synaptic strength between neurons. Furthermore, this synchronous network activity led to a significant increase in neuronal differentiation from the co-cultured neural precursor cells. When applied directly to precursor cells, glycine and Mg2+-free solution did not induce neuronal differentiation. Synaptic plasticity-induced neuronal differentiation of precursor cells was observed in the presence of GABAergic neurotransmission blockers but was dependent on NMDA-mediated Ca2+ influx. Most importantly, neuronal differentiation required the release of brain-derived neurotrophic factor (BDNF from the underlying substrate hippocampal neurons as well as TrkB receptor phosphorylation in precursor cells. This suggests that activity-dependent stem cell differentiation within the hippocampal network is mediated via synaptically evoked BDNF signaling.
Analysis of complex networks using aggressive abstraction.
Colbaugh, Richard; Glass, Kristin.; Willard, Gerald
2008-10-01
This paper presents a new methodology for analyzing complex networks in which the network of interest is first abstracted to a much simpler (but equivalent) representation, the required analysis is performed using the abstraction, and analytic conclusions are then mapped back to the original network and interpreted there. We begin by identifying a broad and important class of complex networks which admit abstractions that are simultaneously dramatically simplifying and property preserving we call these aggressive abstractions -- and which can therefore be analyzed using the proposed approach. We then introduce and develop two forms of aggressive abstraction: 1.) finite state abstraction, in which dynamical networks with uncountable state spaces are modeled using finite state systems, and 2.) onedimensional abstraction, whereby high dimensional network dynamics are captured in a meaningful way using a single scalar variable. In each case, the property preserving nature of the abstraction process is rigorously established and efficient algorithms are presented for computing the abstraction. The considerable potential of the proposed approach to complex networks analysis is illustrated through case studies involving vulnerability analysis of technological networks and predictive analysis for social processes.
Benchmark analysis of railway networks and undertakings
Hansen, I.A.; Wiggenraad, P.B.L.; Wolff, J.W.
2013-01-01
Benchmark analysis of railway networks and companies has been stimulated by the European policy of deregulation of transport markets, the opening of national railway networks and markets to new entrants and separation of infrastructure and train operation. Recent international railway benchmarking s
Consistency Analysis of Network Traffic Repositories
Lastdrager, Elmer; Pras, Aiko
2009-01-01
Traffic repositories with TCP/IP header information are very important for network analysis. Researchers often assume that such repositories reliably represent all traffic that has been flowing over the network; little thoughts are made regarding the consistency of these repositories. Still, for var
Spectrum-Based and Collaborative Network Topology Analysis and Visualization
Hu, Xianlin
2013-01-01
Networks are of significant importance in many application domains, such as World Wide Web and social networks, which often embed rich topological information. Since network topology captures the organization of network nodes and links, studying network topology is very important to network analysis. In this dissertation, we study networks by…
A Network Model to Describe the Terminal Differentiation of B Cells
Méndez, Akram; Mendoza, Luis
2016-01-01
Terminal differentiation of B cells is an essential process for the humoral immune response in vertebrates and is achieved by the concerted action of several transcription factors in response to antigen recognition and extracellular signals provided by T-helper cells. While there is a wealth of experimental data regarding the molecular and cellular signals involved in this process, there is no general consensus regarding the structure and dynamical properties of the underlying regulatory network controlling this process. We developed a dynamical model of the regulatory network controlling terminal differentiation of B cells. The structure of the network was inferred from experimental data available in the literature, and its dynamical behavior was analyzed by modeling the network both as a discrete and a continuous dynamical systems. The steady states of these models are consistent with the patterns of activation reported for the Naive, GC, Mem, and PC cell types. Moreover, the models are able to describe the patterns of differentiation from the precursor Naive to any of the GC, Mem, or PC cell types in response to a specific set of extracellular signals. We simulated all possible single loss- and gain-of-function mutants, corroborating the importance of Pax5, Bcl6, Bach2, Irf4, and Blimp1 as key regulators of B cell differentiation process. The model is able to represent the directional nature of terminal B cell differentiation and qualitatively describes key differentiation events from a precursor cell to terminally differentiated B cells. PMID:26751566
Principal component analysis using neural network
杨建刚; 孙斌强
2002-01-01
The authors present their analysis of the differential equation dX ( t )/dt = AX ( t ) - XT( t ) BX( t)X( t), where A is an unsymmetrical real matrix, B is a positive definite symmetric real matrix,X E Rn ; showing that the equation characterizes a class of continuous type full-feedback artificial neural network; We give the analytic expression of the solution; discuss its asymptotic behavior; and finally present the result showing that, in almost all cases, one and only one of following eases is true. 1. For any initial value X0∈Rn, the solution approximates asymptotically to zero vector. In thin cane, the real part of each eigenvalue of A is non-positive. 2. For any initial value X0 outside a proper subspace of Rn,the solution approximates asymptoticaUy to a nontrivial constant vector Y( X0 ). In this cane, the eigenvalue of A with maximal real part is the positive number λ=Ⅱ Y (X0)ⅡB2 and Y (X0) is the corre-sponding eigenvector. 3. For any initial value X0 outsidea proper subspace of Rn, the solution approximates asymptotically to a non-constant periodic function Y( X0 , t ). Then the eigenvalues of A with maximal real part is a pair of conjugate complex numbers which can be computed.
Principal component analysis using neural network
杨建刚; 孙斌强
2002-01-01
The authors present their analysis of the differential equation dX(t)/dt=AX(t)-XT(t)BX(t)X(t), where A is an unsymmetrical real matrix, B is a positive definite symmetric real matrix, X∈Rn; showing that the equation characterizes a class of continuous type full-feedback artificial neural network; We give the analytic expression of the solution; discuss its asymptotic behavior; and finally present the result showing that, in almost all cases, one and only one of following cases is true. 1. For any initial value X0∈Rn, the solution approximates asymptotically to zero vector. In this case, the real part of each eigenvalue of A is non-positive. 2. For any initial value X0 outside a proper subspace of Rn, the solution approximates asymptotically to a nontrivial constant vector (X0). In this case, the eigenvalue of A with maximal real part is the positive number λ=‖(X0)‖2B and (X0) is the corresponding eigenvector. 3. For any initial value X0 outside a proper subspace of Rn, the solution approximates asymptotically to a non-constant periodic function (X0,t). Then the eigenvalues of A with maximal real part is a pair of conjugate complex numbers which can be computed.
Extending Stochastic Network Calculus to Loss Analysis
Chao Luo
2013-01-01
Full Text Available Loss is an important parameter of Quality of Service (QoS. Though stochastic network calculus is a very useful tool for performance evaluation of computer networks, existing studies on stochastic service guarantees mainly focused on the delay and backlog. Some efforts have been made to analyse loss by deterministic network calculus, but there are few results to extend stochastic network calculus for loss analysis. In this paper, we introduce a new parameter named loss factor into stochastic network calculus and then derive the loss bound through the existing arrival curve and service curve via this parameter. We then prove that our result is suitable for the networks with multiple input flows. Simulations show the impact of buffer size, arrival traffic, and service on the loss factor.
Extending stochastic network calculus to loss analysis.
Luo, Chao; Yu, Li; Zheng, Jun
2013-01-01
Loss is an important parameter of Quality of Service (QoS). Though stochastic network calculus is a very useful tool for performance evaluation of computer networks, existing studies on stochastic service guarantees mainly focused on the delay and backlog. Some efforts have been made to analyse loss by deterministic network calculus, but there are few results to extend stochastic network calculus for loss analysis. In this paper, we introduce a new parameter named loss factor into stochastic network calculus and then derive the loss bound through the existing arrival curve and service curve via this parameter. We then prove that our result is suitable for the networks with multiple input flows. Simulations show the impact of buffer size, arrival traffic, and service on the loss factor.
Constructing an Intelligent Patent Network Analysis Method
Chao-Chan Wu
2012-11-01
Full Text Available Patent network analysis, an advanced method of patent analysis, is a useful tool for technology management. This method visually displays all the relationships among the patents and enables the analysts to intuitively comprehend the overview of a set of patents in the field of the technology being studied. Although patent network analysis possesses relative advantages different from traditional methods of patent analysis, it is subject to several crucial limitations. To overcome the drawbacks of the current method, this study proposes a novel patent analysis method, called the intelligent patent network analysis method, to make a visual network with great precision. Based on artificial intelligence techniques, the proposed method provides an automated procedure for searching patent documents, extracting patent keywords, and determining the weight of each patent keyword in order to generate a sophisticated visualization of the patent network. This study proposes a detailed procedure for generating an intelligent patent network that is helpful for improving the efficiency and quality of patent analysis. Furthermore, patents in the field of Carbon Nanotube Backlight Unit (CNT-BLU were analyzed to verify the utility of the proposed method.
Computer network environment planning and analysis
Dalphin, John F.
1989-01-01
The GSFC Computer Network Environment provides a broadband RF cable between campus buildings and ethernet spines in buildings for the interlinking of Local Area Networks (LANs). This system provides terminal and computer linkage among host and user systems thereby providing E-mail services, file exchange capability, and certain distributed computing opportunities. The Environment is designed to be transparent and supports multiple protocols. Networking at Goddard has a short history and has been under coordinated control of a Network Steering Committee for slightly more than two years; network growth has been rapid with more than 1500 nodes currently addressed and greater expansion expected. A new RF cable system with a different topology is being installed during summer 1989; consideration of a fiber optics system for the future will begin soon. Summmer study was directed toward Network Steering Committee operation and planning plus consideration of Center Network Environment analysis and modeling. Biweekly Steering Committee meetings were attended to learn the background of the network and the concerns of those managing it. Suggestions for historical data gathering have been made to support future planning and modeling. Data Systems Dynamic Simulator, a simulation package developed at NASA and maintained at GSFC was studied as a possible modeling tool for the network environment. A modeling concept based on a hierarchical model was hypothesized for further development. Such a model would allow input of newly updated parameters and would provide an estimation of the behavior of the network.
UMA/GAN network architecture analysis
Yang, Liang; Li, Wensheng; Deng, Chunjian; Lv, Yi
2009-07-01
This paper is to critically analyze the architecture of UMA which is one of Fix Mobile Convergence (FMC) solutions, and also included by the third generation partnership project(3GPP). In UMA/GAN network architecture, UMA Network Controller (UNC) is the key equipment which connects with cellular core network and mobile station (MS). UMA network could be easily integrated into the existing cellular networks without influencing mobile core network, and could provides high-quality mobile services with preferentially priced indoor voice and data usage. This helps to improve subscriber's experience. On the other hand, UMA/GAN architecture helps to integrate other radio technique into cellular network which includes WiFi, Bluetooth, and WiMax and so on. This offers the traditional mobile operators an opportunity to integrate WiMax technique into cellular network. In the end of this article, we also give an analysis of potential influence on the cellular core networks ,which is pulled by UMA network.
Statistical Analysis of Bus Networks in India
2016-01-01
In this paper, we model the bus networks of six major Indian cities as graphs in L-space, and evaluate their various statistical properties. While airline and railway networks have been extensively studied, a comprehensive study on the structure and growth of bus networks is lacking. In India, where bus transport plays an important role in day-to-day commutation, it is of significant interest to analyze its topological structure and answer basic questions on its evolution, growth, robustness and resiliency. Although the common feature of small-world property is observed, our analysis reveals a wide spectrum of network topologies arising due to significant variation in the degree-distribution patterns in the networks. We also observe that these networks although, robust and resilient to random attacks are particularly degree-sensitive. Unlike real-world networks, such as Internet, WWW and airline, that are virtual, bus networks are physically constrained. Our findings therefore, throw light on the evolution of such geographically and constrained networks that will help us in designing more efficient bus networks in the future. PMID:27992590
Equilibrium Analysis for Anycast in WDM Networks
唐矛宁; 王汉兴
2005-01-01
In this paper, the wavelength-routed WDM network, was analyzed for the dynamic case where the arrival of anycast requests was modeled by a state-dependent Poisson process. The equilibrium analysis was also given with the UWNC algorithm.
An Improved Differential Evolution Trained Neural Network Scheme for Nonlinear System Identification
Bidyadhar Subudhi; Debashisha Jena
2009-01-01
This paper prescnts an improved nonlinear system identification scheme using differential evolution (DE), neural network (NN) and Levenberg Marquardt algorithm (LM). With a view to achieve better convergence of NN weights optimization during the training, the DE and LM are used in a combined framework to train the NN. We present the convergence analysis of the DE and demonstrate the efficacy of the proposed improved system identification algorithm by exploiting the combined DE and LM training of the NN and suitably implementing it together with other system identification methods, namely NN and DE+NN on a numbcr of examples including a practical case study. The identification rcsults obtained through a series of simulation studies of these methods on different nonlinear systems demonstrate that the proposed DE and LM trained NN approach to nonlinear system identification can yield better identification results in terms of time of convergence and less identification error.
Yang Zhang
2013-01-01
Full Text Available We introduce a continuum modeling method to approximate a class of large wireless networks by nonlinear partial differential equations (PDEs. This method is based on the convergence of a sequence of underlying Markov chains of the network indexed by N, the number of nodes in the network. As N goes to infinity, the sequence converges to a continuum limit, which is the solution of a certain nonlinear PDE. We first describe PDE models for networks with uniformly located nodes and then generalize to networks with nonuniformly located, and possibly mobile, nodes. Based on the PDE models, we develop a method to control the transmissions in nonuniform networks so that the continuum limit is invariant under perturbations in node locations. This enables the networks to maintain stable global characteristics in the presence of varying node locations.
Behavioral analysis of network flow traffic
Heller, Mark D.
2010-01-01
Approved for public release, distribution unlimited Network Behavior Analysis (NBA) is a technique to enhance network security by passively monitoring aggregate traffic patterns and noting unusual action or departures from normal operations. The analysis is typically performed offline, due to the huge volume of input data, in contrast to conventional intrusion prevention solutions based on deep packet inspection, signature detection, and real-time blocking. After establishing a benchmar...
Özgür Başkan
2014-09-01
Full Text Available Differential Evolution algorithm has effectively been used to solve engineering optimization problems recently. The Differential Evolution algorithm, which uses similar principles with Genetic Algorithms, is more robust on obtaining optimal solution than many other heuristic algorithms with its simpler structure. In this study, Differential Evolution algorithm is applied to the transportation network design problems and its effectiveness on the solution is investigated. In this context, Differential Evolution based models are developed using bi-level programming approach for the solution of the transportation network design problem and determination of the on-street parking places in urban road networks. In these models, optimal investment and parking strategies are investigated on the upper level. On the lower level, deterministic traffic assignment problem, which represents drivers' responses, is solved using Frank-Wolfe algorithm and VISUM traffic modeling software. In order to determine the effectiveness of the proposed models, numerical applications are carried out on Sioux-Falls test network. Results showed that the Differential Evolution algorithm may effectively been used for the solution of transportation network design problems.
5-HTTLPR differentially predicts brain network responses to emotional faces
Fisher, Patrick M; Grady, Cheryl L; Madsen, Martin K
2015-01-01
The effects of the 5-HTTLPR polymorphism on neural responses to emotionally salient faces have been studied extensively, focusing on amygdala reactivity and amygdala-prefrontal interactions. Despite compelling evidence that emotional face paradigms engage a distributed network of brain regions in...
TRANSIENT ANALYSIS OF NONUNIFORM TRANSMISSION LINES WITH NONLINEAR TERMINAL NETWORKS
无
2006-01-01
A semi-analytical method in time domain is presented for analysis of the transient response of nonuniform transmission lines. In this method, the telegraph equations in time domain is differenced in space domain first, and is transformed into a set of first-order differential equations of voltage and current with respect to time. By integrating these differential equations with respect to time, and precise computation, the solution of these differential equations can be obtained. This method can solve the transient response of various kinds of transmission lines with arbitrary terminal networks. Particularly, it can analyze the nonuniform lines with initial conditions, for which there is no existing effective method to analyze the time response so far. The results obtained with this method are stable and accurate. Two examples are given to illustrate the application of this method.
Malleshaiah, Mohan; Padi, Megha; Rué, Pau; Quackenbush, John; Martinez-Arias, Alfonso; Gunawardena, Jeremy
2016-02-01
Pluripotent cells give rise to distinct cell types during development and are regulated by often self-reinforcing molecular networks. How such networks allow cells to differentiate is less well understood. Here, we use integrative methods to show that external signals induce reorganization of the mouse embryonic stem cell pluripotency network and that a sub-network of four factors, Nac1, Oct4, Tcf3, and Sox2, regulates their differentiation into the alternative mesendodermal and neuroectodermal fates. In the mesendodermal fate, Nac1 and Oct4 were constrained within quantitative windows, whereas Sox2 and Tcf3 were repressed. In contrast, in the neuroectodermal fate, Sox2 and Tcf3 were constrained while Nac1 and Oct4 were repressed. In addition, we show that Nac1 coordinates differentiation by activating Oct4 and inhibiting both Sox2 and Tcf3. Reorganization of progenitor cell networks around shared factors might be a common differentiation strategy and our integrative approach provides a general methodology for delineating such networks.
Mohan Malleshaiah
2016-02-01
Full Text Available Pluripotent cells give rise to distinct cell types during development and are regulated by often self-reinforcing molecular networks. How such networks allow cells to differentiate is less well understood. Here, we use integrative methods to show that external signals induce reorganization of the mouse embryonic stem cell pluripotency network and that a sub-network of four factors, Nac1, Oct4, Tcf3, and Sox2, regulates their differentiation into the alternative mesendodermal and neuroectodermal fates. In the mesendodermal fate, Nac1 and Oct4 were constrained within quantitative windows, whereas Sox2 and Tcf3 were repressed. In contrast, in the neuroectodermal fate, Sox2 and Tcf3 were constrained while Nac1 and Oct4 were repressed. In addition, we show that Nac1 coordinates differentiation by activating Oct4 and inhibiting both Sox2 and Tcf3. Reorganization of progenitor cell networks around shared factors might be a common differentiation strategy and our integrative approach provides a general methodology for delineating such networks.
1st International Conference on Network Analysis
Kalyagin, Valery; Pardalos, Panos
2013-01-01
This volume contains a selection of contributions from the "First International Conference in Network Analysis," held at the University of Florida, Gainesville, on December 14-16, 2011. The remarkable diversity of fields that take advantage of Network Analysis makes the endeavor of gathering up-to-date material in a single compilation a useful, yet very difficult, task. The purpose of this volume is to overcome this difficulty by collecting the major results found by the participants and combining them in one easily accessible compilation. Network analysis has become a major research topic over the last several years. The broad range of applications that can be described and analyzed by means of a network is bringing together researchers, practitioners and other scientific communities from numerous fields such as Operations Research, Computer Science, Transportation, Energy, Social Sciences, and more. The contributions not only come from different fields, but also cover a broad range of topics relevant to the...
Historical Network Analysis of the Web
Brügger, Niels
2013-01-01
This article discusses some of the fundamental methodological challenges related to doing historical network analyses of the web based on material in web archives. Since the late 1990s many countries have established extensive national web archives, and software supported network analysis...... of the online web has for a number of years gained currency within Internet studies. However, the combination of these two phenomena—historical network analysis of material in web archives—can at best be characterized as an emerging new area of study. Most of the methodological challenges within this new area...... revolve around the specific nature of archived web material. On the basis of an introduction to the processes involved in web archiving as well as of the characteristics of archived web material, the article outlines and scrutinizes some of the major challenges which may arise when doing network analysis...
Visualization and Analysis of Complex Covert Networks
Memon, Bisharat
systems that are covert and hence inherently complex. My Ph.D. is positioned within the wider framework of CrimeFighter project. The framework envisions a number of key knowledge management processes that are involved in the workflow, and the toolbox provides supporting tools to assist human end......This report discusses and summarize the results of my work so far in relation to my Ph.D. project entitled "Visualization and Analysis of Complex Covert Networks". The focus of my research is primarily on development of methods and supporting tools for visualization and analysis of networked......-users (intelligence analysts) in harvesting, filtering, storing, managing, structuring, mining, analyzing, interpreting, and visualizing data about offensive networks. The methods and tools proposed and discussed in this work can also be applied to analysis of more generic complex networks....
Arrell, D Kent; Niederländer, Nicolas J; Faustino, Randolph S; Behfar, Atta; Terzic, Andre
2008-02-01
In the developing embryo, instructive guidance from the ventral endoderm secures cardiac program induction within the anterolateral mesoderm. Endoderm-guided cardiogenesis, however, has yet to be resolved at the proteome level. Here, through cardiopoietic priming of the endoderm with the reprogramming cytokine tumor necrosis factor alpha (TNFalpha), candidate effectors of embryonic stem cell cardiac differentiation were delineated by comparative proteomics. Differential two-dimensional gel electrophoretic mapping revealed that more than 75% of protein species increased >1.5-fold in the TNFalpha-primed versus unprimed endodermal secretome. Protein spot identification by linear ion trap quadrupole (LTQ) tandem mass spectrometry (MS/MS) and validation by shotgun LTQ-Fourier transform MS/MS following multidimensional chromatography mapped 99 unique proteins from 153 spot assignments. A definitive set of 48 secretome proteins was deduced by iterative bioinformatic screening using algorithms for detection of canonical and noncanonical indices of secretion. Protein-protein interaction analysis, in conjunction with respective expression level changes, revealed a nonstochastic TNFalpha-centric secretome network with a scale-free hierarchical architecture. Cardiovascular development was the primary developmental function of the resolved TNFalpha-anchored network. Functional cooperativity of the derived cardioinductive network was validated through direct application of the TNFalpha-primed secretome on embryonic stem cells, potentiating cardiac commitment and sarcomerogenesis. Conversely, inhibition of primary network hubs negated the procardiogenic effects of TNFalpha priming. Thus, proteomic cartography establishes a systems biology framework for the endodermal secretome network guiding stem cell cardiopoiesis.
Multilayer Network Analysis of Nuclear Reactions
Zhu, Liang; Ma, Yu-Gang; Chen, Qu; Han, Ding-Ding
2016-08-01
The nuclear reaction network is usually studied via precise calculation of differential equation sets, and much research interest has been focused on the characteristics of nuclides, such as half-life and size limit. In this paper, however, we adopt the methods from both multilayer and reaction networks, and obtain a distinctive view by mapping all the nuclear reactions in JINA REACLIB database into a directed network with 4 layers: neutron, proton, 4He and the remainder. The layer names correspond to reaction types decided by the currency particles consumed. This combined approach reveals that, in the remainder layer, the β-stability has high correlation with node degree difference and overlapping coefficient. Moreover, when reaction rates are considered as node strength, we find that, at lower temperatures, nuclide half-life scales reciprocally with its out-strength. The connection between physical properties and topological characteristics may help to explore the boundary of the nuclide chart.
Multilayer network analysis of nuclear reactions
Zhu, Liang; Chen, Qu; Han, Ding-Ding
2016-01-01
The nuclear reaction network is usually studied via precise calculation of differential equation sets, and much research interest has been focused on the characteristics of nuclides, such as half-life and size limit. In this paper, however, we adopt the methods from both multilayer and reaction networks, and obtain a distinctive view by mapping all the nuclear reactions in JINA REACLIB database into a directed network with 4 layers: neutron, proton, $^4$He and the remainder. The layer names correspond to reaction types decided by the currency particles consumed. This combined approach reveals that, in the remainder layer, the $\\beta$-stability has high correlation with node degree difference and overlapping coefficient. Moreover, when reaction rates are considered as node strength, we find that, at lower temperatures, nuclide half-life scales reciprocally with its out-strength. The connection between physical properties and topological characteristics may help to explore the boundary of the nuclide chart.
Network Anomaly Detection Based on Wavelet Analysis
Ali A. Ghorbani
2008-11-01
Full Text Available Signal processing techniques have been applied recently for analyzing and detecting network anomalies due to their potential to find novel or unknown intrusions. In this paper, we propose a new network signal modelling technique for detecting network anomalies, combining the wavelet approximation and system identification theory. In order to characterize network traffic behaviors, we present fifteen features and use them as the input signals in our system. We then evaluate our approach with the 1999 DARPA intrusion detection dataset and conduct a comprehensive analysis of the intrusions in the dataset. Evaluation results show that the approach achieves high-detection rates in terms of both attack instances and attack types. Furthermore, we conduct a full day's evaluation in a real large-scale WiFi ISP network where five attack types are successfully detected from over 30 millions flows.
Trimming of mammalian transcriptional networks using network component analysis
Liao James C
2010-10-01
Full Text Available Abstract Background Network Component Analysis (NCA has been used to deduce the activities of transcription factors (TFs from gene expression data and the TF-gene binding relationship. However, the TF-gene interaction varies in different environmental conditions and tissues, but such information is rarely available and cannot be predicted simply by motif analysis. Thus, it is beneficial to identify key TF-gene interactions under the experimental condition based on transcriptome data. Such information would be useful in identifying key regulatory pathways and gene markers of TFs in further studies. Results We developed an algorithm to trim network connectivity such that the important regulatory interactions between the TFs and the genes were retained and the regulatory signals were deduced. Theoretical studies demonstrated that the regulatory signals were accurately reconstructed even in the case where only three independent transcriptome datasets were available. At least 80% of the main target genes were correctly predicted in the extreme condition of high noise level and small number of datasets. Our algorithm was tested with transcriptome data taken from mice under rapamycin treatment. The initial network topology from the literature contains 70 TFs, 778 genes, and 1423 edges between the TFs and genes. Our method retained 1074 edges (i.e. 75% of the original edge number and identified 17 TFs as being significantly perturbed under the experimental condition. Twelve of these TFs are involved in MAPK signaling or myeloid leukemia pathways defined in the KEGG database, or are known to physically interact with each other. Additionally, four of these TFs, which are Hif1a, Cebpb, Nfkb1, and Atf1, are known targets of rapamycin. Furthermore, the trimmed network was able to predict Eno1 as an important target of Hif1a; this key interaction could not be detected without trimming the regulatory network. Conclusions The advantage of our new algorithm
Pathogenic Network Analysis Predicts Candidate Genes for Cervical Cancer
Yun-Xia Zhang
2016-01-01
Full Text Available Purpose. The objective of our study was to predicate candidate genes in cervical cancer (CC using a network-based strategy and to understand the pathogenic process of CC. Methods. A pathogenic network of CC was extracted based on known pathogenic genes (seed genes and differentially expressed genes (DEGs between CC and normal controls. Subsequently, cluster analysis was performed to identify the subnetworks in the pathogenic network using ClusterONE. Each gene in the pathogenic network was assigned a weight value, and then candidate genes were obtained based on the weight distribution. Eventually, pathway enrichment analysis for candidate genes was performed. Results. In this work, a total of 330 DEGs were identified between CC and normal controls. From the pathogenic network, 2 intensely connected clusters were extracted, and a total of 52 candidate genes were detected under the weight values greater than 0.10. Among these candidate genes, VIM had the highest weight value. Moreover, candidate genes MMP1, CDC45, and CAT were, respectively, enriched in pathway in cancer, cell cycle, and methane metabolism. Conclusion. Candidate pathogenic genes including MMP1, CDC45, CAT, and VIM might be involved in the pathogenesis of CC. We believe that our results can provide theoretical guidelines for future clinical application.
DNA microsatellite analysis for tomato genetic differentiation
Miskoska-Milevska Elizabeta
2015-01-01
Full Text Available Commonly used method for determination of the genetic diversity among the populations is the test for genetic differentiation. DNA microsatellite markers are usually used to investigate the genetic structure of natural populations. The aim of this study was to evaluate the applicability of eight DNA microsatellite loci (LECH13, LE21085, LEMDDNa, LEEF1Aa, LELEUZIP, LE20592, TMS9 and LE2A11 in genetic differentiation of six morphologically different tomato varieties (var. grandifolium from subsp. cultum; var. cerasiforme - red and yellow, var. pruniforme and var. pyriforme from subsp. subspontaneum; and var. racemigerum from subsp. spontaneum. The fragment analyses was performed using Applied Biosystems DNA analyzer (ABI 3130 and GeneMapper® Software program. The data were analysed using the specific program Power Marker Software. The average number of detected alleles was 3,625. Also, the average PIC value for all 8 DNA microsatellites loci was 0,3571. The genetic differentiation test in the researched tomato subspecies showed minor differentiation for locus LELEUZIP (- 0,0009, modest differentiation for locus LECH13 (0,0896, locus LEMDDNa (0,0896 and locus LE21085 (0,0551 and major differentiation for locus LE2A11 (0,7633, locus LEEF1Aa (0,6167, locus TMS9 (0.4967 and locus LE20592 (0,4263. On the other hand, in the estimated tomato varieties, locus LE21085 (0,0297, locus LECH13 (0,0256 and locus LELEUZIP (0,0005 showed minor differentiation, locus LEMDDNa (0,1333 showed modest differentiation, while locus TMS9 (0,5929, locus LEEF1Aa (0,5006, locus LE2A11 (0,4013 and locus LE20592 (0,2606 showed major differentiation. The eight DNA microsatellite loci can be applicable solution for tomato genetic differentiation. The overall results suggest that these microsatellite loci could be used in further population genetic studies of tomatoes.
Using Differential Evolution to Optimize Learning from Signals and Enhance Network Security
Harmer, Paul K [Air Force Institute of Technology; Temple, Michael A [Air Force Institute of Technology; Buckner, Mark A [ORNL; Farquhar, Ethan [ORNL
2011-01-01
Computer and communication network attacks are commonly orchestrated through Wireless Access Points (WAPs). This paper summarizes proof-of-concept research activity aimed at developing a physical layer Radio Frequency (RF) air monitoring capability to limit unauthorizedWAP access and mprove network security. This is done using Differential Evolution (DE) to optimize the performance of a Learning from Signals (LFS) classifier implemented with RF Distinct Native Attribute (RF-DNA) fingerprints. Performance of the resultant DE-optimized LFS classifier is demonstrated using 802.11a WiFi devices under the most challenging conditions of intra-manufacturer classification, i.e., using emissions of like-model devices that only differ in serial number. Using identical classifier input features, performance of the DE-optimized LFS classifier is assessed relative to a Multiple Discriminant Analysis / Maximum Likelihood (MDA/ML) classifier that has been used for previous demonstrations. The comparative assessment is made using both Time Domain (TD) and Spectral Domain (SD) fingerprint features. For all combinations of classifier type, feature type, and signal-to-noise ratio considered, results show that the DEoptimized LFS classifier with TD features is uperior and provides up to 20% improvement in classification accuracy with proper selection of DE parameters.
Secure Application-Aware Service Differentiation in Public Area Wireless Networks
Weisong Shi; Sharun Santhosh; Hanping Lufei
2005-01-01
We are witnessing the increasing demand for pervasive Internet access from public area wireless networks (PAWNs). As their popularity grows, the inherent untrusted nature of public places and the diverse service requirements of end users are two key issues that need to be addressed. We have proposed two approaches to address these issues. First,the Home-based Authentication Protocol (HAP) that provides a framework by which to establish trust between a nomadic client and a service provider using a trusted third party (home). Second, we argue that the best-effort-based service model provided by many access points is not enough to satisfy the end user fairness and to maximize the wireless link utilization for a diverse user population. We have proposed an application-aware service differentiation (AASD) mechanism that takes both application semantics and user requirements into consideration. Our analysis of this framework shows several fruitful results. The total authentication latency increases with the number of clients but at a rate that is much less than linear increasing latency. Also, in comparison with two other bandwidth allocation approaches, the best effort and static access control, our proposed application-aware service differentiation method, outperforms them in terms of the client fairness and wireless bandwidth utilization.
Lozhkin, V.; Tarkhov, D.; Timofeev, V.; Lozhkina, O.; Vasilyev, A.
2016-11-01
The paper presents a novel differential neural network model estimating the dispersion of CO emissions from a peat fire near a highway. We have developed approaches for the optimization of the model on the base of simulated and experimental measurements of CO concentrations in the area of dispersion of the smoke cloud. The numerical solutions of the problem are presented in the form of neural network approximations by the Gaussian model and in the form of neural network approximate solutions of partial differential equations. The trained neural network model can be used for the prediction of emergency when wind speed and direction and other fire parameters are changing. The method is also recommended for the development of air quality monitoring and predicting information systems.
Social network analysis applied to team sports analysis
Clemente, Filipe Manuel; Mendes, Rui Sousa
2016-01-01
Explaining how graph theory and social network analysis can be applied to team sports analysis, This book presents useful approaches, models and methods that can be used to characterise the overall properties of team networks and identify the prominence of each team player. Exploring the different possible network metrics that can be utilised in sports analysis, their possible applications and variances from situation to situation, the respective chapters present an array of illustrative case studies. Identifying the general concepts of social network analysis and network centrality metrics, readers are shown how to generate a methodological protocol for data collection. As such, the book provides a valuable resource for students of the sport sciences, sports engineering, applied computation and the social sciences.
Medical image analysis with artificial neural networks.
Jiang, J; Trundle, P; Ren, J
2010-12-01
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging. Copyright © 2010 Elsevier Ltd. All rights reserved.
Kinetic analysis of complex metabolic networks
Stephanopoulos, G. [MIT, Cambridge, MA (United States)
1996-12-31
A new methodology is presented for the analysis of complex metabolic networks with the goal of metabolite overproduction. The objective is to locate a small number of reaction steps in a network that have maximum impact on network flux amplification and whose rate can also be increased without functional network derangement. This method extends the concepts of Metabolic Control Analysis to groups of reactions and offers the means for calculating group control coefficients as measures of the control exercised by groups of reactions on the overall network fluxes and intracellular metabolite pools. It is further demonstrated that the optimal strategy for the effective increase of network fluxes, while maintaining an uninterrupted supply of intermediate metabolites, is through the coordinated amplification of multiple (as opposed to a single) reaction steps. Satisfying this requirement invokes the concept of the concentration control to coefficient, which emerges as a critical parameter in the identification of feasible enzymatic modifications with maximal impact on the network flux. A case study of aromatic aminoacid production is provided to illustrate these concepts.
Fast network centrality analysis using GPUs
Shi Zhiao
2011-05-01
Full Text Available Abstract Background With the exploding volume of data generated by continuously evolving high-throughput technologies, biological network analysis problems are growing larger in scale and craving for more computational power. General Purpose computation on Graphics Processing Units (GPGPU provides a cost-effective technology for the study of large-scale biological networks. Designing algorithms that maximize data parallelism is the key in leveraging the power of GPUs. Results We proposed an efficient data parallel formulation of the All-Pairs Shortest Path problem, which is the key component for shortest path-based centrality computation. A betweenness centrality algorithm built upon this formulation was developed and benchmarked against the most recent GPU-based algorithm. Speedup between 11 to 19% was observed in various simulated scale-free networks. We further designed three algorithms based on this core component to compute closeness centrality, eccentricity centrality and stress centrality. To make all these algorithms available to the research community, we developed a software package gpu-fan (GPU-based Fast Analysis of Networks for CUDA enabled GPUs. Speedup of 10-50× compared with CPU implementations was observed for simulated scale-free networks and real world biological networks. Conclusions gpu-fan provides a significant performance improvement for centrality computation in large-scale networks. Source code is available under the GNU Public License (GPL at http://bioinfo.vanderbilt.edu/gpu-fan/.
Chen, Ye; Wolanyk, Nathaniel; Ilker, Tunc; Gao, Shouguo; Wang, Xujing
Methods developed based on bifurcation theory have demonstrated their potential in driving network identification for complex human diseases, including the work by Chen, et al. Recently bifurcation theory has been successfully applied to model cellular differentiation. However, there one often faces a technical challenge in driving network prediction: time course cellular differentiation study often only contains one sample at each time point, while driving network prediction typically require multiple samples at each time point to infer the variation and interaction structures of candidate genes for the driving network. In this study, we investigate several methods to identify both the critical time point and the driving network through examination of how each time point affects the autocorrelation and phase locking. We apply these methods to a high-throughput sequencing (RNA-Seq) dataset of 42 subsets of thymocytes and mature peripheral T cells at multiple time points during their differentiation (GSE48138 from GEO). We compare the predicted driving genes with known transcription regulators of cellular differentiation. We will discuss the advantages and limitations of our proposed methods, as well as potential further improvements of our methods.
Sympatry inference and network analysis in biogeography.
Dos Santos, Daniel A; Fernández, Hugo R; Cuezzo, María Gabriela; Domínguez, Eduardo
2008-06-01
A new approach for biogeography to find patterns of sympatry, based on network analysis, is proposed. Biogeographic analysis focuses basically on sympatry patterns of species. Sympatry is a network (= relational) datum, but it has never been analyzed before using relational tools such as Network Analysis. Our approach to biogeographic analysis consists of two parts: first the sympatry inference and second the network analysis method (NAM). The sympatry inference method was designed to propose sympatry hypothesis, constructing a basal sympatry network based on punctual data, independent of a priori distributional area determination. In this way, two or more species are considered sympatric when there is interpenetration and relative proximity among their records of occurrence. In nature, groups of species presenting within-group sympatry and between-group allopatry constitute natural units (units of co-occurrence). These allopatric units are usually connected by intermediary species. The network analysis method (NAM) that we propose here is based on the identification and removal of intermediary species to segregate units of co-occurrence, using the betweenness measure and the clustering coefficient. The species ranges of the units of co-occurrence obtained are transferred to a map, being considered as candidates to areas of endemism. The new approach was implemented on three different real complex data sets (one of them a classic example previously used in biogeography) resulting in (1) independence of predefined spatial units; (2) definition of co-occurrence patterns from the sympatry network structure, not from species range similarities; (3) higher stability in results despite scale changes; (4) identification of candidates to areas of endemism supported by strictly endemic species; (5) identification of intermediary species with particular biological attributes.
Control of chaos in delay differential equations, in a network of oscillators and in model cortex
Babloyantz, A.; Lourenço, C.; Sepulchre, J. A.
We extend the Ott-Grebogi-Yorke method to the stabilization of unstable orbits in a network of oscillators exhibiting spatiotemporal chaotic activity, wherein the perturbation is applied to the variables of the system. With the help of numerical simulations we show that a method developed by Pyragas can stabilize unstable orbits in a one variable delay differential equation and in a model cortical network with delay. We discuss the relevance of these results in the physiological processes of the brain.
PIT Overload Analysis in Content Centric Networks
Virgilio, Matteo; Marchetto, Guido; Sisto, Riccardo
2013-01-01
Content Centric Networking represents a paradigm shift in the evolution and definition of modern network protocols. Many research efforts have been made with the purpose of proving the feasibility and the scalability of this proposal. Our main contribution is to provide an analysis of the Pending Interest Table memory requirements in real deployment scenarios, especially considering the impact of distributed denial of service attacks. In fact, the state that the protocol maintains for each re...
Multilayer Analysis and Visualization of Networks
De Domenico, Manlio; Arenas, Alex
2014-01-01
Multilayer relationships among and information about biological entities must be accompanied by the means to analyze, visualize, and obtain insights from such data. We report a methodology and a collection of algorithms for the analysis of multilayer networks in our new open-source software (muxViz). We demonstrate the ability of muxViz to analyze and interactively visualize multilayer data using empirical genetic and neuronal networks.
Applying centrality measures to impact analysis: A coauthorship network analysis
Yan, Erjia
2010-01-01
Many studies on coauthorship networks focus on network topology and network statistical mechanics. This article takes a different approach by studying micro-level network properties, with the aim to apply centrality measures to impact analysis. Using coauthorship data from 16 journals in the field of library and information science (LIS) with a time span of twenty years (1988-2007), we construct an evolving coauthorship network and calculate four centrality measures (closeness, betweenness, degree and PageRank) for authors in this network. We find out that the four centrality measures are significantly correlated with citation counts. We also discuss the usability of centrality measures in author ranking, and suggest that centrality measures can be useful indicators for impact analysis.
Stability analysis of impulsive functional differential equations
Stamova, Ivanka
2009-01-01
This book is devoted to impulsive functional differential equations which are a natural generalization of impulsive ordinary differential equations (without delay) and of functional differential equations (without impulses). At the present time the qualitative theory of such equationsis under rapid development. After a presentation of the fundamental theory of existence, uniqueness and continuability of solutions, a systematic development of stability theory for that class of problems is given which makes the book unique. It addresses to a wide audience such as mathematicians, applied research
Logical Modeling and Dynamical Analysis of Cellular Networks.
Abou-Jaoudé, Wassim; Traynard, Pauline; Monteiro, Pedro T; Saez-Rodriguez, Julio; Helikar, Tomáš; Thieffry, Denis; Chaouiya, Claudine
2016-01-01
The logical (or logic) formalism is increasingly used to model regulatory and signaling networks. Complementing these applications, several groups contributed various methods and tools to support the definition and analysis of logical models. After an introduction to the logical modeling framework and to several of its variants, we review here a number of recent methodological advances to ease the analysis of large and intricate networks. In particular, we survey approaches to determine model attractors and their reachability properties, to assess the dynamical impact of variations of external signals, and to consistently reduce large models. To illustrate these developments, we further consider several published logical models for two important biological processes, namely the differentiation of T helper cells and the control of mammalian cell cycle.
Identifying glioblastoma gene networks based on hypergeometric test analysis.
Vasileios Stathias
Full Text Available Patient specific therapy is emerging as an important possibility for many cancer patients. However, to identify such therapies it is essential to determine the genomic and transcriptional alterations present in one tumor relative to control samples. This presents a challenge since use of a single sample precludes many standard statistical analysis techniques. We reasoned that one means of addressing this issue is by comparing transcriptional changes in one tumor with those observed in a large cohort of patients analyzed by The Cancer Genome Atlas (TCGA. To test this directly, we devised a bioinformatics pipeline to identify differentially expressed genes in tumors resected from patients suffering from the most common malignant adult brain tumor, glioblastoma (GBM. We performed RNA sequencing on tumors from individual GBM patients and filtered the results through the TCGA database in order to identify possible gene networks that are overrepresented in GBM samples relative to controls. Importantly, we demonstrate that hypergeometric-based analysis of gene pairs identifies gene networks that validate experimentally. These studies identify a putative workflow for uncovering differentially expressed patient specific genes and gene networks for GBM and other cancers.
Selected papers on analysis and differential equations
Nomizu, Katsumi
2003-01-01
This volume contains translations of papers that originally appeared in the Japanese journal, Sugaku. The papers range over a variety of topics, including nonlinear partial differential equations, C^*-algebras, and Schrödinger operators.
Network analysis of human glaucomatous optic nerve head astrocytes
Bhattacharya Sanjoy K
2009-05-01
Full Text Available Abstract Background Astrocyte activation is a characteristic response to injury in the central nervous system, and can be either neurotoxic or neuroprotective, while the regulation of both roles remains elusive. Methods To decipher the regulatory elements controlling astrocyte-mediated neurotoxicity in glaucoma, we conducted a systems-level functional analysis of gene expression, proteomic and genetic data associated with reactive optic nerve head astrocytes (ONHAs. Results Our reconstruction of the molecular interactions affected by glaucoma revealed multi-domain biological networks controlling activation of ONHAs at the level of intercellular stimuli, intracellular signaling and core effectors. The analysis revealed that synergistic action of the transcription factors AP-1, vitamin D receptor and Nuclear Factor-kappaB in cross-activation of multiple pathways, including inflammatory cytokines, complement, clusterin, ephrins, and multiple metabolic pathways. We found that the products of over two thirds of genes linked to glaucoma by genetic analysis can be functionally interconnected into one epistatic network via experimentally-validated interactions. Finally, we built and analyzed an integrative disease pathology network from a combined set of genes revealed in genetic studies, genes differentially expressed in glaucoma and closely connected genes/proteins in the interactome. Conclusion Our results suggest several key biological network modules that are involved in regulating neurotoxicity of reactive astrocytes in glaucoma, and comprise potential targets for cell-based therapy.
GLOBAL STABILITY ANALYSIS IN CELLULAR NEURAL NETWORKS WITH UNBOUNDED TIME DELAYS
张继业
2004-01-01
Without assuming the boundedness and differentiability of the activation functions,the conditions ensuring existence,uniqueness,and global asymptotical stability of the equilibrium point of cellular neural networks with unbounded time delays and variable delays were studied.Using the idea of vector Liapunov method,the intero-differential inequalities with unbounded delay and variable delays were constructed.By the stability analysis of the intero-differential inequalities,the sufficient conditions for global asymptotic stability of cellular neural networks were obtained.
Lijun Zhang
2014-01-01
Full Text Available An integral-differential model equation arising from neuronal networks with very general kernel functions is considered in this paper. The kernel functions we study here include pure excitations, lateral inhibition, lateral excitations, and more general synaptic couplings (e.g., oscillating kernel functions. The main goal of this paper is to prove the existence and uniqueness of the traveling wave front solutions. The main idea we apply here is to reduce the nonlinear integral-differential equation into a solvable differential equation and test whether the solution we get is really a wave front solution of the model equation.
Social network analysis of study environment
Blaženka Divjak
2010-06-01
Full Text Available Student working environment influences student learning and achievement level. In this respect social aspects of students’ formal and non-formal learning play special role in learning environment. The main research problem of this paper is to find out if students' academic performance influences their position in different students' social networks. Further, there is a need to identify other predictors of this position. In the process of problem solving we use the Social Network Analysis (SNA that is based on the data we collected from the students at the Faculty of Organization and Informatics, University of Zagreb. There are two data samples: in the basic sample N=27 and in the extended sample N=52. We collected data on social-demographic position, academic performance, learning and motivation styles, student status (full-time/part-time, attitudes towards individual and teamwork as well as informal cooperation. Afterwards five different networks (exchange of learning materials, teamwork, informal communication, basic and aggregated social network were constructed. These networks were analyzed with different metrics and the most important were betweenness, closeness and degree centrality. The main result is, firstly, that the position in a social network cannot be forecast only by academic success and, secondly, that part-time students tend to form separate groups that are poorly connected with full-time students. In general, position of a student in social networks in study environment can influence student learning as well as her/his future employability and therefore it is worthwhile to be investigated.
Network analysis of eight industrial symbiosis systems
Zhang, Yan; Zheng, Hongmei; Shi, Han; Yu, Xiangyi; Liu, Gengyuan; Su, Meirong; Li, Yating; Chai, Yingying
2016-06-01
Industrial symbiosis is the quintessential characteristic of an eco-industrial park. To divide parks into different types, previous studies mostly focused on qualitative judgments, and failed to use metrics to conduct quantitative research on the internal structural or functional characteristics of a park. To analyze a park's structural attributes, a range of metrics from network analysis have been applied, but few researchers have compared two or more symbioses using multiple metrics. In this study, we used two metrics (density and network degree centralization) to compare the degrees of completeness and dependence of eight diverse but representative industrial symbiosis networks. Through the combination of the two metrics, we divided the networks into three types: weak completeness, and two forms of strong completeness, namely "anchor tenant" mutualism and "equality-oriented" mutualism. The results showed that the networks with a weak degree of completeness were sparse and had few connections among nodes; for "anchor tenant" mutualism, the degree of completeness was relatively high, but the affiliated members were too dependent on core members; and the members in "equality-oriented" mutualism had equal roles, with diverse and flexible symbiotic paths. These results revealed some of the systems' internal structure and how different structures influenced the exchanges of materials, energy, and knowledge among members of a system, thereby providing insights into threats that may destabilize the network. Based on this analysis, we provide examples of the advantages and effectiveness of recent improvement projects in a typical Chinese eco-industrial park (Shandong Lubei).
Information flow analysis of interactome networks.
Patrycja Vasilyev Missiuro
2009-04-01
Full Text Available Recent studies of cellular networks have revealed modular organizations of genes and proteins. For example, in interactome networks, a module refers to a group of interacting proteins that form molecular complexes and/or biochemical pathways and together mediate a biological process. However, it is still poorly understood how biological information is transmitted between different modules. We have developed information flow analysis, a new computational approach that identifies proteins central to the transmission of biological information throughout the network. In the information flow analysis, we represent an interactome network as an electrical circuit, where interactions are modeled as resistors and proteins as interconnecting junctions. Construing the propagation of biological signals as flow of electrical current, our method calculates an information flow score for every protein. Unlike previous metrics of network centrality such as degree or betweenness that only consider topological features, our approach incorporates confidence scores of protein-protein interactions and automatically considers all possible paths in a network when evaluating the importance of each protein. We apply our method to the interactome networks of Saccharomyces cerevisiae and Caenorhabditis elegans. We find that the likelihood of observing lethality and pleiotropy when a protein is eliminated is positively correlated with the protein's information flow score. Even among proteins of low degree or low betweenness, high information scores serve as a strong predictor of loss-of-function lethality or pleiotropy. The correlation between information flow scores and phenotypes supports our hypothesis that the proteins of high information flow reside in central positions in interactome networks. We also show that the ranks of information flow scores are more consistent than that of betweenness when a large amount of noisy data is added to an interactome. Finally, we
Differential synchronization in default and task-specific networks of the human brain
Aaron eKirschner
2012-05-01
Full Text Available On a regional scale the brain is organized into dynamic functional networks. The activity within one of these, the default network, can be dissociated from that in other task-specific networks. All brain networks are connected structurally, but evidently are only transiently connected functionally. One hypothesis as to how such transient functional coupling occurs is that network formation and dissolution is mediated, or at least accompanied, by increases and decreases in oscillatory synchronization between constituent brain regions. If so, then we should be able to find transient differences in intra-network synchronization between the default network and a task-specific network. In order to investigate this hypothesis we conducted two experiments in which subjects engaged in a Sustained Attention to Response Task (SART while having brain activity recorded via high-density electroencephalography (EEG. We found that during periods when attention was focused internally (mind-wandering there was significantly more neural phase synchronization between brain regions associated with the default network, whereas during periods when subjects were focused on performing the visual task there was significantly more neural phase synchrony within a task-specific brain network that shared some of the same brain regions. These differences in network synchrony occurred in each of theta, alpha, and gamma frequency bands. A similar pattern of differential oscillatory power changes, indicating modulation of local synchronization by attention state, was also found. These results provide further evidence that the human brain is intrinsically organized into default and task-specific brain networks, and confirm that oscillatory synchronization is a potential mechanism for functional coupling within these networks.
Complex networks analysis of language complexity
Amancio, Diego R; Oliveira, Osvaldo N; Costa, Luciano da F; 10.1209/0295-5075/100/58002
2013-01-01
Methods from statistical physics, such as those involving complex networks, have been increasingly used in quantitative analysis of linguistic phenomena. In this paper, we represented pieces of text with different levels of simplification in co-occurrence networks and found that topological regularity correlated negatively with textual complexity. Furthermore, in less complex texts the distance between concepts, represented as nodes, tended to decrease. The complex networks metrics were treated with multivariate pattern recognition techniques, which allowed us to distinguish between original texts and their simplified versions. For each original text, two simplified versions were generated manually with increasing number of simplification operations. As expected, distinction was easier for the strongly simplified versions, where the most relevant metrics were node strength, shortest paths and diversity. Also, the discrimination of complex texts was improved with higher hierarchical network metrics, thus point...
A statistical analysis of UK financial networks
Chu, J.; Nadarajah, S.
2017-04-01
In recent years, with a growing interest in big or large datasets, there has been a rise in the application of large graphs and networks to financial big data. Much of this research has focused on the construction and analysis of the network structure of stock markets, based on the relationships between stock prices. Motivated by Boginski et al. (2005), who studied the characteristics of a network structure of the US stock market, we construct network graphs of the UK stock market using same method. We fit four distributions to the degree density of the vertices from these graphs, the Pareto I, Fréchet, lognormal, and generalised Pareto distributions, and assess the goodness of fit. Our results show that the degree density of the complements of the market graphs, constructed using a negative threshold value close to zero, can be fitted well with the Fréchet and lognormal distributions.
Shattering and Compressing Networks for Centrality Analysis
Sarıyüce, Ahmet Erdem; Kaya, Kamer; Çatalyürek, Ümit V
2012-01-01
Who is more important in a network? Who controls the flow between the nodes or whose contribution is significant for connections? Centrality metrics play an important role while answering these questions. The betweenness metric is useful for network analysis and implemented in various tools. Since it is one of the most computationally expensive kernels in graph mining, several techniques have been proposed for fast computation of betweenness centrality. In this work, we propose and investigate techniques which compress a network and shatter it into pieces so that the rest of the computation can be handled independently for each piece. Although we designed and tuned the shattering process for betweenness, it can be adapted for other centrality metrics in a straightforward manner. Experimental results show that the proposed techniques can be a great arsenal to reduce the centrality computation time for various types of networks.
Domino effect analysis using Bayesian networks.
Khakzad, Nima; Khan, Faisal; Amyotte, Paul; Cozzani, Valerio
2013-02-01
A new methodology is introduced based on Bayesian network both to model domino effect propagation patterns and to estimate the domino effect probability at different levels. The flexible structure and the unique modeling techniques offered by Bayesian network make it possible to analyze domino effects through a probabilistic framework, considering synergistic effects, noisy probabilities, and common cause failures. Further, the uncertainties and the complex interactions among the domino effect components are captured using Bayesian network. The probabilities of events are updated in the light of new information, and the most probable path of the domino effect is determined on the basis of the new data gathered. This study shows how probability updating helps to update the domino effect model either qualitatively or quantitatively. The methodology is applied to a hypothetical example and also to an earlier-studied case study. These examples accentuate the effectiveness of Bayesian network in modeling domino effects in processing facility. © 2012 Society for Risk Analysis.
Automated Analysis of Security in Networking Systems
Buchholtz, Mikael
2004-01-01
It has for a long time been a challenge to built secure networking systems. One way to counter this problem is to provide developers of software applications for networking systems with easy-to-use tools that can check security properties before the applications ever reach the marked. These tools...... will both help raise the general level of awareness of the problems and prevent the most basic flaws from occurring. This thesis contributes to the development of such tools. Networking systems typically try to attain secure communication by applying standard cryptographic techniques. In this thesis...... attacks, and attacks launched by insiders. Finally, the perspectives for the application of the analysis techniques are discussed, thereby, coming a small step closer to providing developers with easy- to-use tools for validating the security of networking applications....
Design of Intelligent Network Performance Analysis Forecast Support System
无
2001-01-01
A system designed for supporting the network performance analysis and forecast effort is pre sented, based on the combination of offline network analysis and online real-time performance forecast. The off-line analysis will perform analysis of specific network node performance, correlation analysis of relative network nodes performance and evolutionary mathematical modeling of long-term network performance mea surements. The online real-time network performance forecast will be based on one so-called hybrid predic tion modeling approach for short-term network performance prediction and trend analysis. Based on the module design, the system proposed has good intelligence, scalability and self-adaptability, which will offer highly effective network performance analysis and forecast tools for network managers, and is one ideal sup port platform for network performance analysis and forecast effort.
Continuous nowhere differentiable functions the monsters of analysis
Jarnicki, Marek
2015-01-01
This book covers the construction, analysis, and theory of continuous nowhere differentiable functions, comprehensively and accessibly. After illuminating the significance of the subject through an overview of its history, the reader is introduced to the sophisticated toolkit of ideas and tricks used to study the explicit continuous nowhere differentiable functions of Weierstrass, Takagi–van der Waerden, Bolzano, and others. Modern tools of functional analysis, measure theory, and Fourier analysis are applied to examine the generic nature of continuous nowhere differentiable functions, as well as linear structures within the (nonlinear) space of continuous nowhere differentiable functions. To round out the presentation, advanced techniques from several areas of mathematics are brought together to give a state-of-the-art analysis of Riemann’s continuous, and purportedly nowhere differentiable, function. For the reader’s benefit, claims requiring elaboration, and open problems, are clearly indicated. An a...
Rolling Force Prediction in Heavy Plate Rolling Based on Uniform Differential Neural Network
Fei Zhang
2016-01-01
Full Text Available Accurate prediction of the rolling force is critical to assuring the quality of the final product in steel manufacturing. Exit thickness of plate for each pass is calculated from roll gap, mill spring, and predicted roll force. Ideal pass scheduling is dependent on a precise prediction of the roll force in each pass. This paper will introduce a concept that allows obtaining the material model parameters directly from the rolling process on an industrial scale by the uniform differential neural network. On the basis of the characteristics that the uniform distribution can fully characterize the solution space and enhance the diversity of the population, uniformity research on differential evolution operator is made to get improved crossover with uniform distribution. When its original function is transferred with a transfer function, the uniform differential evolution algorithms can quickly solve complex optimization problems. Neural network structure and weights threshold are optimized by uniform differential evolution algorithm, and a uniform differential neural network is formed to improve rolling force prediction accuracy in process control system.
Tensor analysis and elementary differential geometry for physicists and engineers
Nguyen-Schäfer, Hung
2017-01-01
This book comprehensively presents topics, such as Dirac notation, tensor analysis, elementary differential geometry of moving surfaces, and k-differential forms. Additionally, two new chapters of Cartan differential forms and Dirac and tensor notations in quantum mechanics are added to this second edition. The reader is provided with hands-on calculations and worked-out examples at which he will learn how to handle the bra-ket notation, tensors, differential geometry, and differential forms; and to apply them to the physical and engineering world. Many methods and applications are given in CFD, continuum mechanics, electrodynamics in special relativity, cosmology in the Minkowski four-dimensional spacetime, and relativistic and non-relativistic quantum mechanics. Tensors, differential geometry, differential forms, and Dirac notation are very useful advanced mathematical tools in many fields of modern physics and computational engineering. They are involved in special and general relativity physics, quantum m...
Asymptotic analysis for functional stochastic differential equations
Bao, Jianhai; Yuan, Chenggui
2016-01-01
This brief treats dynamical systems that involve delays and random disturbances. The study is motivated by a wide variety of systems in real life in which random noise has to be taken into consideration and the effect of delays cannot be ignored. Concentrating on such systems that are described by functional stochastic differential equations, this work focuses on the study of large time behavior, in particular, ergodicity. This brief is written for probabilists, applied mathematicians, engineers, and scientists who need to use delay systems and functional stochastic differential equations in their work. Selected topics from the brief can also be used in a graduate level topics course in probability and stochastic processes.
Improved ultrasonic differentiation model for structural coal types based on neural network
TIAN Zi-jian; WANG Fu-zhong; LI Tao; BAI Shan-shan
2009-01-01
In order to solve the difficulty of detailed recognition of subdivisions of structural coal types, a differentiation model that combines BP neural network with an ultrasonic reflection method is proposed. Structural coal types are recognized based on a suit-able consideration of ultrasonic speed, an ultrasonic attenuation coefficient, characteristics of ultrasonic transmission and other parameters relating to structural coal types. We have focused on a computational model of ultrasonic speed, attenuation coefficient in coal and differentiation algorithm of structural coal types based on a BP neural network. Experiments demonstrate that the model can distinguish structural coal types effectively. It is important for the improved ultrasonic differentiation model to predict coal and gas outbursts.
Differential space-time modulation based on orthogonal design for cooperative network
Ding Sheng; Yan Kai; Qiu Yunzhou; Zhu Minghua; Liu Haitao
2008-01-01
Differential modulation was widely used for wireless networks in which channel estimation was difficult. Based on orthogonal design, a novel distributed differential space-time coding/decoding scheme for M-PSK modulations was proposed, which had a high code rate of 2/3 and second-order diversity for the two-user cooperative networks. The performance of decode-and-forward (DF) protocols was evaluated. Simulations show that the differential space-time modulation scheme in this paper has better bit error rate (BER) performance or higher code rate than the schemes proposed by Tarasak and Wang when interuser channel states are good enough. The impacts of transmission error between two users for the whole system BER performance were also investigated.
Complex network analysis of state spaces for random Boolean networks
Shreim, Amer [Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, AB, T2N 1N4 (Canada); Berdahl, Andrew [Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, AB, T2N 1N4 (Canada); Sood, Vishal [Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, AB, T2N 1N4 (Canada); Grassberger, Peter [Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, AB, T2N 1N4 (Canada); Paczuski, Maya [Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, AB, T2N 1N4 (Canada)
2008-01-15
We apply complex network analysis to the state spaces of random Boolean networks (RBNs). An RBN contains N Boolean elements each with K inputs. A directed state space network (SSN) is constructed by linking each dynamical state, represented as a node, to its temporal successor. We study the heterogeneity of these SSNs at both local and global scales, as well as sample to-sample fluctuations within an ensemble of SSNs. We use in-degrees of nodes as a local topological measure, and the path diversity (Shreim A et al 2007 Phys. Rev. Lett. 98 198701) of an SSN as a global topological measure. RBNs with 2 {<=} K {<=} 5 exhibit non-trivial fluctuations at both local and global scales, while K = 2 exhibits the largest sample-to-sample (possibly non-self-averaging) fluctuations. We interpret the observed 'multi scale' fluctuations in the SSNs as indicative of the criticality and complexity of K = 2 RBNs. 'Garden of Eden' (GoE) states are nodes on an SSN that have in-degree zero. While in-degrees of non-GoE nodes for K > 1 SSNs can assume any integer value between 0 and 2{sup N}, for K = 1 all the non-GoE nodes in a given SSN have the same in-degree which is always a power of two.
Complex network analysis of time series
Gao, Zhong-Ke; Small, Michael; Kurths, Jürgen
2016-12-01
Revealing complicated behaviors from time series constitutes a fundamental problem of continuing interest and it has attracted a great deal of attention from a wide variety of fields on account of its significant importance. The past decade has witnessed a rapid development of complex network studies, which allow to characterize many types of systems in nature and technology that contain a large number of components interacting with each other in a complicated manner. Recently, the complex network theory has been incorporated into the analysis of time series and fruitful achievements have been obtained. Complex network analysis of time series opens up new venues to address interdisciplinary challenges in climate dynamics, multiphase flow, brain functions, ECG dynamics, economics and traffic systems.
Complex Network Characteristics and Invulnerability Simulating Analysis of Supply Chain
Hui-Huang Chen; Ai-Min Lin
2012-01-01
To study the characteristics of the complex supply chain, a invulnerability analysis method based on the complex network theory is proposed. The topological structure and dynamic characteristics of the complex supply chain network were analyzed. The fact was found that the network is with general characteristics of the complex network, and with the characteristics of small-world network and scale-free network. A simulation experiment was made on the invulnerability of the supply chain network...
Differential analysis of matrix convex functions
Hansen, Frank; Tomiyama, Jun
2007-01-01
We analyze matrix convex functions of a fixed order defined in a real interval by differential methods as opposed to the characterization in terms of divided differences given by Kraus [F. Kraus, Über konvekse Matrixfunktionen, Math. Z. 41 (1936) 18-42]. We obtain for each order conditions for ma...
Neural Network Analysis of Breast Cancer from Mammographic Evaluation
P. Abdolmaleki
2006-06-01
Full Text Available Background/Objective: Mammographic differentiation of benign lesions from malignancies is a difficult task. We developed an artificial neural network (ANN as a diagnostic aid in mammography using radiographic features as input. Materials & Methods: A three-layered ANN was used to differentiate malignant from benign findings in a group of patients with proven breast lesions on the basis of morphological data extracted from conventional mammograms. Our database included 122 patient records on 14qualitative variables. The database was randomly divided into training and validation samples including 82 and 40 patient records, respectively, to construct the ANN and validate its performance. Sensitivity, specificity, accuracy and receiver operating characteristic curve (ROC analysis for this method and the radiologist were compared. Results: Our results showed that the neural network model was able to correctly classify 30 out of 40 cases presented in the validation sample. Comparing the output with that of the radiologist, showed a reasonable diagnostic accuracy (75%, a moderate specificity (64% and a relatively high sensitivity (89%. Conclusion: A diagnostic aid was developed that accurately differentiates malignant from benign pattern using radiological features extracted from mammograms.
Diversity Performance Analysis on Multiple HAP Networks
Feihong Dong
2015-06-01
Full Text Available One of the main design challenges in wireless sensor networks (WSNs is achieving a high-data-rate transmission for individual sensor devices. The high altitude platform (HAP is an important communication relay platform for WSNs and next-generation wireless networks. Multiple-input multiple-output (MIMO techniques provide the diversity and multiplexing gain, which can improve the network performance effectively. In this paper, a virtual MIMO (V-MIMO model is proposed by networking multiple HAPs with the concept of multiple assets in view (MAV. In a shadowed Rician fading channel, the diversity performance is investigated. The probability density function (PDF and cumulative distribution function (CDF of the received signal-to-noise ratio (SNR are derived. In addition, the average symbol error rate (ASER with BPSK and QPSK is given for the V-MIMO model. The system capacity is studied for both perfect channel state information (CSI and unknown CSI individually. The ergodic capacity with various SNR and Rician factors for different network configurations is also analyzed. The simulation results validate the effectiveness of the performance analysis. It is shown that the performance of the HAPs network in WSNs can be significantly improved by utilizing the MAV to achieve overlapping coverage, with the help of the V-MIMO techniques.
Link-space formalism for network analysis.
Smith, David M D; Lee, Chiu Fan; Onnela, Jukka-Pekka; Johnson, Neil F
2008-03-01
We introduce the link-space formalism for analyzing network models with degree-degree correlations. The formalism is based on a statistical description of the fraction of links l(i,j) connecting nodes of degrees i and j. To demonstrate its use, we apply the framework to some pedagogical network models, namely, random attachment, Barabási-Albert preferential attachment, and the classical Erdos and Rényi random graph. For these three models the link-space matrix can be solved analytically. We apply the formalism to a simple one-parameter growing network model whose numerical solution exemplifies the effect of degree-degree correlations for the resulting degree distribution. We also employ the formalism to derive the degree distributions of two very simple network decay models, more specifically, that of random link deletion and random node deletion. The formalism allows detailed analysis of the correlations within networks and we also employ it to derive the form of a perfectly nonassortative network for arbitrary degree distribution.
Nonlinear Time Series Analysis via Neural Networks
Volná, Eva; Janošek, Michal; Kocian, Václav; Kotyrba, Martin
This article deals with a time series analysis based on neural networks in order to make an effective forex market [Moore and Roche, J. Int. Econ. 58, 387-411 (2002)] pattern recognition. Our goal is to find and recognize important patterns which repeatedly appear in the market history to adapt our trading system behaviour based on them.
Mixed Methods Analysis of Enterprise Social Networks
Behrendt, Sebastian; Richter, Alexander; Trier, Matthias
2014-01-01
The increasing use of enterprise social networks (ESN) generates vast amounts of data, giving researchers and managerial decision makers unprecedented opportunities for analysis. However, more transparency about the available data dimensions and how these can be combined is needed to yield accurate...
Nonlinear eigenvalue approach to differential Riccati equations for contraction analysis
Kawano, Yu; Ohtsuka, Toshiyuki
2017-01-01
In this paper, we extend the eigenvalue method of the algebraic Riccati equation to the differential Riccati equation (DRE) in contraction analysis. One of the main results is showing that solutions to the DRE can be expressed as functions of nonlinear eigenvectors of the differential Hamiltonian ma
Capacity analysis of vehicular communication networks
Lu, Ning
2013-01-01
This SpringerBrief focuses on the network capacity analysis of VANETs, a key topic as fundamental guidance on design and deployment of VANETs is very limited. Moreover, unique characteristics of VANETs impose distinguished challenges on such an investigation. This SpringerBrief first introduces capacity scaling laws for wireless networks and briefly reviews the prior arts in deriving the capacity of VANETs. It then studies the unicast capacity considering the socialized mobility model of VANETs. With vehicles communicating based on a two-hop relaying scheme, the unicast capacity bound is deriv
Historical Network Analysis of the Web
Brügger, Niels
2013-01-01
of the online web has for a number of years gained currency within Internet studies. However, the combination of these two phenomena—historical network analysis of material in web archives—can at best be characterized as an emerging new area of study. Most of the methodological challenges within this new area...... at the Danish parliamentary elections in 2011, 2007, and 2001. As the Internet grows older historical studies of networks on the web will probably become more widespread and therefore it may be about time to begin debating the methodological challenges within this emerging field....
Dynamic quality of service differentiation using fixed code weight in optical CDMA networks
Kakaee, Majid H.; Essa, Shawnim I.; Abd, Thanaa H.; Seyedzadeh, Saleh
2015-11-01
The emergence of network-driven applications, such as internet, video conferencing, and online gaming, brings in the need for a network the environments with capability of providing diverse Quality of Services (QoS). In this paper, a new code family of novel spreading sequences, called a Multi-Service (MS) code, has been constructed to support multiple services in Optical- Code Division Multiple Access (CDMA) system. The proposed method uses fixed weight for all services, however reducing the interfering codewords for the users requiring higher QoS. The performance of the proposed code is demonstrated using mathematical analysis. It shown that the total number of served users with satisfactory BER of 10-9 using NB=2 is 82, while they are only 36 and 10 when NB=3 and 4 respectively. The developed MS code is compared with variable-weight codes such as Variable Weight-Khazani Syed (VW-KS) and Multi-Weight-Random Diagonal (MW-RD). Different numbers of basic users (NB) are used to support triple-play services (audio, data and video) with different QoS requirements. Furthermore, reference to the BER of 10-12, 10-9, and 10-3 for video, data and audio, respectively, the system can support up to 45 total users. Hence, results show that the technique can clearly provide a relative QoS differentiation with lower value of basic users can support larger number of subscribers as well as better performance in terms of acceptable BER of 10-9 at fixed code weight.
Lee, Jae-Hyeok; Shim, Wooyoung; Choolakadavil Khalid, Najeeb; Kang, Won-Seok; Lee, Minsu; Kim, Hyo-Sop; Choi, Je; Lee, Gwang; Kim, Jae-Ho
2015-01-28
Studies on the interaction of cells with single-walled carbon nanotubes (SWCNTs) have been receiving increasing attention owing to their potential for various cellular applications. In this report, we investigated the interactions between biological cells and nanostructured SWCNTs films and focused on how morphological structures of SWCNT films affected cellular behavior such as cell proliferation and differentiation. One directionally aligned SWCNT Langmuir-Blodgett (LB) film and random network SWCNT film were fabricated by LB and vacuum filteration methods, respectively. We demonstrate that our SWCNT LB and network film based scaffolds do not show any cytotoxicity, while on the other hand, these scaffolds promote differentiation property of rat mesenchymal stem cells (rMSCs) when compared with that on conventional tissue culture polystyrene substrates. Especially, the SWCNT network film with average thickness and roughness values of 95 ± 5 and 9.81 nm, respectively, demonstrated faster growth rate and higher cell thickness for rMSCs. These results suggest that systematic manipulation of the thickness, roughness, and directional alignment of SWCNT films would provide the convenient strategy for controlling the growth and maintenance of the differentiation property of stem cells. The SWCNT film could be an alternative culture substrate for various stem cells, which often require close control of the growth and differentiation properties.
Schiesser, William E
2014-01-01
Features a solid foundation of mathematical and computational tools to formulate and solve real-world PDE problems across various fields With a step-by-step approach to solving partial differential equations (PDEs), Differential Equation Analysis in Biomedical Science and Engineering: Partial Differential Equation Applications with R successfully applies computational techniques for solving real-world PDE problems that are found in a variety of fields, including chemistry, physics, biology, and physiology. The book provides readers with the necessary knowledge to reproduce and extend the com
Schiesser, William E
2014-01-01
Features a solid foundation of mathematical and computational tools to formulate and solve real-world ODE problems across various fields With a step-by-step approach to solving ordinary differential equations (ODEs), Differential Equation Analysis in Biomedical Science and Engineering: Ordinary Differential Equation Applications with R successfully applies computational techniques for solving real-worldODE problems that are found in a variety of fields, including chemistry, physics, biology,and physiology. The book provides readers with the necessary knowledge to reproduce andextend the comp
Lie symmetry analysis of some time fractional partial differential equations
El Kinani, E. H.; Ouhadan, A.
2015-04-01
This paper uses Lie symmetry analysis to reduce the number of independent variables of time fractional partial differential equations. Then symmetry properties have been employed to construct some exact solutions.
Mathematical Analysis of Urban Spatial Networks
Blanchard, Philippe
2009-01-01
Cities can be considered to be among the largest and most complex artificial networks created by human beings. Due to the numerous and diverse human-driven activities, urban network topology and dynamics can differ quite substantially from that of natural networks and so call for an alternative method of analysis. The intent of the present monograph is to lay down the theoretical foundations for studying the topology of compact urban patterns, using methods from spectral graph theory and statistical physics. These methods are demonstrated as tools to investigate the structure of a number of real cities with widely differing properties: medieval German cities, the webs of city canals in Amsterdam and Venice, and a modern urban structure such as found in Manhattan. Last but not least, the book concludes by providing a brief overview of possible applications that will eventually lead to a useful body of knowledge for architects, urban planners and civil engineers.
A network analysis of Sibiu County, Romania
Grama, Cristina-Nicol
2013-01-01
Network science methods have proved to be able to provide useful insights from both a theoretical and a practical point of view in that they can better inform governance policies in complex dynamic environments. The tourism research community has provided an increasing number of works that analyse destinations from a network science perspective. However, most of the studies refer to relatively small samples of actors and linkages. With this note we provide a full network study, although at a preliminary stage, that reports a complete analysis of a Romanian destination (Sibiu). Our intention is to increase the set of similar studies with the aim of supporting the investigations in structural and dynamical characteristics of tourism destinations.
Intentional risk management through complex networks analysis
Chapela, Victor; Moral, Santiago; Romance, Miguel
2015-01-01
This book combines game theory and complex networks to examine intentional technological risk through modeling. As information security risks are in constant evolution, the methodologies and tools to manage them must evolve to an ever-changing environment. A formal global methodology is explained in this book, which is able to analyze risks in cyber security based on complex network models and ideas extracted from the Nash equilibrium. A risk management methodology for IT critical infrastructures is introduced which provides guidance and analysis on decision making models and real situations. This model manages the risk of succumbing to a digital attack and assesses an attack from the following three variables: income obtained, expense needed to carry out an attack, and the potential consequences for an attack. Graduate students and researchers interested in cyber security, complex network applications and intentional risk will find this book useful as it is filled with a number of models, methodologies a...
Micro-macro analysis of complex networks.
Marchiori, Massimo; Possamai, Lino
2015-01-01
Complex systems have attracted considerable interest because of their wide range of applications, and are often studied via a "classic" approach: study a specific system, find a complex network behind it, and analyze the corresponding properties. This simple methodology has produced a great deal of interesting results, but relies on an often implicit underlying assumption: the level of detail on which the system is observed. However, in many situations, physical or abstract, the level of detail can be one out of many, and might also depend on intrinsic limitations in viewing the data with a different level of abstraction or precision. So, a fundamental question arises: do properties of a network depend on its level of observability, or are they invariant? If there is a dependence, then an apparently correct network modeling could in fact just be a bad approximation of the true behavior of a complex system. In order to answer this question, we propose a novel micro-macro analysis of complex systems that quantitatively describes how the structure of complex networks varies as a function of the detail level. To this extent, we have developed a new telescopic algorithm that abstracts from the local properties of a system and reconstructs the original structure according to a fuzziness level. This way we can study what happens when passing from a fine level of detail ("micro") to a different scale level ("macro"), and analyze the corresponding behavior in this transition, obtaining a deeper spectrum analysis. The obtained results show that many important properties are not universally invariant with respect to the level of detail, but instead strongly depend on the specific level on which a network is observed. Therefore, caution should be taken in every situation where a complex network is considered, if its context allows for different levels of observability.
Research of differentiated QoS routing in GMPLS-based IP/WDM networks
Wang, YiYun; Zeng, QingJi; Cao, JunWen
2004-04-01
At this point in technology's evolution, the simplicity, elegance, extensibility, and broad compatibility of the Internet protocol suite has made it the automatic choice for most forms of communication. The attempts at resolution of this apparent dichotomy consist of a collection of technologies and philosophies known as Quality of Service. In an IP network, QoS defines the ability to compensate for traffic characteristics without compromising average throughput. Clearly, optimizing QoS performance for all traffic types on an IP network presents a daunting challenge. To partially address this challenge, several Internet Engineering Task Force groups have been working on standardized approaches for IP-based QoS technologies. The IETF"s approaches fall into four categories: prioritization using differentiated services, reservation using integrated services, label switching using multi-protocol label switching, bandwidth management using the subnet bandwidth manager. Differentiated services classify per-hop behaviors on the basis of a Diffserv code point attached to the type of service byte in each packet"s IP header. This DSCP approach represents a form of soft QoS that rather coarsely classifies services through packet marking. The differentiated QoS routing in GMPLS-based IP/WDM Networks are a promising candidate for the next generation optical Internet networks. By using a unified control plane, such networks make more efficient usage of network resources both at the IP layer and the WDM optical layer. In this paper, we consider prioritized routing of bandwidth-guaranteed Label Switched paths (LSPs) providing service differentiation between classes of high and normal priority traffic. The QoS delay requirements are assumed to be translated into bandwidth and O-E-O conversion requirements. We present a graphical representation of the integrated network state which is different from other conventional graphical representations in that it models the cost of usage of
Comparison of Artificial Neural Network Architecture in Solving Ordinary Differential Equations
Susmita Mall
2013-01-01
Full Text Available This paper investigates the solution of Ordinary Differential Equations (ODEs with initial conditions using Regression Based Algorithm (RBA and compares the results with arbitrary- and regression-based initial weights for different numbers of nodes in hidden layer. Here, we have used feed forward neural network and error back propagation method for minimizing the error function and for the modification of the parameters (weights and biases. Initial weights are taken as combination of random as well as by the proposed regression based model. We present the method for solving a variety of problems and the results are compared. Here, the number of nodes in hidden layer has been fixed according to the degree of polynomial in the regression fitting. For this, the input and output data are fitted first with various degree polynomials using regression analysis and the coefficients involved are taken as initial weights to start with the neural training. Fixing of the hidden nodes depends upon the degree of the polynomial. For the example problems, the analytical results have been compared with neural results with arbitrary and regression based weights with four, five, and six nodes in hidden layer and are found to be in good agreement.
Sander, K F
1964-01-01
Linear Network Theory covers the significant algebraic aspect of network theory, with minimal reference to practical circuits. The book begins the presentation of network analysis with the exposition of networks containing resistances only, and follows it up with a discussion of networks involving inductance and capacity by way of the differential equations. Classification and description of certain networks, equivalent networks, filter circuits, and network functions are also covered. Electrical engineers, technicians, electronics engineers, electricians, and students learning the intricacies
Empirical Bayes Model Comparisons for Differential Methylation Analysis
Mingxiang Teng
2012-01-01
Full Text Available A number of empirical Bayes models (each with different statistical distribution assumptions have now been developed to analyze differential DNA methylation using high-density oligonucleotide tiling arrays. However, it remains unclear which model performs best. For example, for analysis of differentially methylated regions for conservative and functional sequence characteristics (e.g., enrichment of transcription factor-binding sites (TFBSs, the sensitivity of such analyses, using various empirical Bayes models, remains unclear. In this paper, five empirical Bayes models were constructed, based on either a gamma distribution or a log-normal distribution, for the identification of differential methylated loci and their cell division—(1, 3, and 5 and drug-treatment-(cisplatin dependent methylation patterns. While differential methylation patterns generated by log-normal models were enriched with numerous TFBSs, we observed almost no TFBS-enriched sequences using gamma assumption models. Statistical and biological results suggest log-normal, rather than gamma, empirical Bayes model distribution to be a highly accurate and precise method for differential methylation microarray analysis. In addition, we presented one of the log-normal models for differential methylation analysis and tested its reproducibility by simulation study. We believe this research to be the first extensive comparison of statistical modeling for the analysis of differential DNA methylation, an important biological phenomenon that precisely regulates gene transcription.
Network analysis reveals multiscale controls on streamwater chemistry
McGuire, Kevin J.; Torgersen, Christian E.; Likens, Gene E.; Buso, Donald C.; Lowe, Winsor H.; Bailey, Scott W.
2014-01-01
By coupling synoptic data from a basin-wide assessment of streamwater chemistry with network-based geostatistical analysis, we show that spatial processes differentially affect biogeochemical condition and pattern across a headwater stream network. We analyzed a high-resolution dataset consisting of 664 water samples collected every 100 m throughout 32 tributaries in an entire fifth-order stream network. These samples were analyzed for an exhaustive suite of chemical constituents. The fine grain and broad extent of this study design allowed us to quantify spatial patterns over a range of scales by using empirical semivariograms that explicitly incorporated network topology. Here, we show that spatial structure, as determined by the characteristic shape of the semivariograms, differed both among chemical constituents and by spatial relationship (flow-connected, flow-unconnected, or Euclidean). Spatial structure was apparent at either a single scale or at multiple nested scales, suggesting separate processes operating simultaneously within the stream network and surrounding terrestrial landscape. Expected patterns of spatial dependence for flow-connected relationships (e.g., increasing homogeneity with downstream distance) occurred for some chemical constituents (e.g., dissolved organic carbon, sulfate, and aluminum) but not for others (e.g., nitrate, sodium). By comparing semivariograms for the different chemical constituents and spatial relationships, we were able to separate effects on streamwater chemistry of (i) fine-scale versus broad-scale processes and (ii) in-stream processes versus landscape controls. These findings provide insight on the hierarchical scaling of local, longitudinal, and landscape processes that drive biogeochemical patterns in stream networks.
A network analysis of developing brain cultures
Christopoulos, V. N.; Boeff, D. V.; Evans, C. D.; Crowe, D. A.; Amirikian, B.; Georgopoulos, A.; Georgopoulos, A. P.
2012-08-01
We recorded electrical activity from four developing embryonic brain cultures (4-40 days in vitro) using multielectrode arrays (MEAs) with 60 embedded electrodes. Data were filtered for local field potentials (LFPs) and downsampled to 1 ms to yield a matrix of time series consisting of 60 electrode × 60 000 time samples per electrode per day per MEA. Each electrode time series was rendered stationary and nonautocorrelated by applying an ARIMA (25, 1, 1) model and taking the residuals (i.e. innovations). Two kinds of analyses were then performed. First, a pairwise crosscorrelation (CC) analysis (±25 1 ms lags) revealed systematic changes in CC with lag, day in vitro (DIV), and inter-electrode distance. Specifically, (i) positive CCs were 1.76× more prevalent and 1.44× stronger (absolute value) than negative ones, and (ii) the strength of CC increased with DIV and decreased with lag and inter-electrode distance. Second, a network equilibrium analysis was based on the instantaneous (1 ms resolution) logratio of the number of electrodes that were above or below their mean, called simultaneous departure from equilibrium, SDE. This measure possesses a major computational advantage over the pairwise crosscorrelation approach because it is very simple and fast to calculate, an important factor for the analysis of large networks. The results obtained with SDE covaried highly with CC over DIV, which further validates the usefulness of this measure as a computationally effective tool for large scale network analysis.
Yan ZHANG; Hongmei ZHENG; Bin CHEN; Naijin YANG
2013-01-01
An important and practical pattern of industrial symbiosis is rapidly developing:eco-industrial parks.In this study,we used social network analysis to study the network connectedness (i.e.,the proportion of the theoretical number of connections that had been achieved) and related attributes of these hybrid ecological and industrial symbiotic systems.This approach provided insights into details of the network's interior and analyzed the overall degree of connectedness and the relationships among the nodes within the network.We then characterized the structural attributes of the network and subnetwork nodes at two levels (core and periphery),thereby providing insights into the operational problems within each eco-industrial park.We chose ten typical ecoindustrial parks in China and around the world and compared the degree of network connectedness of these systems that resulted from exchanges of products,byproducts,and wastes.By analyzing the density and nodal degree,we determined the relative power and status of the nodes in these networks,as well as other structural attributes such as the core-periphery structure and the degree of sub-network connectedness.The results reveal the operational problems created by the structure of the industrial networks and provide a basis for improving the degree of completeness,thereby increasing their potential for sustainable development and enriching the methods available for the study of industrial symbiosis.
A user’s guide to network analysis in R
Luke, Douglas
2015-01-01
Presenting a comprehensive resource for the mastery of network analysis in R, the goal of Network Analysis with R is to introduce modern network analysis techniques in R to social, physical, and health scientists. The mathematical foundations of network analysis are emphasized in an accessible way and readers are guided through the basic steps of network studies: network conceptualization, data collection and management, network description, visualization, and building and testing statistical models of networks. As with all of the books in the Use R! series, each chapter contains extensive R code and detailed visualizations of datasets. Appendices will describe the R network packages and the datasets used in the book. An R package developed specifically for the book, available to readers on GitHub, contains relevant code and real-world network datasets as well.
Dynamic data analysis modeling data with differential equations
Ramsay, James
2017-01-01
This text focuses on the use of smoothing methods for developing and estimating differential equations following recent developments in functional data analysis and building on techniques described in Ramsay and Silverman (2005) Functional Data Analysis. The central concept of a dynamical system as a buffer that translates sudden changes in input into smooth controlled output responses has led to applications of previously analyzed data, opening up entirely new opportunities for dynamical systems. The technical level has been kept low so that those with little or no exposure to differential equations as modeling objects can be brought into this data analysis landscape. There are already many texts on the mathematical properties of ordinary differential equations, or dynamic models, and there is a large literature distributed over many fields on models for real world processes consisting of differential equations. However, a researcher interested in fitting such a model to data, or a statistician interested in...
Differential Neural Networks for Identification and Filtering in Nonlinear Dynamic Games
Emmanuel García
2014-01-01
Full Text Available This paper deals with the problem of identifying and filtering a class of continuous-time nonlinear dynamic games (nonlinear differential games subject to additive and undesired deterministic perturbations. Moreover, the mathematical model of this class is completely unknown with the exception of the control actions of each player, and even though the deterministic noises are known, their power (or their effect is not. Therefore, two differential neural networks are designed in order to obtain a feedback (perfect state information pattern for the mentioned class of games. In this way, the stability conditions for two state identification errors and for a filtering error are established, the upper bounds of these errors are obtained, and two new learning laws for each neural network are suggested. Finally, an illustrating example shows the applicability of this approach.
Ransdell, Joseph L; Nair, Satish S; Schulz, David J
2013-06-12
Biological and theoretical evidence suggest that individual neurons may achieve similar outputs by differentially balancing variable underlying ionic conductances. Despite the substantial amount of data consistent with this idea, a direct biological demonstration that cells with conserved output, particularly within the same network, achieve these outputs via different solutions has been difficult to achieve. Here we demonstrate definitively that neurons from native neural networks with highly similar output achieve this conserved output by differentially tuning underlying conductance magnitudes. Multiple motor neurons of the crab (Cancer borealis) cardiac ganglion have highly conserved output within a preparation, despite showing a 2-4-fold range of conductance magnitudes. By blocking subsets of these currents, we demonstrate that the remaining conductances become unbalanced, causing disparate output as a result. Therefore, as strategies to understand neuronal excitability become increasingly sophisticated, it is important that such variability in excitability of neurons, even among those within the same individual, is taken into account.
Phosphoproteomics-based systems analysis of signal transduction networks
Hiroko eKozuka-Hata
2012-01-01
Full Text Available Signal transduction systems coordinate complex cellular information to regulate biological events such as cell proliferation and differentiation. Although the accumulating evidence on widespread association of signaling molecules has revealed essential contribution of phosphorylation-dependent interaction networks to cellular regulation, their dynamic behavior is mostly yet to be analyzed. Recent technological advances regarding mass spectrometry-based quantitative proteomics have enabled us to describe the comprehensive status of phosphorylated molecules in a time-resolved manner. Computational analyses based on the phosphoproteome dynamics accelerate generation of novel methodologies for mathematical analysis of cellular signaling. Phosphoproteomics-based numerical modeling can be used to evaluate regulatory network elements from a statistical point of view. Integration with transcriptome dynamics also uncovers regulatory hubs at the transcriptional level. These omics-based computational methodologies, which have firstly been applied to representative signaling systems such as the epidermal growth factor receptor pathway, have now opened up a gate for systems analysis of signaling networks involved in immune response and cancer.
Introduction to stream network habitat analysis
Bartholow, John M.; Waddle, Terry J.
1986-01-01
Increasing demands on stream resources by a variety of users have resulted in an increased emphasis on studies that evaluate the cumulative effects of basinwide water management programs. Network habitat analysis refers to the evaluation of an entire river basin (or network) by predicting its habitat response to alternative management regimes. The analysis principally focuses on the biological and hydrological components of the riv er basin, which include both micro- and macrohabitat. (The terms micro- and macrohabitat are further defined and discussed later in this document.) Both conceptual and analytic models are frequently used for simplifying and integrating the various components of the basin. The model predictions can be used in developing management recommendations to preserve, restore, or enhance instream fish habitat. A network habitat analysis should begin with a clear and concise statement of the study objectives and a thorough understanding of the institutional setting in which the study results will be applied. This includes the legal, social, and political considerations inherent in any water management setting. The institutional environment may dictate the focus and level of detail required of the study to a far greater extent than the technical considerations. After the study objectives, including species on interest, and institutional setting are collectively defined, the technical aspects should be scoped to determine the spatial and temporal requirements of the analysis. A macro level approach should be taken first to identify critical biological elements and requirements. Next, habitat availability is quantified much as in a "standard" river segment analysis, with the likely incorporation of some macrohabitat components, such as stream temperature. Individual river segments may be aggregated to represent the networkwide habitat response of alternative water management schemes. Things learned about problems caused or opportunities generated may
Analysis of linear partial differential operators
Hörmander , Lars
2005-01-01
This volume is an expanded version of Chapters III, IV, V and VII of my 1963 book "Linear partial differential operators". In addition there is an entirely new chapter on convolution equations, one on scattering theory, and one on methods from the theory of analytic functions of several complex variables. The latter is somewhat limited in scope though since it seems superfluous to duplicate the monographs by Ehrenpreis and by Palamodov on this subject. The reader is assumed to be familiar with distribution theory as presented in Volume I. Most topics discussed here have in fact been encountered in Volume I in special cases, which should provide the necessary motivation and background for a more systematic and precise exposition. The main technical tool in this volume is the Fourier- Laplace transformation. More powerful methods for the study of operators with variable coefficients will be developed in Volume III. However, constant coefficient theory has given the guidance for all that work. Although the field...
Principal component analysis networks and algorithms
Kong, Xiangyu; Duan, Zhansheng
2017-01-01
This book not only provides a comprehensive introduction to neural-based PCA methods in control science, but also presents many novel PCA algorithms and their extensions and generalizations, e.g., dual purpose, coupled PCA, GED, neural based SVD algorithms, etc. It also discusses in detail various analysis methods for the convergence, stabilizing, self-stabilizing property of algorithms, and introduces the deterministic discrete-time systems method to analyze the convergence of PCA/MCA algorithms. Readers should be familiar with numerical analysis and the fundamentals of statistics, such as the basics of least squares and stochastic algorithms. Although it focuses on neural networks, the book only presents their learning law, which is simply an iterative algorithm. Therefore, no a priori knowledge of neural networks is required. This book will be of interest and serve as a reference source to researchers and students in applied mathematics, statistics, engineering, and other related fields.
Inference and contradictory analysis for binary neural networks
郭宝龙; 郭雷
1996-01-01
A weak-inference theory and a contradictory analysis for binary neural networks (BNNs).are presented.The analysis indicates that the essential reason why a neural network is changing its slates is the existence of superior contradiction inside the network,and that the process by which a neural network seeks a solution corresponds to eliminating the superior contradiction.Different from general constraint satisfaction networks,the solutions found by BNNs may contain inferior contradiction but not superior contradiction.
Service network analysis for agricultural mental health
Fuller Jeffrey D
2009-05-01
Full Text Available Abstract Background Farmers represent a subgroup of rural and remote communities at higher risk of suicide attributed to insecure economic futures, self-reliant cultures and poor access to health services. Early intervention models are required that tap into existing farming networks. This study describes service networks in rural shires that relate to the mental health needs of farming families. This serves as a baseline to inform service network improvements. Methods A network survey of mental health related links between agricultural support, health and other human services in four drought declared shires in comparable districts in rural New South Wales, Australia. Mental health links covered information exchange, referral recommendations and program development. Results 87 agencies from 111 (78% completed a survey. 79% indicated that two thirds of their clients needed assistance for mental health related problems. The highest mean number of interagency links concerned information exchange and the frequency of these links between sectors was monthly to three monthly. The effectiveness of agricultural support and health sector links were rated as less effective by the agricultural support sector than by the health sector (p Conclusion Aligning with agricultural agencies is important to build effective mental health service pathways to address the needs of farming populations. Work is required to ensure that these agricultural support agencies have operational and effective links to primary mental health care services. Network analysis provides a baseline to inform this work. With interventions such as local mental health training and joint service planning to promote network development we would expect to see over time an increase in the mean number of links, the frequency in which these links are used and the rated effectiveness of these links.
Wontakal, Sandeep N.; Guo, Xingyi; Smith, Cameron; MacCarthy, Thomas; Emery H Bresnick; Bergman, Aviv; Snyder, Michael P.; Weissman, Sherman M.; Zheng, Deyou; Skoultchi, Arthur I.
2012-01-01
Two mechanisms that play important roles in cell fate decisions are control of a “core transcriptional network” and repression of alternative transcriptional programs by antagonizing transcription factors. Whether these two mechanisms operate together is not known. Here we report that GATA-1, SCL, and Klf1 form an erythroid core transcriptional network by co-occupying >300 genes. Importantly, we find that PU.1, a negative regulator of terminal erythroid differentiation, is a highly integrated...
The Application of Social Network Analysis to Team Sports
Lusher, Dean; Robins, Garry; Kremer, Peter
2010-01-01
This article reviews how current social network analysis might be used to investigate individual and group behavior in sporting teams. Social network analysis methods permit researchers to explore social relations between team members and their individual-level qualities simultaneously. As such, social network analysis can be seen as augmenting…
Context Specific and Differential Gene Co-expression Networks via Bayesian Biclustering.
Gao, Chuan; McDowell, Ian C; Zhao, Shiwen; Brown, Christopher D; Engelhardt, Barbara E
2016-07-01
Identifying latent structure in high-dimensional genomic data is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-regulated genes that covary in all of the samples or in only a subset of the samples. Our biclustering method, BicMix, allows overcomplete representations of the data, computational tractability, and joint modeling of unknown confounders and biological signals. Compared with related biclustering methods, BicMix recovers latent structure with higher precision across diverse simulation scenarios as compared to state-of-the-art biclustering methods. Further, we develop a principled method to recover context specific gene co-expression networks from the estimated sparse biclustering matrices. We apply BicMix to breast cancer gene expression data and to gene expression data from a cardiovascular study cohort, and we recover gene co-expression networks that are differential across ER+ and ER- samples and across male and female samples. We apply BicMix to the Genotype-Tissue Expression (GTEx) pilot data, and we find tissue specific gene networks. We validate these findings by using our tissue specific networks to identify trans-eQTLs specific to one of four primary tissues.
Achieving QoS for Aeronautical Telecommunication Networks over Differentiated Services
Bai, Haowei; Atiquzzaman, Mohammed; Ivancic, William
2001-01-01
Aeronautical Telecommunication Network (ATN) has been developed by the International Civil Aviation Organization to integrate Air-Ground and Ground-Ground data communication for aeronautical applications into a single network serving Air Traffic Control and Aeronautical Operational Communications. To carry time critical information required for aeronautical applications, ATN provides different Quality of Services (QoS) to applications. ATN has therefore, been designed as a standalone network which implies building an expensive separate network for ATN. However, the cost of operating ATN can be reduced if it can be run over a public network such as the Internet. Although the current Internet does not provide QoS, the next generation Internet is expected to provide QoS to applications. The objective of this paper is to investigate the possibility of providing QoS to ATN applications when it is run over the next generation Internet. Differentiated Services (DiffServ), one of the protocols proposed for the next generation Internet, will allow network service providers to offer different QoS to customers. Our results show that it is possible to provide QoS to ATN applications when they run over a DiffServ backbone.
Dynamical modeling and analysis of large cellular regulatory networks
Bérenguier, D.; Chaouiya, C.; Monteiro, P. T.; Naldi, A.; Remy, E.; Thieffry, D.; Tichit, L.
2013-06-01
The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.
Todinov, Michael T
2013-01-01
Repairable flow networks are a new area of research, which analyzes the repair and flow disruption caused by failures of components in static flow networks. This book addresses a gap in current network research by developing the theory, algorithms and applications related to repairable flow networks and networks with disturbed flows. The theoretical results presented in the book lay the foundations of a new generation of ultra-fast algorithms for optimizing the flow in networks after failures or congestion, and the high computational speed creates the powerful possibility of optimal control
Distinguishing manipulated stocks via trading network analysis
Sun, Xiao-Qian; Cheng, Xue-Qi; Shen, Hua-Wei; Wang, Zhao-Yang
2011-10-01
Manipulation is an important issue for both developed and emerging stock markets. For the study of manipulation, it is critical to analyze investor behavior in the stock market. In this paper, an analysis of the full transaction records of over a hundred stocks in a one-year period is conducted. For each stock, a trading network is constructed to characterize the relations among its investors. In trading networks, nodes represent investors and a directed link connects a stock seller to a buyer with the total trade size as the weight of the link, and the node strength is the sum of all edge weights of a node. For all these trading networks, we find that the node degree and node strength both have tails following a power-law distribution. Compared with non-manipulated stocks, manipulated stocks have a high lower bound of the power-law tail, a high average degree of the trading network and a low correlation between the price return and the seller-buyer ratio. These findings may help us to detect manipulated stocks.
Ahmed N. U.
2004-01-01
Full Text Available We consider optimum feedback control strategy for computer communication network, in particular, the access control mechanism. The dynamic model representing the source and the access control system is described by a system of stochastic differential equations developed in our previous works. Simulated annealing (SA was used to optimize the parameters of the control law based on neural network. This technique was found to be computationally intensive. In this paper, we have proposed to use a more powerful algorithm known as recursive random search (RRS. By using this technique, we have been able to reduce the computation time by a factor of five without compromising the optimality. This is very important for optimization of high-dimensional systems serving a large number of aggregate users. The results show that the proposed control law can improve the network performance by improving throughput, reducing multiplexor and TB losses, and relaxing, not avoiding, congestion.
Huseyin Ceylan
2013-01-01
Full Text Available This study proposes a traffic congestion minimization model in which the traffic signal setting optimization is performed through a combined simulation-optimization model. In this model, the TRANSYT traffic simulation software is combined with Differential Evolution (DE optimization algorithm, which is based on the natural selection paradigm. In this context, the EQuilibrium Network Design (EQND problem is formulated as a bilevel programming problem in which the upper level is the minimization of the total network performance index. In the lower level, the traffic assignment problem, which represents the route choice behavior of the road users, is solved using the Path Flow Estimator (PFE as a stochastic user equilibrium assessment. The solution of the bilevel EQND problem is carried out by the proposed Differential Evolution and TRANSYT with PFE, the so-called DETRANSPFE model, on a well-known signal controlled test network. Performance of the proposed model is compared to that of two previous works where the EQND problem has been solved by Genetic-Algorithms- (GAs- and Harmony-Search- (HS- based models. Results show that the DETRANSPFE model outperforms the GA- and HS-based models in terms of the network performance index and the computational time required.
Analysis and visualization of citation networks
Zhao, Dangzhi
2015-01-01
Citation analysis-the exploration of reference patterns in the scholarly and scientific literature-has long been applied in a number of social sciences to study research impact, knowledge flows, and knowledge networks. It has important information science applications as well, particularly in knowledge representation and in information retrieval.Recent years have seen a burgeoning interest in citation analysis to help address research, management, or information service issues such as university rankings, research evaluation, or knowledge domain visualization. This renewed and growing interest
An Intelligent technical analysis using neural network
Reza Raei
2011-07-01
Full Text Available Technical analysis has been one of the most popular methods for stock market predictions for the past few decades. There have been enormous technical analysis methods to study the behavior of stock market for different kinds of trading markets such as currency, commodity or stock. In this paper, we propose two different methods based on volume adjusted moving average and ease of movement for stock trading. These methods are used with and without generalized regression neural network methods and the results are compared with each other. The preliminary results on historical stock price of 20 firms indicate that there is no meaningful difference between various proposed models of this paper.
Differential PIXE analysis of Mesoamerican jewelry items
Demortier, G.; Ruvalcaba-Sil, J. L.
1996-09-01
Gold jewelry items of Mesoamerican origin (from Peru, Colombia, Mexico, etc,…) are usually cast in Tumbaga: a man-made gold-copper-silver alloy containing a large proportion of copper. In order to give the objects a colour close to that of pure gold, ancient Mesoamerican goldsmiths experimented with a procedure to eliminate less noble metals (like copper and silver) from the surface. RBS may be used to identify a possible enrichment in gold in the most external layer of the items but due to the low capability of this technique to separate scattered particles on gold and silver and due to the low Rutherford cross section for α-particles on copper by comparison with those on gold, the determination of the exact depth depletion of copper cannot be easily reached. Differential PIXE is an appropriate method to achieve this goal. It takes the relative X-ray intensities of Cu and Au lines into account. By varying the incident proton energy, this ratio is modified in a completely different way if the sample is homogeneous or exhibits a layered or depth profile structure.
Analysis and synthesis of distributed-lumped-active networks by digital computer
1973-01-01
The use of digital computational techniques in the analysis and synthesis of DLA (distributed lumped active) networks is considered. This class of networks consists of three distinct types of elements, namely, distributed elements (modeled by partial differential equations), lumped elements (modeled by algebraic relations and ordinary differential equations), and active elements (modeled by algebraic relations). Such a characterization is applicable to a broad class of circuits, especially including those usually referred to as linear integrated circuits, since the fabrication techniques for such circuits readily produce elements which may be modeled as distributed, as well as the more conventional lumped and active ones.
Network analysis in public health: history, methods, and applications.
Luke, Douglas A; Harris, Jenine K
2007-01-01
Network analysis is an approach to research that is uniquely suited to describing, exploring, and understanding structural and relational aspects of health. It is both a methodological tool and a theoretical paradigm that allows us to pose and answer important ecological questions in public health. In this review we trace the history of network analysis, provide a methodological overview of network techniques, and discuss where and how network analysis has been used in public health. We show how network analysis has its roots in mathematics, statistics, sociology, anthropology, psychology, biology, physics, and computer science. In public health, network analysis has been used to study primarily disease transmission, especially for HIV/AIDS and other sexually transmitted diseases; information transmission, particularly for diffusion of innovations; the role of social support and social capital; the influence of personal and social networks on health behavior; and the interorganizational structure of health systems. We conclude with future directions for network analysis in public health.
Communicating oscillatory networks: frequency domain analysis
Ihekwaba Adaoha EC
2011-12-01
Full Text Available Abstract Background Constructing predictive dynamic models of interacting signalling networks remains one of the great challenges facing systems biology. While detailed dynamical data exists about individual pathways, the task of combining such data without further lengthy experimentation is highly nontrivial. The communicating links between pathways, implicitly assumed to be unimportant and thus excluded, are precisely what become important in the larger system and must be reinstated. To maintain the delicate phase relationships between signals, signalling networks demand accurate dynamical parameters, but parameters optimised in isolation and under varying conditions are unlikely to remain optimal when combined. The computational burden of estimating parameters increases exponentially with increasing system size, so it is crucial to find precise and efficient ways of measuring the behaviour of systems, in order to re-use existing work. Results Motivated by the above, we present a new frequency domain-based systematic analysis technique that attempts to address the challenge of network assembly by defining a rigorous means to quantify the behaviour of stochastic systems. As our focus we construct a novel coupled oscillatory model of p53, NF-kB and the mammalian cell cycle, based on recent experimentally verified mathematical models. Informed by online databases of protein networks and interactions, we distilled their key elements into simplified models containing the most significant parts. Having coupled these systems, we constructed stochastic models for use in our frequency domain analysis. We used our new technique to investigate the crosstalk between the components of our model and measure the efficacy of certain network-based heuristic measures. Conclusions We find that the interactions between the networks we study are highly complex and not intuitive: (i points of maximum perturbation do not necessarily correspond to points of maximum
Integrated Adaptive Analysis and Visualization of Satellite Network Data Project
National Aeronautics and Space Administration — We propose to develop a system that enables integrated and adaptive analysis and visualization of satellite network management data. Integrated analysis and...
A methodology for the analysis of differential coexpression across the human lifespan
Gillis Jesse
2009-09-01
Full Text Available Abstract Background Differential coexpression is a change in coexpression between genes that may reflect 'rewiring' of transcriptional networks. It has previously been hypothesized that such changes might be occurring over time in the lifespan of an organism. While both coexpression and differential expression of genes have been previously studied in life stage change or aging, differential coexpression has not. Generalizing differential coexpression analysis to many time points presents a methodological challenge. Here we introduce a method for analyzing changes in coexpression across multiple ordered groups (e.g., over time and extensively test its validity and usefulness. Results Our method is based on the use of the Haar basis set to efficiently represent changes in coexpression at multiple time scales, and thus represents a principled and generalizable extension of the idea of differential coexpression to life stage data. We used published microarray studies categorized by age to test the methodology. We validated the methodology by testing our ability to reconstruct Gene Ontology (GO categories using our measure of differential coexpression and compared this result to using coexpression alone. Our method allows significant improvement in characterizing these groups of genes. Further, we examine the statistical properties of our measure of differential coexpression and establish that the results are significant both statistically and by an improvement in semantic similarity. In addition, we found that our method finds more significant changes in gene relationships compared to several other methods of expressing temporal relationships between genes, such as coexpression over time. Conclusion Differential coexpression over age generates significant and biologically relevant information about the genes producing it. Our Haar basis methodology for determining age-related differential coexpression performs better than other tested methods. The
Solving Generalised Riccati Differential Equations by Homotopy Analysis Method
J. Vahidi
2013-07-01
Full Text Available In this paper, the quadratic Riccati differential equation is solved by means of an analytic technique, namely the homotopy analysis method (HAM. Comparisons are made between Adomian’s decomposition method (ADM and the exact solution and the homotopy analysis method. The results reveal that the proposed method is very effective and simple.
STABILITY ANALYSIS OF HOPFIELD NEURAL NETWORKS WITH TIME DELAY
王林山; 徐道义
2002-01-01
The global asymptotic stability for Hopfield neural networks with time delay was investigated. A theorem and two corollaries were obtained, in which the boundedness and differentiability offjon R in some articles were deleted. Some sufficient conditions for the existence of global asymptotic stable equilibrium of the networks in this paper are better than the sufficient conditions in quoted articles.
Design Criteria For Networked Image Analysis System
Reader, Cliff; Nitteberg, Alan
1982-01-01
Image systems design is currently undergoing a metamorphosis from the conventional computing systems of the past into a new generation of special purpose designs. This change is motivated by several factors, notably among which is the increased opportunity for high performance with low cost offered by advances in semiconductor technology. Another key issue is a maturing in understanding of problems and the applicability of digital processing techniques. These factors allow the design of cost-effective systems that are functionally dedicated to specific applications and used in a utilitarian fashion. Following an overview of the above stated issues, the paper presents a top-down approach to the design of networked image analysis systems. The requirements for such a system are presented, with orientation toward the hospital environment. The three main areas are image data base management, viewing of image data and image data processing. This is followed by a survey of the current state of the art, covering image display systems, data base techniques, communications networks and software systems control. The paper concludes with a description of the functional subystems and architectural framework for networked image analysis in a production environment.
Simultaneity Analysis In A Wireless Sensor Network
Malović Miodrag
2015-06-01
Full Text Available An original wireless sensor network for vibration measurements was designed. Its primary purpose is modal analysis of vibrations of large structures. A number of experiments have been performed to evaluate the system, with special emphasis on the influence of different effects on simultaneity of data acquired from remote nodes, which is essential for modal analysis. One of the issues is that quartz crystal oscillators, which provide time reading on the devices, are optimized for use in the room temperature and exhibit significant frequency variations if operated outside the 20–30°C range. Although much research was performed to optimize algorithms of synchronization in wireless networks, the subject of temperature fluctuations was not investigated and discussed in proportion to its significance. This paper describes methods used to evaluate data simultaneity and some algorithms suitable for its improvement in small to intermediate size ad-hoc wireless sensor networks exposed to varying temperatures often present in on-site civil engineering measurements.
Estimation and analysis of Galileo differential code biases
Li, Min; Yuan, Yunbin; Wang, Ningbo; Li, Zishen; Li, Ying; Huo, Xingliang
2017-03-01
When sensing the Earth's ionosphere using dual-frequency pseudorange observations of global navigation satellite systems (GNSS), the satellite and receiver differential code biases (DCBs) account for one of the main sources of error. For the Galileo system, limited knowledge is available about the determination and characteristic analysis of the satellite and receiver DCBs. To better understand the characteristics of satellite and receiver DCBs of Galileo, the IGGDCB (IGG, Institute of Geodesy and Geophysics, Wuhan, China) method is extended to estimate the satellite and receiver DCBs of Galileo, with the combined use of GPS and Galileo observations. The experimental data were collected from the Multi-GNSS Experiment network, covering the period of 2013-2015. The stability of both Galileo satellite and receiver DCBs over a time period of 36 months was thereby analyzed for the current state of the Galileo system. Good agreement of Galileo satellite DCBs is found between the IGGDCB-based DCB estimates and those from the German Aerospace Center (DLR), at the level of 0.22 ns. Moreover, high-level stability of the Galileo satellite DCB estimates is obtained over the selected time span (less than 0.25 ns in terms of standard deviation) by both IGGDCB and DLR algorithms. The Galileo receiver DCB estimates are also relatively stable for the case in which the receiver hardware device stays unchanged. It can also be concluded that the receiver DCB estimates are rather sensitive to the change of the firmware version and that the receiver antenna type has no great impact on receiver DCBs.
Differential proteome analysis of chikungunya virus infection on host cells.
Christina Li-Ping Thio
Full Text Available BACKGROUND: Chikungunya virus (CHIKV is an emerging mosquito-borne alphavirus that has caused multiple unprecedented and re-emerging outbreaks in both tropical and temperate countries. Despite ongoing research efforts, the underlying factors involved in facilitating CHIKV replication during early infection remains ill-characterized. The present study serves to identify host proteins modulated in response to early CHIKV infection using a proteomics approach. METHODOLOGY AND PRINCIPAL FINDINGS: The whole cell proteome profiles of CHIKV-infected and mock control WRL-68 cells were compared and analyzed using two-dimensional gel electrophoresis (2-DGE. Fifty-three spots were found to be differentially modulated and 50 were successfully identified by MALDI-TOF/TOF. Eight were significantly up-regulated and 42 were down-regulated. The mRNA expressions of 15 genes were also found to correlate with the corresponding protein expression. STRING network analysis identified several biological processes to be affected, including mRNA processing, translation, energy production and cellular metabolism, ubiquitin-proteasome pathway (UPP and cell cycle regulation. CONCLUSION/SIGNIFICANCE: This study constitutes a first attempt to investigate alteration of the host cellular proteome during early CHIKV infection. Our proteomics data showed that during early infection, CHIKV affected the expression of proteins that are involved in mRNA processing, host metabolic machinery, UPP, and cyclin-dependent kinase 1 (CDK1 regulation (in favour of virus survival, replication and transmission. While results from this study complement the proteomics results obtained from previous late host response studies, functional characterization of these proteins is warranted to reinforce our understanding of their roles during early CHIKV infection in humans.
Yonglong Yu; Dong Zhu; Chaoying Ma; Hui Cao; Yaping Wang; Yanhao Xu; Wenying Zhang; Yueming Yan
2016-01-01
Wheat seed development is an important physiological process of seed maturation and directly affects wheat yield and quality. In this study, we performed dynamic transcriptome microarray analysis of an elite Chinese bread wheat cultivar (Jimai 20) during grain development using the GeneChip Wheat Genome Array. Grain morphology and scanning electron microscope observations showed that the period of 11–15 days post-anthesis (DPA) was a key stage for the synthesis and accumulation of seed starch. Genome-wide transcriptional profiling and significance analysis of microarrays revealed that the period from 11 to 15 DPA was more important than the 15–20 DPA stage for the synthesis and accumulation of nutritive reserves. Series test of cluster analysis of differential genes revealed five statistically significant gene expression profiles. Gene ontology annotation and enrichment analysis gave further informa-tion about differentially expressed genes, and MapMan analysis revealed expression changes within functional groups during seed development. Metabolic pathway network analysis showed that major and minor metabolic pathways regulate one another to ensure regular seed development and nutritive reserve accumulation. We performed gene co-expression network analysis to identify genes that play vital roles in seed development and identified several key genes involved in important metabolic pathways. The transcriptional expression of eight key genes involved in starch and protein synthesis and stress defense was further validated by qRT-PCR. Our results provide new insight into the molecular mechanisms of wheat seed development and the determinants of yield and quality.
Yonglong Yu
2016-04-01
Full Text Available Wheat seed development is an important physiological process of seed maturation and directly affects wheat yield and quality. In this study, we performed dynamic transcriptome microarray analysis of an elite Chinese bread wheat cultivar (Jimai 20 during grain development using the GeneChip Wheat Genome Array. Grain morphology and scanning electron microscope observations showed that the period of 11–15 days post-anthesis (DPA was a key stage for the synthesis and accumulation of seed starch. Genome-wide transcriptional profiling and significance analysis of microarrays revealed that the period from 11 to 15 DPA was more important than the 15–20 DPA stage for the synthesis and accumulation of nutritive reserves. Series test of cluster analysis of differential genes revealed five statistically significant gene expression profiles. Gene ontology annotation and enrichment analysis gave further information about differentially expressed genes, and MapMan analysis revealed expression changes within functional groups during seed development. Metabolic pathway network analysis showed that major and minor metabolic pathways regulate one another to ensure regular seed development and nutritive reserve accumulation. We performed gene co-expression network analysis to identify genes that play vital roles in seed development and identified several key genes involved in important metabolic pathways. The transcriptional expression of eight key genes involved in starch and protein synthesis and stress defense was further validated by qRT-PCR. Our results provide new insight into the molecular mechanisms of wheat seed development and the determinants of yield and quality.
Expert Network for Die Casing Defect Analysis
Jiadi WANG; Yongfeng JIANG; Chen LU; Wenjiang DING
2003-01-01
Due to the competition and high cost associated with die casting defects, it is urgent to adopt a rapid and effective method for defect analysis. In this research, a novel expert network approach was proposed to avoid some disadvantages of rulebased expert system. The main objective of the system is to assist die casting engineer in identifying defect, determining the probable causes of defect and proposing remedies to eliminate the defect. 14 common die casting defects could be identified quickly by expert system on the basis of their characteristics. BP neural network in combination with expert system was applied to map the complex relationship between causes and defects, and further explained the cause determination process.Cause determination gives due consideration to practical process conditions. Finally, corrective measures were recommended to eliminate the defect and implemented in the sequence of difficulty.
Integrative bayesian network analysis of genomic data.
Ni, Yang; Stingo, Francesco C; Baladandayuthapani, Veerabhadran
2014-01-01
Rapid development of genome-wide profiling technologies has made it possible to conduct integrative analysis on genomic data from multiple platforms. In this study, we develop a novel integrative Bayesian network approach to investigate the relationships between genetic and epigenetic alterations as well as how these mutations affect a patient's clinical outcome. We take a Bayesian network approach that admits a convenient decomposition of the joint distribution into local distributions. Exploiting the prior biological knowledge about regulatory mechanisms, we model each local distribution as linear regressions. This allows us to analyze multi-platform genome-wide data in a computationally efficient manner. We illustrate the performance of our approach through simulation studies. Our methods are motivated by and applied to a multi-platform glioblastoma dataset, from which we reveal several biologically relevant relationships that have been validated in the literature as well as new genes that could potentially be novel biomarkers for cancer progression.
Network analysis of online bidding activity
Yang, I.; Oh, E.; Kahng, B.
2006-07-01
With the advent of digital media, people are increasingly resorting to online channels for commercial transactions. The online auction is a prototypical example. In such online transactions, the pattern of bidding activity is more complex than traditional offline transactions; this is because the number of bidders participating in a given transaction is not bounded and the bidders can also easily respond to the bidding instantaneously. By using the recently developed network theory, we study the interaction patterns between bidders (items) who (that) are connected when they bid for the same item (if the item is bid by the same bidder). The resulting network is analyzed by using the hierarchical clustering algorithm, which is used for clustering analysis for expression data from DNA microarrays. A dendrogram is constructed for the item subcategories; this dendrogram is compared to a traditional classification scheme. The implication of the difference between the two is discussed.
Dissipativity Analysis of Neural Networks with Time-varying Delays
Yan Sun; Bao-Tong Cui
2008-01-01
A new definition of dissipativity for neural networks is presented in this paper. By constructing proper Lyapunov func- tionals and using some analytic techniques, sufficient conditions are given to ensure the dissipativity of neural networks with or without time-varying parametric uncertainties and the integro-differential neural networks in terms of linear matrix inequalities. Numerical examples are given to illustrate the effectiveness of the obtained results.
Network meta-analysis: an introduction for clinicians.
Rouse, Benjamin; Chaimani, Anna; Li, Tianjing
2017-02-01
Network meta-analysis is a technique for comparing multiple treatments simultaneously in a single analysis by combining direct and indirect evidence within a network of randomized controlled trials. Network meta-analysis may assist assessing the comparative effectiveness of different treatments regularly used in clinical practice and, therefore, has become attractive among clinicians. However, if proper caution is not taken in conducting and interpreting network meta-analysis, inferences might be biased. The aim of this paper is to illustrate the process of network meta-analysis with the aid of a working example on first-line medical treatment for primary open-angle glaucoma. We discuss the key assumption of network meta-analysis, as well as the unique considerations for developing appropriate research questions, conducting the literature search, abstracting data, performing qualitative and quantitative synthesis, presenting results, drawing conclusions, and reporting the findings in a network meta-analysis.
Transcriptional master regulator analysis in breast cancer genetic networks.
Tovar, Hugo; García-Herrera, Rodrigo; Espinal-Enríquez, Jesús; Hernández-Lemus, Enrique
2015-12-01
Gene regulatory networks account for the delicate mechanisms that control gene expression. Under certain circumstances, gene regulatory programs may give rise to amplification cascades. Such transcriptional cascades are events in which activation of key-responsive transcription factors called master regulators trigger a series of gene expression events. The action of transcriptional master regulators is then important for the establishment of certain programs like cell development and differentiation. However, such cascades have also been related with the onset and maintenance of cancer phenotypes. Here we present a systematic implementation of a series of algorithms aimed at the inference of a gene regulatory network and analysis of transcriptional master regulators in the context of primary breast cancer cells. Such studies were performed in a highly curated database of 880 microarray gene expression experiments on biopsy-captured tissue corresponding to primary breast cancer and healthy controls. Biological function and biochemical pathway enrichment analyses were also performed to study the role that the processes controlled - at the transcriptional level - by such master regulators may have in relation to primary breast cancer. We found that transcription factors such as AGTR2, ZNF132, TFDP3 and others are master regulators in this gene regulatory network. Sets of genes controlled by these regulators are involved in processes that are well-known hallmarks of cancer. This kind of analyses may help to understand the most upstream events in the development of phenotypes, in particular, those regarding cancer biology.
Understanding resilience in industrial symbiosis networks: insights from network analysis.
Chopra, Shauhrat S; Khanna, Vikas
2014-08-01
Industrial symbiotic networks are based on the principles of ecological systems where waste equals food, to develop synergistic networks. For example, industrial symbiosis (IS) at Kalundborg, Denmark, creates an exchange network of waste, water, and energy among companies based on contractual dependency. Since most of the industrial symbiotic networks are based on ad-hoc opportunities rather than strategic planning, gaining insight into disruptive scenarios is pivotal for understanding the balance of resilience and sustainability and developing heuristics for designing resilient IS networks. The present work focuses on understanding resilience as an emergent property of an IS network via a network-based approach with application to the Kalundborg Industrial Symbiosis (KIS). Results from network metrics and simulated disruptive scenarios reveal Asnaes power plant as the most critical node in the system. We also observe a decrease in the vulnerability of nodes and reduction in single points of failure in the system, suggesting an increase in the overall resilience of the KIS system from 1960 to 2010. Based on our findings, we recommend design strategies, such as increasing diversity, redundancy, and multi-functionality to ensure flexibility and plasticity, to develop resilient and sustainable industrial symbiotic networks.
Applications of social media and social network analysis
Kazienko, Przemyslaw
2015-01-01
This collection of contributed chapters demonstrates a wide range of applications within two overlapping research domains: social media analysis and social network analysis. Various methodologies were utilized in the twelve individual chapters including static, dynamic and real-time approaches to graph, textual and multimedia data analysis. The topics apply to reputation computation, emotion detection, topic evolution, rumor propagation, evaluation of textual opinions, friend ranking, analysis of public transportation networks, diffusion in dynamic networks, analysis of contributors to commun
Wontakal, Sandeep N; Guo, Xingyi; Smith, Cameron; MacCarthy, Thomas; Bresnick, Emery H; Bergman, Aviv; Snyder, Michael P; Weissman, Sherman M; Zheng, Deyou; Skoultchi, Arthur I
2012-03-06
Two mechanisms that play important roles in cell fate decisions are control of a "core transcriptional network" and repression of alternative transcriptional programs by antagonizing transcription factors. Whether these two mechanisms operate together is not known. Here we report that GATA-1, SCL, and Klf1 form an erythroid core transcriptional network by co-occupying >300 genes. Importantly, we find that PU.1, a negative regulator of terminal erythroid differentiation, is a highly integrated component of this network. GATA-1, SCL, and Klf1 act to promote, whereas PU.1 represses expression of many of the core network genes. PU.1 also represses the genes encoding GATA-1, SCL, Klf1, and important GATA-1 cofactors. Conversely, in addition to repressing PU.1 expression, GATA-1 also binds to and represses >100 PU.1 myelo-lymphoid gene targets in erythroid progenitors. Mathematical modeling further supports that this dual mechanism of repressing both the opposing upstream activator and its downstream targets provides a synergistic, robust mechanism for lineage specification. Taken together, these results amalgamate two key developmental principles, namely, regulation of a core transcriptional network and repression of an alternative transcriptional program, thereby enhancing our understanding of the mechanisms that establish cellular identity.
Duan, Lian; Huang, Lihong
2014-09-01
In this paper, we investigate a class of memristor-based neural networks with general mixed delays involving both time-varying delays and distributed delays. By using the Mawhin-like coincidence theorem, together with the differential inclusion theory, M-matrix properties and differential inequality techniques, some novel criteria are established for ensuring the periodicity and dissipativity for the addressed neural networks. Finally, two numerical examples with simulations are presented to demonstrate the effectiveness of the theoretical results.
Samarasinghe, S; Ling, H
2017-02-04
In this paper, we show how to extend our previously proposed novel continuous time Recurrent Neural Networks (RNN) approach that retains the advantage of continuous dynamics offered by Ordinary Differential Equations (ODE) while enabling parameter estimation through adaptation, to larger signalling networks using a modular approach. Specifically, the signalling network is decomposed into several sub-models based on important temporal events in the network. Each sub-model is represented by the proposed RNN and trained using data generated from the corresponding ODE model. Trained sub-models are assembled into a whole system RNN which is then subjected to systems dynamics and sensitivity analyses. The concept is illustrated by application to G1/S transition in cell cycle using Iwamoto et al. (2008) ODE model. We decomposed the G1/S network into 3 sub-models: (i) E2F transcription factor release; (ii) E2F and CycE positive feedback loop for elevating cyclin levels; and (iii) E2F and CycA negative feedback to degrade E2F. The trained sub-models accurately represented system dynamics and parameters were in good agreement with the ODE model. The whole system RNN however revealed couple of parameters contributing to compounding errors due to feedback and required refinement to sub-model 2. These related to the reversible reaction between CycE/CDK2 and p27, its inhibitor. The revised whole system RNN model very accurately matched dynamics of the ODE system. Local sensitivity analysis of the whole system model further revealed the most dominant influence of the above two parameters in perturbing G1/S transition, giving support to a recent hypothesis that the release of inhibitor p27 from Cyc/CDK complex triggers cell cycle stage transition. To make the model useful in a practical setting, we modified each RNN sub-model with a time relay switch to facilitate larger interval input data (≈20min) (original model used data for 30s or less) and retrained them that produced
Ferroelectric Field-Effect Transistor Differential Amplifier Circuit Analysis
Phillips, Thomas A.; MacLeod, Todd C.; Ho, Fat D.
2008-01-01
There has been considerable research investigating the Ferroelectric Field-Effect Transistor (FeFET) in memory circuits. However, very little research has been performed in applying the FeFET to analog circuits. This paper investigates the use of FeFETs in a common analog circuit, the differential amplifier. The two input Metal-Oxide-Semiconductor (MOS) transistors in a general MOS differential amplifier circuit are replaced with FeFETs. Resistors are used in place of the other three MOS transistors. The FeFET model used in the analysis has been previously reported and was based on experimental device data. Because of the FeFET hysteresis, the FeFET differential amplifier has four different operating modes depending on whether the FeFETs are positively or negatively polarized. The FeFET differential amplifier operation in the different modes was analyzed by calculating the amplifier voltage transfer and gain characteristics shown in figures 2 through 5. Comparisons were made between the FeFET differential amplifier and the standard MOS differential amplifier. Possible applications and benefits of the FeFET differential amplifier are discussed.
Lorraine Komisarjevsky Tyler
2013-05-01
Full Text Available The core human capacity of syntactic analysis involves a left hemisphere network involving left inferior frontal gyrus (LIFG and posterior middle temporal gyrus (LMTG and the anatomical connections between them. Here we use MEG to determine the spatio-temporal properties of syntactic computations in this network. Listeners heard spoken sentences containing a local syntactic ambiguity (e.g. …landing planes…, at the offset of which they heard a disambiguating verb and decided whether it was an acceptable/unacceptable continuation of the sentence. We charted the time-course of processing and resolving syntactic ambiguity by measuring MEG responses from the onset of each word in the ambiguous phrase and the disambiguating word. We used representational similarity analysis (RSA to characterize syntactic information represented in the LIFG and LpMTG over time and to investigate their relationship to each other. Testing a variety of lexico-syntactic and ambiguity models against the MEG data, our results suggest early lexico-syntactic responses in the LpMTG and later effects of ambiguity in the LIFG, pointing to a clear differentiation in the functional roles of these two regions. Our results suggest the LpMTG represents and transmits lexical information to the LIFG, which responds to and resolves the ambiguity.
Tensor and vector analysis with applications to differential geometry
Springer, C E
2012-01-01
Concise and user-friendly, this college-level text assumes only a knowledge of basic calculus in its elementary and gradual development of tensor theory. The introductory approach bridges the gap between mere manipulation and a genuine understanding of an important aspect of both pure and applied mathematics.Beginning with a consideration of coordinate transformations and mappings, the treatment examines loci in three-space, transformation of coordinates in space and differentiation, tensor algebra and analysis, and vector analysis and algebra. Additional topics include differentiation of vect
Community-enhanced Network Representation Learning for Network Analysis
Tu, Cunchao; Zeng, Xiangkai; Liu, Zhiyuan; Sun, Maosong
2016-01-01
Network representation learning (NRL) aims to build low-dimensional vectors for vertices in a network. Most existing NRL methods focus on learning representations from local context of vertices (such as their neighbors). Nevertheless, vertices in many complex networks also exhibit significant global patterns widely known as communities. It's a common sense that vertices in the same community tend to connect densely, and usually share common attributes. These patterns are expected to improve NRL and benefit relevant evaluation tasks, such as link prediction and vertex classification. In this work, we propose a novel NRL model by introducing community information of vertices to learn more discriminative network representations, named as Community-enhanced Network Representation Learning (CNRL). CNRL simultaneously detects community distribution of each vertex and learns embeddings of both vertices and communities. In this way, we can obtain more informative representation of a vertex accompanying with its commu...
Network-based analysis of proteomic profiles
Wong, Limsoon
2016-01-26
Mass spectrometry (MS)-based proteomics is a widely used and powerful tool for profiling systems-wide protein expression changes. It can be applied for various purposes, e.g. biomarker discovery in diseases and study of drug responses. Although RNA-based high-throughput methods have been useful in providing glimpses into the underlying molecular processes, the evidences they provide are indirect. Furthermore, RNA and corresponding protein levels have been known to have poor correlation. On the other hand, MS-based proteomics tend to have consistency issues (poor reproducibility and inter-sample agreement) and coverage issues (inability to detect the entire proteome) that need to be urgently addressed. In this talk, I will discuss how these issues can be addressed by proteomic profile analysis techniques that use biological networks (especially protein complexes) as the biological context. In particular, I will describe several techniques that we have been developing for network-based analysis of proteomics profile. And I will present evidence that these techniques are useful in identifying proteomics-profile analysis results that are more consistent, more reproducible, and more biologically coherent, and that these techniques allow expansion of the detected proteome to uncover and/or discover novel proteins.
Yi Gan
2014-01-01
Full Text Available Aim: The objective of this study is to characterize differential proteomic expression among well-differentiation and poor-differentiation colorectal carcinoma tissues and normal mucous epithelium. Materials and Methods: The study is based on quantitative 2-dimensional gel electrophoresis and analyzed by PDquest. Results: Excluding redundancies due to proteolysis and posttranslational modified isoforms of over 600 protein spots, 11 proteins were revealed as regulated with statistical variance being within the 95 th confidence level and were identified by peptide mass fingerprinting in matrix assisted laser desorption/ionization time-of-flight mass spectrometry. Progression-associated proteins belong to the functional complexes of tumorigenesis, proliferation, differentiation, metabolism, and the regulation of major histocompatibility complex processing and other functions. Partial but significant overlap was revealed with previous proteomics and transcriptomics studies in CRC. Among various differentiation stage of CRC tissues, we identified calreticulin precursor, MHC class I antigen (human leukocyte antigen A , glutathione S-transferase pi1, keratin 8, heat shock protein 27, tubulin beta chain, triosephosphate, fatty acid-binding protein, hemoglobin (deoxy mutant with val b 1 replaced by met (HBB, and zinc finger protein 312 (FEZF2. Conclusions: Their functional networks were analyzed by Ingenuity systems Ingenuity Pathways Analysis and revealed the potential roles as novel biomarkers for progression in various differentiation stages of CRC.
China ASON Network Migration Scenarios and Their Quantitative Analysis
Soichiro; Araki; Itaru; Nishioka; Yoshihiko; Suemura
2003-01-01
This paper proposes two migration scenarios from China ring networks to ASON mesh networks. In our quantitative analysis with ASON/GMPLS simulator, a subnetwork protection scheme achieved best balanced performance in resource utilization and restoration time.
China ASON Network Migration Scenarios and Their Quantitative Analysis
Guoying Zhang; Soichiro Araki; Itaru Nishioka; Yoshihiko Suemura
2003-01-01
This paper proposes two migration scenarios from China rin g networks to ASON mesh networks . In our quantitative analysis with ASON/GMPLS simulator, a subnetwork protection scheme achieved best balanced performance in resource utilization and restoration time.
Automated Analysis of Security in Networking Systems
Buchholtz, Mikael
2004-01-01
will both help raise the general level of awareness of the problems and prevent the most basic flaws from occurring. This thesis contributes to the development of such tools. Networking systems typically try to attain secure communication by applying standard cryptographic techniques. In this thesis......-experts users. The feasibility of the techiques is illustrated by a proof-of-concept implementation of a control ow analysis developed for LySa. From a techincal point of view, this implementation also interesting because it encodes in nite sets of algebraic terms, which denote encryption, as a nite number...
Social sciences via network analysis and computation
Kanduc, Tadej
2015-01-01
In recent years information and communication technologies have gained significant importance in the social sciences. Because there is such rapid growth of knowledge, methods and computer infrastructure, research can now seamlessly connect interdisciplinary fields such as business process management, data processing and mathematics. This study presents some of the latest results, practices and state-of-the-art approaches in network analysis, machine learning, data mining, data clustering and classifications in the contents of social sciences. It also covers various real-life examples such as t
Identifying changes in the support networks of end-of-life carers using social network analysis.
Leonard, Rosemary; Horsfall, Debbie; Noonan, Kerrie
2015-06-01
End-of-life caring is often associated with reduced social networks for both the dying person and for the carer. However, those adopting a community participation and development approach, see the potential for the expansion and strengthening of networks. This paper uses Knox, Savage and Harvey's definitions of three generations social network analysis to analyse the caring networks of people with a terminal illness who are being cared for at home and identifies changes in these caring networks that occurred over the period of caring. Participatory network mapping of initial and current networks was used in nine focus groups. The analysis used key concepts from social network analysis (size, density, transitivity, betweenness and local clustering) together with qualitative analyses of the group's reflections on the maps. The results showed an increase in the size of the networks and that ties between the original members of the network strengthened. The qualitative data revealed the importance between core and peripheral network members and the diverse contributions of the network members. The research supports the value of third generation social network analysis and the potential for end-of-life caring to build social capital. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
Economic analysis of spider web airline networks
无
2009-01-01
The distinct network organization, management, service and operating characteristics of US Southwest Airlines are key elements of its success compared with other airlines. As a network organization type, the spider web airline network has received more attention. In this paper, we analyzed the relation between the spider web airline network and spider web, and the structure of spider web airline network, built the assignment model of the spider web airline network,and investigated the economics concerned.
Advantages of Social Network Analysis in Educational Research
Ushakov, K. M.; Kukso, K. N.
2015-01-01
Currently one of the main tools for the large scale studies of schools is statistical analysis. Although it is the most common method and it offers greatest opportunities for analysis, there are other quantitative methods for studying schools, such as network analysis. We discuss the potential advantages that network analysis has for educational…
Large-scale differential display analysis of T helper cell differentiation.
Ojala, Pekka; Virtanen, Eveliina; Chen, Zhi
2007-03-01
We have developed a novel large-scale multicapillary fluorescent differential display (FDD) platform amenable to further automation. The power of the method is demonstrated by the analysis of T helper cell differentiation. Eight RNA samples from wild type, Stat4 knockout and Stat6 knockout mice were analyzed with 16 anchoring primers and 24 arbitrary primers, resulting in 285 294 sample peaks. Visually selected patterns of differential expression suggest two major regulatory mechanisms: activation and Stat4 genotype. A subset of the findings is reproduced in the confirmatory differential display (DD) that included technical and biological replicates. In a small fragment identification pilot study, we identify Ifi27 and Cct8 to be up-regulated by T cell activation. We present a method for the analysis of electropherogram similarity across large datasets, based on correlation of low-resolution representations of electrophoretic data. We show how it can be applied to analyze experimental and technical variables. Using this method, we demonstrate the effect of activation and genotype. In addition, agreement of our real experimental data to the theoretical basis of DD, as well as issues in anchoring primer selectivity, are studied.
Network Analysis of Urban Traffic with Big Bus Data
Zhao, Kai
2016-01-01
Urban traffic analysis is crucial for traffic forecasting systems, urban planning and, more recently, various mobile and network applications. In this paper, we analyse urban traffic with network and statistical methods. Our analysis is based on one big bus dataset containing 45 million bus arrival samples in Helsinki. We mainly address following questions: 1. How can we identify the areas that cause most of the traffic in the city? 2. Why there is a urban traffic? Is bus traffic a key cause of the urban traffic? 3. How can we improve the urban traffic systems? To answer these questions, first, the betweenness is used to identify the most import areas that cause most traffics. Second, we find that bus traffic is not an important cause of urban traffic using statistical methods. We differentiate the urban traffic and the bus traffic in a city. We use bus delay as an identification of the urban traffic, and the number of bus as an identification of the bus traffic. Third, we give our solutions on how to improve...
Partial differential equations modeling, analysis and numerical approximation
Le Dret, Hervé
2016-01-01
This book is devoted to the study of partial differential equation problems both from the theoretical and numerical points of view. After presenting modeling aspects, it develops the theoretical analysis of partial differential equation problems for the three main classes of partial differential equations: elliptic, parabolic and hyperbolic. Several numerical approximation methods adapted to each of these examples are analyzed: finite difference, finite element and finite volumes methods, and they are illustrated using numerical simulation results. Although parts of the book are accessible to Bachelor students in mathematics or engineering, it is primarily aimed at Masters students in applied mathematics or computational engineering. The emphasis is on mathematical detail and rigor for the analysis of both continuous and discrete problems. .
Analysis and perturbation of degree correlation in complex networks
Xiang, Ju; Hu, Tao; Zhang, Yan
2015-01-01
Degree correlation is an important topological property common to many real-world networks. In this paper, the statistical measures for characterizing the degree correlation in networks are investigated analytically. We give an exact proof of the consistency for the statistical measures, reveal the general linear relation in the degree correlation, which provide a simple and interesting perspective on the analysis of the degree correlation in complex networks. By using the general linear analysis, we investigate the perturbation of the degree correlation in complex networks caused by the addition of few nodes and the rich club. The results show that the assortativity of homogeneous networks such as the ER graphs is easily to be affected strongly by the simple structural changes, while it has only slight variation for heterogeneous networks with broad degree distribution such as the scale-free networks. Clearly, the homogeneous networks are more sensitive for the perturbation than the heterogeneous networks.
Packet flow analysis in IP networks via abstract interpretation
Komondoor, Raghavan; Seetharam, Deva P; Balodia, Sudha
2011-01-01
Static analysis (aka offline analysis) of a model of an IP network is useful for understanding, debugging, and verifying packet flow properties of the network. There have been static analysis approaches proposed in the literature for networks based on model checking as well as graph reachability. Abstract interpretation is a method that has typically been applied to static analysis of programs. We propose a new, abstract-interpretation based approach for analysis of networks. We formalize our approach, mention its correctness guarantee, and demonstrate its flexibility in addressing multiple network-analysis problems that have been previously solved via tailor-made approaches. Finally, we investigate an application of our analysis to a novel problem -- inferring a high-level policy for the network -- which has been addressed in the past only in the restricted single-router setting.
Drost, Derek R; Benedict, Catherine I; Berg, Arthur; Novaes, Evandro; Novaes, Carolina R D B; Yu, Qibin; Dervinis, Christopher; Maia, Jessica M; Yap, John; Miles, Brianna; Kirst, Matias
2010-05-04
A fundamental goal of systems biology is to identify genetic elements that contribute to complex phenotypes and to understand how they interact in networks predictive of system response to genetic variation. Few studies in plants have developed such networks, and none have examined their conservation among functionally specialized organs. Here we used genetical genomics in an interspecific hybrid population of the model hardwood plant Populus to uncover transcriptional networks in xylem, leaves, and roots. Pleiotropic eQTL hotspots were detected and used to construct coexpression networks a posteriori, for which regulators were predicted based on cis-acting expression regulation. Networks were shown to be enriched for groups of genes that function in biologically coherent processes and for cis-acting promoter motifs with known roles in regulating common groups of genes. When contrasted among xylem, leaves, and roots, transcriptional networks were frequently conserved in composition, but almost invariably regulated by different loci. Similarly, the genetic architecture of gene expression regulation is highly diversified among plant organs, with less than one-third of genes with eQTL detected in two organs being regulated by the same locus. However, colocalization in eQTL position increases to 50% when they are detected in all three organs, suggesting conservation in the genetic regulation is a function of ubiquitous expression. Genes conserved in their genetic regulation among all organs are primarily cis regulated (approximately 92%), whereas genes with eQTL in only one organ are largely trans regulated. Trans-acting regulation may therefore be the primary driver of differentiation in function between plant organs.
Haitao Zhang
2011-12-01
Full Text Available In the networked control system with random time delay in forward and feedback channels, a kind of controller based on Smith compensator and signal neuron incomplete differential forward PID is presented. First, using root locus method and simulink simulation software, the influences of network’s time delay on the system stability and dynamic performance are analyzed. Then, combined with incomplete differential forward PID control algorithm, Smith compensation model is established. Compared with existing Smith compensator, the proposed control model is easy to be implemented, and can also get better control performance in the case of miss-matching compensator model. Finally, the simulation research on a DC motor is done, and the simulation results show the effectiveness of the proposed method.
Co-occurrence network analysis of Chinese and English poems
Liang, Wei; Wang, Yanli; Shi, Yuming; Chen, Guanrong
2015-02-01
A total of 572 co-occurrence networks of Chinese characters and words as well as English words are constructed from both Chinese and English poems. It is found that most of the networks have small-world features; more Chinese networks have scale-free properties and hierarchical structures as compared with the English networks; all the networks are disassortative, and the disassortativeness of the Chinese word networks is more prominent than those of the English networks; the spectral densities of the Chinese word networks and English networks are similar, but they are different from those of the ER, BA, and WS networks. For the above observed phenomena, analysis is provided with interpretation from a linguistic perspective.
Grunspan, Daniel Z.; Wiggins, Benjamin L.; Goodreau, Steven M.
2014-01-01
Social interactions between students are a major and underexplored part of undergraduate education. Understanding how learning relationships form in undergraduate classrooms, as well as the impacts these relationships have on learning outcomes, can inform educators in unique ways and improve educational reform. Social network analysis (SNA)…
Antonova, I; Bänninger, A; Dierks, T; Griskova-Bulanova, I; Koenig, T; Kohler, A
2015-10-01
Recent functional magnetic resonance imaging (fMRI) studies consistently revealed contributions of fronto-parietal and related networks to the execution of a visuospatial judgment task, the so-called "Clock Task". However, due to the low temporal resolution of fMRI, the exact cortical dynamics and timing of processing during task performance could not be resolved until now. In order to clarify the detailed cortical activity and temporal dynamics, 14 healthy subjects performed an established version of the "Clock Task", which comprises a visuospatial task (angle discrimination) and a control task (color discrimination) with the same stimulus material, in an electroencephalography (EEG) experiment. Based on the time-resolved analysis of network activations (microstate analysis), differences in timing between the angle compared to the color discrimination task were found after sensory processing in a time window starting around 200 ms. Significant differences between the two tasks were observed in an analysis window from 192 ms to 776 ms. We divided this window in two parts: an early phase - from 192 ms to ∼440 ms, and a late phase - from ∼440 ms to 776 ms. For both tasks, the order of network activations and the types of networks were the same, but, in each phase, activations for the two conditions were dominated by differing network states with divergent temporal dynamics. Our results provide an important basis for the assessment of deviations in processing dynamics during visuospatial tasks in clinical populations.
Covariance analysis of differential drag-based satellite cluster flight
Ben-Yaacov, Ohad; Ivantsov, Anatoly; Gurfil, Pini
2016-06-01
One possibility for satellite cluster flight is to control relative distances using differential drag. The idea is to increase or decrease the drag acceleration on each satellite by changing its attitude, and use the resulting small differential acceleration as a controller. The most significant advantage of the differential drag concept is that it enables cluster flight without consuming fuel. However, any drag-based control algorithm must cope with significant aerodynamical and mechanical uncertainties. The goal of the current paper is to develop a method for examination of the differential drag-based cluster flight performance in the presence of noise and uncertainties. In particular, the differential drag control law is examined under measurement noise, drag uncertainties, and initial condition-related uncertainties. The method used for uncertainty quantification is the Linear Covariance Analysis, which enables us to propagate the augmented state and filter covariance without propagating the state itself. Validation using a Monte-Carlo simulation is provided. The results show that all uncertainties have relatively small effect on the inter-satellite distance, even in the long term, which validates the robustness of the used differential drag controller.
Analysis of High Degree Nodes in a Social Network
Nadeem Akhtar
2013-05-01
Full Text Available Online Social Network platforms(e.g. Facebook, LinkedIn, Flickr, Instant Messenger etc provide a deeper comprehension of social networks and hence render the basis for social network analysis. The huge amount of data from these sites has given a boost to the researchers who examine a network from different perspectives through various SNA methods. The development of network analysis tools have further helped to extract actionable patterns which are useful for business, consumers, and users. This study is a part of the growing body of research on Social Network Analysis and make use of a Facebook network to analyze the attributes of high degree nodes (users having greater number of friends and to uncover the hidden relationships of that network. Results show that there is little association among high degree nodes
Das, Pritha; Calhoun, Vince; Malhi, Gin S
2014-09-01
Bipolar disorder (BD) and borderline personality (BPD) disorder share clinical features such as emotional lability and poor interpersonal functioning but the course of illness and treatment differs in these groups, which suggests that the underlying neurobiology of BD and BPD is likely to be different. Understanding the neural mechanisms behind the pathophysiology of BD and BPD will facilitate accurate diagnosis and inform the administration of targeted treatment. Since deficits in social cognition or emotion regulation or in the self-referential processing system can give rise to these clinical features, and impairment in these domains have been observed in both patient groups, functional connectivity within and between networks subserving these processes during resting was investigated using functional magnetic resonance imaging. Data were acquired from 16 patients with BD, 14 patients with BPD, and 13 healthy controls (HC) and functional connectivity strength was correlated with scores using the Difficulties in Emotion Regulation Scale. Functional network connectivity (FNC) patterns differentiated BD and BPD patients from HC. In BD, FNC was increased while in BPD it was decreased. In BD impaired FNC was evident primarily among networks involved in self-referential processing while in BPD it also involved the emotion regulatory network. Impaired FNC displayed an association with impulsivity in BPD and emotional clarity and emotional awareness in BD. This study shows that BD and BPD can perhaps be differentiated using resting state FNC approach and that the neural mechanisms underpinning overlapping symptoms discernibly differ between the groups. These findings provide a potential platform for elucidating the targeted effects of psychological interventions in both disorders.
Function spaces and partial differential equations volume 2 : contemporary analysis
Taheri, Ali
2015-01-01
This is a book written primarily for graduate students and early researchers in the fields of Analysis and Partial Differential Equations (PDEs). Coverage of the material is essentially self-contained, extensive and novel with great attention to details and rigour.
Variational analysis and generalized differentiation I basic theory
Mordukhovich, Boris S
2006-01-01
Contains a study of the basic concepts and principles of variational analysis and generalized differentiation in both finite-dimensional and infinite-dimensional spaces. This title presents many applications to problems in optimization, equilibria, stability and sensitivity, control theory, economics, mechanics, and more.
Huang, S.; Ingber, D. E.
2000-01-01
Development of characteristic tissue patterns requires that individual cells be switched locally between different phenotypes or "fates;" while one cell may proliferate, its neighbors may differentiate or die. Recent studies have revealed that local switching between these different gene programs is controlled through interplay between soluble growth factors, insoluble extracellular matrix molecules, and mechanical forces which produce cell shape distortion. Although the precise molecular basis remains unknown, shape-dependent control of cell growth and function appears to be mediated by tension-dependent changes in the actin cytoskeleton. However, the question remains: how can a generalized physical stimulus, such as cell distortion, activate the same set of genes and signaling proteins that are triggered by molecules which bind to specific cell surface receptors. In this article, we use computer simulations based on dynamic Boolean networks to show that the different cell fates that a particular cell can exhibit may represent a preprogrammed set of common end programs or "attractors" which self-organize within the cell's regulatory networks. In this type of dynamic network model of information processing, generalized stimuli (e.g., mechanical forces) and specific molecular cues elicit signals which follow different trajectories, but eventually converge onto one of a small set of common end programs (growth, quiescence, differentiation, apoptosis, etc.). In other words, if cells use this type of information processing system, then control of cell function would involve selection of preexisting (latent) behavioral modes of the cell, rather than instruction by specific binding molecules. Importantly, the results of the computer simulation closely mimic experimental data obtained with living endothelial cells. The major implication of this finding is that current methods used for analysis of cell function that rely on characterization of linear signaling pathways or
Su, Li-Ning; Song, Xiao-Qing; Wei, Hui-Ping; Yin, Hai-Feng
Bone mesenchymal stem cells (BMSCs) differentiated into neurons have been widely proposed for use in cell therapy of many neurological disorders. It is therefore important to understand the molecular mechanisms underlying this differentiation. We screened differentially expressed genes between immature neural tissues and untreated BMSCs to identify the genes responsible for neuronal differentiation from BMSCs. GSE68243 gene microarray data of rat BMSCs and GSE18860 gene microarray data of rat neurons were received from the Gene Expression Omnibus database. Transcriptome Analysis Console software showed that 1248 genes were up-regulated and 1273 were down-regulated in neurons compared with BMSCs. Gene Ontology functional enrichment, protein-protein interaction networks, functional modules, and hub genes were analyzed using DAVID, STRING 10, BiNGO tool, and Network Analyzer software, revealing that nine hub genes, Nrcam, Sema3a, Mapk8, Dlg4, Slit1, Creb1, Ntrk2, Cntn2, and Pax6, may play a pivotal role in neuronal differentiation from BMSCs. Seven genes, Dcx, Nrcam, sema3a, Cntn2, Slit1, Ephb1, and Pax6, were shown to be hub nodes within the neuronal development network, while six genes, Fgf2, Tgfβ1, Vegfa, Serpine1, Il6, and Stat1, appeared to play an important role in suppressing neuronal differentiation. However, additional studies are required to confirm these results.
Modeling and analysis of retinoic acid induced differentiation of uncommitted precursor cells.
Tasseff, Ryan; Nayak, Satyaprakash; Song, Sang Ok; Yen, Andrew; Varner, Jeffrey D
2011-05-01
Manipulation of differentiation programs has therapeutic potential in a spectrum of human cancers and neurodegenerative disorders. In this study, we integrated computational and experimental methods to unravel the response of a lineage uncommitted precursor cell-line, HL-60, to Retinoic Acid (RA). HL-60 is a human myeloblastic leukemia cell-line used extensively to study human differentiation programs. Initially, we focused on the role of the BLR1 receptor in RA-induced differentiation and G1/0-arrest in HL-60. BLR1, a putative G protein-coupled receptor expressed following RA exposure, is required for RA-induced cell-cycle arrest and differentiation and causes persistent MAPK signaling. A mathematical model of RA-induced cell-cycle arrest and differentiation was formulated and tested against BLR1 wild-type (wt) knock-out and knock-in HL-60 cell-lines with and without RA. The current model described the dynamics of 729 proteins and protein complexes interconnected by 1356 interactions. An ensemble strategy was used to compensate for uncertain model parameters. The ensemble of HL-60 models recapitulated the positive feedback between BLR1 and MAPK signaling. The ensemble of models also correctly predicted Rb and p47phox regulation and the correlation between p21-CDK4-cyclin D formation and G1/0-arrest following exposure to RA. Finally, we investigated the robustness of the HL-60 network architecture to structural perturbations and generated experimentally testable hypotheses for future study. Taken together, the model presented here was a first step toward a systematic framework for analysis of programmed differentiation. These studies also demonstrated that mechanistic network modeling can help prioritize experimental directions by generating falsifiable hypotheses despite uncertainty.
Capacity Analysis for Dynamic Space Networks
Yang Lu; Bo Li; Wenjing Kang; Gongliang Liu; Xueting Li
2015-01-01
To evaluate transmission rate of highly dynamic space networks, a new method for studying space network capacity is proposed in this paper. Using graph theory, network capacity is defined as the maximum amount of flows ground stations can receive per unit time. Combined with a hybrid constellation model, network capacity is calculated and further analyzed for practical cases. Simulation results show that network capacity will increase to different extents as link capacity, minimum ground elevation constraint and satellite onboard processing capability change. Considering the efficiency and reliability of communication networks, how to scientifically design satellite networks is also discussed.
Traffic chaotic dynamics modeling and analysis of deterministic network
Wu, Weiqiang; Huang, Ning; Wu, Zhitao
2016-07-01
Network traffic is an important and direct acting factor of network reliability and performance. To understand the behaviors of network traffic, chaotic dynamics models were proposed and helped to analyze nondeterministic network a lot. The previous research thought that the chaotic dynamics behavior was caused by random factors, and the deterministic networks would not exhibit chaotic dynamics behavior because of lacking of random factors. In this paper, we first adopted chaos theory to analyze traffic data collected from a typical deterministic network testbed — avionics full duplex switched Ethernet (AFDX, a typical deterministic network) testbed, and found that the chaotic dynamics behavior also existed in deterministic network. Then in order to explore the chaos generating mechanism, we applied the mean field theory to construct the traffic dynamics equation (TDE) for deterministic network traffic modeling without any network random factors. Through studying the derived TDE, we proposed that chaotic dynamics was one of the nature properties of network traffic, and it also could be looked as the action effect of TDE control parameters. A network simulation was performed and the results verified that the network congestion resulted in the chaotic dynamics for a deterministic network, which was identical with expectation of TDE. Our research will be helpful to analyze the traffic complicated dynamics behavior for deterministic network and contribute to network reliability designing and analysis.
Modified Homotopy Analysis Method for Nonlinear Fractional Partial Differential Equations
D. Ziane
2017-05-01
Full Text Available In this paper, a combined form of natural transform with homotopy analysis method is proposed to solve nonlinear fractional partial differential equations. This method is called the fractional homotopy analysis natural transform method (FHANTM. The FHANTM can easily be applied to many problems and is capable of reducing the size of computational work. The fractional derivative is described in the Caputo sense. The results show that the FHANTM is an appropriate method for solving nonlinear fractional partial differentia equation.
Sindbæk, Søren Michael
2015-01-01
Long-distance communication has emerged as a particular focus for archaeologicalexploration using network theory, analysis, and modelling. The promise is apparentlyobvious: communication in the past doubtlessly had properties of complex, dynamicnetworks, and archaeological datasets almost certainly...... preserve patterns of thisinteraction. Formal network analysis and modelling holds the potential to identify anddemonstrate such patterns, where traditional methods often prove inadequate. Thearchaeological study of communication networks in the past, however, calls for radically different analytical...... this is not a problem of network analysis, but network synthesis: theclassic problem of cracking codes or reconstructing black-box circuits. It is proposedthat archaeological approaches to network synthesis must involve a contextualreading of network data: observations arising from individual contexts, morphologies...
Design and Analysis of Fully Integrated Differential VCOs
M. Prochaska
2005-01-01
Full Text Available Oscillators play a decisive role for electronic equipment in many fields - like communication, navigation or data processing. Especially oscillators are key building blocks in integrated transceivers for wired and wireless communication systems. In this context the study of fully integrated differential VCOs has received attention. In this paper we present an analytic analysis of the steady state oscillation of integrated differential VCOs which is based on a nonlinear model of the oscillator. The outcomes of this are design formulas for the amplitude as well as the stability of the oscillator which take the nonlinearity of the circuit into account.
Differentiation-Based Analysis of Environmental Management and Corporate Performance
SHAN Dong-ming; MU Xin
2007-01-01
By building a duopoly model based on product differentiation, both of the clean firm's and the dirty firm's performances are studied under the assumptions that consumers have different preferences for the product environmental attributes, and that the product cost increases with the environmental attribute. The analysis results show that under either the case with no environmental regulation or that with a tariff levied on the dirty product, the clean firm would always get more profit. In addition, the stricter the regulation is, the more profit the clean firm would obtain. This can verify that from the view of product differentiation, a firm could improve its corporate competitiveness with environmental management.
Analysis and monitoring design for networks
Fedorov, V.; Flanagan, D.; Rowan, T.; Batsell, S.
1998-06-01
The idea of applying experimental design methodologies to develop monitoring systems for computer networks is relatively novel even though it was applied in other areas such as meteorology, seismology, and transportation. One objective of a monitoring system should always be to collect as little data as necessary to be able to monitor specific parameters of the system with respect to assigned targets and objectives. This implies a purposeful monitoring where each piece of data has a reason to be collected and stored for future use. When a computer network system as large and complex as the Internet is the monitoring subject, providing an optimal and parsimonious observing system becomes even more important. Many data collection decisions must be made by the developers of a monitoring system. These decisions include but are not limited to the following: (1) The type data collection hardware and software instruments to be used; (2) How to minimize interruption of regular network activities during data collection; (3) Quantification of the objectives and the formulation of optimality criteria; (4) The placement of data collection hardware and software devices; (5) The amount of data to be collected in a given time period, how large a subset of the available data to collect during the period, the length of the period, and the frequency of data collection; (6) The determination of the data to be collected (for instance, selection of response and explanatory variables); (7) Which data will be retained and how long (i.e., data storage and retention issues); and (8) The cost analysis of experiments. Mathematical statistics, and, in particular, optimal experimental design methods, may be used to address the majority of problems generated by 3--7. In this study, the authors focus their efforts on topics 3--5.
Social Network Analysis: A case study of the Islamist terrorist network
Medina, Richard M [ORNL
2012-01-01
Social Network Analysis is a compilation of methods used to identify and analyze patterns in social network systems. This article serves as a primer on foundational social network concepts and analyses and builds a case study on the global Islamist terrorist network to illustrate the use and usefulness of these methods. The Islamist terrorist network is a system composed of multiple terrorist organizations that are socially connected and work toward the same goals. This research utilizes traditional social network, as well as small-world, and scale-free analyses to characterize this system on individual, network and systemic levels. Leaders in the network are identified based on their positions in the social network and the network structure is categorized. Finally, two vital nodes in the network are removed and this version of the network is compared with the previous version to make implications of strengths, weaknesses and vulnerabilities. The Islamist terrorist network structure is found to be a resilient and efficient structure, even with important social nodes removed. Implications for counterterrorism are given from the results of each analysis.
Park, Yun-Jong; Koh, Jin; Kwon, Jin Teak; Park, Yong-Seok; Yang, Lijun; Cha, Seunghee
2017-01-01
Severe xerostomia (dry mouth) compromises the quality of life in patients with Sjögren’s syndrome or radiation therapy for head and neck cancer. A clinical management of xerostomia is often unsatisfactory as most interventions are palliative with limited efficacy. Following up our previous study demonstrating that mouse BM-MSCs are capable of differentiating into salivary epithelial cells in a co-culture system, we further explored the molecular basis that governs the MSC reprogramming by utilizing high-throughput iTRAQ-2D-LC-MS/MS-based proteomics. Our data revealed the novel induction of pancreas-specific transcription factor 1a (PTF1α), muscle, intestine and stomach expression-1 (MIST-1), and achaete-scute complex homolog 3 (ASCL3) in 7 day co-cultured MSCs but not in control MSCs. More importantly, a common notion of pancreatic-specific expression of PTF1 α was challenged for the first time by our verification of PTF1 α expression in the mouse salivary glands. Furthermore, a molecular network simulation of our selected putative MSC reprogramming factors demonstrated evidence for their perspective roles in salivary gland development. In conclusion, quantitative proteomics with extensive data analyses narrowed down a set of MSC reprograming factors potentially contributing to salivary gland regeneration. Identification of their differential/synergistic impact on MSC conversion warrants further investigation. PMID:28158262
6th International Conference on Network Analysis
Nikolaev, Alexey; Pardalos, Panos; Prokopyev, Oleg
2017-01-01
This valuable source for graduate students and researchers provides a comprehensive introduction to current theories and applications in optimization methods and network models. Contributions to this book are focused on new efficient algorithms and rigorous mathematical theories, which can be used to optimize and analyze mathematical graph structures with massive size and high density induced by natural or artificial complex networks. Applications to social networks, power transmission grids, telecommunication networks, stock market networks, and human brain networks are presented. Chapters in this book cover the following topics: Linear max min fairness Heuristic approaches for high-quality solutions Efficient approaches for complex multi-criteria optimization problems Comparison of heuristic algorithms New heuristic iterative local search Power in network structures Clustering nodes in random graphs Power transmission grid structure Network decomposition problems Homogeneity hypothesis testing Network analy...
Node Exchange Network and its Statistical Analysis
Toyota, N
2005-01-01
In considering a social network, there are cases where people is transferred to another place. Then the physical (direct) relations among nodes are lost by the movement. In terms of a network theory, some nodes break the present connections with neighboring nodes, move and there build new connections of nodes. For simplicity we here consider only that two nodes exchange the place each other on a network. Such exchange is assumed to be constantly carried out. We study this dynamic network (node exchange network NEN) and uncover some new features which usual networks do not contain. We mainly consider average path length and the diameter. Lastly we consider a propagation of one virus on the network by a computer simulation. They are compared to other networks investigated hitherto. The relation to a scale free network is also discussed.
An asymptotic analysis of closed queueing networks with branching populations
Bayer, N.; Coffman, E.G.; Kogan, Y.A.
1995-01-01
Closed queueing networks have proven to be valuable tools for system performance analysis. In this paper, we broaden the applications of such networks by incorporating populations of {em branching customers: whenever a customer completes service at some node of the network, it is replaced by N>=0 cu
Traffic Driven Analysis of Cellular and WiFi Networks
Paul, Utpal Kumar
2012-01-01
Since the days Internet traffic proliferated, measurement, monitoring and analysis of network traffic have been critical to not only the basic understanding of large networks, but also to seek improvements in resource management, traffic engineering and security. At the current times traffic in wireless local and wide area networks are facing…
A Network Text Analysis of David Ayer's "Fury"
Hunter, Starling David; Smith, Susan
2015-01-01
Network Text Analysis (NTA) involves the creation of networks of words and/or concepts from linguistic data. Its key insight is that the position of words and concepts in a text network provides vital clues to the central and underlying themes of the text as a whole. Recent research has relied on inductive approaches to identify these themes. In…
Traffic Driven Analysis of Cellular and WiFi Networks
Paul, Utpal Kumar
2012-01-01
Since the days Internet traffic proliferated, measurement, monitoring and analysis of network traffic have been critical to not only the basic understanding of large networks, but also to seek improvements in resource management, traffic engineering and security. At the current times traffic in wireless local and wide area networks are facing…
Network Analysis of Cosmic Structures : Network Centrality and Topological Environment
Hong, Sungryong
2015-01-01
We apply simple analyses techniques developed for the study of complex networks to the study of the cosmic web, the large scale galaxy distribution. In this paper, we measure three network centralities (ranks of topological importance), Degree Centrality (DC), Closeness Centrality (CL), and Betweenness Centrality (BC) from a network built from the Cosmological Evolution Survey (COSMOS) catalog. We define 8 galaxy populations according to the centrality measures; Void, Wall, and Cluster by DC, Main Branch and Dangling Leaf by BC, and Kernel, Backbone, and Fracture by CL. We also define three populations by voronoi tessellation density to compare these with the DC selection. We apply the topological selections to galaxies in the (photometric) redshift range $0.91
Co-occurrence network analysis of modern Chinese poems
Liang, Wei; Wang, Yanli; Shi, Yuming; Chen, Guanrong
2015-02-01
A total of 606 co-occurrence networks of Chinese characters and words are constructed from rhymes, free verses, and prose poems. It is found that 98.5 % of networks have scale-free properties, while 19.8 % of networks do not have small-world features, especially the clustering coefficients in 5.6 % of networks are zero. In addition, 61.4 % of networks have significant hierarchical structures, and 98 % of networks are disassortative. For the above observed phenomena, analysis is provided with interpretation from a linguistic perspective.
Stability analysis of peer-to-peer networks against churn
Bivas Mitra; Sujoy Ghose; Niloy Ganguly; Fernando Peruani
2008-08-01
Users of the peer-to-peer system join and leave the network randomly, which makes the overlay network dynamic and unstable in nature. In this paper, we propose an analytical framework to assess the robustness of p2p networks in the face of user churn. We model the peer churn through degree-independent as well as degree-dependent node failure. Lately, superpeer networks are becoming the most widely used topology among the p2p networks. Therefore, we perform the stability analysis of superpeer networks as a case study. We validate the analytically derived results with the help of simulation.
Ahmed, Faraz; Jin, Rong; Liu, Alex X.
2013-01-01
Online social networks are being increasingly used for analyzing various societal phenomena such as epidemiology, information dissemination, marketing and sentiment flow. Popular analysis techniques such as clustering and influential node analysis, require the computation of eigenvectors of the real graph's adjacency matrix. Recent de-anonymization attacks on Netflix and AOL datasets show that an open access to such graphs pose privacy threats. Among the various privacy preserving models, Dif...
Network Meta-Analysis of Onychomycosis Treatments
Gupta, Aditya K.; Daigle, Deanne; Foley, Kelly A.
2015-01-01
Background Many onychomycosis treatments have not been directly compared in head-to-head clinical trials. Objective: To determine the relative efficacy of onychomycosis treatments using network meta-analysis (NMA). Methods We conducted a systematic review and NMA of mycological cure rates. Results Nineteen trials were included in the network. Terbinafine 250 mg was significantly superior to all treatments except itraconazole 400 mg pulse therapy. The itraconazole 400 mg pulse regimen was significantly superior to all topicals except efinaconazole 10% nail solution. Itraconazole 200 mg was significantly superior to all topical treatments, while fluconazole 150-450 mg, efinaconazole 10% nail solution, tavaborole 5% nail solution, ciclopirox nail lacquer 8%, terbinafine nail solution, and amorolfine 5% nail lacquer were significantly superior to placebo. Conclusions Newly developed topicals have improved the odds ratios (ORs) of mycological cure, yet these ORs were not significantly greater than preexisting topical treatments. Further experience with these agents will reveal their clinical significance, and head-to-head trials are warranted. © 2015 S. Karger AG, Basel PMID:27170937
Experiences in set-up and usage of a geodetic real-time differential correction network
Martin, Sven; Jahn, Cord-Hinrich
2000-10-01
Global Navigation Satellite Systems (GNSS) are commonly used for geodetic and land surveying applications. The stand alone accuracy provided by these GNSS are insufficient for the majority of these operations (GPS, 1995), therefore some form of differential correction method is required. Accordingly, the state survey offices of Germany have installed a differential correction service for geodetic applications. Code- and phase-corrections are broadcast in the VHF-band using the RTCM V2.1 format (RTCM, 1994). One major problem is that the accuracy depends on the distance to a reference station (length of baseline) because of residual orbit and atmospheric biases. To achieve a more precise solution, a number of reference stations are connected together to form a network. Within this network these influences are computed and a set of "area correction parameters" are also transmitted in RTCM message Type 59. Field trials and measurements have confirmed the high accuracy of this service. This paper describes the system itself, investigations of communication methods as well as site planning. In addition measurements from field trials will be presented to demonstrate the high accuracy in a real-time environment.
Differentiation of closely related fungi by electronic nose analysis
Karlshøj, Kristian; Nielsen, Per Væggemose; Larsen, Thomas Ostenfeld
2007-01-01
In this work the potential of electronic nose analysis for differentiation of closely related fun has been described. A total of 20 isolates of the cheese-associated species Geotrichum candidum, Penicillium camemberti, P.nordicum, and Proqueford and its closely related species P paneum, P carneum...... as well as the noacheese ociated P. expansum have been investigated by electronic nose, GC-MS, and LGMS analysis. The isolates were inoculated on yeast extract sucroseagar in 20-mL headspace flasks and electronicnose analysis was performed daily for a-74period. To assess which volatile metabolites...... by high pressure liquid chromatography, coupled-to a diode array detector and a time of flight mass spectrometer. Several mycotoxins were detected in samples from the specles P.nordicum, P.roqueforti, P.paneum, P.carneum, and P.expansum. Differentiation of closely related mycotoxin producing fungi...
Tensor analysis and elementary differential geometry for physicists and engineers
Nguyen-Schäfer, Hung
2014-01-01
Tensors and methods of differential geometry are very useful mathematical tools in many fields of modern physics and computational engineering including relativity physics, electrodynamics, computational fluid dynamics (CFD), continuum mechanics, aero and vibroacoustics, and cybernetics. This book comprehensively presents topics, such as bra-ket notation, tensor analysis, and elementary differential geometry of a moving surface. Moreover, authors intentionally abstain from giving mathematically rigorous definitions and derivations that are however dealt with as precisely as possible. The reader is provided with hands-on calculations and worked-out examples at which he will learn how to handle the bra-ket notation, tensors and differential geometry and to use them in the physical and engineering world. The target audience primarily comprises graduate students in physics and engineering, research scientists, and practicing engineers.
"Selfish" algorithm for optimizing the network survivability analysis
Poroseva, Svetlana V
2012-01-01
In Nature, the primary goal of any network is to survive. This is less obvious for engineering networks (electric power, gas, water, transportation systems etc.) that are expected to operate under normal conditions most of time. As a result, the ability of a network to withstand massive sudden damage caused by adverse events (or survivability) has not been among traditional goals in the network design. Reality, however, calls for the adjustment of design priorities. As modern networks develop toward increasing their size, complexity, and integration, the likelihood of adverse events increases too due to technological development, climate change, and activities in the political arena among other factors. Under such circumstances, a network failure has an unprecedented effect on lives and economy. To mitigate the impact of adverse events on the network operability, the survivability analysis must be conducted at the early stage of the network design. Such analysis requires the development of new analytical and ...
Analysis of pharmacogenomic variants associated with population differentiation.
Bora Yeon
Full Text Available In the present study, we systematically investigated population differentiation of drug-related (DR genes in order to identify common genetic features underlying population-specific responses to drugs. To do so, we used the International HapMap project release 27 Data and Pharmacogenomics Knowledge Base (PharmGKB database. First, we compared four measures for assessing population differentiation: the chi-square test, the analysis of variance (ANOVA F-test, Fst, and Nearest Shrunken Centroid Method (NSCM. Fst showed high sensitivity with stable specificity among varying sample sizes; thus, we selected Fst for determining population differentiation. Second, we divided DR genes from PharmGKB into two groups based on the degree of population differentiation as assessed by Fst: genes with a high level of differentiation (HD gene group and genes with a low level of differentiation (LD gene group. Last, we conducted a gene ontology (GO analysis and pathway analysis. Using all genes in the human genome as the background, the GO analysis and pathway analysis of the HD genes identified terms related to cell communication. "Cell communication" and "cell-cell signaling" had the lowest Benjamini-Hochberg's q-values (0.0002 and 0.0006, respectively, and "drug binding" was highly enriched (16.51 despite its relatively high q-value (0.0142. Among the 17 genes related to cell communication identified in the HD gene group, five genes (STX4, PPARD, DCK, GRIK4, and DRD3 contained single nucleotide polymorphisms with Fst values greater than 0.5. Specifically, the Fst values for rs10871454, rs6922548, rs3775289, rs1954787, and rs167771 were 0.682, 0.620, 0.573, 0.531, and 0.510, respectively. In the analysis using DR genes as the background, the HD gene group contained six significant terms. Five were related to reproduction, and one was "Wnt signaling pathway," which has been implicated in cancer. Our analysis suggests that the HD gene group from PharmGKB is
Katz Centrality of Markovian Temporal Networks: Analysis and Optimization
Ogura, Masaki
2016-01-01
Identifying important nodes in complex networks is a fundamental problem in network analysis. Although a plethora of measures has been proposed to identify important nodes in static (i.e., time-invariant) networks, there is a lack of tools in the context of temporal networks (i.e., networks whose connectivity dynamically changes over time). The aim of this paper is to propose a system-theoretic approach for identifying important nodes in temporal networks. In this direction, we first propose a generalization of the popular Katz centrality measure to the family of Markovian temporal networks using tools from the theory of Markov jump linear systems. We then show that Katz centrality in Markovian temporal networks can be efficiently computed using linear programming. Finally, we propose a convex program for optimizing the Katz centrality of a given node by tuning the weights of the temporal network in a cost-efficient manner. Numerical simulations illustrate the effectiveness of the obtained results.
Comparative Analysis of Routing Attacks in Ad Hoc Network
Bipul Syam Purkayastha
2012-03-01
Full Text Available In the mobile ad hoc networks the major role is played by the routing protocols in order to route the data from one mobile node to another mobile node. But in such mobile networks, routing protocols are vulnerable to various kinds of security attacks such as blackhole node attacks. The routing protocols of MANET are unprotected and hence resulted into the network with the malicious mobile nodes in the network. These malicious nodes in the network are basically acts as attacks in the network. In this paper, we modify the existing DSR protocol with the functionality of attacks detection without affecting overall performance of the network. Also, we are considering the various attacks on mobile ad hoc network called blackhole attack, flooding attack and show the comparative analysis of these attacks using network simulator ns-2.
Differential analysis for high density tiling microarray data
Kapranov Philipp
2007-09-01
Full Text Available Abstract Background High density oligonucleotide tiling arrays are an effective and powerful platform for conducting unbiased genome-wide studies. The ab initio probe selection method employed in tiling arrays is unbiased, and thus ensures consistent sampling across coding and non-coding regions of the genome. These arrays are being increasingly used to study the associated processes of transcription, transcription factor binding, chromatin structure and their association. Studies of differential expression and/or regulation provide critical insight into the mechanics of transcription and regulation that occurs during the developmental program of a cell. The time-course experiment, which comprises an in-vivo system and the proposed analyses, is used to determine if annotated and un-annotated portions of genome manifest coordinated differential response to the induced developmental program. Results We have proposed a novel approach, based on a piece-wise function – to analyze genome-wide differential response. This enables segmentation of the response based on protein-coding and non-coding regions; for genes the methodology also partitions differential response with a 5' versus 3' versus intra-genic bias. Conclusion The algorithm built upon the framework of Significance Analysis of Microarrays, uses a generalized logic to define regions/patterns of coordinated differential change. By not adhering to the gene-centric paradigm, discordant differential expression patterns between exons and introns have been identified at a FDR of less than 12 percent. A co-localization of differential binding between RNA Polymerase II and tetra-acetylated histone has been quantified at a p-value -13. The prototype R code has been made available as supplementary material [see Additional file 1]. Additional file 1 gsam_prototypercode.zip. File archive comprising of prototype R code for gSAM implementation including readme and examples. Click here for file
Differential analysis for high density tiling microarray data.
Ghosh, Srinka; Hirsch, Heather A; Sekinger, Edward A; Kapranov, Philipp; Struhl, Kevin; Gingeras, Thomas R
2007-09-24
High density oligonucleotide tiling arrays are an effective and powerful platform for conducting unbiased genome-wide studies. The ab initio probe selection method employed in tiling arrays is unbiased, and thus ensures consistent sampling across coding and non-coding regions of the genome. These arrays are being increasingly used to study the associated processes of transcription, transcription factor binding, chromatin structure and their association. Studies of differential expression and/or regulation provide critical insight into the mechanics of transcription and regulation that occurs during the developmental program of a cell. The time-course experiment, which comprises an in-vivo system and the proposed analyses, is used to determine if annotated and un-annotated portions of genome manifest coordinated differential response to the induced developmental program. We have proposed a novel approach, based on a piece-wise function - to analyze genome-wide differential response. This enables segmentation of the response based on protein-coding and non-coding regions; for genes the methodology also partitions differential response with a 5' versus 3' versus intra-genic bias. The algorithm built upon the framework of Significance Analysis of Microarrays, uses a generalized logic to define regions/patterns of coordinated differential change. By not adhering to the gene-centric paradigm, discordant differential expression patterns between exons and introns have been identified at a FDR of less than 12 percent. A co-localization of differential binding between RNA Polymerase II and tetra-acetylated histone has been quantified at a p-value < 0.003; it is most significant at the 5' end of genes, at a p-value < 10-13. The prototype R code has been made available as supplementary material [see Additional file 1].
Visual analysis and exploration of complex corporate shareholder networks
Tekušová, Tatiana; Kohlhammer, Jörn
2008-01-01
The analysis of large corporate shareholder network structures is an important task in corporate governance, in financing, and in financial investment domains. In a modern economy, large structures of cross-corporation, cross-border shareholder relationships exist, forming complex networks. These networks are often difficult to analyze with traditional approaches. An efficient visualization of the networks helps to reveal the interdependent shareholding formations and the controlling patterns. In this paper, we propose an effective visualization tool that supports the financial analyst in understanding complex shareholding networks. We develop an interactive visual analysis system by combining state-of-the-art visualization technologies with economic analysis methods. Our system is capable to reveal patterns in large corporate shareholder networks, allows the visual identification of the ultimate shareholders, and supports the visual analysis of integrated cash flow and control rights. We apply our system on an extensive real-world database of shareholder relationships, showing its usefulness for effective visual analysis.
Robustness Analysis of Real Network Topologies Under Multiple Failure Scenarios
Manzano, M.; Marzo, J. L.; Calle, E.
2012-01-01
on topological characteristics. Recently approaches also consider the services supported by such networks. In this paper we carry out a robustness analysis of five real backbone telecommunication networks under defined multiple failure scenarios, taking into account the consequences of the loss of established......Nowadays the ubiquity of telecommunication networks, which underpin and fulfill key aspects of modern day living, is taken for granted. Significant large-scale failures have occurred in the last years affecting telecommunication networks. Traditionally, network robustness analysis has been focused...
Method and tool for network vulnerability analysis
Swiler, Laura Painton; Phillips, Cynthia A.
2006-03-14
A computer system analysis tool and method that will allow for qualitative and quantitative assessment of security attributes and vulnerabilities in systems including computer networks. The invention is based on generation of attack graphs wherein each node represents a possible attack state and each edge represents a change in state caused by a single action taken by an attacker or unwitting assistant. Edges are weighted using metrics such as attacker effort, likelihood of attack success, or time to succeed. Generation of an attack graph is accomplished by matching information about attack requirements (specified in "attack templates") to information about computer system configuration (contained in a configuration file that can be updated to reflect system changes occurring during the course of an attack) and assumed attacker capabilities (reflected in "attacker profiles"). High risk attack paths, which correspond to those considered suited to application of attack countermeasures given limited resources for applying countermeasures, are identified by finding "epsilon optimal paths."
Polish and English wordnets -- statistical analysis of interconnected networks
Bujok, Maksymilian; Fronczak, Agata
2014-01-01
Wordnets are semantic networks containing nouns, verbs, adjectives, and adverbs organized according to linguistic principles, by means of semantic relations. In this work, we adopt a complex network perspective to perform a comparative analysis of the English and Polish wordnets. We determine their similarities and show that the networks exhibit some of the typical characteristics observed in other real-world networks. We analyse interlingual relations between both wordnets and deliberate over the problem of mapping the Polish lexicon onto the English one.
Analysis of Wideband Beamformers Designed with Artificial Neural Networks
1990-12-01
TECHNICAL REPORT 0-90-1 ANALYSIS OF WIDEBAND BEAMFORMERS DESIGNED WITH ARTIFICIAL NEURAL NETWORKS by Cary Cox Instrumentation Services Division...included. A briel tutorial on beamformers and neural networks is also provided. 14. SUBJECT TERMS 15, NUMBER OF PAGES Artificial neural networks Fecdforwa:,l...Beamformers Designed with Artificial Neural Networks ". The study was conducted under the general supervision of Messrs. George P. Bonner, Chief
Graph spectral analysis of protein interaction network evolution
Thorne, Thomas; Stumpf, Michael P. H.
2012-01-01
We present an analysis of protein interaction network data via the comparison of models of network evolution to the observed data. We take a Bayesian approach and perform posterior density estimation using an approximate Bayesian computation with sequential Monte Carlo method. Our approach allows us to perform model selection over a selection of potential network growth models. The methodology we apply uses a distance defined in terms of graph spectra which captures the network data more natu...
The Connectivity Analysis of Intermittent Connected Wireless Network
Li Yun; Zhou Yahui; Liu Qilie; Wang Xiaoying
2009-01-01
The connectivity is a basic and important characteristic to the network, it expresses the situation of link connectivity directly, and provides important reference for the entire network plan. Using statistics and probability Theory, this article emphasizes the probability between any two nodes in the network which nodes are equally distributed and the connectivity of whole network. At last, this article has made verification through simulation and has made out a conclusion, the simulation result agrees with theoretical analysis.
Power distribution and performance analysis for wireless communication networks
Zhao, Dongmei
2012-01-01
This book provides an analysis of transmission power and network performance in different wireless communication networks. It presents the latest research and techniques for power and interference control and performance modeling in wireless communication networks with different network topologies, air interfaces, and transmission techniques. While studying the power distributions and resource management, the reader will also learn basic methodology and skills for problem formulations, can ascertain the complexity for designing radio resource management strategies in modern wireless communicat
Complex Network Analysis of Brazilian Power Grid
Martins, Gabriela C; Ribeiro, Fabiano L; Forgerini, Fabricio L
2016-01-01
Power Grids and other delivery networks has been attracted some attention by the network literature last decades. Despite the Power Grids dynamics has been controlled by computer systems and human operators, the static features of this type of network can be studied and analyzed. The topology of the Brazilian Power Grid (BPG) was studied in this work. We obtained the spatial structure of the BPG from the ONS (electric systems national operator), consisting of high-voltage transmission lines, generating stations and substations. The local low-voltage substations and local power delivery as well the dynamic features of the network were neglected. We analyze the complex network of the BPG and identify the main topological information, such as the mean degree, the degree distribution, the network size and the clustering coefficient to caracterize the complex network. We also detected the critical locations on the network and, therefore, the more susceptible points to lead to a cascading failure and even to a blac...
Analysis of Network Security Spasms and Circumvention
Sangamithra A
2016-04-01
Full Text Available Network is one of the rapid growing technology in today’s world. Network is attached in our life from small things to large things. Everywhere network is spreaded, where people are surrounded by network. We can get more advantage in various fields by using the network. But at the same time there is a lot of things is there to attack the security of the network. In this paper we discuss about the main common attacks in the network , about the causes of the attack and how to recover from that. So this will be helpful for the researcher to come up with the best prevention of the attacks in network.
QoS Differentiated and Fair Packet Scheduling in Broadband Wireless Access Networks
Zhang Yan
2009-01-01
Full Text Available This paper studies the packet scheduling problem in Broadband Wireless Access (BWA networks. The key difficulties of the BWA scheduling problem lie in the high variability of wireless channel capacity and the unknown model of packet arrival process. It is difficult for traditional heuristic scheduling algorithms to handle the situation and guarantee satisfying performance in BWA networks. In this paper, we introduce learning-based approach for a better solution. Specifically, we formulate the packet scheduling problem as an average cost Semi-Markov Decision Process (SMDP. Then, we solve the SMDP by using reinforcement learning. A feature-based linear approximation and the Temporal-Difference learning technique are employed to produce a near optimal solution of the corresponding SMDP problem. The proposed algorithm, called Reinforcement Learning Scheduling (RLS, has in-built capability of self-training. It is able to adaptively and timely regulate its scheduling policy according to the instantaneous network conditions. Simulation results indicate that RLS outperforms two classical scheduling algorithms and simultaneously considers: (i effective QoS differentiation, (ii high bandwidth utilization, and (iii both short-term and long-term fairness.
Analysis of Municipal Pipe Network Franchise Institution
Yong, Sun; Haichuan, Tian; Feng, Xu; Huixia, Zhou
Franchise institution of municipal pipe network has some particularity due to the characteristic of itself. According to the exposition of Chinese municipal pipe network industry franchise institution, the article investigates the necessity of implementing municipal pipe network franchise institution in China, the role of government in the process and so on. And this offers support for the successful implementation of municipal pipe network franchise institution in China.
Transcriptome analysis of differentiating spermatogonia stimulated with kit ligand.
Rossi, Pellegrino; Lolicato, Francesca; Grimaldi, Paola; Dolci, Susanna; Di Sauro, Annarita; Filipponi, Doria; Geremia, Raffaele
2008-01-01
Kit ligand (KL) is a survival factor and a mitogenic stimulus for differentiating spermatogonia. However, it is not known whether KL also plays a role in the differentiative events that lead to meiotic entry of these cells. We performed a wide genome analysis of difference in gene expression induced by treatment with KL of spermatogonia from 7-day-old mice, using gene chips spanning the whole mouse genome. The analysis revealed that the pattern of RNA expression induced by KL is compatible with the qualitative changes of the cell cycle that occur during the subsequent cell divisions in type A and B spermatogonia, i.e. the progressive lengthening of the S phase and the shortening of the G2/M transition. Moreover, KL up-regulates in differentiating spermatogonia the expression of early meiotic genes (for instance: Lhx8, Nek1, Rnf141, Xrcc3, Tpo1, Tbca, Xrcc2, Mesp1, Phf7, Rtel1), whereas it down-regulates typical spermatogonial markers (for instance: Pole, Ptgs2, Zfpm2, Egr2, Egr3, Gsk3b, Hnrpa1, Fst, Ptch2). Since KL modifies the expression of several genes known to be up-regulated or down-regulated in spermatogonia during the transition from the mitotic to the meiotic cell cycle, these results are consistent with a role of the KL/kit interaction in the induction of their meiotic differentiation.
The Analysis of User Behaviour of a Network Management Training Tool using a Neural Network
Helen Donelan
2005-10-01
Full Text Available A novel method for the analysis and interpretation of data that describes the interaction between trainee network managers and a network management training tool is presented. A simulation based approach is currently being used to train network managers, through the use of a simulated network. The motivation is to provide a tool for exposing trainees to a life like situation without disrupting a live network. The data logged by this system describes the detailed interaction between trainee network manager and simulated network. The work presented here provides an analysis of this interaction data that enables an assessment of the capabilities of the trainee network manager as well as an understanding of how the network management tasks are being approached. A neural network architecture is implemented in order to perform an exploratory data analysis of the interaction data. The neural network employs a novel form of continuous self-organisation to discover key features in the data and thus provide new insights into the learning and teaching strategies employed.
A Social Network Analysis of Occupational Segregation
Buhai, Ioan Sebastian; van der Leij, Marco
We develop a social network model of occupational segregation between different social groups, generated by the existence of positive inbreeding bias among individuals from the same group. If network referrals are important for job search, then expected homophily in the contact network structure...
Analysis of neural networks through base functions
van der Zwaag, B.J.; Slump, Cornelis H.; Spaanenburg, L.
Problem statement. Despite their success-story, neural networks have one major disadvantage compared to other techniques: the inability to explain comprehensively how a trained neural network reaches its output; neural networks are not only (incorrectly) seen as a "magic tool" but possibly even more
Analysis of Neural Networks through Base Functions
Zwaag, van der B.J.; Slump, C.H.; Spaanenburg, L.
2002-01-01
Problem statement. Despite their success-story, neural networks have one major disadvantage compared to other techniques: the inability to explain comprehensively how a trained neural network reaches its output; neural networks are not only (incorrectly) seen as a "magic tool" but possibly even more
Daskalaki, Andriani; Jozefczuk, Justyna; Lehrach, Hans; Adjaye, James; Wierling, Christoph
2011-06-01
A more detailed understanding of the differentiation of human embryonic and induced pluripotent stem cells into hepatocyte-like cells can help to improve therapies for liver diseases, like steatohepatitis. In this work we used microarray-based expression data to analyze the in vitro differentiation of human embryonic stem cells into hepatocytes. Pathway analysis has been carried out on gene expression data of different stages of the differentiation process from embryonic stem cells into hepatocyte-like cells via definitive endoderm and hepatic endoderm. Based on pathway analysis we identified signaling pathways, like the GPCR signaling pathway as well as FOXA2 regulatory networks. Based on these highly enriched pathways we constructed a model prototype to better understand and study the differentiation of stem cells into hepatocytes.
Compensatory fuzzy logic for intelligent social network analysis
Maikel Y. Leyva-Vázquez
2014-10-01
Full Text Available Fuzzy graph theory has gained in visibility for social network analysis. In this work fuzzy logic and their role in modeling social relational networks is discussed. We present a proposal for extending the fuzzy logic framework to intelligent social network analysis using the good properties of robustness and interpretability of compensatory fuzzy logic. We apply this approach to the concept path importance taking into account the length and strength of the connection. Results obtained with our model are more consistent with the way human make decisions. Additionally a case study to illustrate the applicability of the proposal on a coauthorship network is developed. Our main outcome is a new model for social network analysis based on compensatory fuzzy logic that gives more robust results and allows compensation. Moreover this approach makes emphasis in using language for social network analysis.
Painlevé analysis for nonlinear partial differential equations
Musette, M
1998-01-01
The Painlevé analysis introduced by Weiss, Tabor and Carnevale (WTC) in 1983 for nonlinear partial differential equations (PDE's) is an extension of the method initiated by Painlevé and Gambier at the beginning of this century for the classification of algebraic nonlinear differential equations (ODE's) without movable critical points. In these lectures we explain the WTC method in its invariant version introduced by Conte in 1989 and its application to solitonic equations in order to find algorithmically their associated so-called ``integrable'' equations but they are generically no more valid for equations modelising physical phenomema. Belonging to this second class, some equations called ``partially integrable'' sometimes keep remnants of integrability. In that case, the singularity analysis may also be useful for building closed form analytic solutions, which necessarily % Conte agree with the singularity structure of the equations. We display the privileged role played by the Riccati equation and syste...
State of the art applications of social network analysis
Can, Fazli; Polat, Faruk
2014-01-01
Social network analysis increasingly bridges the discovery of patterns in diverse areas of study as more data becomes available and complex. Yet the construction of huge networks from large data often requires entirely different approaches for analysis including; graph theory, statistics, machine learning and data mining. This work covers frontier studies on social network analysis and mining from different perspectives such as social network sites, financial data, e-mails, forums, academic research funds, XML technology, blog content, community detection and clique finding, prediction of user
Network analysis and synthesis a modern systems theory approach
Anderson, Brian D O
2006-01-01
Geared toward upper-level undergraduates and graduate students, this book offers a comprehensive look at linear network analysis and synthesis. It explores state-space synthesis as well as analysis, employing modern systems theory to unite the classical concepts of network theory. The authors stress passive networks but include material on active networks. They avoid topology in dealing with analysis problems and discuss computational techniques. The concepts of controllability, observability, and degree are emphasized in reviewing the state-variable description of linear systems. Explorations
The classification and analysis of dynamic networks
Guo Jin-Li
2007-01-01
In this paper we, firstly, classify the complex networks in which the nodes are of the lifetime distribution. Secondly, in order to study complex networks in terms of queuing system and homogeneous Markov chain, we establish the relation between the complex networks and queuing system, providing a new way of studying complex networks. Thirdly, we prove that there exist stationary degree distributions of M-G-P network, and obtain the analytic expression of the distribution by means of Markov chain theory. We also obtain the average path length and clustering coefficient of the network. The results show that M-G-P network is not only scale-free but also of a small-world feature in proper conditions.
Statistical Network Analysis for Functional MRI: Mean Networks and Group Comparisons.
Cedric E Ginestet
2014-05-01
Full Text Available Comparing networks in neuroscience is hard, because the topological properties of a given network are necessarily dependent on the number of edges of that network. This problem arises in the analysis of both weighted and unweighted networks. The term density is often used in this context, in order to refer to the mean edge weight of a weighted network, or to the number of edges in an unweighted one. Comparing families of networks is therefore statistically difficult because differences in topology are necessarily associated with differences in density. In this review paper, we consider this problem from two different perspectives, which include (i the construction of summary networks, such as how to compute and visualize the mean network from a sample of network-valued data points; and (ii how to test for topological differences, when two families of networks also exhibit significant differences in density. In the first instance, we show that the issue of summarizing a family of networks can be conducted by either adopting a mass-univariate approach, which produces a statistical parametric network (SPN, or by directly computing the mean network, provided that a metric has been specified on the space of all networks with a given number of nodes. In the second part of this review, we then highlight the inherent problems associated with the comparison of topological functions of families of networks that differ in density. In particular, we show that a wide range of topological summaries, such as global efficiency and network modularity are highly sensitive to differences in density. Moreover, these problems are not restricted to unweighted metrics, as we demonstrate that the same issues remain present when considering the weighted versions of these metrics. We conclude by encouraging caution, when reporting such statistical comparisons, and by emphasizing the importance of constructing summary networks.
A Differential Abundance Analysis of HD219175 A and B
Hua-Wei Zhang; Gang Zhao
2005-01-01
The abundances of the wide binary pair HD 219175 A and B are determined and compared using a line-by-line differential analysis. No evidence for difference has been found in the abundances of Fe, O, Na, Mg, Al, Si, K, Ca, Sc,Ti, V, Cr, Mn, Ni, Cu and Ba. Our results support a physical relation between the two components of HD 219175.
An Analysis of Stopping Criteria in Artificial Neural Networks
1994-03-01
ARTIFICIAL NEURAL NETWORKS THESIS Bruce Kostal Captain, USAF AFIT/GST/ENS/94M 07 D I ELECTE APR...ANALYSIS OF STOPPING CRITERIA IN ARTIFICIAL NEURAL NETWORKS THESIS Bruce Kostal Captain, USAF AFIT/GST/ENS/94M-07 ETIC ELECTE 94-12275 APR2 1994 U Approved...for public release; distributi6 unlimited D94i •6 AFIT/GST/ENS/94M-07 AN ANALYSIS OF STOPPING CRITERIA IN ARTIFICIAL NEURAL NETWORKS
Regulatory network analysis of microRNAs and genes in imatinib-resistant chronic myeloid leukemia.
Soltani, Ismael; Gharbi, Hanen; Hassine, Islem Ben; Bouguerra, Ghada; Douzi, Kais; Teber, Mouheb; Abbes, Salem; Menif, Samia
2016-09-16
Targeted therapy in the form of selective breakpoint cluster region-abelson (BCR/ABL) tyrosine kinase inhibitor (imatinib mesylate) has successfully been introduced in the treatment of the chronic myeloid leukemia (CML). However, acquired resistance against imatinib mesylate (IM) has been reported in nearly half of patients and has been recognized as major issue in clinical practice. Multiple resistance genes and microRNAs (miRNAs) are thought to be involved in the IM resistance process. These resistance genes and miRNAs tend to interact with each other through a regulatory network. Therefore, it is crucial to study the impact of these interactions in the IM resistance process. The present study focused on miRNA and gene network analysis in order to elucidate the role of interacting elements and to understand their functional contribution in therapeutic failure. Unlike previous studies which were centered only on genes or miRNAs, the prime focus of the present study was on relationships. To this end, three regulatory networks including differentially expressed, related, and global networks were constructed and analyzed in search of similarities and differences. Regulatory associations between miRNAs and their target genes, transcription factors and miRNAs, as well as miRNAs and their host genes were also macroscopically investigated. Certain key pathways in the three networks, especially in the differentially expressed network, were featured. The differentially expressed network emerged as a fault map of IM-resistant CML. Theoretically, the IM resistance process could be prevented by correcting the included errors. The present network-based approach to study resistance miRNAs and genes might help in understanding the molecular mechanisms of IM resistance in CML as well as in the improvement of CML therapy.
Multifractality and Network Analysis of Phase Transition
Li, Wei; Yang, Chunbin; Han, Jihui; Su, Zhu; Zou, Yijiang
2017-01-01
Many models and real complex systems possess critical thresholds at which the systems shift dramatically from one sate to another. The discovery of early-warnings in the vicinity of critical points are of great importance to estimate how far the systems are away from the critical states. Multifractal Detrended Fluctuation analysis (MF-DFA) and visibility graph method have been employed to investigate the multifractal and geometrical properties of the magnetization time series of the two-dimensional Ising model. Multifractality of the time series near the critical point has been uncovered from the generalized Hurst exponents and singularity spectrum. Both long-term correlation and broad probability density function are identified to be the sources of multifractality. Heterogeneous nature of the networks constructed from magnetization time series have validated the fractal properties. Evolution of the topological quantities of the visibility graph, along with the variation of multifractality, serve as new early-warnings of phase transition. Those methods and results may provide new insights about the analysis of phase transition problems and can be used as early-warnings for a variety of complex systems. PMID:28107414
Service-Oriented Enterprise Network Performance Analysis
ZENG Sen; HUANG Shuangxi; FAN Yushun
2009-01-01
The service-oriented architecture (SOA) and the model-driven architecture (MDA) have been rec-ognized as major evolutionary steps in enterprise integration (El) in service-oriented computing environ-ments. Service-oriented enterprise (SOE) networks (SOEN) are emerging with the significant advances of El, SOA, and MDA. However, the implementation and optimization of SOEN is still lacking integrated SOA, MDA, and performance analysis and optimization (PAO) methods. This paper introduces an integrated solu-tion of SOA and MDA with a simulation-based three-stage PAO method with stage 1 being an analytic hier-archy process (AHP)-based comprehensive performance calculation for service matching and binding, stage 2 being a simulation-based comprehensive performance evaluation for business process/service composition, and stage 3 being a business process simulation-based performance optimization for SOEN. The SOE architecture, performance analysis framework, performance indicators, and performance opera-tors are discussed. The system uses MDA as the system development philosophy, SOA as the system im-plementation infrastructure, and the simulation-based PAO methods to analyze and optimize the SOEN performance. A case study of the SOEN illustrates the usage of the integrated solution.
Process Analysis in Container Shipping Network Structure Form Change
Chen Chao
2012-03-01
Full Text Available Being aimed at the influence of ship-size and cargo-demand changes on container shipping networks, to reveal the evolution process of container shipping networks structure form, this paper respectively designed the operation models for two major container shipping networks structure forms: Multi-port-calling network and Hub-and-spoke network, to maximizing the investment efficiency. Based on the above models, a comprehensively integrated operation model of container shipping networks is built and the evolution process of container shipping networks structure form with changing of both ship-size and cargo demands is analyzed. Finally, through a case study, results show that the comprehensive integrated operation model is very effective in the analysis of evolution process of container shipping networks structure forms.
Centrality measures in temporal networks with time series analysis
Huang, Qiangjuan; Zhao, Chengli; Zhang, Xue; Wang, Xiaojie; Yi, Dongyun
2017-05-01
The study of identifying important nodes in networks has a wide application in different fields. However, the current researches are mostly based on static or aggregated networks. Recently, the increasing attention to networks with time-varying structure promotes the study of node centrality in temporal networks. In this paper, we define a supra-evolution matrix to depict the temporal network structure. With using of the time series analysis, the relationships between different time layers can be learned automatically. Based on the special form of the supra-evolution matrix, the eigenvector centrality calculating problem is turned into the calculation of eigenvectors of several low-dimensional matrices through iteration, which effectively reduces the computational complexity. Experiments are carried out on two real-world temporal networks, Enron email communication network and DBLP co-authorship network, the results of which show that our method is more efficient at discovering the important nodes than the common aggregating method.
Reliability Analysis of Wireless Sensor Networks Using Markovian Model
Jin Zhu
2012-01-01
Full Text Available This paper investigates reliability analysis of wireless sensor networks whose topology is switching among possible connections which are governed by a Markovian chain. We give the quantized relations between network topology, data acquisition rate, nodes' calculation ability, and network reliability. By applying Lyapunov method, sufficient conditions of network reliability are proposed for such topology switching networks with constant or varying data acquisition rate. With the conditions satisfied, the quantity of data transported over wireless network node will not exceed node capacity such that reliability is ensured. Our theoretical work helps to provide a deeper understanding of real-world wireless sensor networks, which may find its application in the fields of network design and topology control.
Pareto distance for multi-layer network analysis
Magnani, Matteo; Rossi, Luca
2013-01-01
Social Network Analysis has been historically applied to single networks, e.g., interaction networks between co-workers. However, the advent of on-line social network sites has emphasized the stratified structure of our social experience. Individuals usually spread their identities over multiple...... services, e.g., Facebook, Twitter, LinkedIn and Foursquare. As a result, the analysis of on-line social networks requires a wider scope and, more technically speaking, models for the representation of this fragmented scenario. The recent introduction of more realistic layered models has however determined...... new research problems related to the extension of traditional single-layer network measures. In this paper we take a step forward over existing approaches by defining a new concept of geodesic distance that includes heterogeneous networks and connections with very limited assumptions regarding...
Optimization of Survivability Analysis for Large-Scale Engineering Networks
Poroseva, S V
2012-01-01
Engineering networks fall into the category of large-scale networks with heterogeneous nodes such as sources and sinks. The survivability analysis of such networks requires the analysis of the connectivity of the network components for every possible combination of faults to determine a network response to each combination of faults. From the computational complexity point of view, the problem belongs to the class of exponential time problems at least. Partially, the problem complexity can be reduced by mapping the initial topology of a complex large-scale network with multiple sources and multiple sinks onto a set of smaller sub-topologies with multiple sources and a single sink connected to the network of sources by a single link. In this paper, the mapping procedure is applied to the Florida power grid.
Aydoğdu, Eylem; Katchy, Anne; Tsouko, Efrosini; Lin, Chin-Yo; Haldosén, Lars-Arne; Helguero, Luisa; Williams, Cecilia
2012-08-01
MicroRNAs (miRNAs) play pivotal roles in stem cell biology, differentiation and oncogenesis and are of high interest as potential breast cancer therapeutics. However, their expression and function during normal mammary differentiation and in breast cancer remain to be elucidated. In order to identify which miRNAs are involved in mammary differentiation, we thoroughly investigated miRNA expression during functional differentiation of undifferentiated, stem cell-like, murine mammary cells using two different large-scale approaches followed by qPCR. Significant changes in expression of 21 miRNAs were observed in repeated rounds of mammary cell differentiation. The majority, including the miR-200 family and known tumor suppressor miRNAs, was upregulated during differentiation. Only four miRNAs, including oncomiR miR-17, were downregulated. Pathway analysis indicated complex interactions between regulated miRNA clusters and major pathways involved in differentiation, proliferation and stem cell maintenance. Comparisons with human breast cancer tumors showed the gene profile from the undifferentiated, stem-like stage clustered with that of poor-prognosis breast cancer. A common nominator in these groups was the E2F pathway, which was overrepresented among genes targeted by the differentiation-induced miRNAs. A subset of miRNAs could further discriminate between human non-cancer and breast cancer cell lines, and miR-200a/miR-200b, miR-146b and miR-148a were specifically downregulated in triple-negative breast cancer cells. We show that miR-200a/miR-200b can inhibit epithelial-mesenchymal transition (EMT)-characteristic morphological changes in undifferentiated, non-tumorigenic mammary cells. Our studies propose EphA2 as a novel and important target gene for miR-200a. In conclusion, we present evidentiary data on how miRNAs are involved in mammary cell differentiation and indicate their related roles in breast cancer.
Exploratory social network analysis with Pajek. - 2nd ed.
de Nooy, W.; Mrvar, A.; Batagelj, V.
2011-01-01
This is an extensively revised and expanded second edition of the successful textbook on social network analysis integrating theory, applications, and network analysis using Pajek. The main structural concepts and their applications in social research are introduced with exercises. Pajek software an
Fluid Analysis of Network Content Dissemination and Cloud Systems
2017-03-06
Final 3. DATES COVERED (From - To) 01 Sep 2015 to 30 Nov 2016 4. TITLE AND SUBTITLE Fluid analysis of network content dissemination and cloud systems...2015 to 30 Nov 2016 AFOSR GRANT NUMBER: FA9550-15-1-0183 TITLE: Fluid analysis of network content dissemination and cloud systems PI: Fernando
Stability Analysis of Transportation Networks with Multiscale Driver Decisions
Como, Giacomo; Acemoglu, Daron; Dahleh, Munther A; Frazzoli, Emilio
2011-01-01
Stability of Wardrop equilibria is analyzed for dynamical transportation networks in which the drivers' route choices are influenced by information at multiple temporal and spatial scales. The considered model involves a continuum of indistinguishable drivers commuting between a common origin/destination pair in an acyclic transportation network. The drivers' route choices are affected by their, relatively infrequent, perturbed best responses to global information about the current network congestion levels, as well as their instantaneous local observation of the immediate surroundings as they transit through the network. A novel model is proposed for the drivers' route choice behavior, exhibiting local consistency with their preference toward globally less congested paths as well as myopic decisions in favor of locally less congested paths. The simultaneous evolution of the traffic congestion on the network and of the aggregate path preference is modeled by a system of coupled ordinary differential equations...
Bifurcation Analysis of Equilibria in Competitive Logistic Networks with Adaptation
Raimondi, A.; Tebaldi, C.
2008-04-01
A general n-node network is considered for which, in absence of interactions, each node is governed by a logistic equation. Interactions among the nodes take place in the form of competition, which also includes adaptive abilities through a (short term) memory effect. As a consequence the dynamics of the network is governed by a system of n2 nonlinear ordinary differential equations. As a first step, equilibria and their stability are investigated analytically for the general network in dependence of the relevant parameters, namely the strength of competition, the adaptation rate and the network size. The existence of classes of invariant subspaces, related to symmetries, allows the introduction of a reduced model, four dimensional, where n appears as a parameter, which give full account of existence and stability for the equilibria in the network.
Moriyama, Tetsuji; Tanaka, Shu; Nakayama, Yasumune; Fukumoto, Masahiro; Tsujimura, Kenji; Yamada, Kohji; Bamba, Takeshi; Yoneda, Yoshihiro; Fukusaki, Eiichiro; Oka, Masahiro
2016-01-01
Transaldolase 1 (TALDO1) is a rate-limiting enzyme involved in the pentose phosphate pathway, which is traditionally thought to occur in the cytoplasm. In this study, we found that the gene TALDO1 has two translational initiation sites, generating two isoforms that differ by the presence of the first 10 N-terminal amino acids. Notably, the long and short isoforms were differentially localised to the cell nucleus and cytoplasm, respectively. Pull-down and in vitro transport assays showed that the long isoform, unlike the short one, binds to importin α and is actively transported into the nucleus in an importin α/β-dependent manner, demonstrating that the 10 N-terminal amino acids are essential for its nuclear localisation. Additionally, we found that these two isoforms can form homo- and/or hetero-dimers with different localisation dynamics. A metabolite analysis revealed that the subcellular localisation of TALDO1 is not crucial for its activity in the pentose phosphate pathway. However, the expression of these two isoforms differentially affected the levels of various metabolites, including components of the tricarboxylic acid cycle, nucleotides, and sugars. These results demonstrate that the nucleocytoplasmic distribution of TALDO1, modulated via alternative translational initiation and dimer formation, plays an important role in a wide range of metabolic networks. PMID:27703206
G. Shangytbayeva
2015-08-01
Full Text Available This study discusses the problem distributed network attacks, formalized of model of linear kind for differentiate distributed network attacks on the basis of a weight coefficients Structured the formalized mathematical models allow to consider structure of the On network to a basis big percent, a measure of influence of each type of attack that gives the fine chance effectively to design to protect information system taking into account information on threats. Based on classification of information threats, characteristic for distributed network attacks it is offered the formalized models of a linear look for differentiation of attacks on the basis of a method of weight coefficients. By these indicators and coefficients it is possible to define the main types of threats in computer systems allowing to design effectively systems of information security taking into account information threats.
Applying temporal network analysis to the venture capital market
Zhang, Xin; Feng, Ling; Zhu, Rongqian; Stanley, H. Eugene
2015-10-01
Using complex network theory to study the investment relationships of venture capital firms has produced a number of significant results. However, previous studies have often neglected the temporal properties of those relationships, which in real-world scenarios play a pivotal role. Here we examine the time-evolving dynamics of venture capital investment in China by constructing temporal networks to represent (i) investment relationships between venture capital firms and portfolio companies and (ii) the syndication ties between venture capital investors. The evolution of the networks exhibits rich variations in centrality, connectivity and local topology. We demonstrate that a temporal network approach provides a dynamic and comprehensive analysis of real-world networks.
SNAP: A General Purpose Network Analysis and Graph Mining Library
Leskovec, Jure
2016-01-01
Large networks are becoming a widely used abstraction for studying complex systems in a broad set of disciplines, ranging from social network analysis to molecular biology and neuroscience. Despite an increasing need to analyze and manipulate large networks, only a limited number of tools are available for this task. Here, we describe Stanford Network Analysis Platform (SNAP), a general-purpose, high-performance system that provides easy to use, high-level operations for analysis and manipulation of large networks. We present SNAP functionality, describe its implementational details, and give performance benchmarks. SNAP has been developed for single big-memory machines and it balances the trade-off between maximum performance, compact in-memory graph representation, and the ability to handle dynamic graphs where nodes and edges are being added or removed over time. SNAP can process massive networks with hundreds of millions of nodes and billions of edges. SNAP offers over 140 different graph algorithms that ...
Vulnerability Analysis for Complex Networks Using Aggressive Abstraction
Colbaugh, Richard
2010-01-01
Large, complex networks are ubiquitous in nature and society, and there is great interest in developing rigorous, scalable methods for identifying and characterizing their vulnerabilities. This paper presents an approach for analyzing the dynamics of complex networks in which the network of interest is first abstracted to a much simpler, but mathematically equivalent, representation, the required analysis is performed on the abstraction, and analytic conclusions are then mapped back to the original network and interpreted there. We begin by identifying a broad and important class of complex networks which admit vulnerability-preserving, finite state abstractions, and develop efficient algorithms for computing these abstractions. We then propose a vulnerability analysis methodology which combines these finite state abstractions with formal analytics from theoretical computer science to yield a comprehensive vulnerability analysis process for networks of realworld scale and complexity. The potential of the prop...
Random matrix analysis of complex networks.
Jalan, Sarika; Bandyopadhyay, Jayendra N
2007-10-01
We study complex networks under random matrix theory (RMT) framework. Using nearest-neighbor and next-nearest-neighbor spacing distributions we analyze the eigenvalues of the adjacency matrix of various model networks, namely, random, scale-free, and small-world networks. These distributions follow the Gaussian orthogonal ensemble statistic of RMT. To probe long-range correlations in the eigenvalues we study spectral rigidity via the Delta_{3} statistic of RMT as well. It follows RMT prediction of linear behavior in semilogarithmic scale with the slope being approximately 1pi;{2} . Random and scale-free networks follow RMT prediction for very large scale. A small-world network follows it for sufficiently large scale, but much less than the random and scale-free networks.
Ying Lu
2013-12-01
Full Text Available To study the problem about the optimal combination of postponement operations in the context of supply chain network, a decision-making model is developed. In the model two kinds of product differentiations and three types of postponement operations are considered. Then a procedure for solving this model is proposed. By using a numerical example, we analyze the effect of cost structure on the decision about postponement operations. The results show that if customizing cost is high, no customizing operation should be adopted, if the subassembly-related costs are large assembling directly the components is more sensible, as well as if lead time is tight and penalty cost is high, only manufacturing postponement should be adopted
Optimal Location and Sizing of UPQC in Distribution Networks Using Differential Evolution Algorithm
Seyed Abbas Taher
2012-01-01
Full Text Available Differential evolution (DE algorithm is used to determine optimal location of unified power quality conditioner (UPQC considering its size in the radial distribution systems. The problem is formulated to find the optimum location of UPQC based on an objective function (OF defined for improving of voltage and current profiles, reducing power loss and minimizing the investment costs considering the OF's weighting factors. Hence, a steady-state model of UPQC is derived to set in forward/backward sweep load flow. Studies are performed on two IEEE 33-bus and 69-bus standard distribution networks. Accuracy was evaluated by reapplying the procedures using both genetic (GA and immune algorithms (IA. Comparative results indicate that DE is capable of offering a nearer global optimal in minimizing the OF and reaching all the desired conditions than GA and IA.
Social Network Analysis in Frontier Capital Markets
2012-06-01
2011. [SG11] Joseph E Stiglitz and Mauro Gallegati. Heterogeneous interacting agent models for understanding monetary economies. Eastern Economic...incorporate the network of interacting individuals, the structure of their interactions, and the consequences of network activity [Kir10]. Stiglitz and...as financial capital. As Stiglitz and Gallegati [SG11] note, “Some network designs may be good at absorbing small shocks, when there can be systemic
Bi, Dongbin; Ning, Hao; Liu, Shuai; Que, Xinxiang; Ding, Kejia
2015-06-01
To explore molecular mechanisms of bladder cancer (BC), network strategy was used to find biomarkers for early detection and diagnosis. The differentially expressed genes (DEGs) between bladder carcinoma patients and normal subjects were screened using empirical Bayes method of the linear models for microarray data package. Co-expression networks were constructed by differentially co-expressed genes and links. Regulatory impact factors (RIF) metric was used to identify critical transcription factors (TFs). The protein-protein interaction (PPI) networks were constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and clusters were obtained through molecular complex detection (MCODE) algorithm. Centralities analyses for complex networks were performed based on degree, stress and betweenness. Enrichment analyses were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Co-expression networks and TFs (based on expression data of global DEGs and DEGs in different stages and grades) were identified. Hub genes of complex networks, such as UBE2C, ACTA2, FABP4, CKS2, FN1 and TOP2A, were also obtained according to analysis of degree. In gene enrichment analyses of global DEGs, cell adhesion, proteinaceous extracellular matrix and extracellular matrix structural constituent were top three GO terms. ECM-receptor interaction, focal adhesion, and cell cycle were significant pathways. Our results provide some potential underlying biomarkers of BC. However, further validation is required and deep studies are needed to elucidate the pathogenesis of BC. Copyright © 2015 Elsevier Ltd. All rights reserved.
Differential Proteomic Analysis of Carbon Ion Radiation in Sheep Sperm
HE Yu-xuan; LI Hong-yan; ZHANG Yong; HE Jian-hua; ZHANG Hong; ZHAO Xing-xu
2013-01-01
This study is first to investigate proteomic changes in sheep sperm induced by carbon ion radiation using two-dimensional electrophoresis (2-DE) analysis in the project of breeding a new variety of sheep. Differential expression proteins were detected using the PDQuest 8.0 software after staining with Coomassie blue. Valid spots were then analyzed through liquid chromatography tandem mass spectrometry (LC-MS/MS). Among the 480 total protein spots displayed in 2-D gels, 6 specific protein spots were observed in sperm gels. A search against protein sequences in the National Center for Biotechnology Information databases (NCBI) indicated that differentially expressed proteins correspond to two proteins, identified to be enolase and transcription factor AP-2-alpha (TFAP-2α). The two proteins were up-regulated in the irradiated sperm. To the best of our knowledge, this study is the first to identify proteomic changes induced by carbon ion radiation in sheep sperm. The analysis of differential expression protein may be useful in identifying new breeding markers in sheep reproduction and in clarifying the mechanisms involved in irradiation or space breeding.
Circadian phase has profound effects on differential expression analysis.
Polly Yingshan Hsu
Full Text Available Circadian rhythms are physiological and behavioral cycles with a period of approximately 24 hours that are generated by an endogenous clock, or oscillator. Found in diverse organisms, they are precisely controlled and provide growth and fitness benefits. Numerous microarray studies examining circadian control of gene expression have reported that a substantial fraction of the genomes of many organisms is clock-controlled. Here we show that a long-period mutant in Arabidopsis, rve8-1, has a global alteration in phase of all clock-controlled genes. After several days in constant environmental conditions, at which point the mutant and control plants have very different circadian phases, we found 1557 genes to be differentially expressed in rve8-1, almost all of which are clock-regulated. However, after adjusting for this phase difference, only a handful show overall expression level differences between rve8-1 and wild type. Thus the apparent differential expression is mainly due to the phase difference between these two genotypes. These findings prompted us to examine the effect of phase on gene expression within a single genotype. Using samples of wild-type plants harvested at thirty-minute intervals, we demonstrated that even this small difference in circadian phase significantly influences the results of differential expression analysis. Our study demonstrates the robust influence of the circadian clock on the transcriptome and provides a cautionary note for all biologists performing genome-level expression analysis.
Mascia, Daniele; Cicchetti, Americo; Damiani, Gianfranco
2013-10-22
Extant research suggests that there is a strong social component to Evidence-Based Medicine (EBM) adoption since professional networks amongst physicians are strongly associated with their attitudes towards EBM. Despite this evidence, it is still unknown whether individual attitudes to use scientific evidence in clinical decision-making influence the position that physicians hold in their professional network. This paper explores how physicians' attitudes towards EBM is related to the network position they occupy within healthcare organizations. Data pertain to a sample of Italian physicians, whose professional network relationships, demographics and work-profile characteristics were collected. A social network analysis was performed to capture the structural importance of physicians in the collaboration network by the means of a core-periphery analysis and the computation of network centrality indicators. Then, regression analysis was used to test the association between the network position of individual clinicians and their attitudes towards EBM. Findings documented that the overall network structure is made up of a dense cohesive core of physicians and of less connected clinicians who occupy the periphery. A negative association between the physicians' attitudes towards EBM and the coreness they exhibited in the professional network was also found. Network centrality indicators confirmed these results documenting a negative association between physicians' propensity to use EBM and their structural importance in the professional network. Attitudes that physicians show towards EBM are related to the part (core or periphery) of the professional networks to which they belong as well as to their structural importance. By identifying virtuous attitudes and behaviors of professionals within their organizations, policymakers and executives may avoid marginalization and stimulate integration and continuity of care, both within and across the boundaries of healthcare
Mathematics of small stochastic reaction networks: a boundary layer theory for eigenstate analysis.
Mjolsness, Eric; Prasad, Upendra
2013-03-14
We study and analyze the stochastic dynamics of a reversible bimolecular reaction A + B ↔ C called the "trivalent reaction." This reaction is of a fundamental nature and is part of many biochemical reaction networks. The stochastic dynamics is given by the stochastic master equation, which is difficult to solve except when the equilibrium state solution is desired. We present a novel way of finding the eigenstates of this system of difference-differential equations, using perturbation analysis of ordinary differential equations arising from approximation of the difference equations. The time evolution of the state probabilities can then be expressed in terms of the eigenvalues and the eigenvectors.
Teacher knowledge of error analysis in differential calculus
Eunice K. Moru
2014-12-01
Full Text Available The study investigated teacher knowledge of error analysis in differential calculus. Two teachers were the sample of the study: one a subject specialist and the other a mathematics education specialist. Questionnaires and interviews were used for data collection. The findings of the study reflect that the teachers’ knowledge of error analysis was characterised by the following assertions, which are backed up with some evidence: (1 teachers identified the errors correctly, (2 the generalised error identification resulted in opaque analysis, (3 some of the identified errors were not interpreted from multiple perspectives, (4 teachers’ evaluation of errors was either local or global and (5 in remedying errors accuracy and efficiency were emphasised more than conceptual understanding. The implications of the findings of the study for teaching include engaging in error analysis continuously as this is one way of improving knowledge for teaching.
Topology design and performance analysis of an integrated communication network
Li, V. O. K.; Lam, Y. F.; Hou, T. C.; Yuen, J. H.
1985-01-01
A research study on the topology design and performance analysis for the Space Station Information System (SSIS) network is conducted. It is begun with a survey of existing research efforts in network topology design. Then a new approach for topology design is presented. It uses an efficient algorithm to generate candidate network designs (consisting of subsets of the set of all network components) in increasing order of their total costs, and checks each design to see if it forms an acceptable network. This technique gives the true cost-optimal network, and is particularly useful when the network has many constraints and not too many components. The algorithm for generating subsets is described in detail, and various aspects of the overall design procedure are discussed. Two more efficient versions of this algorithm (applicable in specific situations) are also given. Next, two important aspects of network performance analysis: network reliability and message delays are discussed. A new model is introduced to study the reliability of a network with dependent failures. For message delays, a collection of formulas from existing research results is given to compute or estimate the delays of messages in a communication network without making the independence assumption. The design algorithm coded in PASCAL is included as an appendix.
Stochastic analysis of Chemical Reaction Networks using Linear Noise Approximation.
Cardelli, Luca; Kwiatkowska, Marta; Laurenti, Luca
2016-11-01
Stochastic evolution of Chemical Reactions Networks (CRNs) over time is usually analyzed through solving the Chemical Master Equation (CME) or performing extensive simulations. Analysing stochasticity is often needed, particularly when some molecules occur in low numbers. Unfortunately, both approaches become infeasible if the system is complex and/or it cannot be ensured that initial populations are small. We develop a probabilistic logic for CRNs that enables stochastic analysis of the evolution of populations of molecular species. We present an approximate model checking algorithm based on the Linear Noise Approximation (LNA) of the CME, whose computational complexity is independent of the population size of each species and polynomial in the number of different species. The algorithm requires the solution of first order polynomial differential equations. We prove that our approach is valid for any CRN close enough to the thermodynamical limit. However, we show on four case studies that it can still provide good approximation even for low molecule counts. Our approach enables rigorous analysis of CRNs that are not analyzable by solving the CME, but are far from the deterministic limit. Moreover, it can be used for a fast approximate stochastic characterization of a CRN.
egoSlider: Visual Analysis of Egocentric Network Evolution.
Wu, Yanhong; Pitipornvivat, Naveen; Zhao, Jian; Yang, Sixiao; Huang, Guowei; Qu, Huamin
2016-01-01
Ego-network, which represents relationships between a specific individual, i.e., the ego, and people connected to it, i.e., alters, is a critical target to study in social network analysis. Evolutionary patterns of ego-networks along time provide huge insights to many domains such as sociology, anthropology, and psychology. However, the analysis of dynamic ego-networks remains challenging due to its complicated time-varying graph structures, for example: alters come and leave, ties grow stronger and fade away, and alter communities merge and split. Most of the existing dynamic graph visualization techniques mainly focus on topological changes of the entire network, which is not adequate for egocentric analytical tasks. In this paper, we present egoSlider, a visual analysis system for exploring and comparing dynamic ego-networks. egoSlider provides a holistic picture of the data through multiple interactively coordinated views, revealing ego-network evolutionary patterns at three different layers: a macroscopic level for summarizing the entire ego-network data, a mesoscopic level for overviewing specific individuals' ego-network evolutions, and a microscopic level for displaying detailed temporal information of egos and their alters. We demonstrate the effectiveness of egoSlider with a usage scenario with the DBLP publication records. Also, a controlled user study indicates that in general egoSlider outperforms a baseline visualization of dynamic networks for completing egocentric analytical tasks.
PC analysis of stochastic differential equations driven by Wiener noise
Le Maitre, Olivier
2015-03-01
A polynomial chaos (PC) analysis with stochastic expansion coefficients is proposed for stochastic differential equations driven by additive or multiplicative Wiener noise. It is shown that for this setting, a Galerkin formalism naturally leads to the definition of a hierarchy of stochastic differential equations governing the evolution of the PC modes. Under the mild assumption that the Wiener and uncertain parameters can be treated as independent random variables, it is also shown that the Galerkin formalism naturally separates parametric uncertainty and stochastic forcing dependences. This enables us to perform an orthogonal decomposition of the process variance, and consequently identify contributions arising from the uncertainty in parameters, the stochastic forcing, and a coupled term. Insight gained from this decomposition is illustrated in light of implementation to simplified linear and non-linear problems; the case of a stochastic bifurcation is also considered.
Quantum Analysis - Non-Commutative Differential and Integral Calculi
Suzuki, Masuo
1997-01-01
A new scheme of quantum analysis, namely a non-commutative calculus of operator derivatives and integrals is introduced. This treats differentiation of an operator-valued function with respect to the relevant operator in a Banach space. In this new scheme, operator derivatives are expressed in terms of the relevant operator and its inner derivation explicitly. Derivatives of hyperoperators are also defined. Some possible applications of the present calculus to quantum statistical physics are briefly discussed. Acknowledgements The author would like to thank Professor H. Araki, Professor K. Aomoto, Professor H. Hiai, Professor N. Obata and Dr. R.I. McLachlan for useful comments. Added in proof. Recently it has been proven that the quantum derivatives {dn f(A)/ dAn} are invariant for any choice of definitions of the differential df(A) satisfying the Leibniz rule and the linearity (M. Suzuki, J. Math. Phys.).->
Dynamical Networks for Smog Pattern Analysis
Zong, Linqi; Zhu, Jia
2015-01-01
Smog, as a form of air pollution, poses as a serious problem to the environment, health, and economy of the world[1-4] . Previous studies on smog mostly focused on the components and the effects of smog [5-10]. However, as the smog happens with increased frequency and duration, the smog pattern which is critical for smog forecast and control, is rarely investigated, mainly due to the complexity of the components, the causes, and the spreading processes of smog. Here we report the first analysis on smog pattern applying the model of dynamical networks with spontaneous recovery. We show that many phenomena such as the sudden outbreak and dissipation of smog and the long duration smog can be revealed with the mathematical mechanism under a random walk simulation. We present real-world air quality index data in accord with the predictions of the model. Also we found that compared to external causes such as pollution spreading from nearby, internal causes such as industrial pollution and vehicle emission generated...
Gao, Leitao; Zhao, Guangshe; Li, Guoqi; Yang, Zhaoxu
2017-03-01
The leader selection problem refers to determining a predefined number of agents as leaders in order to minimize the mean-square deviation from consensus in stochastically forced networks. The original leader selection problem is formulated as a non-convex optimization problem where matrix variables are involved. By relaxing the constraints, a convex optimization model can be obtained. By introducing a chain rule of matrix differentiation, we can obtain the gradient of the cost function which consists matrix variables. We develop a "revisited projected gradient method" (RPGM) and a "probabilistic projected gradient method" (PPGM) to solve the two formulated convex and non-convex optimization problems, respectively. The convergence property of both methods is established. For convex optimization model, the global optimal solution can be achieved by RPGM, while for the original non-convex optimization model, a suboptimal solution is achieved by PPGM. Simulation results ranging from the synthetic to real-life networks are provided to show the effectiveness of RPGM and PPGM. This works will deepen the understanding of leader selection problems and enable applications in various real-life distributed control problems.
Eldaief, Mark C; Deckersbach, Thilo; Carlson, Lindsay E; Beucke, Jan C; Dougherty, Darin D
2012-04-01
The brain's default network (DN) is comprised of several cortical regions demonstrating robust intrinsic connectivity at rest. The authors sought to examine the differential effects of emotional reasoning and reasoning under certainty upon the DN through the employment of an event-related fMRI design in healthy participants. Participants were presented with syllogistic arguments which were organized into a 2 × 2 factorial design in which the first factor was emotional salience and the second factor was certainty/uncertainty. We demonstrate that regions of the DN were activated both during reasoning that is emotionally salient and during reasoning which is more certain, suggesting that these processes are neurally instantiated on a network level. In addition, we present evidence that emotional reasoning preferentially activates the dorsomedial (dMPFC) subsystem of the DN, whereas reasoning in the context of certainty activates areas specific to the DN's medial temporal (MTL) subsystem. We postulate that emotional reasoning mobilizes the dMPFC subsystem of the DN because this type of reasoning relies upon the recruitment of introspective and self-relevant data such as personal bias and temperament. In contrast, activation of the MTL subsystem during certainty argues that this form of reasoning involves the recruitment of mnemonic and semantic associations to derive conclusions.
Yu, Wei-Ming; Appler, Jessica M; Kim, Ye-Hyun; Nishitani, Allison M; Holt, Jeffrey R; Goodrich, Lisa V
2013-12-10
Information flow through neural circuits is determined by the nature of the synapses linking the subtypes of neurons. How neurons acquire features distinct to each synapse remains unknown. We show that the transcription factor Mafb drives the formation of auditory ribbon synapses, which are specialized for rapid transmission from hair cells to spiral ganglion neurons (SGNs). Mafb acts in SGNs to drive differentiation of the large postsynaptic density (PSD) characteristic of the ribbon synapse. In Mafb mutant mice, SGNs fail to develop normal PSDs, leading to reduced synapse number and impaired auditory responses. Conversely, increased Mafb accelerates synaptogenesis. Moreover, Mafb is responsible for executing one branch of the SGN differentiation program orchestrated by the Gata3 transcriptional network. Remarkably, restoration of Mafb rescues the synapse defect in Gata3 mutants. Hence, Mafb is a powerful regulator of cell-type specific features of auditory synaptogenesis that offers a new entry point for treating hearing loss. DOI: http://dx.doi.org/10.7554/eLife.01341.001.
Design and analysis of the satellite laser communications network
Ren, Pei-an; Qian, Fengchen; Liu, Qiang; Jin, Linlin
2015-02-01
A satellite laser communications network structure with two layers and multiple domains has been proposed, which performance has been simulated by OPENT. To simulation, we design several OPNET models of the network's components based on a satellite constellation with two layers and multiple domains, as network model, node model, MAC layer protocol and optical antenna model. The network model consists of core layer and access layer. The core network consists of four geostationary orbit (GEO) satellites which are uniformly distributed in the geostationary orbit. The access network consists of 6 low Earth orbit (LEO) satellites which is the walker delta (walk-δ) constellation with three orbit planes. In access layer, each plane has two satellites, and the constellation is stably. The satellite constellation presented for space laser network can meet the demand of coverage in the middle and low latitude by a few satellites. Also several terminal device models such as the space laser transmitter, receiver, protocol layer module and optical antenna have been designed according to the inter-satellite links in different orbits t from GEO to LEO or GEO to ground. The influence to network of different transmitting throughput, receiving throughput, network protocol and average time delay are simulated. Simulation results of network coverage, connectivity and traffic load performance in different scenes show that the satellite laser network presented by the paper can be fit for high-speed satellite communications. Such analysis can provide effective reference for the research of satellite laser networking and communication protocol.
McLinden, Daniel
2013-02-01
Concept mapping is a method that creates a visual representation that illustrates the thoughts, ideas, or planned actions that arise from a group of stakeholders on a particular issue. Social network analysis is a method that likewise creates a visual representation of data; a network map typically represents people and the connections, or lack thereof, between these people regarding a particular issue. While the goals of these two methods differ, the underlying data structures are similar; a network of relationships between data elements. Social network analysis is explored here as a supplement to concept mapping. A secondary analysis of a concept map to define to leadership needs was conducted using social network analysis. The methods and the implications for supplementing the analysis of concept maps and debriefing results with stakeholders are discussed.
Graphical tools for network meta-analysis in STATA.
Anna Chaimani
Full Text Available Network meta-analysis synthesizes direct and indirect evidence in a network of trials that compare multiple interventions and has the potential to rank the competing treatments according to the studied outcome. Despite its usefulness network meta-analysis is often criticized for its complexity and for being accessible only to researchers with strong statistical and computational skills. The evaluation of the underlying model assumptions, the statistical technicalities and presentation of the results in a concise and understandable way are all challenging aspects in the network meta-analysis methodology. In this paper we aim to make the methodology accessible to non-statisticians by presenting and explaining a series of graphical tools via worked examples. To this end, we provide a set of STATA routines that can be easily employed to present the evidence base, evaluate the assumptions, fit the network meta-analysis model and interpret its results.
Graphical tools for network meta-analysis in STATA.
Chaimani, Anna; Higgins, Julian P T; Mavridis, Dimitris; Spyridonos, Panagiota; Salanti, Georgia
2013-01-01
Network meta-analysis synthesizes direct and indirect evidence in a network of trials that compare multiple interventions and has the potential to rank the competing treatments according to the studied outcome. Despite its usefulness network meta-analysis is often criticized for its complexity and for being accessible only to researchers with strong statistical and computational skills. The evaluation of the underlying model assumptions, the statistical technicalities and presentation of the results in a concise and understandable way are all challenging aspects in the network meta-analysis methodology. In this paper we aim to make the methodology accessible to non-statisticians by presenting and explaining a series of graphical tools via worked examples. To this end, we provide a set of STATA routines that can be easily employed to present the evidence base, evaluate the assumptions, fit the network meta-analysis model and interpret its results.
Lie Symmetry Analysis of the Hopf Functional-Differential Equation
Daniel D. Janocha
2015-08-01
Full Text Available In this paper, we extend the classical Lie symmetry analysis from partial differential equations to integro-differential equations with functional derivatives. We continue the work of Oberlack and Wacławczyk (2006, Arch. Mech. 58, 597, (2013, J. Math. Phys. 54, 072901, where the extended Lie symmetry analysis is performed in the Fourier space. Here, we introduce a method to perform the extended Lie symmetry analysis in the physical space where we have to deal with the transformation of the integration variable in the appearing integral terms. The method is based on the transformation of the product y(xdx appearing in the integral terms and applied to the functional formulation of the viscous Burgers equation. The extended Lie symmetry analysis furnishes all known symmetries of the viscous Burgers equation and is able to provide new symmetries associated with the Hopf formulation of the viscous Burgers equation. Hence, it can be employed as an important tool for applications in continuum mechanics.
Trauma-Exposed Latina Immigrants' Networks: A Social Network Analysis Approach.
Hurtado-de-Mendoza, Alejandra; Serrano, Adriana; Gonzales, Felisa A; Fernandez, Nicole C; Cabling, Mark; Kaltman, Stacey
2016-11-01
Trauma exposure among Latina immigrants is common. Social support networks can buffer the impact of trauma on mental health. This study characterizes the social networks of trauma-exposed Latina immigrants using a social network analysis perspective. In 2011-2012 a convenience sample (n=28) of Latina immigrants with trauma exposure and presumptive depression or posttraumatic stress disorder was recruited from a community clinic in Washington DC. Participants completed a social network assessment and listed up to ten persons in their network (alters). E-Net was used to describe the aggregate structural, interactional, and functional characteristics of networks and Node-XL was used in a case study to diagram one network. Most participants listed children (93%), siblings (82%), and friends (71%) as alters, and most alters lived in the US (69%). Perceived emotional support and positive social interaction were higher compared to tangible, language, information, and financial support. A case study illustrates the use of network visualizations to assess the strengths and weaknesses of social networks. Targeted social network interventions to enhance supportive networks among trauma-exposed Latina immigrants are warranted.
RCytoscape: tools for exploratory network analysis
Shannon, P.T.; Grimes, M.; Kutlu, B.; Bot, J.J.; Galas, D.J.
2013-01-01
Background: Biomolecular pathways and networks are dynamic and complex, and the perturbations to them which cause disease are often multiple, heterogeneous and contingent. Pathway and network visualizations, rendered on a computer or published on paper, however, tend to be static, lacking in detail,
Stochastic stabilization analysis of networked control systems
Ma Changlin; Fang Huajing
2007-01-01
Considering the stochastic delay problems existing in networked control systems, a new control mode is proposed for networked control systems whose delay is longer than a sampling period. Under the control mode, the mathematical model of such a system is established. A stochastic stabilization condition for the system is given. The maximum delay can be derived from the stabilization condition.
Force network analysis using complementary energy
Borgart, A.; Liem, Y.
2011-01-01
The method presented solves statically indeterminate force networks, which are graphical representations of forces (force polygons) of a structure, by using complementary energy. Statically indeterminate force networks, which have nodes where four or more members come together, have for each node mu
Numerical solution of differential equations using multiquadric radial basis functions networks.
Mai-Duy, N; Tran-Cong, T
2001-03-01
This paper presents mesh-free procedures for solving linear differential equations (ODEs and elliptic PDEs) based on multiquadric (MQ) radial basis function networks (RBFNs). Based on our study of approximation of function and its derivatives using RBFNs that was reported in an earlier paper (Mai-Duy, N. & Tran-Cong, T. (1999). Approximation of function and its derivatives using radial basis function networks. Neural networks, submitted), new RBFN approximation procedures are developed in this paper for solving DEs, which can also be classified into two types: a direct (DRBFN) and an indirect (IRBFN) RBFN procedure. In the present procedures, the width of the RBFs is the only adjustable parameter according to a(i) = betad(i), where d(i) is the distance from the ith centre to the nearest centre. The IRBFN method is more accurate than the DRBFN one and experience so far shows that beta can be chosen in the range 7 < or = beta 10 for the former. Different combinations of RBF centres and collocation points (uniformly and randomly distributed) are tested on both regularly and irregularly shaped domains. The results for a 1D Poisson's equation show that the DRBFN and the IRBFN procedures achieve a norm of error of at least O(1.0 x 10(-4)) and O(1.0 x 10(-8)), respectively, with a centre density of 50. Similarly, the results for a 2D Poisson's equation show that the DRBFN and the IRBFN procedures achieve a norm of error of at least O(1.0 x 10(-3)) and O(1.0 x10(-6)) respectively, with a centre density of 12 X 12.
Silvia Yumi Bando
Full Text Available We previously described - studying transcriptional signatures of hippocampal CA3 explants - that febrile (FS and afebrile (NFS forms of refractory mesial temporal lobe epilepsy constitute two distinct genomic phenotypes. That network analysis was based on a limited number (hundreds of differentially expressed genes (DE networks among a large set of valid transcripts (close to two tens of thousands. Here we developed a methodology for complex network visualization (3D and analysis that allows the categorization of network nodes according to distinct hierarchical levels of gene-gene connections (node degree and of interconnection between node neighbors (concentric node degree. Hubs are highly connected nodes, VIPs have low node degree but connect only with hubs, and high-hubs have VIP status and high overall number of connections. Studying the whole set of CA3 valid transcripts we: i obtained complete transcriptional networks (CO for FS and NFS phenotypic groups; ii examined how CO and DE networks are related; iii characterized genomic and molecular mechanisms underlying FS and NFS phenotypes, identifying potential novel targets for therapeutic interventions. We found that: i DE hubs and VIPs are evenly distributed inside the CO networks; ii most DE hubs and VIPs are related to synaptic transmission and neuronal excitability whereas most CO hubs, VIPs and high hubs are related to neuronal differentiation, homeostasis and neuroprotection, indicating compensatory mechanisms. Complex network visualization and analysis is a useful tool for systems biology approaches to multifactorial diseases. Network centrality observed for hubs, VIPs and high hubs of CO networks, is consistent with the network disease model, where a group of nodes whose perturbation leads to a disease phenotype occupies a central position in the network. Conceivably, the chance for exerting therapeutic effects through the modulation of particular genes will be higher if these genes
Geng, Xiaofang; Xu, Tiantian; Niu, Zhipeng; Zhou, Xiaochun; Zhao, Lijun; Xie, Zhaohui; Xue, Deming; Zhang, Fuchun; Xu, Cunshuan
2014-01-01
Following amputation, the newt has the remarkable ability to regenerate its limb, and this process involves dedifferentiation, proliferation and differentiation. To investigate the potential proteome during a dynamic network of Chinese fire-bellied newt limb regeneration (CNLR), two-dimensional fluorescence difference gel electrophoresis (2D-DIGE) and mass spectrum (MS) were applied to examine changes in the proteome that occurred at 11 time points after amputation. Meanwhile, several proteins were selected to validate their expression levels by Western blot. The results revealed that 1476 proteins had significantly changed as compared to the control group. Gene Ontology annotation and protein network analysis by Ingenuity Pathway Analysis 9.0 (IPA) software suggested that the differentially expressed proteins were involved in 33 kinds of physiological activities including signal transduction, cell proliferation, cell differentiation, etc. Among these proteins, 407 proteins participated in cell differentiation with 212 proteins in the differentiation of skin cell, myocyte, neurocyte, chondrocyte and osteocyte, and 37 proteins participated in signaling pathways of BCC, CRH, CXCR4, GnRH, GPCR and IL1 which regulated cell differentiation and redifferentiation. On the other hand, the signal transduction activity and cell differentiation activity were analyzed by IPA based on the changes in the expression of these proteins. The results showed that BCC, CRH, CXCR4, GnRH, GPCR and IL1 signaling pathways played an important role in regulating the differentiation of skin cell, myocyte, neurocyte, chondrocyte and osteocyte during CNLR.
Hybrid Dynamic Network Data Envelopment Analysis
Ling Li
2015-01-01
Full Text Available Conventional DEA models make no hypothesis concerning the internal operations in a static situation. To open the “black box” and work with dynamic assessment issues synchronously, we put forward a hybrid model for evaluating the relative efficiencies of a set of DMUs over an observed time period with a composite of network DEA and dynamic DEA. We vertically deal with intermediate products between divisions with assignable inputs in the network structure and, horizontally, we extend network structure by means of a dynamic pattern with unrelated activities between two succeeding periods. The hybrid dynamic network DEA model proposed in this paper enables us to (i pry into the internal operations of DEA by another network structure, (ii obtain dynamic change of period efficiency, and (iii gain the overall dynamic efficiency of DMUs over the entire observed periods. We finally illustrate the calculation procedure of the proposed approach by a numerical example.
Complex networks analysis in socioeconomic models
Varela, Luis M; Ausloos, Marcel; Carrete, Jesus
2014-01-01
This chapter aims at reviewing complex networks models and methods that were either developed for or applied to socioeconomic issues, and pertinent to the theme of New Economic Geography. After an introduction to the foundations of the field of complex networks, the present summary adds insights on the statistical mechanical approach, and on the most relevant computational aspects for the treatment of these systems. As the most frequently used model for interacting agent-based systems, a brief description of the statistical mechanics of the classical Ising model on regular lattices, together with recent extensions of the same model on small-world Watts-Strogatz and scale-free Albert-Barabasi complex networks is included. Other sections of the chapter are devoted to applications of complex networks to economics, finance, spreading of innovations, and regional trade and developments. The chapter also reviews results involving applications of complex networks to other relevant socioeconomic issues, including res...
Luque, John S; Tyson, Dinorah Martinez; Bynum, Shalanda A; Noel-Thomas, Shalewa; Wells, Kristen J; Vadaparampil, Susan T; Gwede, Clement K; Meade, Cathy D
2011-11-01
The Tampa Bay Community Cancer Network (TBCCN) is one of the Community Network Program sites funded (2005-10) by the National Cancer Institute's Center to Reduce Cancer Health Disparities. TBCCN was tasked to form a sustainable, community-based partnership network focused on the goal of reducing cancer health disparities among racial-ethnic minority and medically underserved populations. This article reports evaluation outcome results from a social network analysis and discusses the varying TBCCN partner roles-in education, training, and research-over a span of three years (2007-09). The network analysis included 20 local community partner organizations covering a tricounty area in Southwest Florida. In addition, multiple externally funded, community-based participatory research pilot projects with community-academic partners have either been completed or are currently in progress, covering research topics including culturally targeted colorectal and prostate cancer screening education, patient navigation focused on preventing cervical cancer in rural Latinas, and community perceptions of biobanking. The social network analysis identified a trend toward increased network decentralization based on betweenness centrality and overall increase in number of linkages, suggesting network sustainability. Degree centrality, trust, and multiplexity exhibited stability over the three-year time period. These results suggest increased interaction and interdependence among partner organizations and less dependence on the cancer center. Social network analysis enabled us to quantitatively evaluate partnership network functioning of TBCCN in terms of network structure and information and resources flows, which are integral to understanding effective coalition practice based on Community Coalition Action Theory ( Butterfoss and Kegler 2009). Sharing the results of the social network analysis with the partnership network is an important component of our coalition building efforts. A
Road Network Vulnerability Analysis Based on Improved Ant Colony Algorithm
Yunpeng Wang
2014-01-01
Full Text Available We present an improved ant colony algorithm-based approach to assess the vulnerability of a road network and identify the critical infrastructures. This approach improves computational efficiency and allows for its applications in large-scale road networks. This research involves defining the vulnerability conception, modeling the traffic utility index and the vulnerability of the road network, and identifying the critical infrastructures of the road network. We apply the approach to a simple test road network and a real road network to verify the methodology. The results show that vulnerability is directly related to traffic demand and increases significantly when the demand approaches capacity. The proposed approach reduces the computational burden and may be applied in large-scale road network analysis. It can be used as a decision-supporting tool for identifying critical infrastructures in transportation planning and management.
Investigation and analysis of network psychology of college students
Zhang Xiaoyan
2013-01-01
Based on basic situational research and analysis carried out on 638 college students using network,we found that as many as 20 percent of the students are not only largely dependent on internet,but also addicted to it.Further biography characteristics analyses for different individuals on the four dimensions of the network forced addiction,tolerance,and time management and interpersonal relationship and health,show that there are significant differences in grades,gender with different education levels of their parents.Further researches on discrepancy that addicted groups have in network entertainment addiction,network information,cyber porn,network relations and network transactions addictions also illustrate that significant discrepancies exist in gender,net age,different discipline and other factors.Finally we put forward some correlative measures to solve the problems of college students network psychology from individuals,schools,and society levels.
Comparative Traffic Performance Analysis of Urban Transportation Network Structures
Amini, Behnam; Mojarradi, Morteza; Derrible, Sybil
2015-01-01
The network structure of an urban transportation system has a significant impact on its traffic performance. This study uses network indicators along with several traffic performance measures including speed, trip length, travel time, and traffic volume, to compare a selection of seven transportation networks with a variety of structures and under different travel demand conditions. The selected network structures are: modified linear, branch, grid, 3-directional grid, 1-ring web, 2-ring web, and radial. For the analysis, a base origin-destination matrix is chosen, to which different growth factors are applied in order to simulate various travel demand conditions. Results show that overall the 2-ring web network offers the most efficient traffic performance, followed by the grid and the 1-ring networks. A policy application of this study is that the branch, 3-directional grid, and radial networks are mostly suited for small cities with uncongested traffic conditions. In contrast, the 2-ring web, grid, and 1-r...
NEXCADE: perturbation analysis for complex networks.
Gitanjali Yadav
Full Text Available Recent advances in network theory have led to considerable progress in our understanding of complex real world systems and their behavior in response to external threats or fluctuations. Much of this research has been invigorated by demonstration of the 'robust, yet fragile' nature of cellular and large-scale systems transcending biology, sociology, and ecology, through application of the network theory to diverse interactions observed in nature such as plant-pollinator, seed-dispersal agent and host-parasite relationships. In this work, we report the development of NEXCADE, an automated and interactive program for inducing disturbances into complex systems defined by networks, focusing on the changes in global network topology and connectivity as a function of the perturbation. NEXCADE uses a graph theoretical approach to simulate perturbations in a user-defined manner, singly, in clusters, or sequentially. To demonstrate the promise it holds for broader adoption by the research community, we provide pre-simulated examples from diverse real-world networks including eukaryotic protein-protein interaction networks, fungal biochemical networks, a variety of ecological food webs in nature as well as social networks. NEXCADE not only enables network visualization at every step of the targeted attacks, but also allows risk assessment, i.e. identification of nodes critical for the robustness of the system of interest, in order to devise and implement context-based strategies for restructuring a network, or to achieve resilience against link or node failures. Source code and license for the software, designed to work on a Linux-based operating system (OS can be downloaded at http://www.nipgr.res.in/nexcade_download.html. In addition, we have developed NEXCADE as an OS-independent online web server freely available to the scientific community without any login requirement at http://www.nipgr.res.in/nexcade.html.
Mapping Extension's Networks: Using Social Network Analysis to Explore Extension's Outreach
Bartholomay, Tom; Chazdon, Scott; Marczak, Mary S.; Walker, Kathrin C.
2011-01-01
The University of Minnesota Extension conducted a social network analysis (SNA) to examine its outreach to organizations external to the University of Minnesota. The study found that its outreach network was both broad in its reach and strong in its connections. The study found that SNA offers a unique method for describing and measuring Extension…
The Applicability of Social Network Analysis to the Study of Networked Learning
Toikkanen, Tarmo; Lipponen, Lasse
2011-01-01
Studying networked learning (NL) by applying social network analysis (SNA) has gained popularity in recent years. However, it appears that in the context of NL the choice of SNA indices is very often dictated by using easily achievable SNA tools. Most studies in this field only involve a single group of students and utilise simple indices, such as…
Network Tools for the Analysis of Proteomic Data.
Chisanga, David; Keerthikumar, Shivakumar; Mathivanan, Suresh; Chilamkurti, Naveen
2017-01-01
Recent advancements in high-throughput technologies such as mass spectrometry have led to an increase in the rate at which data is generated and accumulated. As a result, standard statistical methods no longer suffice as a way of analyzing such gigantic amounts of data. Network analysis, the evaluation of how nodes relate to one another, has over the years become an integral tool for analyzing high throughput proteomic data as they provide a structure that helps reduce the complexity of the underlying data.Computational tools, including pathway databases and network building tools, have therefore been developed to store, analyze, interpret, and learn from proteomics data. These tools enable the visualization of proteins as networks of signaling, regulatory, and biochemical interactions. In this chapter, we provide an overview of networks and network theory fundamentals for the analysis of proteomics data. We further provide an overview of interaction databases and network tools which are frequently used for analyzing proteomics data.
Multifractal analysis of weighted networks by a modified sandbox algorithm
Song, Yu-Qin; Yu, Zu-Guo; Li, Bao-Gen
2015-01-01
Complex networks have attracted growing attention in many fields. As a generalization of fractal analysis, multifractal analysis (MFA) is a useful way to systematically describe the spatial heterogeneity of both theoretical and experimental fractal patterns. Some algorithms for MFA of unweighted complex networks have been proposed in the past a few years, including the sandbox (SB) algorithm recently employed by our group. In this paper, a modified SB algorithm (we call it SBw algorithm) is proposed for MFA of weighted networks.First, we use the SBw algorithm to study the multifractal property of two families of weighted fractal networks (WFNs): "Sierpinski" WFNs and "Cantor dust" WFNs. We also discuss how the fractal dimension and generalized fractal dimensions change with the edge-weights of the WFN. From the comparison between the theoretical and numerical fractal dimensions of these networks, we can find that the proposed SBw algorithm is efficient and feasible for MFA of weighted networks. Then, we apply...
Jia, Yujie; Nie, Kun; Li, Jing; Liang, Xinyue; Zhang, Xuezhu
2016-11-01
In order to investigate the pathogenic targets and associated biological process of Alzheimer's disease in the present study, mRNA expression profiles (GSE28146) and microRNA (miRNA) expression profiles (GSE16759) were downloaded from the Gene Expression Omnibus database. In GSE28146, eight control samples, and Alzheimer's disease samples comprising seven incipient, eight moderate, seven severe Alzheimer's disease samples, were included. The Affy package in R was used for background correction and normalization of the raw microarray data. The differentially expressed genes (DEGs) and differentially expressed miRNAs were identified using the Limma package. In addition, mRNAs were clustered using weighted gene correlation network analysis, and modules found to be significantly associated with the stages of Alzheimer's disease were screened out. The Database for Annotation, Visualization, and Integrated Discovery was used to perform Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. The target genes of the differentially expressed miRNAs were identified using the miRWalk database. Compared with the control samples, 175,59 genes and 90 DEGs were identified in the incipient, moderate and severe Alzheimer's disease samples, respectively. A module, which contained 1,592 genes was found to be closely associated with the stage of Alzheimer's disease and biological processes. In addition, pathways associated with Alzheimer's disease and other neurological diseases were found to be enriched in those genes. A total of 139 overlapped genes were identified between those genes and the DEGs in the three groups. From the miRNA expression profiles, 189 miRNAs were found differentially expressed in the samples from patients with Alzheimer's disease and 1,647 target genes were obtained. In addition, five overlapped genes were identified between those 1,647 target genes and the 139 genes, and these genes may be important pathogenic targets for Alzheimer
Santos, Márcia C T; Tegge, Allison N; Correa, Bruna R; Mahesula, Swetha; Kohnke, Luana Q; Qiao, Mei; Ferreira, Marco A R; Kokovay, Erzsebet; Penalva, Luiz O F
2016-01-01
The ventricular-subventricular zone harbors neural stem cells (NSCs) that can differentiate into neurons, astrocytes, and oligodendrocytes. This process requires loss of stem cell properties and gain of characteristics associated with differentiated cells. miRNAs function as important drivers of this transition; miR-124, -128, and -137 are among the most relevant ones and have been shown to share commonalities and act as proneurogenic regulators. We conducted biological and genomic analyses to dissect their target repertoire during neurogenesis and tested the hypothesis that they act cooperatively to promote differentiation. To map their target genes, we transfected NSCs with antagomiRs and analyzed differences in their mRNA profile throughout differentiation with respect to controls. This strategy led to the identification of 910 targets for miR-124, 216 for miR-128, and 652 for miR-137. The target sets show extensive overlap. Inspection by gene ontology and network analysis indicated that transcription factors are a major component of these miRNAs target sets. Moreover, several of these transcription factors form a highly interconnected network. Sp1 was determined to be the main node of this network and was further investigated. Our data suggest that miR-124, -128, and -137 act synergistically to regulate Sp1 expression. Sp1 levels are dramatically reduced as cells differentiate and silencing of its expression reduced neuronal production and affected NSC viability and proliferation. In summary, our results show that miRNAs can act cooperatively and synergistically to regulate complex biological processes like neurogenesis and that transcription factors are heavily targeted to branch out their regulatory effect. © 2015 AlphaMed Press.
Rakkiyappan, R; Velmurugan, G; Cao, Jinde
2015-04-01
In this paper, the problem of the existence, uniqueness and uniform stability of memristor-based fractional-order neural networks (MFNNs) with two different types of memductance functions is extensively investigated. Moreover, we formulate the complex-valued memristor-based fractional-order neural networks (CVMFNNs) with two different types of memductance functions and analyze the existence, uniqueness and uniform stability of such networks. By using Banach contraction principle and analysis technique, some sufficient conditions are obtained to ensure the existence, uniqueness and uniform stability of the considered MFNNs and CVMFNNs with two different types of memductance functions. The analysis results establish from the theory of fractional-order differential equations with discontinuous right-hand sides. Finally, four numerical examples are presented to show the effectiveness of our theoretical results.
王怀宇; 刘陕西
2002-01-01
Objective: To elucidate the molecular mechanism of the differentiation of acute promyelocytic leukemia (APL) cell line NB4 induced by realgar. Methods: The response of NB4 cell to realgar was explored with a cDNA microarray representing 1003 different human genes. Results: The analysis of gene expression profiles indicated that 8 genes were up-regulated and 33 genes were down-regulated 48 hrs after realgar treatment. Among the 8 up-regulated genes, 2 genes were involved in ubiquitin proteasome degradation pathway. Some genes related to RNA processing, protein synthesis and signal transduction were down-regulated. Conclusion: The ubiquitin-proteasome degradation pathway may play an important role in the degradation of PML/RAR α fusion protein and the differentiation of NB4 cells.
Tan, Zhi; Cao, Hongyu
2008-11-01
Network service support to ensure quality of service (QoS) is a key requirement for many applications, we present a novel management plane oriented service architecture which provides network resource scheduling service in large-scale GMPLS/ASON network environment based on the service level agreement(SLA) conducted between service customers and service providers. It applies the updated service-oriented and policy translation structure with excellent expansibility and efficiency in the running process. The architecture contains four components: Application Monitor Service, SLA Management, Policy management and Service management. In addition, vertical service mapping and differentiated-resilience provisioning schemes of service level agreement(SLA) applied to the GMPLS/ASON networks are discussed, which is expected to be the near- and long-term network technology thanks, among other things, to the great bandwidth capacity offered by optical devices.
Pedro L. Faggion
2005-08-01
Full Text Available Adjustment strategies associated to the methodology applied used to the implantation of a gravity network of high precision in Paraná are presented. A network was implanted with stations in 21 places in the State of Paraná and one in the state of São Paulo To reduce the risk of the losing of points of that gravity network, they were established on the points of the GPS High Precision Network of Paraná, which possess a relatively homogeneous geographical distribution. For each one of the gravity lines belonging to the loops implanted for the network, it was possible to obtain three or six observations. In the first strategy, of adjustment investigated, for the net, it was considered, as observation, the medium value of the observations obtained for each gravity line. In the second strategy, of the adjustment, the observations were considered independent. The comparison of those strategies revealed that only the precision criteria is not enough to indicate the great solution of a gravity network. It was verified that there is need to use an additional criterion for analysis of the adjusted solution of the network, besides the precision criteria. The reliability criterion for geodesic networks, which becomes separated in reliability internal and external reliability it was used. The reliability internal it was used to verify the rigidity with which the network reacts in the detection and quantification of existent gross errors in the observations, and the reliability external in the quantification of the influence on the adjusted parameters of the errors non located. They are presented the aspects that differentiate the obtained solutions, when they combine the precision criteria and reliability criteria in the analysis of the quality of a gravity network.
Differential item functioning analysis by applying multiple comparison procedures.
Eusebi, Paolo; Kreiner, Svend
2015-01-01
Analysis within a Rasch measurement framework aims at development of valid and objective test score. One requirement of both validity and objectivity is that items do not show evidence of differential item functioning (DIF). A number of procedures exist for the assessment of DIF including those based on analysis of contingency tables by Mantel-Haenszel tests and partial gamma coefficients. The aim of this paper is to illustrate Multiple Comparison Procedures (MCP) for analysis of DIF relative to a variable defining a very large number of groups, with an unclear ordering with respect to the DIF effect. We propose a single step procedure controlling the false discovery rate for DIF detection. The procedure applies for both dichotomous and polytomous items. In addition to providing evidence against a hypothesis of no DIF, the procedure also provides information on subset of groups that are homogeneous with respect to the DIF effect. A stepwise MCP procedure for this purpose is also introduced.
Statistical and machine learning approaches for network analysis
Dehmer, Matthias
2012-01-01
Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internation
Energy balance for analysis of complex metabolic networks.
Beard, Daniel A.; Liang, Shou-dan; Qian, Hong
2002-01-01
Predicting behavior of large-scale biochemical networks represents one of the greatest challenges of bioinformatics and computational biology. Computational tools for predicting fluxes in biochemical networks are applied in the fields of integrated and systems biology, bioinformatics, and genomics, and to aid in drug discovery and identification of potential drug targets. Approaches, such as flux balance analysis (FBA), that account for the known stoichiometry of the reaction network while av...
Quantitative analysis of access strategies to remoteinformation in network services
Olsen, Rasmus Løvenstein; Schwefel, Hans-Peter; Hansen, Martin Bøgsted
2006-01-01
Remote access to dynamically changing information elements is a required functionality for various network services, including routing and instances of context-sensitive networking. Three fundamentally different strategies for such access are investigated in this paper: (1) a reactive approach......, network delay characterization) and specific requirements on mismatch probability, traffic overhead, and access delay. Finally, the analysis is applied to the use-case of context-sensitive service discovery....
An Approach to Structural Approximation Analysis by Artificial Neural Networks
陆金桂; 周济; 王浩; 陈新度; 余俊; 肖世德
1994-01-01
This paper theoretically proves that a three-layer neural network can be applied to implementing exactly the function between the stresses and displacements and the design variables of any elastic structure based on the Kolmogorov’s mapping neural network existence theorem. A new approach to the structural approximation analysis with the global characteristic based on artificial neural networks is presented. The computer simulation experiments made by this paper show that the new approach is effective.
Quantitative analysis of access strategies to remoteinformation in network services
Olsen, Rasmus Løvenstein; Schwefel, Hans-Peter; Hansen, Martin Bøgsted
2006-01-01
Remote access to dynamically changing information elements is a required functionality for various network services, including routing and instances of context-sensitive networking. Three fundamentally different strategies for such access are investigated in this paper: (1) a reactive approach......, network delay characterization) and specific requirements on mismatch probability, traffic overhead, and access delay. Finally, the analysis is applied to the use-case of context-sensitive service discovery....
Towards the integration of social network analysis in an inter-organizational networks perspective
Bergenholtz, Carsten; Waldstrøm, Christian
This conceptual paper deals with the issue of studying inter-organizational networks while applying social network analysis (SNA). SNA is a widely recognized technique in network research, particularly within intra-organizational settings, while there seems to be a significant gap in the inter......-organizational setting. Based on a literature review of both SNA as a methodology and/or theory and the field of inter-organizational networks, the aim is to gain an overview in order to provide a clear setting for SNA in inter-organizational research....
Linear analysis of degree correlations in complex networks
JU XIANG; TAO HU; YAN ZHANG; KE HU; YAN-NI TANG; YUAN-YUAN GAO; KE DENG
2016-12-01
Many real-world networks such as the protein–protein interaction networks and metabolic networks often display nontrivial correlations between degrees of vertices connected by edges. Here, we analyse the statistical methods used usually to describe the degree correlation in the networks, and analytically give linear relation in the degree correlation. It provides a simple and interesting perspective on the analysis of the degree correlation in networks, which is usefully complementary to the existing methods for degree correlation in networks. Especially, the slope in the linear relation corresponds exactly to the degree correlation coefficient in networks, meaning that it can not only characterize the level of degree correlation in networks, but also reflects the speed that the average nearest neighbours’ degree varies with the vertex degree. Finally, we applied our results to several real-world networks, validating the conclusions of the linear analysis of degree correlation. We hope that the work in this paper can be helpful for further understanding the degree correlation in complex networks.
Multivariable Discriminant Analysis for the Differential Diagnosis of Microcytic Anemia
Eloísa Urrechaga
2013-01-01
Full Text Available Introduction. Iron deficiency anemia and thalassemia are the most common causes of microcytic anemia. Powerful statistical computer programming enables sensitive discriminant analyses to aid in the diagnosis. We aimed at investigating the performance of the multiple discriminant analysis (MDA to the differential diagnosis of microcytic anemia. Methods. The training group was composed of 200 β-thalassemia carriers, 65 α-thalassemia carriers, 170 iron deficiency anemia (IDA, and 45 mixed cases of thalassemia and acute phase response or iron deficiency. A set of potential predictor parameters that could detect differences among groups were selected: Red Blood Cells (RBC, hemoglobin (Hb, mean cell volume (MCV, mean cell hemoglobin (MCH, and RBC distribution width (RDW. The functions obtained with MDA analysis were applied to a set of 628 consecutive patients with microcytic anemia. Results. For classifying patients into two groups (genetic anemia and acquired anemia, only one function was needed; 87.9% β-thalassemia carriers, and 83.3% α-thalassemia carriers, and 72.1% in the mixed group were correctly classified. Conclusion. Linear discriminant functions based on hemogram data can aid in differentiating between IDA and thalassemia, so samples can be efficiently selected for further analysis to confirm the presence of genetic anemia.
A Social Network Analysis of Occupational Segregation
Buhai, Ioan Sebastian; van der Leij, Marco
We develop a social network model of occupational segregation between different social groups, generated by the existence of positive inbreeding bias among individuals from the same group. If network referrals are important for job search, then expected homophily in the contact network structure...... induces different career choices for individuals from different social groups. This further translates into stable occupational segregation equilibria in the labor market. We derive the conditions for wage and unemployment inequality in the segregation equilibria and characterize first and second best...
Natural Time Analysis and Complex Networks
Sarlis, Nicholas; Skordas, Efthimios; Lazaridou, Mary; Varotsos, Panayiotis
2013-04-01
Here, we review the analysis of complex time series in a new time domain, termed natural time, introduced by our group [1,2]. This analysis conforms to the desire to reduce uncertainty and extract signal information as much as possible [3]. It enables [4] the distinction between the two origins of self-similarity when analyzing data from complex systems, i.e., whether self-similarity solely results from long-range temporal correlations (the process's memory only) or solely from the process's increments infinite variance (heavy tails in their distribution). Natural time analysis captures the dynamical evolution of a complex system and identifies [5] when the system enters a critical stage. Hence, this analysis plays a key role in predicting forthcoming catastrophic events in general. Relevant examples, compiled in a recent monograph [6], have been presented in diverse fields, including Solid State Physics [7], Statistical Physics (for example systems exhibiting self-organized criticality [8]), Cardiology [9,10], Earth Sciences [11] (Geophysics, Seismology), Environmental Sciences (e.g. see Ref. [12]), etc. Other groups have proposed and developed a network approach to earthquake events with encouraging results. A recent study [13] reveals that this approach is strengthened if we combine it with natural time analysis. In particular, we find [13,14] that the study of the spatial distribution of the variability [15] of the order parameter fluctuations, defined in natural time, provides important information on the dynamical evolution of the system. 1. P. Varotsos, N. Sarlis, and E. Skordas, Practica of Athens Academy, 76, 294-321, 2001. 2. P.A. Varotsos, N.V. Sarlis, and E.S. Skordas, Phys. Rev. E, 66, 011902 , 2002. 3. S. Abe, N.V. Sarlis, E.S. Skordas, H.K. Tanaka and P.A. Varotsos, Phys. Rev. Lett. 94, 170601, 2005. 4. P.A. Varotsos, N.V. Sarlis, E.S. Skordas, H.K. Tanaka and M.S. Lazaridou, Phys. Rev. E, 74, 021123, 2006. 5. P.Varotsos, N. V. Sarlis, E. S. Skordas
Numerical Analysis for Functional Differential and Integral Equations
Hermann BRUNNER; Tao TANG; Stefan VANDEWALLE
2009-01-01
@@ From December 3-6,2007,the Department of Mathematics at Hong Kong Baptist University hosted the International Workshop on Numerical Analysis and Computational Methods for Functional Differential and Integral Equations. This workshop,organized by Hermann Brunner of Memorial University of Newfoundland (Canada) & Hong Kong Baptist University,Leevan Ling and Tao Tang of Hong Kong Baptist University,and Chengjian Zhang of Huazhong University of Science and Technology (China) brought together some 40 members of research groups in Hong Kong,Taiwan and the mainland of China,Belgium,Canada,Japan,and Portugal.
Statistics for proteomics: experimental design and 2-DE differential analysis.
Chich, Jean-François; David, Olivier; Villers, Fanny; Schaeffer, Brigitte; Lutomski, Didier; Huet, Sylvie
2007-04-15
Proteomics relies on the separation of complex protein mixtures using bidimensional electrophoresis. This approach is largely used to detect the expression variations of proteins prepared from two or more samples. Recently, attention was drawn on the reliability of the results published in literature. Among the critical points identified were experimental design, differential analysis and the problem of missing data, all problems where statistics can be of help. Using examples and terms understandable by biologists, we describe how a collaboration between biologists and statisticians can improve reliability of results and confidence in conclusions.
Mathematical Analysis of a PDE System for Biological Network Formation
Haskovec, Jan
2015-02-04
Motivated by recent physics papers describing rules for natural network formation, we study an elliptic-parabolic system of partial differential equations proposed by Hu and Cai [13, 15]. The model describes the pressure field thanks to Darcy\\'s type equation and the dynamics of the conductance network under pressure force effects with a diffusion rate D >= 0 representing randomness in the material structure. We prove the existence of global weak solutions and of local mild solutions and study their long term behavior. It turns out that, by energy dissipation, steady states play a central role to understand the network formation capacity of the system. We show that for a large diffusion coefficient D, the zero steady state is stable, while network formation occurs for small values of D due to the instability of the zero steady state, and the borderline case D = 0 exhibits a large class of dynamically stable (in the linearized sense) steady states.
Biological networks to the analysis of microarray data
FANG Zhuo; LUO Qingming; ZHANG Guoqing; LI Yixue
2006-01-01
Microarray technology, which permits rapid and large-scale screening for patterns of gene expressions, usually generates a large amount of data. How to mine the biological meanings under these data is one of the main challenges in bioinformatics. Compared to the pure mathematical techniques, those methods incorporated with some prior biological knowledge generally bring better interpretations.Recently, a new analysis, in which the knowledge of biological networks such as metabolic network and protein interaction network is introduced, is widely applied to microarray data analysis. The microarray data analysis based on biological networks contains two main research aspects: identification of active components in biological networks and assessment of gene sets significance. In this paper, we briefly review the progress of these two categories of analyses, especially some representative methods.
Kathleen Oros Klein
2016-08-01
Full Text Available In a variety of solid cancers, missense mutations in the well-established TP53 tumour suppressor gene may lead to presence of a partially-functioning protein molecule, whereas mutations affecting the protein encoding reading frame, often referred to as null mutations, result in the absence of p53 protein. Both types of mutations have been observed in the same cancer type. As the resulting tumour biology may be quite different between these two groups, we used RNA-sequencing data from The Cancer Genome Atlas (TCGA from four different cancers with poor prognosis, namely ovarian, breast, lung and skin cancers, to compare the patterns of co-expression of genes in tumours grouped according to their TP53 missense or null mutation status. We used Weighted Gene Coexpression Network analysis (WGCNA and a new test statistic built on differences between groups in the measures of gene connectivity. For each cancer, our analysis identified a set of genes showing differential coexpression patterns between the TP53 missense- and null mutation-carrying groups that was robust to the choice of the tuning parameter in WGCNA. After comparing these sets of genes across the four cancers, one gene (KIR3DL2 consistently showed differential coexpression patterns between the null and missense groups. KIR3DL2 is known to play an important role in regulating the immune response, which is consistent with our observation that this gene’s strongly-correlated partners implicated many immune-related pathways. Examining mutation-type-related changes in correlations between sets of genes may provide new insight into tumour biology.
Assessing Group Interaction with Social Language Network Analysis
Scholand, Andrew J.; Tausczik, Yla R.; Pennebaker, James W.
In this paper we discuss a new methodology, social language network analysis (SLNA), that combines tools from social language processing and network analysis to assess socially situated working relationships within a group. Specifically, SLNA aims to identify and characterize the nature of working relationships by processing artifacts generated with computer-mediated communication systems, such as instant message texts or emails. Because social language processing is able to identify psychological, social, and emotional processes that individuals are not able to fully mask, social language network analysis can clarify and highlight complex interdependencies between group members, even when these relationships are latent or unrecognized.
Acerbi, Enzo; Viganò, Elena; Poidinger, Michael; Mortellaro, Alessandra; Zelante, Teresa; Stella, Fabio
2016-03-15
T helper 17 (TH17) cells represent a pivotal adaptive cell subset involved in multiple immune disorders in mammalian species. Deciphering the molecular interactions regulating TH17 cell differentiation is particularly critical for novel drug target discovery designed to control maladaptive inflammatory conditions. Using continuous time Bayesian networks over a time-course gene expression dataset, we inferred the global regulatory network controlling TH17 differentiation. From the network, we identified the Prdm1 gene encoding the B lymphocyte-induced maturation protein 1 as a crucial negative regulator of human TH17 cell differentiation. The results have been validated by perturbing Prdm1 expression on freshly isolated CD4(+) naïve T cells: reduction of Prdm1 expression leads to augmentation of IL-17 release. These data unravel a possible novel target to control TH17 polarization in inflammatory disorders. Furthermore, this study represents the first in vitro validation of continuous time Bayesian networks as gene network reconstruction method and as hypothesis generation tool for wet-lab biological experiments.
Bandwidth Analysis of Smart Meter Network Infrastructure
Balachandran, Kardi; Olsen, Rasmus Løvenstein; Pedersen, Jens Myrup
2014-01-01
Advanced Metering Infrastructure (AMI) is a net-work infrastructure in Smart Grid, which links the electricity customers to the utility company. This network enables smart services by making it possible for the utility company to get an overview of their customers power consumption and also control...... devices in their costumers household e.g. heat pumps. With these smart services, utility companies can do load balancing on the grid by shifting load using resources the customers have. The problem investigated in this paper is what bandwidth require-ments can be expected when implementing such network...... to utilize smart meters and which existing broadband network technologies can facilitate this smart meter service. Initially, scenarios for smart meter infrastructure are identified. The paper defines abstraction models which cover the AMI scenarios. When the scenario has been identified a general overview...
Privacy Analysis in Mobile Social Networks
Sapuppo, Antonio
2012-01-01
Nowadays, mobile social networks are capable of promoting social networking benefits during physical meetings, in order to leverage interpersonal affinities not only among acquaintances, but also between strangers. Due to their foundation on automated sharing of personal data in the physical...... factors: inquirer, purpose of disclosure, access & control of the disclosed information, location familiarity and current activity of the user. This research can serve as relevant input for the design of privacy management models in mobile social networks....... surroundings of the user, these networks are subject to crucial privacy threats. Privacy management systems must be capable of accurate selection of data disclosure according to human data sensitivity evaluation. Therefore, it is crucial to research and comprehend an individual's personal information...
Transcription regulatory networks analysis using CAGE
Tegnér, Jesper N.
2009-10-01
Mapping out cellular networks in general and transcriptional networks in particular has proved to be a bottle-neck hampering our understanding of biological processes. Integrative approaches fusing computational and experimental technologies for decoding transcriptional networks at a high level of resolution is therefore of uttermost importance. Yet, this is challenging since the control of gene expression in eukaryotes is a complex multi-level process influenced by several epigenetic factors and the fine interplay between regulatory proteins and the promoter structure governing the combinatorial regulation of gene expression. In this chapter we review how the CAGE data can be integrated with other measurements such as expression, physical interactions and computational prediction of regulatory motifs, which together can provide a genome-wide picture of eukaryotic transcriptional regulatory networks at a new level of resolution. © 2010 by Pan Stanford Publishing Pte. Ltd. All rights reserved.
Network analysis using organizational risk analyzer
无
2008-01-01
The tool system of the organizational risk analyzer (ORA) to study the network of East Turkistan terrorists is selected. The model of the relationships among its personnel, knowledge, resources and task entities is represented by the meta-matrix in ORA, with which to analyze the risks and vulnerabilities of organizational structure quantitatively, and obtain the last vulnerabilities and risks of the organization. Case study in this system shows that it should be a shortcut to destroy effectively the network...
Stochastic modeling and analysis of telecoms networks
Decreusefond, Laurent
2012-01-01
This book addresses the stochastic modeling of telecommunication networks, introducing the main mathematical tools for that purpose, such as Markov processes, real and spatial point processes and stochastic recursions, and presenting a wide list of results on stability, performances and comparison of systems.The authors propose a comprehensive mathematical construction of the foundations of stochastic network theory: Markov chains, continuous time Markov chains are extensively studied using an original martingale-based approach. A complete presentation of stochastic recursions from an
Analysis and Design of Complex Networks
2014-12-02
protocols for higher throughput and lower delays in WiFi , protocols for generating secret keys in wireless networks. (a) Papers published in peer...Reservation for Channel Access in Wireless LANs, IEEE/ACM Transactions on Networking, (02 2013): 0. doi: 10.1109/TNET.2012.2202323 Amin Aminzadeh Gohari...Venkat Anantharam. Evaluation of Marton’s Inner Bound for the General Broadcast Channel , IEEE Transactions on Information Theory, (03 2012): 0. doi
A Graph Oriented Approach for Network Forensic Analysis
Wang, Wei
2010-01-01
Network forensic analysis is a process that analyzes intrusion evidence captured from networked environment to identify suspicious entities and stepwise actions in an attack scenario. Unfortunately, the overwhelming amount and low quality of output from security sensors make it difficult for analysts to obtain a succinct high-level view of complex…
Analysis and control of flows in pressurized hydraulic networks
Gupta, R.K.
2006-01-01
Analysis, design and flow control problems in pressurized hydraulic networks such as water transmission and distribution systems consisting of pipes and other appurtenant components such as reservoirs, pumps, valves and surge devices are dealt with from the prospective of network synthesis aiming at
Analysis and control of flows in pressurized hydraulic networks
Gupta, R.K.
2006-01-01
Analysis, design and flow control problems in pressurized hydraulic networks such as water transmission and distribution systems consisting of pipes and other appurtenant components such as reservoirs, pumps, valves and surge devices are dealt with from the prospective of network synthesis aiming at
An Analysis of the Structure and Evolution of Networks
Hua, Guangying
2011-01-01
As network research receives more and more attention from both academic researchers and practitioners, network analysis has become a fast growing field attracting many researchers from diverse fields such as physics, computer science, and sociology. This dissertation provides a review of theory and research on different real data sets from the…
Transient stability analysis of a distribution network with distributed generators
Xyngi, I.; Ishchenko, A.; Popov, M.; Van der Sluis, L.
2009-01-01
This letter describes the transient stability analysis of a 10-kV distribution network with wind generators, microturbines, and CHP plants. The network being modeled in Matlab/Simulink takes into account detailed dynamic models of the generators. Fault simulations at various locations are investigat