Characterization of the SOS meta-regulon in the human gut microbiome.

PubMed

Cornish, Joseph P; Sanchez-Alberola, Neus; O'Neill, Patrick K; O'Keefe, Ronald; Gheba, Jameel; Erill, Ivan

2014-05-01

Data from metagenomics projects remain largely untapped for the analysis of transcriptional regulatory networks. Here, we provide proof-of-concept that metagenomic data can be effectively leveraged to analyze regulatory networks by characterizing the SOS meta-regulon in the human gut microbiome. We combine well-established in silico and in vitro techniques to mine the human gut microbiome data and determine the relative composition of the SOS network in a natural setting. Our analysis highlights the importance of translesion synthesis as a primary function of the SOS response. We predict the association of this network with three novel protein clusters involved in cell wall biogenesis, chromosome partitioning and restriction modification, and we confirm binding of the SOS response transcriptional repressor to sites in the promoter of a cell wall biogenesis enzyme, a phage integrase and a death-on-curing protein. We discuss the implications of these findings and the potential for this approach for metagenome analysis.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Jialin Frank; Martínez, Maria Gabriela; Anderson, C Lindsay

This work presents a preliminary analysis considering impact of a grid-connected microgrid on network transmission of the power system. The locational marginal prices of the power system are used to strategically place the microgrid to avoid congestion problems. In addition, a Monte Carlo simulation approach is implemented to confirm that network congestion can be attenuated if appropriate price-based signals are set to define the import and export dynamic between the two systems.

An agent-based model of centralized institutions, social network technology, and revolution.

PubMed

Makowsky, Michael D; Rubin, Jared

2013-01-01

This paper sheds light on the general mechanisms underlying large-scale social and institutional change. We employ an agent-based model to test the impact of authority centralization and social network technology on preference falsification and institutional change. We find that preference falsification is increasing with centralization and decreasing with social network range. This leads to greater cascades of preference revelation and thus more institutional change in highly centralized societies and this effect is exacerbated at greater social network ranges. An empirical analysis confirms the connections that we find between institutional centralization, social radius, preference falsification, and institutional change.

Quantitative proteomic analysis of cultured skin fibroblast cells derived from patients with triglyceride deposit cardiomyovasculopathy

PubMed Central

2013-01-01

Background Triglyceride deposit cardiomyovasculopathy (TGCV) is a rare disease, characterized by the massive accumulation of triglyceride (TG) in multiple tissues, especially skeletal muscle, heart muscle and the coronary artery. TGCV is caused by mutation of adipose triglyceride lipase, which is an essential molecule for the hydrolysis of TG. TGCV is at high risk for skeletal myopathy and heart dysfunction, and therefore premature death. Development of therapeutic methods for TGCV is highly desirable. This study aims to discover specific molecules responsible for TGCV pathogenesis. Methods To identify differentially expressed proteins in TGCV patient cells, the stable isotope labeling with amino acids in cell culture (SILAC) method coupled with LC-MS/MS was performed using skin fibroblast cells derived from two TGCV patients and three healthy volunteers. Altered protein expression in TGCV cells was confirmed using the selected reaction monitoring (SRM) method. Microarray-based transcriptome analysis was simultaneously performed to identify changes in gene expression in TGCV cells. Results Using SILAC proteomics, 4033 proteins were quantified, 53 of which showed significantly altered expression in both TGCV patient cells. Twenty altered proteins were chosen and confirmed using SRM. SRM analysis successfully quantified 14 proteins, 13 of which showed the same trend as SILAC proteomics. The altered protein expression data set was used in Ingenuity Pathway Analysis (IPA), and significant networks were identified. Several of these proteins have been previously implicated in lipid metabolism, while others represent new therapeutic targets or markers for TGCV. Microarray analysis quantified 20743 transcripts, and 252 genes showed significantly altered expression in both TGCV patient cells. Ten altered genes were chosen, 9 of which were successfully confirmed using quantitative RT-PCR. Biological networks of altered genes were analyzed using an IPA search. Conclusions We performed the SILAC- and SRM-based identification-through-confirmation study using skin fibroblast cells derived from TGCV patients, and first identified altered proteins specific for TGCV. Microarray analysis also identified changes in gene expression. The functional networks of the altered proteins and genes are discussed. Our findings will be exploited to elucidate the pathogenesis of TGCV and discover clinically relevant molecules for TGCV in the near future. PMID:24360150

Exploring Wound-Healing Genomic Machinery with a Network-Based Approach

PubMed Central

Vitali, Francesca; Marini, Simone; Balli, Martina; Grosemans, Hanne; Sampaolesi, Maurilio; Lussier, Yves A.; Cusella De Angelis, Maria Gabriella; Bellazzi, Riccardo

2017-01-01

The molecular mechanisms underlying tissue regeneration and wound healing are still poorly understood despite their importance. In this paper we develop a bioinformatics approach, combining biology and network theory to drive experiments for better understanding the genetic underpinnings of wound healing mechanisms and for selecting potential drug targets. We start by selecting literature-relevant genes in murine wound healing, and inferring from them a Protein-Protein Interaction (PPI) network. Then, we analyze the network to rank wound healing-related genes according to their topological properties. Lastly, we perform a procedure for in-silico simulation of a treatment action in a biological pathway. The findings obtained by applying the developed pipeline, including gene expression analysis, confirms how a network-based bioinformatics method is able to prioritize candidate genes for in vitro analysis, thus speeding up the understanding of molecular mechanisms and supporting the discovery of potential drug targets. PMID:28635674

Minimum spanning tree analysis of the human connectome

PubMed Central

Sommer, Iris E.; Bohlken, Marc M.; Tewarie, Prejaas; Draaisma, Laurijn; Zalesky, Andrew; Di Biase, Maria; Brown, Jesse A.; Douw, Linda; Otte, Willem M.; Mandl, René C.W.; Stam, Cornelis J.

2018-01-01

Abstract One of the challenges of brain network analysis is to directly compare network organization between subjects, irrespective of the number or strength of connections. In this study, we used minimum spanning tree (MST; a unique, acyclic subnetwork with a fixed number of connections) analysis to characterize the human brain network to create an empirical reference network. Such a reference network could be used as a null model of connections that form the backbone structure of the human brain. We analyzed the MST in three diffusion‐weighted imaging datasets of healthy adults. The MST of the group mean connectivity matrix was used as the empirical null‐model. The MST of individual subjects matched this reference MST for a mean 58%–88% of connections, depending on the analysis pipeline. Hub nodes in the MST matched with previously reported locations of hub regions, including the so‐called rich club nodes (a subset of high‐degree, highly interconnected nodes). Although most brain network studies have focused primarily on cortical connections, cortical–subcortical connections were consistently present in the MST across subjects. Brain network efficiency was higher when these connections were included in the analysis, suggesting that these tracts may be utilized as the major neural communication routes. Finally, we confirmed that MST characteristics index the effects of brain aging. We conclude that the MST provides an elegant and straightforward approach to analyze structural brain networks, and to test network topological features of individual subjects in comparison to empirical null models. PMID:29468769

Performance Confirmation Data Aquisition System

DOE Office of Scientific and Technical Information (OSTI.GOV)

D.W. Markman

2000-10-27

The purpose of this analysis is to identify and analyze concepts for the acquisition of data in support of the Performance Confirmation (PC) program at the potential subsurface nuclear waste repository at Yucca Mountain. The scope and primary objectives of this analysis are to: (1) Review the criteria for design as presented in the Performance Confirmation Data Acquisition/Monitoring System Description Document, by way of the Input Transmittal, Performance Confirmation Input Criteria (CRWMS M&O 1999c). (2) Identify and describe existing and potential new trends in data acquisition system software and hardware that would support the PC plan. The data acquisition softwaremore » and hardware will support the field instruments and equipment that will be installed for the observation and perimeter drift borehole monitoring, and in-situ monitoring within the emplacement drifts. The exhaust air monitoring requirements will be supported by a data communication network interface with the ventilation monitoring system database. (3) Identify the concepts and features that a data acquisition system should have in order to support the PC process and its activities. (4) Based on PC monitoring needs and available technologies, further develop concepts of a potential data acquisition system network in support of the PC program and the Site Recommendation and License Application.« less

Smooth information flow in temperature climate network reflects mass transport

NASA Astrophysics Data System (ADS)

Hlinka, Jaroslav; Jajcay, Nikola; Hartman, David; Paluš, Milan

2017-03-01

A directed climate network is constructed by Granger causality analysis of air temperature time series from a regular grid covering the whole Earth. Using winner-takes-all network thresholding approach, a structure of a smooth information flow is revealed, hidden to previous studies. The relevance of this observation is confirmed by comparison with the air mass transfer defined by the wind field. Their close relation illustrates that although the information transferred due to the causal influence is not a physical quantity, the information transfer is tied to the transfer of mass and energy.

Photonics: From target recognition to lesion detection

NASA Technical Reports Server (NTRS)

Henry, E. Michael

1994-01-01

Since 1989, Martin Marietta has invested in the development of an innovative concept for robust real-time pattern recognition for any two-dimensioanal sensor. This concept has been tested in simulation, and in laboratory and field hardware, for a number of DOD and commercial uses from automatic target recognition to manufacturing inspection. We have now joined Rose Health Care Systems in developing its use for medical diagnostics. The concept is based on determining regions of interest by using optical Fourier bandpassing as a scene segmentation technique, enhancing those regions using wavelet filters, passing the enhanced regions to a neural network for analysis and initial pattern identification, and following this initial identification with confirmation by optical correlation. The optical scene segmentation and pattern confirmation are performed by the same optical module. The neural network is a recursive error minimization network with a small number of connections and nodes that rapidly converges to a global minimum.

Analysis of the SOS response of Vibrio and other bacteria with multiple chromosomes.

PubMed

Sanchez-Alberola, Neus; Campoy, Susana; Barbé, Jordi; Erill, Ivan

2012-02-03

The SOS response is a well-known regulatory network present in most bacteria and aimed at addressing DNA damage. It has also been linked extensively to stress-induced mutagenesis, virulence and the emergence and dissemination of antibiotic resistance determinants. Recently, the SOS response has been shown to regulate the activity of integrases in the chromosomal superintegrons of the Vibrionaceae, which encompasses a wide range of pathogenic species harboring multiple chromosomes. Here we combine in silico and in vitro techniques to perform a comparative genomics analysis of the SOS regulon in the Vibrionaceae, and we extend the methodology to map this transcriptional network in other bacterial species harboring multiple chromosomes. Our analysis provides the first comprehensive description of the SOS response in a family (Vibrionaceae) that includes major human pathogens. It also identifies several previously unreported members of the SOS transcriptional network, including two proteins of unknown function. The analysis of the SOS response in other bacterial species with multiple chromosomes uncovers additional regulon members and reveals that there is a conserved core of SOS genes, and that specialized additions to this basic network take place in different phylogenetic groups. Our results also indicate that across all groups the main elements of the SOS response are always found in the large chromosome, whereas specialized additions are found in the smaller chromosomes and plasmids. Our findings confirm that the SOS response of the Vibrionaceae is strongly linked with pathogenicity and dissemination of antibiotic resistance, and suggest that the characterization of the newly identified members of this regulon could provide key insights into the pathogenesis of Vibrio. The persistent location of key SOS genes in the large chromosome across several bacterial groups confirms that the SOS response plays an essential role in these organisms and sheds light into the mechanisms of evolution of global transcriptional networks involved in adaptability and rapid response to environmental changes, suggesting that small chromosomes may act as evolutionary test beds for the rewiring of transcriptional networks.

Dynamic Functional Connectivity States Between the Dorsal and Ventral Sensorimotor Networks Revealed by Dynamic Conditional Correlation Analysis of Resting-State Functional Magnetic Resonance Imaging.

PubMed

Syed, Maleeha F; Lindquist, Martin A; Pillai, Jay J; Agarwal, Shruti; Gujar, Sachin K; Choe, Ann S; Caffo, Brian; Sair, Haris I

2017-12-01

Functional connectivity in resting-state functional magnetic resonance imaging (rs-fMRI) has received substantial attention since the initial findings of Biswal et al. Traditional network correlation metrics assume that the functional connectivity in the brain remains stationary over time. However, recent studies have shown that robust temporal fluctuations of functional connectivity among as well as within functional networks exist, challenging this assumption. In this study, these dynamic correlation differences were investigated between the dorsal and ventral sensorimotor networks by applying the dynamic conditional correlation model to rs-fMRI data of 20 healthy subjects. k-Means clustering was used to determine an optimal number of discrete connectivity states (k = 10) of the sensorimotor system across all subjects. Our analysis confirms the existence of differences in dynamic correlation between the dorsal and ventral networks, with highest connectivity found within the ventral motor network.

An Agent-Based Model of Centralized Institutions, Social Network Technology, and Revolution

PubMed Central

Makowsky, Michael D.; Rubin, Jared

2013-01-01

This paper sheds light on the general mechanisms underlying large-scale social and institutional change. We employ an agent-based model to test the impact of authority centralization and social network technology on preference falsification and institutional change. We find that preference falsification is increasing with centralization and decreasing with social network range. This leads to greater cascades of preference revelation and thus more institutional change in highly centralized societies and this effect is exacerbated at greater social network ranges. An empirical analysis confirms the connections that we find between institutional centralization, social radius, preference falsification, and institutional change. PMID:24278280

"Us and them": a social network analysis of physicians' professional networks and their attitudes towards EBM.

PubMed

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 providers.

Social insect colony as a biological regulatory system: modelling information flow in dominance networks.

PubMed

Nandi, Anjan K; Sumana, Annagiri; Bhattacharya, Kunal

2014-12-06

Social insects provide an excellent platform to investigate flow of information in regulatory systems since their successful social organization is essentially achieved by effective information transfer through complex connectivity patterns among the colony members. Network representation of such behavioural interactions offers a powerful tool for structural as well as dynamical analysis of the underlying regulatory systems. In this paper, we focus on the dominance interaction networks in the tropical social wasp Ropalidia marginata-a species where behavioural observations indicate that such interactions are principally responsible for the transfer of information between individuals about their colony needs, resulting in a regulation of their own activities. Our research reveals that the dominance networks of R. marginata are structurally similar to a class of naturally evolved information processing networks, a fact confirmed also by the predominance of a specific substructure-the 'feed-forward loop'-a key functional component in many other information transfer networks. The dynamical analysis through Boolean modelling confirms that the networks are sufficiently stable under small fluctuations and yet capable of more efficient information transfer compared to their randomized counterparts. Our results suggest the involvement of a common structural design principle in different biological regulatory systems and a possible similarity with respect to the effect of selection on the organization levels of such systems. The findings are also consistent with the hypothesis that dominance behaviour has been shaped by natural selection to co-opt the information transfer process in such social insect species, in addition to its primal function of mediation of reproductive competition in the colony. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

MRI correlates of general intelligence in neurotypical adults.

PubMed

Malpas, Charles B; Genc, Sila; Saling, Michael M; Velakoulis, Dennis; Desmond, Patricia M; O'Brien, Terence J

2016-02-01

There is growing interest in the neurobiological substrate of general intelligence. Psychometric estimates of general intelligence are reduced in a range of neurological disorders, leading to practical application as sensitive, but non-specific, markers of cerebral disorder. This study examined estimates of general intelligence in neurotypical adults using diffusion tensor imaging and resting-state functional connectivity analysis. General intelligence was related to white matter organisation across multiple brain regions, confirming previous work in older healthy adults. We also found that variation in general intelligence was related to a large functional sub-network involving all cortical lobes of the brain. These findings confirm that individual variance in general intelligence is related to diffusely represented brain networks. Copyright © 2015 Elsevier Ltd. All rights reserved.

Minimum spanning tree analysis of the human connectome.

PubMed

van Dellen, Edwin; Sommer, Iris E; Bohlken, Marc M; Tewarie, Prejaas; Draaisma, Laurijn; Zalesky, Andrew; Di Biase, Maria; Brown, Jesse A; Douw, Linda; Otte, Willem M; Mandl, René C W; Stam, Cornelis J

2018-06-01

One of the challenges of brain network analysis is to directly compare network organization between subjects, irrespective of the number or strength of connections. In this study, we used minimum spanning tree (MST; a unique, acyclic subnetwork with a fixed number of connections) analysis to characterize the human brain network to create an empirical reference network. Such a reference network could be used as a null model of connections that form the backbone structure of the human brain. We analyzed the MST in three diffusion-weighted imaging datasets of healthy adults. The MST of the group mean connectivity matrix was used as the empirical null-model. The MST of individual subjects matched this reference MST for a mean 58%-88% of connections, depending on the analysis pipeline. Hub nodes in the MST matched with previously reported locations of hub regions, including the so-called rich club nodes (a subset of high-degree, highly interconnected nodes). Although most brain network studies have focused primarily on cortical connections, cortical-subcortical connections were consistently present in the MST across subjects. Brain network efficiency was higher when these connections were included in the analysis, suggesting that these tracts may be utilized as the major neural communication routes. Finally, we confirmed that MST characteristics index the effects of brain aging. We conclude that the MST provides an elegant and straightforward approach to analyze structural brain networks, and to test network topological features of individual subjects in comparison to empirical null models. © 2018 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Biomarker MicroRNAs for Diagnosis of Oral Squamous Cell Carcinoma Identified Based on Gene Expression Data and MicroRNA-mRNA Network Analysis

PubMed Central

Zhang, Hui; Li, Tangxin; Zheng, Linqing

2017-01-01

Oral squamous cell carcinoma is one of the most malignant tumors with high mortality rate worldwide. Biomarker discovery is critical for early diagnosis and precision treatment of this disease. MicroRNAs are small noncoding RNA molecules which often regulate essential biological processes and are good candidates for biomarkers. By integrative analysis of both the cancer-associated gene expression data and microRNA-mRNA network, miR-148b-3p, miR-629-3p, miR-27a-3p, and miR-142-3p were screened as novel diagnostic biomarkers for oral squamous cell carcinoma based on their unique regulatory abilities in the network structure of the conditional microRNA-mRNA network and their important functions. These findings were confirmed by literature verification and functional enrichment analysis. Future experimental validation is expected for the further investigation of their molecular mechanisms. PMID:29098014

Network structure and travel time perception.

PubMed

Parthasarathi, Pavithra; Levinson, David; Hochmair, Hartwig

2013-01-01

The purpose of this research is to test the systematic variation in the perception of travel time among travelers and relate the variation to the underlying street network structure. Travel survey data from the Twin Cities metropolitan area (which includes the cities of Minneapolis and St. Paul) is used for the analysis. Travelers are classified into two groups based on the ratio of perceived and estimated commute travel time. The measures of network structure are estimated using the street network along the identified commute route. T-test comparisons are conducted to identify statistically significant differences in estimated network measures between the two traveler groups. The combined effect of these estimated network measures on travel time is then analyzed using regression models. The results from the t-test and regression analyses confirm the influence of the underlying network structure on the perception of travel time.

Organisational adaptation in an activist network: social networks, leadership, and change in al-Muhajiroun.

PubMed

Kenney, Michael; Horgan, John; Horne, Cale; Vining, Peter; Carley, Kathleen M; Bigrigg, Michael W; Bloom, Mia; Braddock, Kurt

2013-09-01

Social networks are said to facilitate learning and adaptation by providing the connections through which network nodes (or agents) share information and experience. Yet, our understanding of how this process unfolds in real-world networks remains underdeveloped. This paper explores this gap through a case study of al-Muhajiroun, an activist network that continues to call for the establishment of an Islamic state in Britain despite being formally outlawed by British authorities. Drawing on organisation theory and social network analysis, we formulate three hypotheses regarding the learning capacity and social network properties of al-Muhajiroun (AM) and its successor groups. We then test these hypotheses using mixed methods. Our methods combine quantitative analysis of three agent-based networks in AM measured for structural properties that facilitate learning, including connectedness, betweenness centrality and eigenvector centrality, with qualitative analysis of interviews with AM activists focusing organisational adaptation and learning. The results of these analyses confirm that al-Muhajiroun activists respond to government pressure by changing their operations, including creating new platforms under different names and adjusting leadership roles among movement veterans to accommodate their spiritual leader's unwelcome exodus to Lebanon. Simple as they are effective, these adaptations have allowed al-Muhajiroun and its successor groups to continue their activism in an increasingly hostile environment. Copyright © 2012 Elsevier Ltd and The Ergonomics Society. All rights reserved.

ECS: efficient communication scheduling for underwater sensor networks.

PubMed

Hong, Lu; Hong, Feng; Guo, Zhongwen; Li, Zhengbao

2011-01-01

TDMA protocols have attracted a lot of attention for underwater acoustic sensor networks (UWSNs), because of the unique characteristics of acoustic signal propagation such as great energy consumption in transmission, long propagation delay and long communication range. Previous TDMA protocols all allocated transmission time to nodes based on discrete time slots. This paper proposes an efficient continuous time scheduling TDMA protocol (ECS) for UWSNs, including the continuous time based and sender oriented conflict analysis model, the transmission moment allocation algorithm and the distributed topology maintenance algorithm. Simulation results confirm that ECS improves network throughput by 20% on average, compared to existing MAC protocols.

Effects of biases in domain wall network evolution. II. Quantitative analysis

NASA Astrophysics Data System (ADS)

Correia, J. R. C. C. C.; Leite, I. S. C. R.; Martins, C. J. A. P.

2018-04-01

Domain walls form at phase transitions which break discrete symmetries. In a cosmological context, they often overclose the Universe (contrary to observational evidence), although one may prevent this by introducing biases or forcing anisotropic evolution of the walls. In a previous work [Correia et al., Phys. Rev. D 90, 023521 (2014), 10.1103/PhysRevD.90.023521], we numerically studied the evolution of various types of biased domain wall networks in the early Universe, confirming that anisotropic networks ultimately reach scaling while those with a biased potential or biased initial conditions decay. We also found that the analytic decay law obtained by Hindmarsh was in good agreement with simulations of biased potentials, but not of biased initial conditions, and suggested that the difference was related to the Gaussian approximation underlying the analytic law. Here, we extend our previous work in several ways. For the cases of biased potential and biased initial conditions, we study in detail the field distributions in the simulations, confirming that the validity (or not) of the Gaussian approximation is the key difference between the two cases. For anisotropic walls, we carry out a more extensive set of numerical simulations and compare them to the canonical velocity-dependent one-scale model for domain walls, finding that the model accurately predicts the linear scaling regime after isotropization. Overall, our analysis provides a quantitative description of the cosmological evolution of these networks.

Analysis of HD 73045 light curve data

NASA Astrophysics Data System (ADS)

Das, Mrinal Kanti; Bhatraju, Naveen Kumar; Joshi, Santosh

2018-04-01

In this work we analyzed the Kepler light curve data of HD 73045. The raw data has been smoothened using standard filters. The power spectrum has been obtained by using a fast Fourier transform routine. It shows the presence of more than one period. In order to take care of any non-stationary behavior, we carried out a wavelet analysis to obtain the wavelet power spectrum. In addition, to identify the scale invariant structure, the data has been analyzed using a multifractal detrended fluctuation analysis. Further to characterize the diversity of embedded patterns in the HD 73045 flux time series, we computed various entropy-based complexity measures e.g. sample entropy, spectral entropy and permutation entropy. The presence of periodic structure in the time series was further analyzed using the visibility network and horizontal visibility network model of the time series. The degree distributions in the two network models confirm such structures.

Effect of Cross-Linking on Free Volume Properties of PEG Based Thiol-Ene Networks

NASA Astrophysics Data System (ADS)

Ramakrishnan, Ramesh; Vasagar, Vivek; Nazarenko, Sergei

According to the Fox and Loshaek theory, in elastomeric networks, free volume decreases linearly with the cross-link density increase. The aim of this study is to show whether the poly(ethylene glycol) (PEG) based multicomponent thiol-ene elastomeric networks demonstrate this model behavior? Networks with a broad cross-link density range were prepared by changing the ratio of the trithiol crosslinker to PEG dithiol and then UV cured with PEG diene while maintaining 1:1 thiol:ene stoichiometry. Pressure-volume-temperature (PVT) data of the networks was generated from the high pressure dilatometry experiments which was fit using the Simha-Somcynsky Equation-of-State analysis to obtain the fractional free volume of the networks. Using Positron Annihilation Lifetime Spectroscopy (PALS) analysis, the average free volume hole size of the networks was also quantified. The fractional free volume and the average free volume hole size showed a linear change with the cross-link density confirming that the Fox and Loshaek theory can be applied to this multicomponent system. Gas diffusivities of the networks showed a good correlation with free volume. A free volume based model was developed to describe the gas diffusivity trends as a function of cross-link density.

Discriminating micropathogen lineages and their reticulate evolution through graph theory-based network analysis: the case of Trypanosoma cruzi, the agent of Chagas disease.

PubMed

Arnaud-Haond, Sophie; Moalic, Yann; Barnabé, Christian; Ayala, Francisco José; Tibayrenc, Michel

2014-01-01

Micropathogens (viruses, bacteria, fungi, parasitic protozoa) share a common trait, which is partial clonality, with wide variance in the respective influence of clonality and sexual recombination on the dynamics and evolution of taxa. The discrimination of distinct lineages and the reconstruction of their phylogenetic history are key information to infer their biomedical properties. However, the phylogenetic picture is often clouded by occasional events of recombination across divergent lineages, limiting the relevance of classical phylogenetic analysis and dichotomic trees. We have applied a network analysis based on graph theory to illustrate the relationships among genotypes of Trypanosoma cruzi, the parasitic protozoan responsible for Chagas disease, to identify major lineages and to unravel their past history of divergence and possible recombination events. At the scale of T. cruzi subspecific diversity, graph theory-based networks applied to 22 isoenzyme loci (262 distinct Multi-Locus-Enzyme-Electrophoresis -MLEE) and 19 microsatellite loci (66 Multi-Locus-Genotypes -MLG) fully confirms the high clustering of genotypes into major lineages or "near-clades". The release of the dichotomic constraint associated with phylogenetic reconstruction usually applied to Multilocus data allows identifying putative hybrids and their parental lineages. Reticulate topology suggests a slightly different history for some of the main "near-clades", and a possibly more complex origin for the putative hybrids than hitherto proposed. Finally the sub-network of the near-clade T. cruzi I (28 MLG) shows a clustering subdivision into three differentiated lesser near-clades ("Russian doll pattern"), which confirms the hypothesis recently proposed by other investigators. The present study broadens and clarifies the hypotheses previously obtained from classical markers on the same sets of data, which demonstrates the added value of this approach. This underlines the potential of graph theory-based network analysis for describing the nature and relationships of major pathogens, thereby opening stimulating prospects to unravel the organization, dynamics and history of major micropathogen lineages.

Application of a data-mining method based on Bayesian networks to lesion-deficit analysis

NASA Technical Reports Server (NTRS)

Herskovits, Edward H.; Gerring, Joan P.

2003-01-01

Although lesion-deficit analysis (LDA) has provided extensive information about structure-function associations in the human brain, LDA has suffered from the difficulties inherent to the analysis of spatial data, i.e., there are many more variables than subjects, and data may be difficult to model using standard distributions, such as the normal distribution. We herein describe a Bayesian method for LDA; this method is based on data-mining techniques that employ Bayesian networks to represent structure-function associations. These methods are computationally tractable, and can represent complex, nonlinear structure-function associations. When applied to the evaluation of data obtained from a study of the psychiatric sequelae of traumatic brain injury in children, this method generates a Bayesian network that demonstrates complex, nonlinear associations among lesions in the left caudate, right globus pallidus, right side of the corpus callosum, right caudate, and left thalamus, and subsequent development of attention-deficit hyperactivity disorder, confirming and extending our previous statistical analysis of these data. Furthermore, analysis of simulated data indicates that methods based on Bayesian networks may be more sensitive and specific for detecting associations among categorical variables than methods based on chi-square and Fisher exact statistics.

Network Structure and Travel Time Perception

PubMed Central

Parthasarathi, Pavithra; Levinson, David; Hochmair, Hartwig

2013-01-01

The purpose of this research is to test the systematic variation in the perception of travel time among travelers and relate the variation to the underlying street network structure. Travel survey data from the Twin Cities metropolitan area (which includes the cities of Minneapolis and St. Paul) is used for the analysis. Travelers are classified into two groups based on the ratio of perceived and estimated commute travel time. The measures of network structure are estimated using the street network along the identified commute route. T-test comparisons are conducted to identify statistically significant differences in estimated network measures between the two traveler groups. The combined effect of these estimated network measures on travel time is then analyzed using regression models. The results from the t-test and regression analyses confirm the influence of the underlying network structure on the perception of travel time. PMID:24204932

Mitochondrial network complexity emerges from fission/fusion dynamics.

PubMed

Zamponi, Nahuel; Zamponi, Emiliano; Cannas, Sergio A; Billoni, Orlando V; Helguera, Pablo R; Chialvo, Dante R

2018-01-10

Mitochondrial networks exhibit a variety of complex behaviors, including coordinated cell-wide oscillations of energy states as well as a phase transition (depolarization) in response to oxidative stress. Since functional and structural properties are often interwinded, here we characterized the structure of mitochondrial networks in mouse embryonic fibroblasts using network tools and percolation theory. Subsequently we perturbed the system either by promoting the fusion of mitochondrial segments or by inducing mitochondrial fission. Quantitative analysis of mitochondrial clusters revealed that structural parameters of healthy mitochondria laid in between the extremes of highly fragmented and completely fusioned networks. We confirmed our results by contrasting our empirical findings with the predictions of a recently described computational model of mitochondrial network emergence based on fission-fusion kinetics. Altogether these results offer not only an objective methodology to parametrize the complexity of this organelle but also support the idea that mitochondrial networks behave as critical systems and undergo structural phase transitions.

Game theoretic approach for cooperative feature extraction in camera networks

NASA Astrophysics Data System (ADS)

Redondi, Alessandro E. C.; Baroffio, Luca; Cesana, Matteo; Tagliasacchi, Marco

2016-07-01

Visual sensor networks (VSNs) consist of several camera nodes with wireless communication capabilities that can perform visual analysis tasks such as object identification, recognition, and tracking. Often, VSN deployments result in many camera nodes with overlapping fields of view. In the past, such redundancy has been exploited in two different ways: (1) to improve the accuracy/quality of the visual analysis task by exploiting multiview information or (2) to reduce the energy consumed for performing the visual task, by applying temporal scheduling techniques among the cameras. We propose a game theoretic framework based on the Nash bargaining solution to bridge the gap between the two aforementioned approaches. The key tenet of the proposed framework is for cameras to reduce the consumed energy in the analysis process by exploiting the redundancy in the reciprocal fields of view. Experimental results in both simulated and real-life scenarios confirm that the proposed scheme is able to increase the network lifetime, with a negligible loss in terms of visual analysis accuracy.

Analysis of the SOS response of Vibrio and other bacteria with multiple chromosomes

PubMed Central

2012-01-01

Background The SOS response is a well-known regulatory network present in most bacteria and aimed at addressing DNA damage. It has also been linked extensively to stress-induced mutagenesis, virulence and the emergence and dissemination of antibiotic resistance determinants. Recently, the SOS response has been shown to regulate the activity of integrases in the chromosomal superintegrons of the Vibrionaceae, which encompasses a wide range of pathogenic species harboring multiple chromosomes. Here we combine in silico and in vitro techniques to perform a comparative genomics analysis of the SOS regulon in the Vibrionaceae, and we extend the methodology to map this transcriptional network in other bacterial species harboring multiple chromosomes. Results Our analysis provides the first comprehensive description of the SOS response in a family (Vibrionaceae) that includes major human pathogens. It also identifies several previously unreported members of the SOS transcriptional network, including two proteins of unknown function. The analysis of the SOS response in other bacterial species with multiple chromosomes uncovers additional regulon members and reveals that there is a conserved core of SOS genes, and that specialized additions to this basic network take place in different phylogenetic groups. Our results also indicate that across all groups the main elements of the SOS response are always found in the large chromosome, whereas specialized additions are found in the smaller chromosomes and plasmids. Conclusions Our findings confirm that the SOS response of the Vibrionaceae is strongly linked with pathogenicity and dissemination of antibiotic resistance, and suggest that the characterization of the newly identified members of this regulon could provide key insights into the pathogenesis of Vibrio. The persistent location of key SOS genes in the large chromosome across several bacterial groups confirms that the SOS response plays an essential role in these organisms and sheds light into the mechanisms of evolution of global transcriptional networks involved in adaptability and rapid response to environmental changes, suggesting that small chromosomes may act as evolutionary test beds for the rewiring of transcriptional networks. PMID:22305460

Prediction and functional analysis of the sweet orange protein-protein interaction network.

PubMed

Ding, Yu-Duan; Chang, Ji-Wei; Guo, Jing; Chen, Dijun; Li, Sen; Xu, Qiang; Deng, Xiu-Xin; Cheng, Yun-Jiang; Chen, Ling-Ling

2014-08-05

Sweet orange (Citrus sinensis) is one of the most important fruits world-wide. Because it is a woody plant with a long growth cycle, genetic studies of sweet orange are lagging behind those of other species. In this analysis, we employed ortholog identification and domain combination methods to predict the protein-protein interaction (PPI) network for sweet orange. The K-nearest neighbors (KNN) classification method was used to verify and filter the network. The final predicted PPI network, CitrusNet, contained 8,195 proteins with 124,491 interactions. The quality of CitrusNet was evaluated using gene ontology (GO) and Mapman annotations, which confirmed the reliability of the network. In addition, we calculated the expression difference of interacting genes (EDI) in CitrusNet using RNA-seq data from four sweet orange tissues, and also analyzed the EDI distribution and variation in different sub-networks. Gene expression in CitrusNet has significant modular features. Target of rapamycin (TOR) protein served as the central node of the hormone-signaling sub-network. All evidence supported the idea that TOR can integrate various hormone signals and affect plant growth. CitrusNet provides valuable resources for the study of biological functions in sweet orange.

Breakdown of interdependent directed networks.

PubMed

Liu, Xueming; Stanley, H Eugene; Gao, Jianxi

2016-02-02

Increasing evidence shows that real-world systems interact with one another via dependency connectivities. Failing connectivities are the mechanism behind the breakdown of interacting complex systems, e.g., blackouts caused by the interdependence of power grids and communication networks. Previous research analyzing the robustness of interdependent networks has been limited to undirected networks. However, most real-world networks are directed, their in-degrees and out-degrees may be correlated, and they are often coupled to one another as interdependent directed networks. To understand the breakdown and robustness of interdependent directed networks, we develop a theoretical framework based on generating functions and percolation theory. We find that for interdependent Erdős-Rényi networks the directionality within each network increases their vulnerability and exhibits hybrid phase transitions. We also find that the percolation behavior of interdependent directed scale-free networks with and without degree correlations is so complex that two criteria are needed to quantify and compare their robustness: the percolation threshold and the integrated size of the giant component during an entire attack process. Interestingly, we find that the in-degree and out-degree correlations in each network layer increase the robustness of interdependent degree heterogeneous networks that most real networks are, but decrease the robustness of interdependent networks with homogeneous degree distribution and with strong coupling strengths. Moreover, by applying our theoretical analysis to real interdependent international trade networks, we find that the robustness of these real-world systems increases with the in-degree and out-degree correlations, confirming our theoretical analysis.

Using sensor networks to study the effect of peripatetic healthcare workers on the spread of hospital-associated infections.

PubMed

Hornbeck, Thomas; Naylor, David; Segre, Alberto M; Thomas, Geb; Herman, Ted; Polgreen, Philip M

2012-11-15

Super-spreading events, in which an individual with measurably high connectivity is responsible for infecting a large number of people, have been observed. Our goal is to determine the impact of hand hygiene noncompliance among peripatetic (eg, highly mobile or highly connected) healthcare workers compared with less-connected workers. We used a mote-based sensor network to record contacts among healthcare workers and patients in a 20-bed intensive care unit. The data collected from this network form the basis for an agent-based simulation to model the spread of nosocomial pathogens with various transmission probabilities. We identified the most- and least-connected healthcare workers. We then compared the effects of hand hygiene noncompliance as a function of connectedness. The data confirm the presence of peripatetic healthcare workers. Also, agent-based simulations using our real contact network data confirm that the average number of infected patients was significantly higher when the most connected healthcare worker did not practice hand hygiene and significantly lower when the least connected healthcare workers were noncompliant. Heterogeneity in healthcare worker contact patterns dramatically affects disease diffusion. Our findings should inform future infection control interventions and encourage the application of social network analysis to study disease transmission in healthcare settings.

ECS: Efficient Communication Scheduling for Underwater Sensor Networks

PubMed Central

Hong, Lu; Hong, Feng; Guo, Zhongwen; Li, Zhengbao

2011-01-01

TDMA protocols have attracted a lot of attention for underwater acoustic sensor networks (UWSNs), because of the unique characteristics of acoustic signal propagation such as great energy consumption in transmission, long propagation delay and long communication range. Previous TDMA protocols all allocated transmission time to nodes based on discrete time slots. This paper proposes an efficient continuous time scheduling TDMA protocol (ECS) for UWSNs, including the continuous time based and sender oriented conflict analysis model, the transmission moment allocation algorithm and the distributed topology maintenance algorithm. Simulation results confirm that ECS improves network throughput by 20% on average, compared to existing MAC protocols. PMID:22163775

A control network of Triton

NASA Astrophysics Data System (ADS)

Davies, Merton E.; Rogers, Patricia G.; Colvin, Tim R.

1991-08-01

A control network for Triton has been computed using a bundle-type analytical triangulation program. The network contains 105 points that were measured on 57 Voyager-2 pictures. The adjustment contained 1010 observation equations and 382 normal equations and resulted in a standard measurement error of 13.36 microns. The coordinates of the control points, the camera orientation angles at the times when the pictures were taken, and Triton's mean radius were determined. A separate statistical analysis confirmed Triton's radius to be 1352.6 + or - 2.4 km. Attempts to tie the control network around the satellite were unsuccessful because discontinuities exist in high-resolution coverage between 66 deg and 289 deg longitude, north of 38 deg latitude, and south of 78 deg latitude.

Integrated Genomics Reveals Convergent Transcriptomic Networks Underlying Chronic Obstructive Pulmonary Disease and Idiopathic Pulmonary Fibrosis.

PubMed

Kusko, Rebecca L; Brothers, John F; Tedrow, John; Pandit, Kusum; Huleihel, Luai; Perdomo, Catalina; Liu, Gang; Juan-Guardela, Brenda; Kass, Daniel; Zhang, Sherry; Lenburg, Marc; Martinez, Fernando; Quackenbush, John; Sciurba, Frank; Limper, Andrew; Geraci, Mark; Yang, Ivana; Schwartz, David A; Beane, Jennifer; Spira, Avrum; Kaminski, Naftali

2016-10-15

Despite shared environmental exposures, idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease are usually studied in isolation, and the presence of shared molecular mechanisms is unknown. We applied an integrative genomic approach to identify convergent transcriptomic pathways in emphysema and IPF. We defined the transcriptional repertoire of chronic obstructive pulmonary disease, IPF, or normal histology lungs using RNA-seq (n = 87). Genes increased in both emphysema and IPF relative to control were enriched for the p53/hypoxia pathway, a finding confirmed in an independent cohort using both gene expression arrays and the nCounter Analysis System (n = 193). Immunohistochemistry confirmed overexpression of HIF1A, MDM2, and NFKBIB members of this pathway in tissues from patients with emphysema or IPF. Using reads aligned across splice junctions, we determined that alternative splicing of p53/hypoxia pathway-associated molecules NUMB and PDGFA occurred more frequently in IPF or emphysema compared with control and validated these findings by quantitative polymerase chain reaction and the nCounter Analysis System on an independent sample set (n = 193). Finally, by integrating parallel microRNA and mRNA-Seq data on the same samples, we identified MIR96 as a key novel regulatory hub in the p53/hypoxia gene-expression network and confirmed that modulation of MIR96 in vitro recapitulates the disease-associated gene-expression network. Our results suggest convergent transcriptional regulatory hubs in diseases as varied phenotypically as chronic obstructive pulmonary disease and IPF and suggest that these hubs may represent shared key responses of the lung to environmental stresses.

Tuning of Nafion® by HKUST-1 as coordination network to enhance proton conductivity for fuel cell applications

NASA Astrophysics Data System (ADS)

Kim, Hee Jin; Talukdar, Krishan; Choi, Sang-June

2016-02-01

Metal-organic frameworks can be intentionally coordinated to achieve improved proton conductivity because they have highly ordered structures and modular nature that serve as a scaffold to anchor acidic groups and develop efficient proton transfer pathways for fuel cell application. Using the concept of a coordination network, the conductivity of Nafion® was tuned by the incorporation of HKUST-1. It has CuII-paddle wheel type nodes and 1,3,5-benzenetricarboxylate struts, feature accessible sites that provides an improved protonic channel depending on the water content. In spite of the fact that HKUST-1 is neutral, coordinated water molecules are contributed adequately acidic by CuII to supply protons to enhance proton conductivity. Water molecules play a vital part in transfer of proton as conducting media and serve as triggers to change proton conductivity through reforming hydrogen bonding networks by water adsorption/desorption process. Increased ion exchange capacity and proton conductivity with lower water uptake of the H3PO4-doped material, and improved thermal stability (as confirmed by thermogravimetric analysis) were achieved. The structure of HKUST-1 was confirmed via field emission scanning electron microscopy and X-ray diffraction, while the porosity and adsorption desorption capacity were characterized by porosity analysis.

Can Social Network Analysis Help Address the High Rates of Bacterial Sexually Transmitted Infections in Saskatchewan?

PubMed

Trecker, Molly A; Dillon, Jo-Anne R; Lloyd, Kathy; Hennink, Maurice; Jolly, Ann; Waldner, Cheryl

2017-06-01

Saskatchewan has one of the highest rates of gonorrhea among the Canadian provinces-more than double the national rate. In light of these high rates, and the growing threat of untreatable infections, improved understanding of gonorrhea transmission dynamics in the province and evaluation of the current system and tools for disease control are important. We extracted data from a cross-sectional sample of laboratory-confirmed gonorrhea cases between 2003 and 2012 from the notifiable disease files of the Regina Qu'Appelle Health Region. The database was stratified by calendar year, and social network analysis combined with statistical modeling was used to identify associations between measures of connection within the network and the odds of repeat gonorrhea and risk of coinfection with chlamydia at the time of diagnosis. Networks were highly fragmented. Younger age and component size were positively associated with being coinfected with chlamydia. Being coinfected, reporting sex trade involvement, and component size were all positively associated with repeat infection. This is the first study to apply social network analysis to gonorrhea transmission in Saskatchewan and contributes important information about the relationship of network connections to gonorrhea/chlamydia coinfection and repeat gonorrhea. This study also suggests several areas for change of systems-related factors that could greatly increase understanding of social networks and enhance the potential for bacterial sexually transmitted infection control in Saskatchewan.

The Influence of Social Network Characteristics on Peer Clustering in Smoking: A Two-Wave Panel Study of 19- and 23-Year-Old Swedes.

PubMed

Miething, Alexander; Rostila, Mikael; Edling, Christofer; Rydgren, Jens

2016-01-01

The present study examines how the composition of social networks and perceived relationship content influence peer clustering in smoking, and how the association changes during the transition from late adolescence to early adulthood. The analysis was based on a Swedish two-wave survey sample comprising ego-centric network data. Respondents were 19 years old in the initial wave, and 23 when the follow-up sample was conducted. 17,227 ego-alter dyads were included in the analyses, which corresponds to an average response rate of 48.7 percent. Random effects logistic regression models were performed to calculate gender-specific average marginal effects of social network characteristics on smoking. The association of egos' and alters' smoking behavior was confirmed and found to be stronger when correlated in the female sample. For females, the associations decreased between age 19 and 23. Interactions between network characteristics and peer clustering in smoking showed that intense social interactions with smokers increase egos' smoking probability. The influence of network structures on peer clustering in smoking decreased during the transition from late adolescence to early adulthood. The study confirmed peer clustering in smoking and revealed that females' smoking behavior in particular is determined by social interactions. Female smokers' propensity to interact with other smokers was found to be associated with the quality of peer relationships, frequent social interactions, and network density. The influence of social networks on peer clustering in smoking decreased during the transition from late adolescence to early adulthood.

The Influence of Social Network Characteristics on Peer Clustering in Smoking: A Two-Wave Panel Study of 19- and 23-Year-Old Swedes

PubMed Central

Rostila, Mikael; Edling, Christofer; Rydgren, Jens

2016-01-01

Objectives The present study examines how the composition of social networks and perceived relationship content influence peer clustering in smoking, and how the association changes during the transition from late adolescence to early adulthood. Methods The analysis was based on a Swedish two-wave survey sample comprising ego-centric network data. Respondents were 19 years old in the initial wave, and 23 when the follow-up sample was conducted. 17,227 ego-alter dyads were included in the analyses, which corresponds to an average response rate of 48.7 percent. Random effects logistic regression models were performed to calculate gender-specific average marginal effects of social network characteristics on smoking. Results The association of egos’ and alters’ smoking behavior was confirmed and found to be stronger when correlated in the female sample. For females, the associations decreased between age 19 and 23. Interactions between network characteristics and peer clustering in smoking showed that intense social interactions with smokers increase egos’ smoking probability. The influence of network structures on peer clustering in smoking decreased during the transition from late adolescence to early adulthood. Conclusions The study confirmed peer clustering in smoking and revealed that females’ smoking behavior in particular is determined by social interactions. Female smokers’ propensity to interact with other smokers was found to be associated with the quality of peer relationships, frequent social interactions, and network density. The influence of social networks on peer clustering in smoking decreased during the transition from late adolescence to early adulthood. PMID:27727314

Bayesian Networks Predict Neuronal Transdifferentiation.

PubMed

Ainsworth, Richard I; Ai, Rizi; Ding, Bo; Li, Nan; Zhang, Kai; Wang, Wei

2018-05-30

We employ the language of Bayesian networks to systematically construct gene-regulation topologies from deep-sequencing single-nucleus RNA-Seq data for human neurons. From the perspective of the cell-state potential landscape, we identify attractors that correspond closely to different neuron subtypes. Attractors are also recovered for cell states from an independent data set confirming our models accurate description of global genetic regulations across differing cell types of the neocortex (not included in the training data). Our model recovers experimentally confirmed genetic regulations and community analysis reveals genetic associations in common pathways. Via a comprehensive scan of all theoretical three-gene perturbations of gene knockout and overexpression, we discover novel neuronal trans-differrentiation recipes (including perturbations of SATB2, GAD1, POU6F2 and ADARB2) for excitatory projection neuron and inhibitory interneuron subtypes. Copyright © 2018, G3: Genes, Genomes, Genetics.

Time-series network analysis of civil aviation in Japan (1985-2005)

NASA Astrophysics Data System (ADS)

Michishita, Ryo; Xu, Bing; Yamada, Ikuho

2008-10-01

Due to the airline deregulation in 1985, a series of new airport developments in the 1990s and 2000s, and the reorganization of airline companies in the 2000s, Japan's air passenger transportation has been dramatically altered in the last two decades in many ways. In this paper, the authors examine how the network and flow structures of domestic air passenger transportation in Japan have geographically changed since 1985. For this purpose, passenger flow data in 1985, 1995, and 2005 were extracted from the Air Transportation Statistical Survey conducted by the Ministry of Land, Infrastructure and Transport, Japan. First, national and regional hub airports are identified via dominant flow and hub function analysis. Then the roles of the hub airports and individual connections over the network are examined with respect to their spatial and network autocorrelations. Spatial and network autocorrelations were evaluated both globally and locally using Moran's I and LISA statistics. The passenger flow data were first examined as a whole and then divided into 3 airline-based categories. Dominant flow and hub function enabled us to detect the hub airports. Structural processes of the hub-and-spoke network were confirmed in each airline through spatial autocorrelation analysis. Network autocorrelation analysis showed that all airlines ingeniously optimized their networks by connecting their routes with large numbers of passengers to other routes with large numbers of passengers, and routes with small numbers of passengers to other routes with small numbers of passengers. The effects of political events and the changes in the strategies of each airline on the whole networks were strongly reflected in the results of this study.

Backbone of complex networks of corporations: the flow of control.

PubMed

Glattfelder, J B; Battiston, S

2009-09-01

We present a methodology to extract the backbone of complex networks based on the weight and direction of links, as well as on nontopological properties of nodes. We show how the methodology can be applied in general to networks in which mass or energy is flowing along the links. In particular, the procedure enables us to address important questions in economics, namely, how control and wealth are structured and concentrated across national markets. We report on the first cross-country investigation of ownership networks, focusing on the stock markets of 48 countries around the world. On the one hand, our analysis confirms results expected on the basis of the literature on corporate control, namely, that in Anglo-Saxon countries control tends to be dispersed among numerous shareholders. On the other hand, it also reveals that in the same countries, control is found to be highly concentrated at the global level, namely, lying in the hands of very few important shareholders. Interestingly, the exact opposite is observed for European countries. These results have previously not been reported as they are not observable without the kind of network analysis developed here.

Social Network Analysis Applied to a Historical Ethnographic Study Surrounding Home Birth.

PubMed

Andina-Diaz, Elena; Ovalle-Perandones, Mª Antonia; Ramos-Vidal, Ignacio; Camacho-Morell, Francisca; Siles-Gonzalez, Jose; Marques-Sanchez, Pilar

2018-04-24

Safety during birth has improved since hospital delivery became standard practice, but the process has also become increasingly medicalised. Hence, recent years have witnessed a growing interest in home births due to the advantages it offers to mothers and their newborn infants. The aims of the present study were to confirm the transition from a home birth model of care to a scenario in which deliveries began to occur almost exclusively in a hospital setting; to define the social networks surrounding home births; and to determine whether geography exerted any influence on the social networks surrounding home births. Adopting a qualitative approach, we recruited 19 women who had given birth at home in the mid 20th century in a rural area in Spain. We employed a social network analysis method. Our results revealed three essential aspects that remain relevant today: the importance of health professionals in home delivery care, the importance of the mother’s primary network, and the influence of the geographical location of the actors involved in childbirth. All of these factors must be taken into consideration when developing strategies for maternal health.

Traumatic brain injury impairs small-world topology

PubMed Central

Pandit, Anand S.; Expert, Paul; Lambiotte, Renaud; Bonnelle, Valerie; Leech, Robert; Turkheimer, Federico E.

2013-01-01

Objective: We test the hypothesis that brain networks associated with cognitive function shift away from a “small-world” organization following traumatic brain injury (TBI). Methods: We investigated 20 TBI patients and 21 age-matched controls. Resting-state functional MRI was used to study functional connectivity. Graph theoretical analysis was then applied to partial correlation matrices derived from these data. The presence of white matter damage was quantified using diffusion tensor imaging. Results: Patients showed characteristic cognitive impairments as well as evidence of damage to white matter tracts. Compared to controls, the graph analysis showed reduced overall connectivity, longer average path lengths, and reduced network efficiency. A particular impact of TBI is seen on a major network hub, the posterior cingulate cortex. Taken together, these results confirm that a network critical to cognitive function shows a shift away from small-world characteristics. Conclusions: We provide evidence that key brain networks involved in supporting cognitive function become less small-world in their organization after TBI. This is likely to be the result of diffuse white matter damage, and may be an important factor in producing cognitive impairment after TBI. PMID:23596068

Backbone of complex networks of corporations: The flow of control

NASA Astrophysics Data System (ADS)

Glattfelder, J. B.; Battiston, S.

2009-09-01

We present a methodology to extract the backbone of complex networks based on the weight and direction of links, as well as on nontopological properties of nodes. We show how the methodology can be applied in general to networks in which mass or energy is flowing along the links. In particular, the procedure enables us to address important questions in economics, namely, how control and wealth are structured and concentrated across national markets. We report on the first cross-country investigation of ownership networks, focusing on the stock markets of 48 countries around the world. On the one hand, our analysis confirms results expected on the basis of the literature on corporate control, namely, that in Anglo-Saxon countries control tends to be dispersed among numerous shareholders. On the other hand, it also reveals that in the same countries, control is found to be highly concentrated at the global level, namely, lying in the hands of very few important shareholders. Interestingly, the exact opposite is observed for European countries. These results have previously not been reported as they are not observable without the kind of network analysis developed here.

Large-scale De Novo Prediction of Physical Protein-Protein Association*

PubMed Central

Elefsinioti, Antigoni; Saraç, Ömer Sinan; Hegele, Anna; Plake, Conrad; Hubner, Nina C.; Poser, Ina; Sarov, Mihail; Hyman, Anthony; Mann, Matthias; Schroeder, Michael; Stelzl, Ulrich; Beyer, Andreas

2011-01-01

Information about the physical association of proteins is extensively used for studying cellular processes and disease mechanisms. However, complete experimental mapping of the human interactome will remain prohibitively difficult in the near future. Here we present a map of predicted human protein interactions that distinguishes functional association from physical binding. Our network classifies more than 5 million protein pairs predicting 94,009 new interactions with high confidence. We experimentally tested a subset of these predictions using yeast two-hybrid analysis and affinity purification followed by quantitative mass spectrometry. Thus we identified 462 new protein-protein interactions and confirmed the predictive power of the network. These independent experiments address potential issues of circular reasoning and are a distinctive feature of this work. Analysis of the physical interactome unravels subnetworks mediating between different functional and physical subunits of the cell. Finally, we demonstrate the utility of the network for the analysis of molecular mechanisms of complex diseases by applying it to genome-wide association studies of neurodegenerative diseases. This analysis provides new evidence implying TOMM40 as a factor involved in Alzheimer's disease. The network provides a high-quality resource for the analysis of genomic data sets and genetic association studies in particular. Our interactome is available via the hPRINT web server at: www.print-db.org. PMID:21836163

Development and psychometric testing of the clinical networks engagement tool

PubMed Central

Hecker, Kent G.; Rabatach, Leora; Noseworthy, Tom W.; White, Deborah E.

2017-01-01

Background Clinical networks are being used widely to facilitate large system transformation in healthcare, by engagement of stakeholders throughout the health system. However, there are no available instruments that measure engagement in these networks. Methods The study purpose was to develop and assess the measurement properties of a multiprofessional tool to measure engagement in clinical network initiatives. Based on components of the International Association of Public Participation Spectrum and expert panel review, we developed 40 items for testing. The draft instrument was distributed to 1,668 network stakeholders across different governance levels (leaders, members, support, frontline stakeholders) in 9 strategic clinical networks in Alberta (January to July 2014). With data from 424 completed surveys (25.4% response rate), descriptive statistics, exploratory and confirmatory factor analysis, Pearson correlations, linear regression, multivariate analysis, and Cronbach alpha were conducted to assess reliability and validity of the scores. Results Sixteen items were retained in the instrument. Exploratory factor analysis indicated a four-factor solution and accounted for 85.7% of the total variance in engagement with clinical network initiatives: global engagement, inform (provided with information), involve (worked together to address concerns), and empower (given final decision-making authority). All subscales demonstrated acceptable reliability (Cronbach alpha 0.87 to 0.99). Both the confirmatory factor analysis and regression analysis confirmed that inform, involve, and empower were all significant predictors of global engagement, with involve as the strongest predictor. Leaders had higher mean scores than frontline stakeholders, while members and support staff did not differ in mean scores. Conclusions This study provided foundational evidence for the use of this tool for assessing engagement in clinical networks. Further work is necessary to evaluate engagement in broader network functions and activities; to assess barriers and facilitators of engagement; and, to elucidate how the maturity of networks and other factors influence engagement. PMID:28350834

Operational problems of Haniwa net as a form of social capital: interdependence between human networks of physicians and information networks.

PubMed

Maeda, Minoru; Araki, Sanae; Suzuki, Muneou; Umemoto, Katsuhiro; Kai, Yukiko; Araki, Kenji

2012-10-01

In August 2009, Miyazaki Health and Welfare Network (Haniwa Net, hereafter referred to as "the Net"), centrally led by University of Miyazaki Hospital (UMH), adopted a center hospital-based system offering a unilateral linkage that enables the viewing of UMH's medical records through a web-based browser (electronic medical records (EMR)). By the end of December 2010, the network had developed into a system of 79 collaborating physicians from within the prefecture. Beginning in August 2010, physicians in 12 medical institutions were visited and asked to speak freely on the operational issues concerning the Net. Recordings and written accounts were coded using the text analysis software MAXQDA 10 to understand the actual state of operations. Analysis of calculations of Kendall's rank correlation confirmed that the interdependency between human networks and information networks is significant. At the same time, while the negative opinions concerning the functions of the Net were somewhat conspicuous, the results showed a correlation between requests and proposals for operational improvements of the Net, clearly indicating the need for a more user-friendly system and a better viewer.

Rumor Spreading Model with Trust Mechanism in Complex Social Networks

NASA Astrophysics Data System (ADS)

Wang, Ya-Qi; Yang, Xiao-Yuan; Han, Yi-Liang; Wang, Xu-An

2013-04-01

In this paper, to study rumor spreading, we propose a novel susceptible-infected-removed (SIR) model by introducing the trust mechanism. We derive mean-field equations that describe the dynamics of the SIR model on homogeneous networks and inhomogeneous networks. Then a steady-state analysis is conducted to investigate the critical threshold and the final size of the rumor spreading. We show that the introduction of trust mechanism reduces the final rumor size and the velocity of rumor spreading, but increases the critical thresholds on both networks. Moreover, the trust mechanism not only greatly reduces the maximum rumor influence, but also postpones the rumor terminal time, which provides us with more time to take measures to control the rumor spreading. The theoretical results are confirmed by sufficient numerical simulations.

Communication Dynamics in Finite Capacity Social Networks

NASA Astrophysics Data System (ADS)

Haerter, Jan O.; Jamtveit, Bjørn; Mathiesen, Joachim

2012-10-01

In communication networks, structure and dynamics are tightly coupled. The structure controls the flow of information and is itself shaped by the dynamical process of information exchanged between nodes. In order to reconcile structure and dynamics, a generic model, based on the local interaction between nodes, is considered for the communication in large social networks. In agreement with data from a large human organization, we show that the flow is non-Markovian and controlled by the temporal limitations of individuals. We confirm the versatility of our model by predicting simultaneously the degree-dependent node activity, the balance between information input and output of nodes, and the degree distribution. Finally, we quantify the limitations to network analysis when it is based on data sampled over a finite period of time.

Bias, belief, and consensus: Collective opinion formation on fluctuating networks

NASA Astrophysics Data System (ADS)

Ngampruetikorn, Vudtiwat; Stephens, Greg J.

2016-11-01

With the advent of online networks, societies have become substantially more interconnected with individual members able to easily both maintain and modify their own social links. Here, we show that active network maintenance exposes agents to confirmation bias, the tendency to confirm one's beliefs, and we explore how this bias affects collective opinion formation. We introduce a model of binary opinion dynamics on a complex, fluctuating network with stochastic rewiring and we analyze these dynamics in the mean-field limit of large networks and fast link rewiring. We show that confirmation bias induces a segregation of individuals with different opinions and stabilizes the consensus state. We further show that bias can have an unusual, nonmonotonic effect on the time to consensus and this suggests a novel avenue for large-scale opinion manipulation.

Bias, belief, and consensus: Collective opinion formation on fluctuating networks.

PubMed

Ngampruetikorn, Vudtiwat; Stephens, Greg J

2016-11-01

With the advent of online networks, societies have become substantially more interconnected with individual members able to easily both maintain and modify their own social links. Here, we show that active network maintenance exposes agents to confirmation bias, the tendency to confirm one's beliefs, and we explore how this bias affects collective opinion formation. We introduce a model of binary opinion dynamics on a complex, fluctuating network with stochastic rewiring and we analyze these dynamics in the mean-field limit of large networks and fast link rewiring. We show that confirmation bias induces a segregation of individuals with different opinions and stabilizes the consensus state. We further show that bias can have an unusual, nonmonotonic effect on the time to consensus and this suggests a novel avenue for large-scale opinion manipulation.

Deep Gaze Velocity Analysis During Mammographic Reading for Biometric Identification of Radiologists

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoon, Hong-Jun; Alamudun, Folami T.; Hudson, Kathy

Several studies have confirmed that the gaze velocity of the human eye can be utilized as a behavioral biometric or personalized biomarker. In this study, we leverage the local feature representation capacity of convolutional neural networks (CNNs) for eye gaze velocity analysis as the basis for biometric identification of radiologists performing breast cancer screening. Using gaze data collected from 10 radiologists reading 100 mammograms of various diagnoses, we compared the performance of a CNN-based classification algorithm with two deep learning classifiers, deep neural network and deep belief network, and a previously presented hidden Markov model classifier. The study showed thatmore » the CNN classifier is superior compared to alternative classification methods based on macro F1-scores derived from 10-fold cross-validation experiments. Our results further support the efficacy of eye gaze velocity as a biometric identifier of medical imaging experts.« less

Deep Gaze Velocity Analysis During Mammographic Reading for Biometric Identification of Radiologists

DOE PAGES

Yoon, Hong-Jun; Alamudun, Folami T.; Hudson, Kathy; ...

2018-01-24

Several studies have confirmed that the gaze velocity of the human eye can be utilized as a behavioral biometric or personalized biomarker. In this study, we leverage the local feature representation capacity of convolutional neural networks (CNNs) for eye gaze velocity analysis as the basis for biometric identification of radiologists performing breast cancer screening. Using gaze data collected from 10 radiologists reading 100 mammograms of various diagnoses, we compared the performance of a CNN-based classification algorithm with two deep learning classifiers, deep neural network and deep belief network, and a previously presented hidden Markov model classifier. The study showed thatmore » the CNN classifier is superior compared to alternative classification methods based on macro F1-scores derived from 10-fold cross-validation experiments. Our results further support the efficacy of eye gaze velocity as a biometric identifier of medical imaging experts.« less

Integrated systems analysis reveals a molecular network underlying autism spectrum disorders

PubMed Central

Li, Jingjing; Shi, Minyi; Ma, Zhihai; Zhao, Shuchun; Euskirchen, Ghia; Ziskin, Jennifer; Urban, Alexander; Hallmayer, Joachim; Snyder, Michael

2014-01-01

Autism is a complex disease whose etiology remains elusive. We integrated previously and newly generated data and developed a systems framework involving the interactome, gene expression and genome sequencing to identify a protein interaction module with members strongly enriched for autism candidate genes. Sequencing of 25 patients confirmed the involvement of this module in autism, which was subsequently validated using an independent cohort of over 500 patients. Expression of this module was dichotomized with a ubiquitously expressed subcomponent and another subcomponent preferentially expressed in the corpus callosum, which was significantly affected by our identified mutations in the network center. RNA-sequencing of the corpus callosum from patients with autism exhibited extensive gene mis-expression in this module, and our immunochemical analysis showed that the human corpus callosum is predominantly populated by oligodendrocyte cells. Analysis of functional genomic data further revealed a significant involvement of this module in the development of oligodendrocyte cells in mouse brain. Our analysis delineates a natural network involved in autism, helps uncover novel candidate genes for this disease and improves our understanding of its molecular pathology. PMID:25549968

Three-dimensional spatial modeling of spines along dendritic networks in human cortical pyramidal neurons

PubMed Central

Larrañaga, Pedro; Benavides-Piccione, Ruth; Fernaud-Espinosa, Isabel; DeFelipe, Javier; Bielza, Concha

2017-01-01

We modeled spine distribution along the dendritic networks of pyramidal neurons in both basal and apical dendrites. To do this, we applied network spatial analysis because spines can only lie on the dendritic shaft. We expanded the existing 2D computational techniques for spatial analysis along networks to perform a 3D network spatial analysis. We analyzed five detailed reconstructions of adult human pyramidal neurons of the temporal cortex with a total of more than 32,000 spines. We confirmed that there is a spatial variation in spine density that is dependent on the distance to the cell body in all dendrites. Considering the dendritic arborizations of each pyramidal cell as a group of instances of the same observation (the neuron), we used replicated point patterns together with network spatial analysis for the first time to search for significant differences in the spine distribution of basal dendrites between different cells and between all the basal and apical dendrites. To do this, we used a recent variant of Ripley’s K function defined to work along networks. The results showed that there were no significant differences in spine distribution along basal arbors of the same neuron and along basal arbors of different pyramidal neurons. This suggests that dendritic spine distribution in basal dendritic arbors adheres to common rules. However, we did find significant differences in spine distribution along basal versus apical networks. Therefore, not only do apical and basal dendritic arborizations have distinct morphologies but they also obey different rules of spine distribution. Specifically, the results suggested that spines are more clustered along apical than in basal dendrites. Collectively, the results further highlighted that synaptic input information processing is different between these two dendritic domains. PMID:28662210

Three-dimensional spatial modeling of spines along dendritic networks in human cortical pyramidal neurons.

PubMed

Anton-Sanchez, Laura; Larrañaga, Pedro; Benavides-Piccione, Ruth; Fernaud-Espinosa, Isabel; DeFelipe, Javier; Bielza, Concha

2017-01-01

We modeled spine distribution along the dendritic networks of pyramidal neurons in both basal and apical dendrites. To do this, we applied network spatial analysis because spines can only lie on the dendritic shaft. We expanded the existing 2D computational techniques for spatial analysis along networks to perform a 3D network spatial analysis. We analyzed five detailed reconstructions of adult human pyramidal neurons of the temporal cortex with a total of more than 32,000 spines. We confirmed that there is a spatial variation in spine density that is dependent on the distance to the cell body in all dendrites. Considering the dendritic arborizations of each pyramidal cell as a group of instances of the same observation (the neuron), we used replicated point patterns together with network spatial analysis for the first time to search for significant differences in the spine distribution of basal dendrites between different cells and between all the basal and apical dendrites. To do this, we used a recent variant of Ripley's K function defined to work along networks. The results showed that there were no significant differences in spine distribution along basal arbors of the same neuron and along basal arbors of different pyramidal neurons. This suggests that dendritic spine distribution in basal dendritic arbors adheres to common rules. However, we did find significant differences in spine distribution along basal versus apical networks. Therefore, not only do apical and basal dendritic arborizations have distinct morphologies but they also obey different rules of spine distribution. Specifically, the results suggested that spines are more clustered along apical than in basal dendrites. Collectively, the results further highlighted that synaptic input information processing is different between these two dendritic domains.

Decoding Network Structure in On-Chip Integrated Flow Cells with Synchronization of Electrochemical Oscillators

NASA Astrophysics Data System (ADS)

Jia, Yanxin; Kiss, István Z.

2017-04-01

The analysis of network interactions among dynamical units and the impact of the coupling on self-organized structures is a challenging task with implications in many biological and engineered systems. We explore the coupling topology that arises through the potential drops in a flow channel in a lab-on-chip device that accommodates chemical reactions on electrode arrays. The networks are revealed by analysis of the synchronization patterns with the use of an oscillatory chemical reaction (nickel electrodissolution) and are further confirmed by direct decoding using phase model analysis. In dual electrode configuration, a variety coupling schemes, (uni- or bidirectional positive or negative) were identified depending on the relative placement of the reference and counter electrodes (e.g., placed at the same or the opposite ends of the flow channel). With three electrodes, the network consists of a superposition of a localized (upstream) and global (all-to-all) coupling. With six electrodes, the unique, position dependent coupling topology resulted spatially organized partial synchronization such that there was a synchrony gradient along the quasi-one-dimensional spatial coordinate. The networked, electrode potential (current) spike generating electrochemical reactions hold potential for construction of an in-situ information processing unit to be used in electrochemical devices in sensors and batteries.

Comparative safety and efficacy of vasopressors for mortality in septic shock: A network meta-analysis.

PubMed

Nagendran, Myura; Maruthappu, Mahiben; Gordon, Anthony C; Gurusamy, Kurinchi S

2016-05-01

Septic shock is a life-threatening condition requiring vasopressor agents to support the circulatory system. Several agents exist with choice typically guided by the specific clinical scenario. We used a network meta-analysis approach to rate the comparative efficacy and safety of vasopressors for mortality and arrhythmia incidence in septic shock patients. We performed a comprehensive electronic database search including Medline, Embase, Science Citation Index Expanded and the Cochrane database. Randomised trials investigating vasopressor agents in septic shock patients and specifically assessing 28-day mortality or arrhythmia incidence were included. A Bayesian network meta-analysis was performed using Markov chain Monte Carlo methods. Thirteen trials of low to moderate risk of bias in which 3146 patients were randomised were included. There was no pairwise evidence to suggest one agent was superior over another for mortality. In the network meta-analysis, vasopressin was significantly superior to dopamine (OR 0.68 (95% CI 0.5 to 0.94)) for mortality. For arrhythmia incidence, standard pairwise meta-analyses confirmed that dopamine led to a higher incidence of arrhythmias than norepinephrine (OR 2.69 (95% CI 2.08 to 3.47)). In the network meta-analysis, there was no evidence of superiority of one agent over another. In this network meta-analysis, vasopressin was superior to dopamine for 28-day mortality in septic shock. Existing pairwise information supports the use of norepinephrine over dopamine. Our findings suggest that dopamine should be avoided in patients with septic shock and that other vasopressor agents should continue to be based on existing guidelines and clinical judgement of the specific presentation of the patient.

Link Prediction in Criminal Networks: A Tool for Criminal Intelligence Analysis

PubMed Central

Berlusconi, Giulia; Calderoni, Francesco; Parolini, Nicola; Verani, Marco; Piccardi, Carlo

2016-01-01

The problem of link prediction has recently received increasing attention from scholars in network science. In social network analysis, one of its aims is to recover missing links, namely connections among actors which are likely to exist but have not been reported because data are incomplete or subject to various types of uncertainty. In the field of criminal investigations, problems of incomplete information are encountered almost by definition, given the obvious anti-detection strategies set up by criminals and the limited investigative resources. In this paper, we work on a specific dataset obtained from a real investigation, and we propose a strategy to identify missing links in a criminal network on the basis of the topological analysis of the links classified as marginal, i.e. removed during the investigation procedure. The main assumption is that missing links should have opposite features with respect to marginal ones. Measures of node similarity turn out to provide the best characterization in this sense. The inspection of the judicial source documents confirms that the predicted links, in most instances, do relate actors with large likelihood of co-participation in illicit activities. PMID:27104948

Defining and characterizing the critical transition state prior to the type 2 diabetes disease

PubMed Central

Zhu, Chunqing; Zhou, Xin; Chen, Pei; Fu, Tianyun; Hu, Zhongkai; Wu, Qian; Liu, Wei; Liu, Daowei; Yu, Yunxian; Zhang, Yan; McElhinney, Doff B.; Li, Yu-Ming; Culver, Devore S; Alfreds, Shaun T.; Stearns, Frank; Sylvester, Karl G.; Widen, Eric

2017-01-01

Background Type 2 diabetes mellitus (T2DM), with increased risk of serious long-term complications, currently represents 8.3% of the adult population. We hypothesized that a critical transition state prior to the new onset T2DM can be revealed through the longitudinal electronic medical record (EMR) analysis. Method We applied the transition-based network entropy methodology which previously identified a dynamic driver network (DDN) underlying the critical T2DM transition at the tissue molecular biological level. To profile pre-disease phenotypical changes that indicated a critical transition state, a cohort of 7,334 patients was assembled from the Maine State Health Information Exchange (HIE). These patients all had their first confirmative diagnosis of T2DM between January 1, 2013 and June 30, 2013. The cohort’s EMRs from the 24 months preceding their date of first T2DM diagnosis were extracted. Results Analysis of these patients’ pre-disease clinical history identified a dynamic driver network (DDN) and an associated critical transition state six months prior to their first confirmative T2DM state. Conclusions This 6-month window before the disease state provides an early warning of the impending T2DM, warranting an opportunity to apply proactive interventions to prevent or delay the new onset of T2DM. PMID:28686739

Understanding the process of social network evolution: Online-offline integrated analysis of social tie formation

PubMed Central

Kwak, Doyeon

2017-01-01

It is important to consider the interweaving nature of online and offline social networks when we examine social network evolution. However, it is difficult to find any research that examines the process of social tie formation from an integrated perspective. In our study, we quantitatively measure offline interactions and examine the corresponding evolution of online social network in order to understand the significance of interrelationship between online and offline social factors in generating social ties. We analyze the radio signal strength indicator sensor data from a series of social events to understand offline interactions among the participants and measure the structural attributes of their existing online Facebook social networks. By monitoring the changes in their online social networks before and after offline interactions in a series of social events, we verify that the ability to develop an offline interaction into an online friendship is tied to the number of social connections that participants previously had, while the presence of shared mutual friends between a pair of participants disrupts potential new connections within the pre-designed offline social events. Thus, while our integrative approach enables us to confirm the theory of preferential attachment in the process of network formation, the common neighbor theory is not supported. Our dual-dimensional network analysis allows us to observe the actual process of social network evolution rather than to make predictions based on the assumption of self-organizing networks. PMID:28542367

Understanding the process of social network evolution: Online-offline integrated analysis of social tie formation.

PubMed

Kwak, Doyeon; Kim, Wonjoon

2017-01-01

It is important to consider the interweaving nature of online and offline social networks when we examine social network evolution. However, it is difficult to find any research that examines the process of social tie formation from an integrated perspective. In our study, we quantitatively measure offline interactions and examine the corresponding evolution of online social network in order to understand the significance of interrelationship between online and offline social factors in generating social ties. We analyze the radio signal strength indicator sensor data from a series of social events to understand offline interactions among the participants and measure the structural attributes of their existing online Facebook social networks. By monitoring the changes in their online social networks before and after offline interactions in a series of social events, we verify that the ability to develop an offline interaction into an online friendship is tied to the number of social connections that participants previously had, while the presence of shared mutual friends between a pair of participants disrupts potential new connections within the pre-designed offline social events. Thus, while our integrative approach enables us to confirm the theory of preferential attachment in the process of network formation, the common neighbor theory is not supported. Our dual-dimensional network analysis allows us to observe the actual process of social network evolution rather than to make predictions based on the assumption of self-organizing networks.

Functional networks inference from rule-based machine learning models.

PubMed

Lazzarini, Nicola; Widera, Paweł; Williamson, Stuart; Heer, Rakesh; Krasnogor, Natalio; Bacardit, Jaume

2016-01-01

Functional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the similarity-based inference paradigm (e.g. gene co-expression) has been the most popular approach. It assumes a functional relationship between genes which are expressed at similar levels across different samples. An alternative to this paradigm is the inference of relationships from the structure of machine learning models. These models are able to capture complex relationships between variables, that often are different/complementary to the similarity-based methods. We propose a protocol to infer functional networks from machine learning models, called FuNeL. It assumes, that genes used together within a rule-based machine learning model to classify the samples, might also be functionally related at a biological level. The protocol is first tested on synthetic datasets and then evaluated on a test suite of 8 real-world datasets related to human cancer. The networks inferred from the real-world data are compared against gene co-expression networks of equal size, generated with 3 different methods. The comparison is performed from two different points of view. We analyse the enriched biological terms in the set of network nodes and the relationships between known disease-associated genes in a context of the network topology. The comparison confirms both the biological relevance and the complementary character of the knowledge captured by the FuNeL networks in relation to similarity-based methods and demonstrates its potential to identify known disease associations as core elements of the network. Finally, using a prostate cancer dataset as a case study, we confirm that the biological knowledge captured by our method is relevant to the disease and consistent with the specialised literature and with an independent dataset not used in the inference process. The implementation of our network inference protocol is available at: http://ico2s.org/software/funel.html.

Descriptive Analysis of Selected Data Referral Networks.

ERIC Educational Resources Information Center

MAXIMA Corp., Silver Spring, MD.

The National Environmental Data Referral Service (NEDRES) is being developed in response to a national need to improve the awareness of and access to a broad range of environmental data files. Two studies were conducted in support of this effort: a survey of prospective users confirming the need for and willingness to pay fees for the service and…

Differences between Peer Victimization in Cyber and Physical Settings and Associated Psychosocial Adjustment in Early Adolescence

ERIC Educational Resources Information Center

Dempsey, Allison G.; Sulkowski, Michael L.; Nichols, Rebecca; Storch, Eric A.

2009-01-01

The increasing use of cyberspace as a social networking forum creates a new medium for youth to become victims of peer aggression. This study used factor analysis techniques to confirm whether survey questions about frequency of cyber victimization formed a distinct latent construct from questions about relational and overt victimization…

Improving Nonlethal Targeting: A Social Network Analysis Method for Military Planners

DTIC Science & Technology

2012-12-01

which primarily positive elements are expected , then negative information becomes perceptually salient as a jolting disconfirmation of those... expectations . We also know that people stop to examine disconfirmations to a much higher degree than confirmations. Negative information is often highly...9  3.  Military Deception Operations .........................................................10  C.  INFLUENCE THEORY

Impact of leakage delay on bifurcation in high-order fractional BAM neural networks.

PubMed

Huang, Chengdai; Cao, Jinde

2018-02-01

The effects of leakage delay on the dynamics of neural networks with integer-order have lately been received considerable attention. It has been confirmed that fractional neural networks more appropriately uncover the dynamical properties of neural networks, but the results of fractional neural networks with leakage delay are relatively few. This paper primarily concentrates on the issue of bifurcation for high-order fractional bidirectional associative memory(BAM) neural networks involving leakage delay. The first attempt is made to tackle the stability and bifurcation of high-order fractional BAM neural networks with time delay in leakage terms in this paper. The conditions for the appearance of bifurcation for the proposed systems with leakage delay are firstly established by adopting time delay as a bifurcation parameter. Then, the bifurcation criteria of such system without leakage delay are successfully acquired. Comparative analysis wondrously detects that the stability performance of the proposed high-order fractional neural networks is critically weakened by leakage delay, they cannot be overlooked. Numerical examples are ultimately exhibited to attest the efficiency of the theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Explosive death of conjugate coupled Van der Pol oscillators on networks

NASA Astrophysics Data System (ADS)

Zhao, Nannan; Sun, Zhongkui; Yang, Xiaoli; Xu, Wei

2018-06-01

Explosive death phenomenon has been gradually gaining attention of researchers due to the research boom of explosive synchronization, and it has been observed recently for the identical or nonidentical coupled systems in all-to-all network. In this work, we investigate the emergence of explosive death in networked Van der Pol (VdP) oscillators with conjugate variables coupling. It is demonstrated that the network structures play a crucial role in identifying the types of explosive death behaviors. We also observe that the damping coefficient of the VdP system not only can determine whether the explosive death state is generated but also can adjust the forward transition point. We further show that the backward transition point is independent of the network topologies and the damping coefficient, which is well confirmed by theoretical analysis. Our results reveal the generality of explosive death phenomenon in different network topologies and are propitious to promote a better comprehension for the oscillation quenching behaviors.

Novel indexes based on network structure to indicate financial market

NASA Astrophysics Data System (ADS)

Zhong, Tao; Peng, Qinke; Wang, Xiao; Zhang, Jing

2016-02-01

There have been various achievements to understand and to analyze the financial market by complex network model. However, current studies analyze the financial network model but seldom present quantified indexes to indicate or forecast the price action of market. In this paper, the stock market is modeled as a dynamic network, in which the vertices refer to listed companies and edges refer to their rank-based correlation based on price series. Characteristics of the network are analyzed and then novel indexes are introduced into market analysis, which are calculated from maximum and fully-connected subnets. The indexes are compared with existing ones and the results confirm that our indexes perform better to indicate the daily trend of market composite index in advance. Via investment simulation, the performance of our indexes is analyzed in detail. The results indicate that the dynamic complex network model could not only serve as a structural description of the financial market, but also work to predict the market and guide investment by indexes.

Skill complementarity enhances heterophily in collaboration networks

PubMed Central

Xie, Wen-Jie; Li, Ming-Xia; Jiang, Zhi-Qiang; Tan, Qun-Zhao; Podobnik, Boris; Zhou, Wei-Xing; Stanley, H. Eugene

2016-01-01

Much empirical evidence shows that individuals usually exhibit significant homophily in social networks. We demonstrate, however, skill complementarity enhances heterophily in the formation of collaboration networks, where people prefer to forge social ties with people who have professions different from their own. We construct a model to quantify the heterophily by assuming that individuals choose collaborators to maximize utility. Using a huge database of online societies, we find evidence of heterophily in collaboration networks. The results of model calibration confirm the presence of heterophily. Both empirical analysis and model calibration show that the heterophilous feature is persistent along the evolution of online societies. Furthermore, the degree of skill complementarity is positively correlated with their production output. Our work sheds new light on the scientific research utility of virtual worlds for studying human behaviors in complex socioeconomic systems. PMID:26743687

Social Network Analysis Applied to a Historical Ethnographic Study Surrounding Home Birth

PubMed Central

2018-01-01

Safety during birth has improved since hospital delivery became standard practice, but the process has also become increasingly medicalised. Hence, recent years have witnessed a growing interest in home births due to the advantages it offers to mothers and their newborn infants. The aims of the present study were to confirm the transition from a home birth model of care to a scenario in which deliveries began to occur almost exclusively in a hospital setting; to define the social networks surrounding home births; and to determine whether geography exerted any influence on the social networks surrounding home births. Adopting a qualitative approach, we recruited 19 women who had given birth at home in the mid 20th century in a rural area in Spain. We employed a social network analysis method. Our results revealed three essential aspects that remain relevant today: the importance of health professionals in home delivery care, the importance of the mother’s primary network, and the influence of the geographical location of the actors involved in childbirth. All of these factors must be taken into consideration when developing strategies for maternal health. PMID:29695089

Two new methods to fit models for network meta-analysis with random inconsistency effects.

PubMed

Law, Martin; Jackson, Dan; Turner, Rebecca; Rhodes, Kirsty; Viechtbauer, Wolfgang

2016-07-28

Meta-analysis is a valuable tool for combining evidence from multiple studies. Network meta-analysis is becoming more widely used as a means to compare multiple treatments in the same analysis. However, a network meta-analysis may exhibit inconsistency, whereby the treatment effect estimates do not agree across all trial designs, even after taking between-study heterogeneity into account. We propose two new estimation methods for network meta-analysis models with random inconsistency effects. The model we consider is an extension of the conventional random-effects model for meta-analysis to the network meta-analysis setting and allows for potential inconsistency using random inconsistency effects. Our first new estimation method uses a Bayesian framework with empirically-based prior distributions for both the heterogeneity and the inconsistency variances. We fit the model using importance sampling and thereby avoid some of the difficulties that might be associated with using Markov Chain Monte Carlo (MCMC). However, we confirm the accuracy of our importance sampling method by comparing the results to those obtained using MCMC as the gold standard. The second new estimation method we describe uses a likelihood-based approach, implemented in the metafor package, which can be used to obtain (restricted) maximum-likelihood estimates of the model parameters and profile likelihood confidence intervals of the variance components. We illustrate the application of the methods using two contrasting examples. The first uses all-cause mortality as an outcome, and shows little evidence of between-study heterogeneity or inconsistency. The second uses "ear discharge" as an outcome, and exhibits substantial between-study heterogeneity and inconsistency. Both new estimation methods give results similar to those obtained using MCMC. The extent of heterogeneity and inconsistency should be assessed and reported in any network meta-analysis. Our two new methods can be used to fit models for network meta-analysis with random inconsistency effects. They are easily implemented using the accompanying R code in the Additional file 1. Using these estimation methods, the extent of inconsistency can be assessed and reported.

Statistical mechanics of unsupervised feature learning in a restricted Boltzmann machine with binary synapses

NASA Astrophysics Data System (ADS)

Huang, Haiping

2017-05-01

Revealing hidden features in unlabeled data is called unsupervised feature learning, which plays an important role in pretraining a deep neural network. Here we provide a statistical mechanics analysis of the unsupervised learning in a restricted Boltzmann machine with binary synapses. A message passing equation to infer the hidden feature is derived, and furthermore, variants of this equation are analyzed. A statistical analysis by replica theory describes the thermodynamic properties of the model. Our analysis confirms an entropy crisis preceding the non-convergence of the message passing equation, suggesting a discontinuous phase transition as a key characteristic of the restricted Boltzmann machine. Continuous phase transition is also confirmed depending on the embedded feature strength in the data. The mean-field result under the replica symmetric assumption agrees with that obtained by running message passing algorithms on single instances of finite sizes. Interestingly, in an approximate Hopfield model, the entropy crisis is absent, and a continuous phase transition is observed instead. We also develop an iterative equation to infer the hyper-parameter (temperature) hidden in the data, which in physics corresponds to iteratively imposing Nishimori condition. Our study provides insights towards understanding the thermodynamic properties of the restricted Boltzmann machine learning, and moreover important theoretical basis to build simplified deep networks.

Effect of microwave treatment on structure of binders based on sodium carboxymethyl starch: FT-IR, FT-Raman and XRD investigations

NASA Astrophysics Data System (ADS)

Kaczmarska, Karolina; Grabowska, Beata; Spychaj, Tadeusz; Zdanowicz, Magdalena; Sitarz, Maciej; Bobrowski, Artur; Cukrowicz, Sylwia

2018-06-01

The paper deals with the influence of the microwave treatment on sodium carboxymethyl starch (CMS-Na) applied as a binder for moulding sands. The Fourier transformation infrared spectrometry (FT-IR), Raman spectroscopy (FT-Raman) and XRD analysis data of native potato starch and three different carboxymethyl starches (CMS-Na) with various degree of substitution (DS) before and after exposition to microwave radiation have been compared. FT-IR studies showed that polar groups present in CMS-Na structure take part in the formation of new hydrogen bonds network after water evaporation. However, these changes depend on DS value of the modified starch. The FT-Raman study confirmed that due to the impact on the samples by microwave, the changes of intensity in the characteristic bands associated with the crystalline regions in the sample were noticed. The X-ray diffraction data for microwave treated CMS-Na samples have been compared with the diffractograms of initial materials and analysis of XRD patterns confirmed that microwave-treated samples exhibit completely amorphous structure. Analysis of structural changes allows to state that the binding of sand grains in moulding sand with CMS-Na polymeric binder consists in the formation of hydrogen bonds networks (physical cross-linking).

Effect of microwave treatment on structure of binders based on sodium carboxymethyl starch: FT-IR, FT-Raman and XRD investigations.

PubMed

Kaczmarska, Karolina; Grabowska, Beata; Spychaj, Tadeusz; Zdanowicz, Magdalena; Sitarz, Maciej; Bobrowski, Artur; Cukrowicz, Sylwia

2018-06-15

The paper deals with the influence of the microwave treatment on sodium carboxymethyl starch (CMS-Na) applied as a binder for moulding sands. The Fourier transformation infrared spectrometry (FT-IR), Raman spectroscopy (FT-Raman) and XRD analysis data of native potato starch and three different carboxymethyl starches (CMS-Na) with various degree of substitution (DS) before and after exposition to microwave radiation have been compared. FT-IR studies showed that polar groups present in CMS-Na structure take part in the formation of new hydrogen bonds network after water evaporation. However, these changes depend on DS value of the modified starch. The FT-Raman study confirmed that due to the impact on the samples by microwave, the changes of intensity in the characteristic bands associated with the crystalline regions in the sample were noticed. The X-ray diffraction data for microwave treated CMS-Na samples have been compared with the diffractograms of initial materials and analysis of XRD patterns confirmed that microwave-treated samples exhibit completely amorphous structure. Analysis of structural changes allows to state that the binding of sand grains in moulding sand with CMS-Na polymeric binder consists in the formation of hydrogen bonds networks (physical cross-linking). Copyright © 2018 Elsevier B.V. All rights reserved.

Thermodynamic Constraints Improve Metabolic Networks.

PubMed

Krumholz, Elias W; Libourel, Igor G L

2017-08-08

In pursuit of establishing a realistic metabolic phenotypic space, the reversibility of reactions is thermodynamically constrained in modern metabolic networks. The reversibility constraints follow from heuristic thermodynamic poise approximations that take anticipated cellular metabolite concentration ranges into account. Because constraints reduce the feasible space, draft metabolic network reconstructions may need more extensive reconciliation, and a larger number of genes may become essential. Notwithstanding ubiquitous application, the effect of reversibility constraints on the predictive capabilities of metabolic networks has not been investigated in detail. Instead, work has focused on the implementation and validation of the thermodynamic poise calculation itself. With the advance of fast linear programming-based network reconciliation, the effects of reversibility constraints on network reconciliation and gene essentiality predictions have become feasible and are the subject of this study. Networks with thermodynamically informed reversibility constraints outperformed gene essentiality predictions compared to networks that were constrained with randomly shuffled constraints. Unconstrained networks predicted gene essentiality as accurately as thermodynamically constrained networks, but predicted substantially fewer essential genes. Networks that were reconciled with sequence similarity data and strongly enforced reversibility constraints outperformed all other networks. We conclude that metabolic network analysis confirmed the validity of the thermodynamic constraints, and that thermodynamic poise information is actionable during network reconciliation. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Gene Expression Correlated with Severe Asthma Characteristics Reveals Heterogeneous Mechanisms of Severe Disease.

PubMed

Modena, Brian D; Bleecker, Eugene R; Busse, William W; Erzurum, Serpil C; Gaston, Benjamin M; Jarjour, Nizar N; Meyers, Deborah A; Milosevic, Jadranka; Tedrow, John R; Wu, Wei; Kaminski, Naftali; Wenzel, Sally E

2017-06-01

Severe asthma (SA) is a heterogeneous disease with multiple molecular mechanisms. Gene expression studies of bronchial epithelial cells in individuals with asthma have provided biological insight and underscored possible mechanistic differences between individuals. Identify networks of genes reflective of underlying biological processes that define SA. Airway epithelial cell gene expression from 155 subjects with asthma and healthy control subjects in the Severe Asthma Research Program was analyzed by weighted gene coexpression network analysis to identify gene networks and profiles associated with SA and its specific characteristics (i.e., pulmonary function tests, quality of life scores, urgent healthcare use, and steroid use), which potentially identified underlying biological processes. A linear model analysis confirmed these findings while adjusting for potential confounders. Weighted gene coexpression network analysis constructed 64 gene network modules, including modules corresponding to T1 and T2 inflammation, neuronal function, cilia, epithelial growth, and repair mechanisms. Although no network selectively identified SA, genes in modules linked to epithelial growth and repair and neuronal function were markedly decreased in SA. Several hub genes of the epithelial growth and repair module were found located at the 17q12-21 locus, near a well-known asthma susceptibility locus. T2 genes increased with severity in those treated with corticosteroids but were also elevated in untreated, mild-to-moderate disease compared with healthy control subjects. T1 inflammation, especially when associated with increased T2 gene expression, was elevated in a subgroup of younger patients with SA. In this hypothesis-generating analysis, gene expression networks in relation to asthma severity provided potentially new insight into biological mechanisms associated with the development of SA and its phenotypes.

Gene Expression Correlated with Severe Asthma Characteristics Reveals Heterogeneous Mechanisms of Severe Disease

PubMed Central

Modena, Brian D.; Bleecker, Eugene R.; Busse, William W.; Erzurum, Serpil C.; Gaston, Benjamin M.; Jarjour, Nizar N.; Meyers, Deborah A.; Milosevic, Jadranka; Tedrow, John R.; Wu, Wei; Kaminski, Naftali

2017-01-01

Rationale: Severe asthma (SA) is a heterogeneous disease with multiple molecular mechanisms. Gene expression studies of bronchial epithelial cells in individuals with asthma have provided biological insight and underscored possible mechanistic differences between individuals. Objectives: Identify networks of genes reflective of underlying biological processes that define SA. Methods: Airway epithelial cell gene expression from 155 subjects with asthma and healthy control subjects in the Severe Asthma Research Program was analyzed by weighted gene coexpression network analysis to identify gene networks and profiles associated with SA and its specific characteristics (i.e., pulmonary function tests, quality of life scores, urgent healthcare use, and steroid use), which potentially identified underlying biological processes. A linear model analysis confirmed these findings while adjusting for potential confounders. Measurements and Main Results: Weighted gene coexpression network analysis constructed 64 gene network modules, including modules corresponding to T1 and T2 inflammation, neuronal function, cilia, epithelial growth, and repair mechanisms. Although no network selectively identified SA, genes in modules linked to epithelial growth and repair and neuronal function were markedly decreased in SA. Several hub genes of the epithelial growth and repair module were found located at the 17q12–21 locus, near a well-known asthma susceptibility locus. T2 genes increased with severity in those treated with corticosteroids but were also elevated in untreated, mild-to-moderate disease compared with healthy control subjects. T1 inflammation, especially when associated with increased T2 gene expression, was elevated in a subgroup of younger patients with SA. Conclusions: In this hypothesis-generating analysis, gene expression networks in relation to asthma severity provided potentially new insight into biological mechanisms associated with the development of SA and its phenotypes. PMID:27984699

Grey-matter network disintegration as predictor of cognitive and motor function with aging.

PubMed

Koini, Marisa; Duering, Marco; Gesierich, Benno G; Rombouts, Serge A R B; Ropele, Stefan; Wagner, Fabian; Enzinger, Christian; Schmidt, Reinhold

2018-06-01

Loss of grey-matter volume with advancing age affects the entire cortex. It has been suggested that atrophy occurs in a network-dependent manner with advancing age rather than in independent brain areas. The relationship between networks of structural covariance (SCN) disintegration and cognitive functioning during normal aging is not fully explored. We, therefore, aimed to (1) identify networks that lose GM integrity with advancing age, (2) investigate if age-related impairment of integrity in GM networks associates with cognitive function and decreasing fine motor skills (FMS), and (3) examine if GM disintegration is a mediator between age and cognition and FMS. T1-weighted scans of n = 257 participants (age range: 20-87) were used to identify GM networks using independent component analysis. Random forest analysis was implemented to examine the importance of network integrity as predictors of memory, executive functions, and FMS. The associations between GM disintegration, age and cognitive performance, and FMS were assessed using mediation analyses. Advancing age was associated with decreasing cognitive performance and FMS. Fourteen of 20 GM networks showed integrity changes with advancing age. Next to age and education, eight networks (fronto-parietal, fronto-occipital, temporal, limbic, secondary somatosensory, cuneal, sensorimotor network, and a cerebellar network) showed an association with cognition and FMS (up to 15.08%). GM networks partially mediated the effect between age and cognition and age and FMS. We confirm an age-related decline in cognitive functioning and FMS in non-demented community-dwelling subjects and showed that aging selectively affects the integrity of GM networks. The negative effect of age on cognition and FMS is associated with distinct GM networks and is partly mediated by their disintegration.

Fisher metric, geometric entanglement, and spin networks

NASA Astrophysics Data System (ADS)

Chirco, Goffredo; Mele, Fabio M.; Oriti, Daniele; Vitale, Patrizia

2018-02-01

Starting from recent results on the geometric formulation of quantum mechanics, we propose a new information geometric characterization of entanglement for spin network states in the context of quantum gravity. For the simple case of a single-link fixed graph (Wilson line), we detail the construction of a Riemannian Fisher metric tensor and a symplectic structure on the graph Hilbert space, showing how these encode the whole information about separability and entanglement. In particular, the Fisher metric defines an entanglement monotone which provides a notion of distance among states in the Hilbert space. In the maximally entangled gauge-invariant case, the entanglement monotone is proportional to a power of the area of the surface dual to the link thus supporting a connection between entanglement and the (simplicial) geometric properties of spin network states. We further extend such analysis to the study of nonlocal correlations between two nonadjacent regions of a generic spin network graph characterized by the bipartite unfolding of an intertwiner state. Our analysis confirms the interpretation of spin network bonds as a result of entanglement and to regard the same spin network graph as an information graph, whose connectivity encodes, both at the local and nonlocal level, the quantum correlations among its parts. This gives a further connection between entanglement and geometry.

Clustering drug-drug interaction networks with energy model layouts: community analysis and drug repurposing.

PubMed

Udrescu, Lucreţia; Sbârcea, Laura; Topîrceanu, Alexandru; Iovanovici, Alexandru; Kurunczi, Ludovic; Bogdan, Paul; Udrescu, Mihai

2016-09-07

Analyzing drug-drug interactions may unravel previously unknown drug action patterns, leading to the development of new drug discovery tools. We present a new approach to analyzing drug-drug interaction networks, based on clustering and topological community detection techniques that are specific to complex network science. Our methodology uncovers functional drug categories along with the intricate relationships between them. Using modularity-based and energy-model layout community detection algorithms, we link the network clusters to 9 relevant pharmacological properties. Out of the 1141 drugs from the DrugBank 4.1 database, our extensive literature survey and cross-checking with other databases such as Drugs.com, RxList, and DrugBank 4.3 confirm the predicted properties for 85% of the drugs. As such, we argue that network analysis offers a high-level grasp on a wide area of pharmacological aspects, indicating possible unaccounted interactions and missing pharmacological properties that can lead to drug repositioning for the 15% drugs which seem to be inconsistent with the predicted property. Also, by using network centralities, we can rank drugs according to their interaction potential for both simple and complex multi-pathology therapies. Moreover, our clustering approach can be extended for applications such as analyzing drug-target interactions or phenotyping patients in personalized medicine applications.

Clustering drug-drug interaction networks with energy model layouts: community analysis and drug repurposing

PubMed Central

Udrescu, Lucreţia; Sbârcea, Laura; Topîrceanu, Alexandru; Iovanovici, Alexandru; Kurunczi, Ludovic; Bogdan, Paul; Udrescu, Mihai

2016-01-01

Analyzing drug-drug interactions may unravel previously unknown drug action patterns, leading to the development of new drug discovery tools. We present a new approach to analyzing drug-drug interaction networks, based on clustering and topological community detection techniques that are specific to complex network science. Our methodology uncovers functional drug categories along with the intricate relationships between them. Using modularity-based and energy-model layout community detection algorithms, we link the network clusters to 9 relevant pharmacological properties. Out of the 1141 drugs from the DrugBank 4.1 database, our extensive literature survey and cross-checking with other databases such as Drugs.com, RxList, and DrugBank 4.3 confirm the predicted properties for 85% of the drugs. As such, we argue that network analysis offers a high-level grasp on a wide area of pharmacological aspects, indicating possible unaccounted interactions and missing pharmacological properties that can lead to drug repositioning for the 15% drugs which seem to be inconsistent with the predicted property. Also, by using network centralities, we can rank drugs according to their interaction potential for both simple and complex multi-pathology therapies. Moreover, our clustering approach can be extended for applications such as analyzing drug-target interactions or phenotyping patients in personalized medicine applications. PMID:27599720

Risk analysis of new oral anticoagulants for gastrointestinal bleeding and intracranial hemorrhage in atrial fibrillation patients: a systematic review and network meta-analysis.

PubMed

Xu, Wei-Wei; Hu, Shen-Jiang; Wu, Tao

2017-07-01

Antithrombotic therapy using new oral anticoagulants (NOACs) in patients with atrial fibrillation (AF) has been generally shown to have a favorable risk-benefit profile. Since there has been dispute about the risks of gastrointestinal bleeding (GIB) and intracranial hemorrhage (ICH), we sought to conduct a systematic review and network meta-analysis using Bayesian inference to analyze the risks of GIB and ICH in AF patients taking NOACs. We analyzed data from 20 randomized controlled trials of 91 671 AF patients receiving anticoagulants, antiplatelet drugs, or placebo. Bayesian network meta-analysis of two different evidence networks was performed using a binomial likelihood model, based on a network in which different agents (and doses) were treated as separate nodes. Odds ratios (ORs) and 95% confidence intervals (CIs) were modeled using Markov chain Monte Carlo methods. Indirect comparisons with the Bayesian model confirmed that aspirin+clopidogrel significantly increased the risk of GIB in AF patients compared to the placebo (OR 0.33, 95% CI 0.01-0.92). Warfarin was identified as greatly increasing the risk of ICH compared to edoxaban 30 mg (OR 3.42, 95% CI 1.22-7.24) and dabigatran 110 mg (OR 3.56, 95% CI 1.10-8.45). We further ranked the NOACs for the lowest risk of GIB (apixaban 5 mg) and ICH (apixaban 5 mg, dabigatran 110 mg, and edoxaban 30 mg). Bayesian network meta-analysis of treatment of non-valvular AF patients with anticoagulants suggested that NOACs do not increase risks of GIB and/or ICH, compared to each other.

Cancer systems biology in the genome sequencing era: part 1, dissecting and modeling of tumor clones and their networks.

PubMed

Wang, Edwin; Zou, Jinfeng; Zaman, Naif; Beitel, Lenore K; Trifiro, Mark; Paliouras, Miltiadis

2013-08-01

Recent tumor genome sequencing confirmed that one tumor often consists of multiple cell subpopulations (clones) which bear different, but related, genetic profiles such as mutation and copy number variation profiles. Thus far, one tumor has been viewed as a whole entity in cancer functional studies. With the advances of genome sequencing and computational analysis, we are able to quantify and computationally dissect clones from tumors, and then conduct clone-based analysis. Emerging technologies such as single-cell genome sequencing and RNA-Seq could profile tumor clones. Thus, we should reconsider how to conduct cancer systems biology studies in the genome sequencing era. We will outline new directions for conducting cancer systems biology by considering that genome sequencing technology can be used for dissecting, quantifying and genetically characterizing clones from tumors. Topics discussed in Part 1 of this review include computationally quantifying of tumor subpopulations; clone-based network modeling, cancer hallmark-based networks and their high-order rewiring principles and the principles of cell survival networks of fast-growing clones. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Wavelength routing beyond the standard graph coloring approach

NASA Astrophysics Data System (ADS)

Blankenhorn, Thomas

2004-04-01

When lightpaths are routed in the planning stage of transparent optical networks, the textbook approach is to use algorithms that try to minimize the overall number of wavelengths used in the . We demonstrate that this method cannot be expected to minimize actual costs when the marginal cost of instlling more wavelengths is a declining function of the number of wavelengths already installed, as is frequently the case. We further demonstrate how cost optimization can theoretically be improved with algorithms based on Prim"s algorithm. Finally, we test this theory with simulaion on a series of actual network topologies, which confirm the theoretical analysis.

Strange non-chaotic attractors in a state controlled-cellular neural network-based quasiperiodically forced MLC circuit

NASA Astrophysics Data System (ADS)

Ezhilarasu, P. Megavarna; Inbavalli, M.; Murali, K.; Thamilmaran, K.

2018-07-01

In this paper, we report the dynamical transitions to strange non-chaotic attractors in a quasiperiodically forced state controlled-cellular neural network (SC-CNN)-based MLC circuit via two different mechanisms, namely the Heagy-Hammel route and the gradual fractalisation route. These transitions were observed through numerical simulations and hardware experiments and confirmed using statistical tools, such as maximal Lyapunov exponent spectrum and its variance and singular continuous spectral analysis. We find that there is a remarkable agreement of the results from both numerical simulations as well as from hardware experiments.

Proteomic and computational analysis of bronchoalveolar proteins during the course of the acute respiratory distress syndrome.

PubMed

Chang, Dong W; Hayashi, Shinichi; Gharib, Sina A; Vaisar, Tomas; King, S Trevor; Tsuchiya, Mitsuhiro; Ruzinski, John T; Park, David R; Matute-Bello, Gustavo; Wurfel, Mark M; Bumgarner, Roger; Heinecke, Jay W; Martin, Thomas R

2008-10-01

Acute lung injury causes complex changes in protein expression in the lungs. Whereas most prior studies focused on single proteins, newer methods allowing the simultaneous study of many proteins could lead to a better understanding of pathogenesis and new targets for treatment. The purpose of this study was to examine the changes in protein expression in the bronchoalveolar lavage fluid (BALF) of patients during the course of the acute respiratory distress syndrome (ARDS). Using two-dimensional difference gel electrophoresis (DIGE), the expression of proteins in the BALF from patients on Days 1 (n = 7), 3 (n = 8), and 7 (n = 5) of ARDS were compared with findings in normal volunteers (n = 9). The patterns of protein expression were analyzed using principal component analysis (PCA). Biological processes that were enriched in the BALF proteins of patients with ARDS were identified using Gene Ontology (GO) analysis. Protein networks that model the protein interactions in the BALF were generated using Ingenuity Pathway Analysis. An average of 991 protein spots were detected using DIGE. Of these, 80 protein spots, representing 37 unique proteins in all of the fluids, were identified using mass spectrometry. PCA confirmed important differences between the proteins in the ARDS and normal samples. GO analysis showed that these differences are due to the enrichment of proteins involved in inflammation, infection, and injury. The protein network analysis showed that the protein interactions in ARDS are complex and redundant, and revealed unexpected central components in the protein networks. Proteomics and protein network analysis reveals the complex nature of lung protein interactions in ARDS. The results provide new insights about protein networks in injured lungs, and identify novel mediators that are likely to be involved in the pathogenesis and progression of acute lung injury.

Gel network shampoo formulation and hair health benefits.

PubMed

Marsh, J M; Brown, M A; Felts, T J; Hutton, H D; Vatter, M L; Whitaker, S; Wireko, F C; Styczynski, P B; Li, C; Henry, I D

2017-10-01

The objective of this work was to create a shampoo formula that contains a stable ordered gel network structure that delivers fatty alcohols inside hair. X-ray diffraction (SAXS and WAXS), SEM and DSC have been used to confirm formation of the ordered Lβ gel network with fatty alcohol (cetyl and stearyl alcohols) and an anionic surfactant (SLE1S). Micro-autoradiography and extraction methods using GC-MS were used to confirm penetration of fatty alcohols into hair, and cyclic fatigue testing was used to measure hair strength. In this work, evidence of a stable Lβ ordered gel network structure created from cetyl and stearyl alcohols and anionic surfactant (SLE1S) is presented, and this is confirmed via scanning electron microscopy images showing lamella layers and differential scanning calorimetry (DSC) showing new melting peaks vs the starting fatty alcohols. Hair washed for 16 repeat cycles with this shampoo showed penetration of fatty alcohols from the gel network into hair as confirmed by a differential extraction method with GC-MS and by radiolabelling of stearyl alcohol and showing its presence inside hair cross-sections. The gel network role in delivering fatty alcohol inside hair is demonstrated by comparing with a shampoo with added fatty alcohol not in an ordered gel network structure. The hair containing fatty alcohol was measured via the Dia-stron cyclic fatigue instrument and showed a significantly higher number of cycles to break vs control. The formation of a stable gel network was confirmed in the formulated shampoo, and it was demonstrated that this gel network is important to deliver cetyl and stearyl alcohols into hair. The presence of fatty alcohol inside hair was shown to deliver a hair strength benefit via cyclic fatigue testing. © 2017 Society of Cosmetic Scientists and the Société Française de Cosmétologie.

Identification of interactive gene networks: a novel approach in gene array profiling of myometrial events during guinea pig pregnancy.

PubMed

Mason, Clifford W; Swaan, Peter W; Weiner, Carl P

2006-06-01

The transition from myometrial quiescence to activation is poorly understood, and the analysis of array data is limited by the available data mining tools. We applied functional analysis and logical operations along regulatory gene networks to identify molecular processes and pathways underlying quiescence and activation. We analyzed some 18,400 transcripts and variants in guinea pig myometrium at stages corresponding to quiescence and activation, and compared them to the nonpregnant (control) counterpart using a functional mapping tool, MetaCore (GeneGo, St Joseph, MI) to identify novel gene networks composed of biological pathways during mid (MP) and late (LP) pregnancy. Genes altered during quiescence and or activation were identified following gene specific comparisons with myometrium from nonpregnant animals, and then linked to curated pathways and formulated networks. The MP and LP networks were subtracted from each other to identify unique genomic events during those periods. For example, changes 2-fold or greater in genes mediating protein biosynthesis, programmed cell death, microtubule polymerization, and microtubule based movement were noted during the transition to LP. We describe a novel approach combining microarrays and genetic data to identify networks associated with normal myometrial events. The resulting insights help identify potential biomarkers and permit future targeted investigations of these pathways or networks to confirm or refute their importance.

Integration of Spatial and Social Network Analysis in Disease Transmission Studies.

PubMed

Emch, Michael; Root, Elisabeth D; Giebultowicz, Sophia; Ali, Mohammad; Perez-Heydrich, Carolina; Yunus, Mohammad

2012-01-01

This study presents a case study of how social network and spatial analytical methods can be used simultaneously for disease transmission modeling. The paper first reviews strategies employed in previous studies and then offers the example of transmission of two bacterial diarrheal diseases in rural Bangladesh. The goal is to understand how diseases vary socially above and beyond the effects of the local neighborhood context. Patterns of cholera and shigellosis incidence are analyzed in space and within kinship-based social networks in Matlab, Bangladesh. Data include a spatially referenced longitudinal demographic database which consists of approximately 200,000 people and laboratory-confirmed cholera and shigellosis cases from 1983 to 2003. Matrices are created of kinship ties between households using a complete network design and distance matrices are also created to model spatial relationships. Moran's I statistics are calculated to measure clustering within both social and spatial matrices. Combined spatial effects-spatial disturbance models are built to simultaneously analyze spatial and social effects while controlling for local environmental context. Results indicate that cholera and shigellosis always clusters in space and only sometimes within social networks. This suggests that the local environment is most important for understanding transmission of both diseases however kinship-based social networks also influence their transmission. Simultaneous spatial and social network analysis can help us better understand disease transmission and this study has offered several strategies on how.

Integration of Spatial and Social Network Analysis in Disease Transmission Studies

PubMed Central

Root, Elisabeth D; Giebultowicz, Sophia; Ali, Mohammad; Perez-Heydrich, Carolina; Yunus, Mohammad

2013-01-01

This study presents a case study of how social network and spatial analytical methods can be used simultaneously for disease transmission modeling. The paper first reviews strategies employed in previous studies and then offers the example of transmission of two bacterial diarrheal diseases in rural Bangladesh. The goal is to understand how diseases vary socially above and beyond the effects of the local neighborhood context. Patterns of cholera and shigellosis incidence are analyzed in space and within kinship-based social networks in Matlab, Bangladesh. Data include a spatially referenced longitudinal demographic database which consists of approximately 200,000 people and laboratory-confirmed cholera and shigellosis cases from 1983 to 2003. Matrices are created of kinship ties between households using a complete network design and distance matrices are also created to model spatial relationships. Moran's I statistics are calculated to measure clustering within both social and spatial matrices. Combined spatial effects-spatial disturbance models are built to simultaneously analyze spatial and social effects while controlling for local environmental context. Results indicate that cholera and shigellosis always clusters in space and only sometimes within social networks. This suggests that the local environment is most important for understanding transmission of both diseases however kinship-based social networks also influence their transmission. Simultaneous spatial and social network analysis can help us better understand disease transmission and this study has offered several strategies on how. PMID:24163443

Time Course of Brain Network Reconfiguration Supporting Inhibitory Control.

PubMed

Popov, Tzvetan; Westner, Britta U; Silton, Rebecca L; Sass, Sarah M; Spielberg, Jeffrey M; Rockstroh, Brigitte; Heller, Wendy; Miller, Gregory A

2018-05-02

Hemodynamic research has recently clarified key nodes and links in brain networks implementing inhibitory control. Although fMRI methods are optimized for identifying the structure of brain networks, the relatively slow temporal course of fMRI limits the ability to characterize network operation. The latter is crucial for developing a mechanistic understanding of how brain networks shift dynamically to support inhibitory control. To address this critical gap, we applied spectrally resolved Granger causality (GC) and random forest machine learning tools to human EEG data in two large samples of adults (test sample n = 96, replication sample n = 237, total N = 333, both sexes) who performed a color-word Stroop task. Time-frequency analysis confirmed that recruitment of inhibitory control accompanied by slower behavioral responses was related to changes in theta and alpha/beta power. GC analyses revealed directionally asymmetric exchanges within frontal and between frontal and parietal brain areas: top-down influence of superior frontal gyrus (SFG) over both dorsal ACC (dACC) and inferior frontal gyrus (IFG), dACC control over middle frontal gyrus (MFG), and frontal-parietal exchanges (IFG, precuneus, MFG). Predictive analytics confirmed a combination of behavioral and brain-derived variables as the best set of predictors of inhibitory control demands, with SFG theta bearing higher classification importance than dACC theta and posterior beta tracking the onset of behavioral response. The present results provide mechanistic insight into the biological implementation of a psychological phenomenon: inhibitory control is implemented by dynamic routing processes during which the target response is upregulated via theta-mediated effective connectivity within key PFC nodes and via beta-mediated motor preparation. SIGNIFICANCE STATEMENT Hemodynamic neuroimaging research has recently clarified regional structures in brain networks supporting inhibitory control. However, due to inherent methodological constraints, much of this research has been unable to characterize the temporal dynamics of such networks (e.g., direction of information flow between nodes). Guided by fMRI research identifying the structure of brain networks supporting inhibitory control, results of EEG source analysis in a test sample ( n = 96) and replication sample ( n = 237) using effective connectivity and predictive analytics strategies advance a model of inhibitory control by characterizing the precise temporal dynamics by which this network operates and exemplify an approach by which mechanistic models can be developed for other key psychological processes. Copyright © 2018 the authors 0270-6474/18/384348-09$15.00/0.

Leveraging social networking sites for disease surveillance and public sensing: the case of the 2013 avian influenza A(H7N9) outbreak in China.

PubMed

Zhang, Emma Xuxiao; Yang, Yinping; Di Shang, Richard; Simons, Joseph John Pyne; Quek, Boon Kiat; Yin, Xiao Feng; See, Wanhan; Oh, Olivia Seen Huey; Nandar, Khine Sein Tun; Ling, Vivienne Ruo Yun; Chan, Pei Pei; Wang, Zhaoxia; Goh, Rick Siow Mong; James, Lyn; Tey, Jeannie Su Hui

2015-01-01

We conducted in-depth analysis on the use of a popular Chinese social networking and microblogging site, Sina Weibo, to monitor an avian influenza A(H7N9) outbreak in China and to assess the value of social networking sites in the surveillance of disease outbreaks that occur overseas. Two data sets were employed for our analysis: a line listing of confirmed cases obtained from conventional public health information channels and case information from Weibo posts. Our findings showed that the level of activity on Weibo corresponded with the number of new cases reported. In addition, the reporting of new cases on Weibo was significantly faster than those of conventional reporting sites and non-local news media. A qualitative review of the functions of Weibo also revealed that Weibo enabled timely monitoring of other outbreak-relevant information, provided access to additional crowd-sourced epidemiological information and was leveraged by the local government as an interactive platform for risk communication and monitoring public sentiment on the policy response. Our analysis demonstrated the potential for social networking sites to be used by public health agencies to enhance traditional communicable disease surveillance systems for the global surveillance of overseas public health threats. Social networking sites also can be used by governments for calibration of response policies and measures and for risk communication.

Leveraging social networking sites for disease surveillance and public sensing: the case of the 2013 avian influenza A(H7N9) outbreak in China

PubMed Central

Zhang, Emma Xuxiao; Yang, Yinping; Di Shang, Richard; Simons, Joseph John Pyne; Quek, Boon Kiat; Yin, Xiao Feng; See, Wanhan; Oh, Olivia Seen Huey; Nandar, Khine Sein Tun; Ling, Vivienne Ruo Yun; Chan, Pei Pei; Wang, Zhaoxia; Goh, Rick Siow Mong; James, Lyn

2015-01-01

We conducted in-depth analysis on the use of a popular Chinese social networking and microblogging site, Sina Weibo, to monitor an avian influenza A(H7N9) outbreak in China and to assess the value of social networking sites in the surveillance of disease outbreaks that occur overseas. Two data sets were employed for our analysis: a line listing of confirmed cases obtained from conventional public health information channels and case information from Weibo posts. Our findings showed that the level of activity on Weibo corresponded with the number of new cases reported. In addition, the reporting of new cases on Weibo was significantly faster than those of conventional reporting sites and non-local news media. A qualitative review of the functions of Weibo also revealed that Weibo enabled timely monitoring of other outbreak-relevant information, provided access to additional crowd-sourced epidemiological information and was leveraged by the local government as an interactive platform for risk communication and monitoring public sentiment on the policy response. Our analysis demonstrated the potential for social networking sites to be used by public health agencies to enhance traditional communicable disease surveillance systems for the global surveillance of overseas public health threats. Social networking sites also can be used by governments for calibration of response policies and measures and for risk communication. PMID:26306219

Diffany: an ontology-driven framework to infer, visualise and analyse differential molecular networks.

PubMed

Van Landeghem, Sofie; Van Parys, Thomas; Dubois, Marieke; Inzé, Dirk; Van de Peer, Yves

2016-01-05

Differential networks have recently been introduced as a powerful way to study the dynamic rewiring capabilities of an interactome in response to changing environmental conditions or stimuli. Currently, such differential networks are generated and visualised using ad hoc methods, and are often limited to the analysis of only one condition-specific response or one interaction type at a time. In this work, we present a generic, ontology-driven framework to infer, visualise and analyse an arbitrary set of condition-specific responses against one reference network. To this end, we have implemented novel ontology-based algorithms that can process highly heterogeneous networks, accounting for both physical interactions and regulatory associations, symmetric and directed edges, edge weights and negation. We propose this integrative framework as a standardised methodology that allows a unified view on differential networks and promotes comparability between differential network studies. As an illustrative application, we demonstrate its usefulness on a plant abiotic stress study and we experimentally confirmed a predicted regulator. Diffany is freely available as open-source java library and Cytoscape plugin from http://bioinformatics.psb.ugent.be/supplementary_data/solan/diffany/.

[Formula: see text]A longitudinal analysis of the attention networks in 6- to 11-year-old children.

PubMed

Lewis, Frances C; Reeve, Robert A; Johnson, Katherine A

2018-02-01

Attention is critical for everyday functioning. Posner and Petersen's model of attention describes three neural networks involved in attention control-the alerting network for arousal, the orienting network for selecting sensory input and reorienting attention, and the executive network for the regulatory control of attention. No longitudinal research has examined relative change in these networks in children. A modified version of the attention network task (ANT) was used to examine changes in the three attention networks, three times over 12 months, in 114 6-, 8- and 10-year-olds. Findings showed that the alerting network continued to develop over this period, the orienting network had stabilized by 6 years, and the conflict network had largely stabilized by 7 years. The reorienting of attention was also assessed using invalid cues, which showed a similar developmental trajectory to the orienting attention network and had stabilized by 6 years. The results confirm that age 6 to 7 years is a critical period in the development of attention, in particular executive attention. The largest improvement over the evaluation period was between 6 and 7 years; however, subtle changes were found in attention beyond 8 years of age.

Complete characterization of the stability of cluster synchronization in complex dynamical networks.

PubMed

Sorrentino, Francesco; Pecora, Louis M; Hagerstrom, Aaron M; Murphy, Thomas E; Roy, Rajarshi

2016-04-01

Synchronization is an important and prevalent phenomenon in natural and engineered systems. In many dynamical networks, the coupling is balanced or adjusted to admit global synchronization, a condition called Laplacian coupling. Many networks exhibit incomplete synchronization, where two or more clusters of synchronization persist, and computational group theory has recently proved to be valuable in discovering these cluster states based on the topology of the network. In the important case of Laplacian coupling, additional synchronization patterns can exist that would not be predicted from the group theory analysis alone. Understanding how and when clusters form, merge, and persist is essential for understanding collective dynamics, synchronization, and failure mechanisms of complex networks such as electric power grids, distributed control networks, and autonomous swarming vehicles. We describe a method to find and analyze all of the possible cluster synchronization patterns in a Laplacian-coupled network, by applying methods of computational group theory to dynamically equivalent networks. We present a general technique to evaluate the stability of each of the dynamically valid cluster synchronization patterns. Our results are validated in an optoelectronic experiment on a five-node network that confirms the synchronization patterns predicted by the theory.

The Extended Language Network: A Meta-Analysis of Neuroimaging Studies on Text Comprehension

PubMed Central

Ferstl, Evelyn C.; Neumann, Jane; Bogler, Carsten; von Cramon, D. Yves

2010-01-01

Language processing in context requires more than merely comprehending words and sentences. Important subprocesses are inferences for bridging successive utterances, the use of background knowledge and discourse context, and pragmatic interpretations. The functional neuroanatomy of these text comprehension processes has only recently been investigated. Although there is evidence for right-hemisphere contributions, reviews have implicated the left lateral prefrontal cortex, left temporal regions beyond Wernicke’s area, and the left dorso-medial prefrontal cortex (dmPFC) for text comprehension. To objectively confirm this extended language network and to evaluate the respective contribution of right hemisphere regions, meta-analyses of 23 neuroimaging studies are reported here. The analyses used replicator dynamics based on activation likelihood estimates. Independent of the baseline, the anterior temporal lobes (aTL) were active bilaterally. In addition, processing of coherent compared with incoherent text engaged the dmPFC and the posterior cingulate cortex. Right hemisphere activations were seen most notably in the analysis of contrasts testing specific subprocesses, such as metaphor comprehension. These results suggest task dependent contributions for the lateral PFC and the right hemisphere. Most importantly, they confirm the role of the aTL and the fronto-medial cortex for language processing in context. PMID:17557297

An individual-based approach to SIR epidemics in contact networks.

PubMed

Youssef, Mina; Scoglio, Caterina

2011-08-21

Many approaches have recently been proposed to model the spread of epidemics on networks. For instance, the Susceptible/Infected/Recovered (SIR) compartmental model has successfully been applied to different types of diseases that spread out among humans and animals. When this model is applied on a contact network, the centrality characteristics of the network plays an important role in the spreading process. However, current approaches only consider an aggregate representation of the network structure, which can result in inaccurate analysis. In this paper, we propose a new individual-based SIR approach, which considers the whole description of the network structure. The individual-based approach is built on a continuous time Markov chain, and it is capable of evaluating the state probability for every individual in the network. Through mathematical analysis, we rigorously confirm the existence of an epidemic threshold below which an epidemic does not propagate in the network. We also show that the epidemic threshold is inversely proportional to the maximum eigenvalue of the network. Additionally, we study the role of the whole spectrum of the network, and determine the relationship between the maximum number of infected individuals and the set of eigenvalues and eigenvectors. To validate our approach, we analytically study the deviation with respect to the continuous time Markov chain model, and we show that the new approach is accurate for a large range of infection strength. Furthermore, we compare the new approach with the well-known heterogeneous mean field approach in the literature. Ultimately, we support our theoretical results through extensive numerical evaluations and Monte Carlo simulations. Published by Elsevier Ltd.

The Orphan Disease Networks

PubMed Central

Zhang, Minlu; Zhu, Cheng; Jacomy, Alexis; Lu, Long J.; Jegga, Anil G.

2011-01-01

The low prevalence rate of orphan diseases (OD) requires special combined efforts to improve diagnosis, prevention, and discovery of novel therapeutic strategies. To identify and investigate relationships based on shared genes or shared functional features, we have conducted a bioinformatic-based global analysis of all orphan diseases with known disease-causing mutant genes. Starting with a bipartite network of known OD and OD-causing mutant genes and using the human protein interactome, we first construct and topologically analyze three networks: the orphan disease network, the orphan disease-causing mutant gene network, and the orphan disease-causing mutant gene interactome. Our results demonstrate that in contrast to the common disease-causing mutant genes that are predominantly nonessential, a majority of orphan disease-causing mutant genes are essential. In confirmation of this finding, we found that OD-causing mutant genes are topologically important in the protein interactome and are ubiquitously expressed. Additionally, functional enrichment analysis of those genes in which mutations cause ODs shows that a majority result in premature death or are lethal in the orthologous mouse gene knockout models. To address the limitations of traditional gene-based disease networks, we also construct and analyze OD networks on the basis of shared enriched features (biological processes, cellular components, pathways, phenotypes, and literature citations). Analyzing these functionally-linked OD networks, we identified several additional OD-OD relations that are both phenotypically similar and phenotypically diverse. Surprisingly, we observed that the wiring of the gene-based and other feature-based OD networks are largely different; this suggests that the relationship between ODs cannot be fully captured by the gene-based network alone. PMID:21664998

An extensive assessment of network alignment algorithms for comparison of brain connectomes.

PubMed

Milano, Marianna; Guzzi, Pietro Hiram; Tymofieva, Olga; Xu, Duan; Hess, Christofer; Veltri, Pierangelo; Cannataro, Mario

2017-06-06

Recently the study of the complex system of connections in neural systems, i.e. the connectome, has gained a central role in neurosciences. The modeling and analysis of connectomes are therefore a growing area. Here we focus on the representation of connectomes by using graph theory formalisms. Macroscopic human brain connectomes are usually derived from neuroimages; the analyzed brains are co-registered in the image domain and brought to a common anatomical space. An atlas is then applied in order to define anatomically meaningful regions that will serve as the nodes of the network - this process is referred to as parcellation. The atlas-based parcellations present some known limitations in cases of early brain development and abnormal anatomy. Consequently, it has been recently proposed to perform atlas-free random brain parcellation into nodes and align brains in the network space instead of the anatomical image space, as a way to deal with the unknown correspondences of the parcels. Such process requires modeling of the brain using graph theory and the subsequent comparison of the structure of graphs. The latter step may be modeled as a network alignment (NA) problem. In this work, we first define the problem formally, then we test six existing state of the art of network aligners on diffusion MRI-derived brain networks. We compare the performances of algorithms by assessing six topological measures. We also evaluated the robustness of algorithms to alterations of the dataset. The results confirm that NA algorithms may be applied in cases of atlas-free parcellation for a fully network-driven comparison of connectomes. The analysis shows MAGNA++ is the best global alignment algorithm. The paper presented a new analysis methodology that uses network alignment for validating atlas-free parcellation brain connectomes. The methodology has been experimented on several brain datasets.

Understanding the response to endurance exercise using a systems biology approach: combining blood metabolomics, transcriptomics and miRNomics in horses.

PubMed

Mach, Núria; Ramayo-Caldas, Yuliaxis; Clark, Allison; Moroldo, Marco; Robert, Céline; Barrey, Eric; López, Jesús Maria; Le Moyec, Laurence

2017-02-17

Endurance exercise in horses requires adaptive processes involving physiological, biochemical, and cognitive-behavioral responses in an attempt to regain homeostasis. We hypothesized that the identification of the relationships between blood metabolome, transcriptome, and miRNome during endurance exercise in horses could provide significant insights into the molecular response to endurance exercise. For this reason, the serum metabolome and whole-blood transcriptome and miRNome data were obtained from ten horses before and after a 160 km endurance competition. We obtained a global regulatory network based on 11 unique metabolites, 263 metabolic genes and 5 miRNAs whose expression was significantly altered at T1 (post- endurance competition) relative to T0 (baseline, pre-endurance competition). This network provided new insights into the cross talk between the distinct molecular pathways (e.g. energy and oxygen sensing, oxidative stress, and inflammation) that were not detectable when analyzing single metabolites or transcripts alone. Single metabolites and transcripts were carrying out multiple roles and thus sharing several biochemical pathways. Using a regulatory impact factor metric analysis, this regulatory network was further confirmed at the transcription factor and miRNA levels. In an extended cohort of 31 independent animals, multiple factor analysis confirmed the strong associations between lactate, methylene derivatives, miR-21-5p, miR-16-5p, let-7 family and genes that coded proteins involved in metabolic reactions primarily related to energy, ubiquitin proteasome and lipopolysaccharide immune responses after the endurance competition. Multiple factor analysis also identified potential biomarkers at T0 for an increased likelihood for failure to finish an endurance competition. To the best of our knowledge, the present study is the first to provide a comprehensive and integrated overview of the metabolome, transcriptome, and miRNome co-regulatory networks that may have a key role in regulating the metabolic and immune response to endurance exercise in horses.

A neural network model of metaphor understanding with dynamic interaction based on a statistical language analysis: targeting a human-like model.

PubMed

Terai, Asuka; Nakagawa, Masanori

2007-08-01

The purpose of this paper is to construct a model that represents the human process of understanding metaphors, focusing specifically on similes of the form an "A like B". Generally speaking, human beings are able to generate and understand many sorts of metaphors. This study constructs the model based on a probabilistic knowledge structure for concepts which is computed from a statistical analysis of a large-scale corpus. Consequently, this model is able to cover the many kinds of metaphors that human beings can generate. Moreover, the model implements the dynamic process of metaphor understanding by using a neural network with dynamic interactions. Finally, the validity of the model is confirmed by comparing model simulations with the results from a psychological experiment.

Detecting malicious chaotic signals in wireless sensor network

NASA Astrophysics Data System (ADS)

Upadhyay, Ranjit Kumar; Kumari, Sangeeta

2018-02-01

In this paper, an e-epidemic Susceptible-Infected-Vaccinated (SIV) model has been proposed to analyze the effect of node immunization and worms attacking dynamics in wireless sensor network. A modified nonlinear incidence rate with cyrtoid type functional response has been considered using sleep and active mode approach. Detailed stability analysis and the sufficient criteria for the persistence of the model system have been established. We also established different types of bifurcation analysis for different equilibria at different critical points of the control parameters. We performed a detailed Hopf bifurcation analysis and determine the direction and stability of the bifurcating periodic solutions using center manifold theorem. Numerical simulations are carried out to confirm the theoretical results. The impact of the control parameters on the dynamics of the model system has been investigated and malicious chaotic signals are detected. Finally, we have analyzed the effect of time delay on the dynamics of the model system.

MANEMO Routing in Practice: Protocol Selection, Expected Performance, and Experimental Evaluation

NASA Astrophysics Data System (ADS)

Tazaki, Hajime; van Meter, Rodney; Wakikawa, Ryuji; Wongsaardsakul, Thirapon; Kanchanasut, Kanchana; Dias de Amorim, Marcelo; Murai, Jun

Motivated by the deployment of post-disaster MANEMO (MANET for NEMO) composed of mobile routers and stations, we evaluate two candidate routing protocols through network simulation, theoretical performance analysis, and field experiments. The first protocol is the widely adopted Optimized Link State Routing protocol (OLSR) and the second is the combination of the Tree Discovery Protocol (TDP) with Network In Node Advertisement (NINA). To the best of our knowledge, this is the first time that these two protocols are compared in both theoretical and practical terms. We focus on the control overhead generated when mobile routers perform a handover. Our results confirm the correctness and operational robustness of both protocols. More interestingly, although in the general case OLSR leads to better results, TDP/NINA outperforms OLSR both in the case of sparse networks and in highly mobile networks, which correspond to the operation point of a large set of post-disaster scenarios.

Network-based stochastic semisupervised learning.

PubMed

Silva, Thiago Christiano; Zhao, Liang

2012-03-01

Semisupervised learning is a machine learning approach that is able to employ both labeled and unlabeled samples in the training process. In this paper, we propose a semisupervised data classification model based on a combined random-preferential walk of particles in a network (graph) constructed from the input dataset. The particles of the same class cooperate among themselves, while the particles of different classes compete with each other to propagate class labels to the whole network. A rigorous model definition is provided via a nonlinear stochastic dynamical system and a mathematical analysis of its behavior is carried out. A numerical validation presented in this paper confirms the theoretical predictions. An interesting feature brought by the competitive-cooperative mechanism is that the proposed model can achieve good classification rates while exhibiting low computational complexity order in comparison to other network-based semisupervised algorithms. Computer simulations conducted on synthetic and real-world datasets reveal the effectiveness of the model.

Research on FBG-Based CFRP Structural Damage Identification Using BP Neural Network

NASA Astrophysics Data System (ADS)

Geng, Xiangyi; Lu, Shizeng; Jiang, Mingshun; Sui, Qingmei; Lv, Shanshan; Xiao, Hang; Jia, Yuxi; Jia, Lei

2018-06-01

A damage identification system of carbon fiber reinforced plastics (CFRP) structures is investigated using fiber Bragg grating (FBG) sensors and back propagation (BP) neural network. FBG sensors are applied to construct the sensing network to detect the structural dynamic response signals generated by active actuation. The damage identification model is built based on the BP neural network. The dynamic signal characteristics extracted by the Fourier transform are the inputs, and the damage states are the outputs of the model. Besides, damages are simulated by placing lumped masses with different weights instead of inducing real damages, which is confirmed to be feasible by finite element analysis (FEA). At last, the damage identification system is verified on a CFRP plate with 300 mm × 300 mm experimental area, with the accurate identification of varied damage states. The system provides a practical way for CFRP structural damage identification.

Social networks among Indigenous peoples in Mexico.

PubMed

Skoufias, Emmanuel; Lunde, Trine; Patrinos, Harry Anthony

2010-01-01

We examine the extent to which social networks among indigenous peoples in Mexico have a significant effect on a variety of human capital investment and economic activities, such as school attendance and work among teenage boys and girls, and migration, welfare participation, employment status, occupation, and sector of employment among adult males and females. Using data from the 10 percent population sample of the 2000 Population and Housing Census of Mexico and the empirical strategy that Bertrand, Luttmer, and Mullainathan (2000) propose, which allows us to take into account the role of municipality and language group fixed effects, we confirm empirically that social network effects play an important role in the economic decisions of indigenous people, especially in rural areas. Our analysis also provides evidence that better access to basic services such as water and electricity increases the size and strength of network effects in rural areas.

Systems genetics identifies Sestrin 3 as a regulator of a proconvulsant gene network in human epileptic hippocampus

PubMed Central

Johnson, Michael R.; Rossetti, Tiziana; Speed, Doug; Srivastava, Prashant K.; Chadeau-Hyam, Marc; Hajji, Nabil; Dabrowska, Aleksandra; Rotival, Maxime; Razzaghi, Banafsheh; Kovac, Stjepana; Wanisch, Klaus; Grillo, Federico W.; Slaviero, Anna; Langley, Sarah R.; Shkura, Kirill; Roncon, Paolo; De, Tisham; Mattheisen, Manuel; Niehusmann, Pitt; O’Brien, Terence J.; Petrovski, Slave; von Lehe, Marec; Hoffmann, Per; Eriksson, Johan; Coffey, Alison J.; Cichon, Sven; Walker, Matthew; Simonato, Michele; Danis, Bénédicte; Mazzuferi, Manuela; Foerch, Patrik; Schoch, Susanne; De Paola, Vincenzo; Kaminski, Rafal M.; Cunliffe, Vincent T.; Becker, Albert J.; Petretto, Enrico

2015-01-01

Gene-regulatory network analysis is a powerful approach to elucidate the molecular processes and pathways underlying complex disease. Here we employ systems genetics approaches to characterize the genetic regulation of pathophysiological pathways in human temporal lobe epilepsy (TLE). Using surgically acquired hippocampi from 129 TLE patients, we identify a gene-regulatory network genetically associated with epilepsy that contains a specialized, highly expressed transcriptional module encoding proconvulsive cytokines and Toll-like receptor signalling genes. RNA sequencing analysis in a mouse model of TLE using 100 epileptic and 100 control hippocampi shows the proconvulsive module is preserved across-species, specific to the epileptic hippocampus and upregulated in chronic epilepsy. In the TLE patients, we map the trans-acting genetic control of this proconvulsive module to Sestrin 3 (SESN3), and demonstrate that SESN3 positively regulates the module in macrophages, microglia and neurons. Morpholino-mediated Sesn3 knockdown in zebrafish confirms the regulation of the transcriptional module, and attenuates chemically induced behavioural seizures in vivo. PMID:25615886

Online social networking services in the management of patients with diabetes mellitus: systematic review and meta-analysis of randomised controlled trials.

PubMed

Toma, Tania; Athanasiou, Thanos; Harling, Leanne; Darzi, Ara; Ashrafian, Hutan

2014-11-01

Social networking services (SNS) can facilitate real-time communication and feedback of blood glucose and other physiological data between patients and healthcare professionals. This systematic review and meta-analysis aims to summarise the current evidence surrounding the role of online social networking services in diabetes care. We performed a systematic literature review of the Medline, EMBASE and PsychINFO databases of all studies reporting HbA1c (glycated haemoglobin) as a measure of glycaemic control for social networking services in diabetes care. HbA1c, clinical outcomes and the type of technology used were extracted. Study quality and publication bias were assessed. SNS interventions beneficially reduced HbA1c when compared to controls, which was confirmed by sensitivity analysis. SNS interventions also significantly improved systolic and diastolic blood pressure, triglycerides and total cholesterol. Subgroup analysis according to diabetes type demonstrated that Type 2 diabetes patients had a significantly greater reduction in HbA1c than those with Type 1 diabetes. Online SNS provide a novel, feasible approach to improving glycaemic control, particularly in patients with Type 2 diabetes. Further mechanistic and cost-effectiveness studies are required to improve our understanding of SNS and its efficacy in diabetes care. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

A neural network device for on-line particle identification in cosmic ray experiments

NASA Astrophysics Data System (ADS)

Scrimaglio, R.; Finetti, N.; D'Altorio, L.; Rantucci, E.; Raso, M.; Segreto, E.; Tassoni, A.; Cardarilli, G. C.

2004-05-01

On-line particle identification is one of the main goals of many experiments in space both for rare event studies and for optimizing measurements along the orbital trajectory. Neural networks can be a useful tool for signal processing and real time data analysis in such experiments. In this document we report on the performances of a programmable neural device which was developed in VLSI analog/digital technology. Neurons and synapses were accomplished by making use of Operational Transconductance Amplifier (OTA) structures. In this paper we report on the results of measurements performed in order to verify the agreement of the characteristic curves of each elementary cell with simulations and on the device performances obtained by implementing simple neural structures on the VLSI chip. A feed-forward neural network (Multi-Layer Perceptron, MLP) was implemented on the VLSI chip and trained to identify particles by processing the signals of two-dimensional position-sensitive Si detectors. The radiation monitoring device consisted of three double-sided silicon strip detectors. From the analysis of a set of simulated data it was found that the MLP implemented on the neural device gave results comparable with those obtained with the standard method of analysis confirming that the implemented neural network could be employed for real time particle identification.

Functional wiring of the yeast kinome revealed by global analysis of genetic network motifs

PubMed Central

Sharifpoor, Sara; van Dyk, Dewald; Costanzo, Michael; Baryshnikova, Anastasia; Friesen, Helena; Douglas, Alison C.; Youn, Ji-Young; VanderSluis, Benjamin; Myers, Chad L.; Papp, Balázs; Boone, Charles; Andrews, Brenda J.

2012-01-01

A combinatorial genetic perturbation strategy was applied to interrogate the yeast kinome on a genome-wide scale. We assessed the global effects of gene overexpression or gene deletion to map an integrated genetic interaction network of synthetic dosage lethal (SDL) and loss-of-function genetic interactions (GIs) for 92 kinases, producing a meta-network of 8700 GIs enriched for pathways known to be regulated by cognate kinases. Kinases most sensitive to dosage perturbations had constitutive cell cycle or cell polarity functions under standard growth conditions. Condition-specific screens confirmed that the spectrum of kinase dosage interactions can be expanded substantially in activating conditions. An integrated network composed of systematic SDL, negative and positive loss-of-function GIs, and literature-curated kinase–substrate interactions revealed kinase-dependent regulatory motifs predictive of novel gene-specific phenotypes. Our study provides a valuable resource to unravel novel functional relationships and pathways regulated by kinases and outlines a general strategy for deciphering mutant phenotypes from large-scale GI networks. PMID:22282571

Topography of brain glucose hypometabolism and epileptic network in glucose transporter 1 deficiency.

PubMed

Akman, Cigdem Inan; Provenzano, Frank; Wang, Dong; Engelstad, Kristin; Hinton, Veronica; Yu, Julia; Tikofsky, Ronald; Ichese, Masonari; De Vivo, Darryl C

2015-02-01

(18)F fluorodeoxyglucose positron emission tomography ((18)F FDG-PET) facilitates examination of glucose metabolism. Previously, we described regional cerebral glucose hypometabolism using (18)F FDG-PET in patients with Glucose transporter 1 Deficiency Syndrome (Glut1 DS). We now expand this observation in Glut1 DS using quantitative image analysis to identify the epileptic network based on the regional distribution of glucose hypometabolism. (18)F FDG-PET scans of 16 Glut1 DS patients and 7 healthy participants were examined using Statistical parametric Mapping (SPM). Summed images were preprocessed for statistical analysis using MATLAB 7.1 and SPM 2 software. Region of interest (ROI) analysis was performed to validate SPM results. Visual analysis of the (18)F FDG-PET images demonstrated prominent regional glucose hypometabolism in the thalamus, neocortical regions and cerebellum bilaterally. Group comparison using SPM analysis confirmed that the regional distribution of glucose hypo-metabolism was present in thalamus, cerebellum, temporal cortex and central lobule. Two mildly affected patients without epilepsy had hypometabolism in cerebellum, inferior frontal cortex, and temporal lobe, but not thalamus. Glucose hypometabolism did not correlate with age at the time of PET imaging, head circumference, CSF glucose concentration at the time of diagnosis, RBC glucose uptake, or CNS score. Quantitative analysis of (18)F FDG-PET imaging in Glut1 DS patients confirmed that hypometabolism was present symmetrically in thalamus, cerebellum, frontal and temporal cortex. The hypometabolism in thalamus correlated with the clinical history of epilepsy. Copyright © 2014. Published by Elsevier B.V.

Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells

PubMed Central

Mason, Mike J; Fan, Guoping; Plath, Kathrin; Zhou, Qing; Horvath, Steve

2009-01-01

Background Recent work has revealed that a core group of transcription factors (TFs) regulates the key characteristics of embryonic stem (ES) cells: pluripotency and self-renewal. Current efforts focus on identifying genes that play important roles in maintaining pluripotency and self-renewal in ES cells and aim to understand the interactions among these genes. To that end, we investigated the use of unsigned and signed network analysis to identify pluripotency and differentiation related genes. Results We show that signed networks provide a better systems level understanding of the regulatory mechanisms of ES cells than unsigned networks, using two independent murine ES cell expression data sets. Specifically, using signed weighted gene co-expression network analysis (WGCNA), we found a pluripotency module and a differentiation module, which are not identified in unsigned networks. We confirmed the importance of these modules by incorporating genome-wide TF binding data for key ES cell regulators. Interestingly, we find that the pluripotency module is enriched with genes related to DNA damage repair and mitochondrial function in addition to transcriptional regulation. Using a connectivity measure of module membership, we not only identify known regulators of ES cells but also show that Mrpl15, Msh6, Nrf1, Nup133, Ppif, Rbpj, Sh3gl2, and Zfp39, among other genes, have important roles in maintaining ES cell pluripotency and self-renewal. We also report highly significant relationships between module membership and epigenetic modifications (histone modifications and promoter CpG methylation status), which are known to play a role in controlling gene expression during ES cell self-renewal and differentiation. Conclusion Our systems biologic re-analysis of gene expression, transcription factor binding, epigenetic and gene ontology data provides a novel integrative view of ES cell biology. PMID:19619308

Synaptic dynamics regulation in response to high frequency stimulation in neuronal networks

NASA Astrophysics Data System (ADS)

Su, Fei; Wang, Jiang; Li, Huiyan; Wei, Xile; Yu, Haitao; Deng, Bin

2018-02-01

High frequency stimulation (HFS) has confirmed its ability in modulating the pathological neural activities. However its detailed mechanism is unclear. This study aims to explore the effects of HFS on neuronal networks dynamics. First, the two-neuron FitzHugh-Nagumo (FHN) networks with static coupling strength and the small-world FHN networks with spike-time-dependent plasticity (STDP) modulated synaptic coupling strength are constructed. Then, the multi-scale method is used to transform the network models into equivalent averaged models, where the HFS intensity is modeled as the ratio between stimulation amplitude and frequency. Results show that in static two-neuron networks, there is still synaptic current projected to the postsynaptic neuron even if the presynaptic neuron is blocked by the HFS. In the small-world networks, the effects of the STDP adjusting rate parameter on the inactivation ratio and synchrony degree increase with the increase of HFS intensity. However, only when the HFS intensity becomes very large can the STDP time window parameter affect the inactivation ratio and synchrony index. Both simulation and numerical analysis demonstrate that the effects of HFS on neuronal network dynamics are realized through the adjustment of synaptic variable and conductance.

Design and Analysis of a Data Fusion Scheme in Mobile Wireless Sensor Networks Based on Multi-Protocol Mobile Agents

PubMed Central

Wu, Chunxue; Wu, Wenliang; Wan, Caihua

2017-01-01

Sensors are increasingly used in mobile environments with wireless network connections. Multiple sensor types measure distinct aspects of the same event. Their measurements are then combined to produce integrated, reliable results. As the number of sensors in networks increases, low energy requirements and changing network connections complicate event detection and measurement. We present a data fusion scheme for use in mobile wireless sensor networks with high energy efficiency and low network delays, that still produces reliable results. In the first phase, we used a network simulation where mobile agents dynamically select the next hop migration node based on the stability parameter of the link, and perform the data fusion at the migration node. Agents use the fusion results to decide if it should return the fusion results to the processing center or continue to collect more data. In the second phase. The feasibility of data fusion at the node level is confirmed by an experimental design where fused data from color sensors show near-identical results to actual physical temperatures. These results are potentially important for new large-scale sensor network applications. PMID:29099793

Antiviral Potential of ERK/MAPK and PI3K/AKT/mTOR Signaling Modulation for Middle East Respiratory Syndrome Coronavirus Infection as Identified by Temporal Kinome Analysis

PubMed Central

Ork, Britini; Hart, Brit J.; Holbrook, Michael R.; Frieman, Matthew B.; Traynor, Dawn; Johnson, Reed F.; Dyall, Julie; Olinger, Gene G.; Hensley, Lisa E.

2014-01-01

Middle East respiratory syndrome coronavirus (MERS-CoV) is a lineage C betacoronavirus, and infections with this virus can result in acute respiratory syndrome with renal failure. Globally, MERS-CoV has been responsible for 877 laboratory-confirmed infections, including 317 deaths, since September 2012. As there is a paucity of information regarding the molecular pathogenesis associated with this virus or the identities of novel antiviral drug targets, we performed temporal kinome analysis on human hepatocytes infected with the Erasmus isolate of MERS-CoV with peptide kinome arrays. bioinformatics analysis of our kinome data, including pathway overrepresentation analysis (ORA) and functional network analysis, suggested that extracellular signal-regulated kinase (ERK)/mitogen-activated protein kinase (MAPK) and phosphoinositol 3-kinase (PI3K)/serine-threonine kinase (AKT)/mammalian target of rapamycin (mTOR) signaling responses were specifically modulated in response to MERS-CoV infection in vitro throughout the course of infection. The overrepresentation of specific intermediates within these pathways determined by pathway and functional network analysis of our kinome data correlated with similar patterns of phosphorylation determined through Western blot array analysis. In addition, analysis of the effects of specific kinase inhibitors on MERS-CoV infection in tissue culture models confirmed these cellular response observations. Further, we have demonstrated that a subset of licensed kinase inhibitors targeting the ERK/MAPK and PI3K/AKT/mTOR pathways significantly inhibited MERS-CoV replication in vitro whether they were added before or after viral infection. Taken together, our data suggest that ERK/MAPK and PI3K/AKT/mTOR signaling responses play important roles in MERS-CoV infection and may represent novel drug targets for therapeutic intervention strategies. PMID:25487801

Neural Systems Underlying Individual Differences in Intertemporal Decision-making.

PubMed

Elton, Amanda; Smith, Christopher T; Parrish, Michael H; Boettiger, Charlotte A

2017-03-01

Excessively choosing immediate over larger future rewards, or delay discounting (DD), associates with multiple clinical conditions. Individual differences in DD likely depend on variations in the activation of and functional interactions between networks, representing possible endophenotypes for associated disorders, including alcohol use disorders (AUDs). Numerous fMRI studies have probed the neural bases of DD, but investigations of large-scale networks remain scant. We addressed this gap by testing whether activation within large-scale networks during Now/Later decision-making predicts individual differences in DD. To do so, we scanned 95 social drinkers (18-40 years old; 50 women) using fMRI during hypothetical choices between small monetary amounts available "today" or larger amounts available later. We identified neural networks engaged during Now/Later choice using independent component analysis and tested the relationship between component activation and degree of DD. The activity of two components during Now/Later choice correlated with individual DD rates: A temporal lobe network positively correlated with DD, whereas a frontoparietal-striatal network negatively correlated with DD. Activation differences between these networks predicted individual differences in DD, and their negative correlation during Now/Later choice suggests functional competition. A generalized psychophysiological interactions analysis confirmed a decrease in their functional connectivity during decision-making. The functional connectivity of these two networks negatively correlates with alcohol-related harm, potentially implicating these networks in AUDs. These findings provide novel insight into the neural underpinnings of individual differences in impulsive decision-making with potential implications for addiction and related disorders in which impulsivity is a defining feature.

Stability and sensitivity of ABR flow control protocols

NASA Astrophysics Data System (ADS)

Tsai, Wie K.; Kim, Yuseok; Chiussi, Fabio; Toh, Chai-Keong

1998-10-01

This tutorial paper surveys the important issues in stability and sensitivity analysis of ABR flow control of ATM networks. THe stability and sensitivity issues are formulated in a systematic framework. Four main cause of instability in ABR flow control are identified: unstable control laws, temporal variations of available bandwidth with delayed feedback control, misbehaving components, and interactions between higher layer protocols and ABR flow control. Popular rate-based ABR flow control protocols are evaluated. Stability and sensitivity is shown to be the fundamental issues when the network has dynamically-varying bandwidth. Simulation result confirming the theoretical studies are provided. Open research problems are discussed.

Comment on high resolution simulations of cosmic strings. 1: Network evoloution

NASA Technical Reports Server (NTRS)

Turok, Neil; Albrecht, Andreas

1990-01-01

Comments are made on recent claims (Albrecht and Turok, 1989) regarding simulations of cosmic string evolution. Specially, it was claimed that results were dominated by a numerical artifact which rounds out kinks on a scale of the order of the correlation length on the network. This claim was based on an approximate analysis of an interpolation equation which is solved herein. The typical rounding scale is actually less than one fifth of the correlation length, and comparable with other numerical cutoffs. Results confirm previous estimates of numerical uncertainties, and show that the approximations poorly represent the real solutions to the interpolation equation.

Mean-field models for heterogeneous networks of two-dimensional integrate and fire neurons.

PubMed

Nicola, Wilten; Campbell, Sue Ann

2013-01-01

We analytically derive mean-field models for all-to-all coupled networks of heterogeneous, adapting, two-dimensional integrate and fire neurons. The class of models we consider includes the Izhikevich, adaptive exponential and quartic integrate and fire models. The heterogeneity in the parameters leads to different moment closure assumptions that can be made in the derivation of the mean-field model from the population density equation for the large network. Three different moment closure assumptions lead to three different mean-field systems. These systems can be used for distinct purposes such as bifurcation analysis of the large networks, prediction of steady state firing rate distributions, parameter estimation for actual neurons and faster exploration of the parameter space. We use the mean-field systems to analyze adaptation induced bursting under realistic sources of heterogeneity in multiple parameters. Our analysis demonstrates that the presence of heterogeneity causes the Hopf bifurcation associated with the emergence of bursting to change from sub-critical to super-critical. This is confirmed with numerical simulations of the full network for biologically reasonable parameter values. This change decreases the plausibility of adaptation being the cause of bursting in hippocampal area CA3, an area with a sizable population of heavily coupled, strongly adapting neurons.

Mean-field models for heterogeneous networks of two-dimensional integrate and fire neurons

PubMed Central

Nicola, Wilten; Campbell, Sue Ann

2013-01-01

We analytically derive mean-field models for all-to-all coupled networks of heterogeneous, adapting, two-dimensional integrate and fire neurons. The class of models we consider includes the Izhikevich, adaptive exponential and quartic integrate and fire models. The heterogeneity in the parameters leads to different moment closure assumptions that can be made in the derivation of the mean-field model from the population density equation for the large network. Three different moment closure assumptions lead to three different mean-field systems. These systems can be used for distinct purposes such as bifurcation analysis of the large networks, prediction of steady state firing rate distributions, parameter estimation for actual neurons and faster exploration of the parameter space. We use the mean-field systems to analyze adaptation induced bursting under realistic sources of heterogeneity in multiple parameters. Our analysis demonstrates that the presence of heterogeneity causes the Hopf bifurcation associated with the emergence of bursting to change from sub-critical to super-critical. This is confirmed with numerical simulations of the full network for biologically reasonable parameter values. This change decreases the plausibility of adaptation being the cause of bursting in hippocampal area CA3, an area with a sizable population of heavily coupled, strongly adapting neurons. PMID:24416013

MARIKA - A model revision system using qualitative analysis of simulations. [of human orientation system

NASA Technical Reports Server (NTRS)

Groleau, Nicolas; Frainier, Richard; Colombano, Silvano; Hazelton, Lyman; Szolovits, Peter

1993-01-01

This paper describes portions of a novel system called MARIKA (Model Analysis and Revision of Implicit Key Assumptions) to automatically revise a model of the normal human orientation system. The revision is based on analysis of discrepancies between experimental results and computer simulations. The discrepancies are calculated from qualitative analysis of quantitative simulations. The experimental and simulated time series are first discretized in time segments. Each segment is then approximated by linear combinations of simple shapes. The domain theory and knowledge are represented as a constraint network. Incompatibilities detected during constraint propagation within the network yield both parameter and structural model alterations. Interestingly, MARIKA diagnosed a data set from the Massachusetts Eye and Ear Infirmary Vestibular Laboratory as abnormal though the data was tagged as normal. Published results from other laboratories confirmed the finding. These encouraging results could lead to a useful clinical vestibular tool and to a scientific discovery system for space vestibular adaptation.

Multimodal integration of fMRI and EEG data for high spatial and temporal resolution analysis of brain networks

PubMed Central

Mantini, D.; Marzetti, L.; Corbetta, M.; Romani, G.L.; Del Gratta, C.

2017-01-01

Two major non-invasive brain mapping techniques, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have complementary advantages with regard to their spatial and temporal resolution. We propose an approach based on the integration of EEG and fMRI, enabling the EEG temporal dynamics of information processing to be characterized within spatially well-defined fMRI large-scale networks. First, the fMRI data are decomposed into networks by means of spatial independent component analysis (sICA), and those associated with intrinsic activity and/or responding to task performance are selected using information from the related time-courses. Next, the EEG data over all sensors are averaged with respect to event timing, thus calculating event-related potentials (ERPs). The ERPs are subjected to temporal ICA (tICA), and the resulting components are localized with the weighted minimum norm (WMNLS) algorithm using the task-related fMRI networks as priors. Finally, the temporal contribution of each ERP component in the areas belonging to the fMRI large-scale networks is estimated. The proposed approach has been evaluated on visual target detection data. Our results confirm that two different components, commonly observed in EEG when presenting novel and salient stimuli respectively, are related to the neuronal activation in large-scale networks, operating at different latencies and associated with different functional processes. PMID:20052528

Large-scale integrative network-based analysis identifies common pathways disrupted by copy number alterations across cancers

PubMed Central

2013-01-01

Background Many large-scale studies analyzed high-throughput genomic data to identify altered pathways essential to the development and progression of specific types of cancer. However, no previous study has been extended to provide a comprehensive analysis of pathways disrupted by copy number alterations across different human cancers. Towards this goal, we propose a network-based method to integrate copy number alteration data with human protein-protein interaction networks and pathway databases to identify pathways that are commonly disrupted in many different types of cancer. Results We applied our approach to a data set of 2,172 cancer patients across 16 different types of cancers, and discovered a set of commonly disrupted pathways, which are likely essential for tumor formation in majority of the cancers. We also identified pathways that are only disrupted in specific cancer types, providing molecular markers for different human cancers. Analysis with independent microarray gene expression datasets confirms that the commonly disrupted pathways can be used to identify patient subgroups with significantly different survival outcomes. We also provide a network view of disrupted pathways to explain how copy number alterations affect pathways that regulate cell growth, cycle, and differentiation for tumorigenesis. Conclusions In this work, we demonstrated that the network-based integrative analysis can help to identify pathways disrupted by copy number alterations across 16 types of human cancers, which are not readily identifiable by conventional overrepresentation-based and other pathway-based methods. All the results and source code are available at http://compbio.cs.umn.edu/NetPathID/. PMID:23822816

Porcine Tissue-Specific Regulatory Networks Derived from Meta-Analysis of the Transcriptome

PubMed Central

Pérez-Montarelo, Dafne; Hudson, Nicholas J.; Fernández, Ana I.; Ramayo-Caldas, Yuliaxis; Dalrymple, Brian P.; Reverter, Antonio

2012-01-01

The processes that drive tissue identity and differentiation remain unclear for most tissue types. So are the gene networks and transcription factors (TF) responsible for the differential structure and function of each particular tissue, and this is particularly true for non model species with incomplete genomic resources. To better understand the regulation of genes responsible for tissue identity in pigs, we have inferred regulatory networks from a meta-analysis of 20 gene expression studies spanning 480 Porcine Affymetrix chips for 134 experimental conditions on 27 distinct tissues. We developed a mixed-model normalization approach with a covariance structure that accommodated the disparity in the origin of the individual studies, and obtained the normalized expression of 12,320 genes across the 27 tissues. Using this resource, we constructed a network, based on the co-expression patterns of 1,072 TF and 1,232 tissue specific genes. The resulting network is consistent with the known biology of tissue development. Within the network, genes clustered by tissue and tissues clustered by site of embryonic origin. These clusters were significantly enriched for genes annotated in key relevant biological processes and confirm gene functions and interactions from the literature. We implemented a Regulatory Impact Factor (RIF) metric to identify the key regulators in skeletal muscle and tissues from the central nervous systems. The normalization of the meta-analysis, the inference of the gene co-expression network and the RIF metric, operated synergistically towards a successful search for tissue-specific regulators. Novel among these findings are evidence suggesting a novel key role of ERCC3 as a muscle regulator. Together, our results recapitulate the known biology behind tissue specificity and provide new valuable insights in a less studied but valuable model species. PMID:23049964

Resource constrained flux balance analysis predicts selective pressure on the global structure of metabolic networks.

PubMed

Abedpour, Nima; Kollmann, Markus

2015-11-23

A universal feature of metabolic networks is their hourglass or bow-tie structure on cellular level. This architecture reflects the conversion of multiple input nutrients into multiple biomass components via a small set of precursor metabolites. However, it is yet unclear to what extent this structural feature is the result of natural selection. We extend flux balance analysis to account for limited cellular resources. Using this model, optimal structure of metabolic networks can be calculated for different environmental conditions. We observe a significant structural reshaping of metabolic networks for a toy-network and E. coli core metabolism if we increase the share of invested resources for switching between different nutrient conditions. Here, hub nodes emerge and the optimal network structure becomes bow-tie-like as a consequence of limited cellular resource constraint. We confirm this theoretical finding by comparing the reconstructed metabolic networks of bacterial species with respect to their lifestyle. We show that bow-tie structure can give a system-level fitness advantage to organisms that live in highly competitive and fluctuating environments. Here, limitation of cellular resources can lead to an efficiency-flexibility tradeoff where it pays off for the organism to shorten catabolic pathways if they are frequently activated and deactivated. As a consequence, generalists that shuttle between diverse environmental conditions should have a more predominant bow-tie structure than specialists that visit just a few isomorphic habitats during their life cycle.

Tooth shape optimization of brushless permanent magnet motors for reducing torque ripples

NASA Astrophysics Data System (ADS)

Hsu, Liang-Yi; Tsai, Mi-Ching

2004-11-01

This paper presents a tooth shape optimization method based on a generic algorithm to reduce the torque ripple of brushless permanent magnet motors under two different magnetization directions. The analysis of this design method mainly focuses on magnetic saturation and cogging torque and the computation of the optimization process is based on an equivalent magnetic network circuit. The simulation results, obtained from the finite element analysis, are used to confirm the accuracy and performance. Finite element analysis results from different tooth shapes are compared to show the effectiveness of the proposed method.

Is Traumatic Brain Injury Associated with Reduced Inter-Hemispheric Functional Connectivity? A Study of Large-Scale Resting State Networks following Traumatic Brain Injury

PubMed Central

Duff, Melissa C.; McAuley, Edward; Kramer, Arthur F.; Voss, Michelle W.

2016-01-01

Abstract Traumatic brain injury (TBI) often has long-term debilitating sequelae in cognitive and behavioral domains. Understanding how TBI impacts functional integrity of brain networks that underlie these domains is key to guiding future approaches to TBI rehabilitation. In the current study, we investigated the differences in inter-hemispheric functional connectivity (FC) of resting state networks (RSNs) between chronic mild-to-severe TBI patients and normal comparisons (NC), focusing on two externally oriented networks (i.e., the fronto-parietal network [FPN] and the executive control network [ECN]), one internally oriented network (i.e., the default mode network [DMN]), and one somato-motor network (SMN). Seed voxel correlation analysis revealed that TBI patients displayed significantly less FC between lateralized seeds and both homologous and non-homologous regions in the opposite hemisphere for externally oriented networks but not for DMN or SMN; conversely, TBI patients showed increased FC within regions of the DMN, especially precuneus and parahippocampal gyrus. Region of interest correlation analyses confirmed the presence of significantly higher inter-hemispheric FC in NC for the FPN (p < 0.01), and ECN (p < 0.05), but not for the DMN (p > 0.05) or SMN (p > 0.05). Further analysis revealed that performance on a neuropsychological test measuring organizational skills and visuo-spatial abilities administered to the TBI group, the Rey-Osterrieth Complex Figure Test, positively correlated with FC between the right FPN and homologous regions. Our findings suggest that distinct RSNs display specific patterns of aberrant FC following TBI; this represents a step forward in the search for biomarkers useful for early diagnosis and treatment of TBI-related cognitive impairment. PMID:25719433

Research Review: Neural response to threat in children, adolescents, and adults after child maltreatment - a quantitative meta-analysis.

PubMed

Hein, Tyler C; Monk, Christopher S

2017-03-01

Child maltreatment is common and has long-term consequences for affective function. Investigations of neural consequences of maltreatment have focused on the amygdala. However, developmental neuroscience indicates that other brain regions are also likely to be affected by child maltreatment, particularly in the social information processing network (SIPN). We conducted a quantitative meta-analysis to: confirm that maltreatment is related to greater bilateral amygdala activation in a large sample that was pooled across studies; investigate other SIPN structures that are likely candidates for altered function; and conduct a data-driven examination to identify additional regions that show altered activation in maltreated children, teens, and adults. We conducted an activation likelihood estimation analysis with 1,733 participants across 20 studies of emotion processing in maltreated individuals. Maltreatment is associated with increased bilateral amygdala activation to emotional faces. One SIPN structure is altered: superior temporal gyrus, of the detection node, is hyperactive in maltreated individuals. The results of the whole-brain corrected analysis also show hyperactivation of the parahippocampal gyrus and insula in maltreated individuals. The meta-analysis confirms that maltreatment is related to increased bilateral amygdala reactivity and also shows that maltreatment affects multiple additional structures in the brain that have received little attention in the literature. Thus, although the majority of studies examining maltreatment and brain function have focused on the amygdala, these findings indicate that the neural consequences of child maltreatment involve a broader network of structures. © 2016 Association for Child and Adolescent Mental Health.

Combined analysis of mRNA and miRNA identifies dehydration and salinity responsive key molecular players in citrus roots.

PubMed

Xie, Rangjin; Zhang, Jin; Ma, Yanyan; Pan, Xiaoting; Dong, Cuicui; Pang, Shaoping; He, Shaolan; Deng, Lie; Yi, Shilai; Zheng, Yongqiang; Lv, Qiang

2017-02-06

Citrus is one of the most economically important fruit crops around world. Drought and salinity stresses adversely affected its productivity and fruit quality. However, the genetic regulatory networks and signaling pathways involved in drought and salinity remain to be elucidated. With RNA-seq and sRNA-seq, an integrative analysis of miRNA and mRNA expression profiling and their regulatory networks were conducted using citrus roots subjected to dehydration and salt treatment. Differentially expressed (DE) mRNA and miRNA profiles were obtained according to fold change analysis and the relationships between miRNAs and target mRNAs were found to be coherent and incoherent in the regulatory networks. GO enrichment analysis revealed that some crucial biological processes related to signal transduction (e.g. 'MAPK cascade'), hormone-mediated signaling pathways (e.g. abscisic acid- activated signaling pathway'), reactive oxygen species (ROS) metabolic process (e.g. 'hydrogen peroxide catabolic process') and transcription factors (e.g., 'MYB, ZFP and bZIP') were involved in dehydration and/or salt treatment. The molecular players in response to dehydration and salt treatment were partially overlapping. Quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) analysis further confirmed the results from RNA-seq and sRNA-seq analysis. This study provides new insights into the molecular mechanisms how citrus roots respond to dehydration and salt treatment.

Combined analysis of mRNA and miRNA identifies dehydration and salinity responsive key molecular players in citrus roots

PubMed Central

Xie, Rangjin; Zhang, Jin; Ma, Yanyan; Pan, Xiaoting; Dong, Cuicui; Pang, Shaoping; He, Shaolan; Deng, Lie; Yi, Shilai; Zheng, Yongqiang; Lv, Qiang

2017-01-01

Citrus is one of the most economically important fruit crops around world. Drought and salinity stresses adversely affected its productivity and fruit quality. However, the genetic regulatory networks and signaling pathways involved in drought and salinity remain to be elucidated. With RNA-seq and sRNA-seq, an integrative analysis of miRNA and mRNA expression profiling and their regulatory networks were conducted using citrus roots subjected to dehydration and salt treatment. Differentially expressed (DE) mRNA and miRNA profiles were obtained according to fold change analysis and the relationships between miRNAs and target mRNAs were found to be coherent and incoherent in the regulatory networks. GO enrichment analysis revealed that some crucial biological processes related to signal transduction (e.g. ‘MAPK cascade’), hormone-mediated signaling pathways (e.g. abscisic acid- activated signaling pathway’), reactive oxygen species (ROS) metabolic process (e.g. ‘hydrogen peroxide catabolic process’) and transcription factors (e.g., ‘MYB, ZFP and bZIP’) were involved in dehydration and/or salt treatment. The molecular players in response to dehydration and salt treatment were partially overlapping. Quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) analysis further confirmed the results from RNA-seq and sRNA-seq analysis. This study provides new insights into the molecular mechanisms how citrus roots respond to dehydration and salt treatment. PMID:28165059

Development of visible-light responsive and mechanically enhanced "smart" UCST interpenetrating network hydrogels.

PubMed

Xu, Yifei; Ghag, Onkar; Reimann, Morgan; Sitterle, Philip; Chatterjee, Prithwish; Nofen, Elizabeth; Yu, Hongyu; Jiang, Hanqing; Dai, Lenore L

2017-12-20

An interpenetrating polymer network (IPN), chlorophyllin-incorporated environmentally responsive hydrogel was synthesized and exhibited the following features: enhanced mechanical properties, upper critical solution temperature (UCST) swelling behavior, and promising visible-light responsiveness. Poor mechanical properties are known challenges for hydrogel-based materials. By forming an interpenetrating network between polyacrylamide (PAAm) and poly(acrylic acid) (PAAc) polymer networks, the mechanical properties of the synthesized IPN hydrogels were significantly improved compared to hydrogels made of a single network of each polymer. The formation of the interpenetrating network was confirmed by Fourier Transform Infrared Spectroscopy (FTIR), the analysis of glass transition temperature, and a unique UCST responsive swelling behavior, which is in contrast to the more prevalent lower critical solution temperature (LCST) behaviour of environmentally responsive hydrogels. The visible-light responsiveness of the synthesized hydrogel also demonstrated a positive swelling behavior, and the effect of incorporating chlorophyllin as the chromophore unit was observed to reduce the average pore size and further enhance the mechanical properties of the hydrogel. This interpenetrating network system shows potential to serve as a new route in developing "smart" hydrogels using visible-light as a simple, inexpensive, and remotely controllable stimulus.

Formation of porous networks on polymeric surfaces by femtosecond laser micromachining

NASA Astrophysics Data System (ADS)

Assaf, Youssef; Kietzig, Anne-Marie

2017-02-01

In this study, porous network structures were successfully created on various polymer surfaces by femtosecond laser micromachining. Six different polymers (poly(tetrafluoroethylene) (PTFE), poly(methyl methacrylate) (PMMA), high density poly(ethylene) (HDPE), poly(lactic acid) (PLA), poly(carbonate) (PC), and poly(ethylene terephthalate) (PET)) were machined at different fluences and pulse numbers, and the resulting structures were identified and compared by lacunarity analysis. At low fluence and pulse numbers, porous networks were confirmed to form on all materials except PLA. Furthermore, all networks except for PMMA were shown to bundle up at high fluence and pulse numbers. In the case of PC, a complete breakdown of the structure at such conditions was observed. Operation slightly above threshold fluence and at low pulse numbers is therefore recommended for porous network formation. Finally, the thickness over which these structures formed was measured and compared to two intrinsic material dependent parameters: the single pulse threshold fluence and the incubation coefficient. Results indicate that a lower threshold fluence at operating conditions favors material removal over structure formation and is hence detrimental to porous network formation. Favorable machining conditions and material-dependent parameters for the formation of porous networks on polymer surfaces have thus been identified.

On the effect of networks of cycle-tracks on the risk of cycling. The case of Seville.

PubMed

Marqués, R; Hernández-Herrador, V

2017-05-01

We analyze the evolution of the risk of cycling in Seville before and after the implementation of a network of segregated cycle tracks in the city. Specifically, we study the evolution of the risk for cyclists of being involved in a collision with a motor vehicle, using data reported by the traffic police along the period 2000-2013, i.e. seven years before and after the network was built. A sudden drop of such risk was observed after the implementation of the network of bikeways. We study, through a multilinear regression analysis, the evolution of the risk by means of explanatory variables representing changes in the built environment, specifically the length of the bikeways and a stepwise jump variable taking the values 0/1 before/after the network was implemented. We found that this last variable has a high value as explanatory variable, even higher than the length of the network, thus suggesting that networking the bikeways has a substantial effect on cycling safety by itself and beyond the mere increase in the length of the bikeways. We also analyze safety in numbers through a non-linear regression analysis. Our results fully agree qualitatively and quantitatively with the results previously reported by Jacobsen (2003), thus providing an independent confirmation of Jacobsen's results. Finally, the mutual causal relationships between the increase in safety, the increase in the number of cyclists and the presence of the network of bikeways are discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Qualitative Discovery in Medical Databases

NASA Technical Reports Server (NTRS)

Maluf, David A.

2000-01-01

Implication rules have been used in uncertainty reasoning systems to confirm and draw hypotheses or conclusions. However a major bottleneck in developing such systems lies in the elicitation of these rules. This paper empirically examines the performance of evidential inferencing with implication networks generated using a rule induction tool called KAT. KAT utilizes an algorithm for the statistical analysis of empirical case data, and hence reduces the knowledge engineering efforts and biases in subjective implication certainty assignment. The paper describes several experiments in which real-world diagnostic problems were investigated; namely, medical diagnostics. In particular, it attempts to show that: (1) with a limited number of case samples, KAT is capable of inducing implication networks useful for making evidential inferences based on partial observations, and (2) observation driven by a network entropy optimization mechanism is effective in reducing the uncertainty of predicted events.

Delay-induced Turing-like waves for one-species reaction-diffusion model on a network

NASA Astrophysics Data System (ADS)

Petit, Julien; Carletti, Timoteo; Asllani, Malbor; Fanelli, Duccio

2015-09-01

A one-species time-delay reaction-diffusion system defined on a complex network is studied. Traveling waves are predicted to occur following a symmetry-breaking instability of a homogeneous stationary stable solution, subject to an external nonhomogeneous perturbation. These are generalized Turing-like waves that materialize in a single-species populations dynamics model, as the unexpected byproduct of the imposed delay in the diffusion part. Sufficient conditions for the onset of the instability are mathematically provided by performing a linear stability analysis adapted to time-delayed differential equations. The method here developed exploits the properties of the Lambert W-function. The prediction of the theory are confirmed by direct numerical simulation carried out for a modified version of the classical Fisher model, defined on a Watts-Strogatz network and with the inclusion of the delay.

An innovative and comprehensive technique to evaluate different measures of medication adherence: The network meta-analysis.

PubMed

Tonin, Fernanda S; Wiecek, Elyssa; Torres-Robles, Andrea; Pontarolo, Roberto; Benrimoj, Shalom Charlie I; Fernandez-Llimos, Fernando; Garcia-Cardenas, Victoria

2018-05-19

Poor medication adherence is associated with adverse health outcomes and higher costs of care. However, inconsistencies in the assessment of adherence are found in the literature. To evaluate the effect of different measures of adherence in the comparative effectiveness of complex interventions to enhance patients' adherence to prescribed medications. A systematic review with network meta-analysis was performed. Electronic searches for relevant pairwise meta-analysis including trials of interventions that aimed to improve medication adherence were performed in PubMed. Data extraction was conducted with eligible trials evaluating short-period adherence follow-up (until 3 months) using any measure of adherence: self-report, pill count, or MEMS (medication event monitoring system). To standardize the results obtained with these different measures, an overall composite measure and an objective composite measure were also calculated. Network meta-analyses for each measure of adherence were built. Rank order and surface under the cumulative ranking curve analyses (SUCRA) were performed. Ninety-one trials were included in the network meta-analyses. The five network meta-analyses demonstrated robustness and reliability. Results obtained for all measures of adherence were similar across them and to both composite measures. For both composite measures, interventions comprising economic + technical components were the best option (90% of probability in SUCRA analysis) with statistical superiority against almost all other interventions and against standard care (odds ratio with 95% credibility interval ranging from 0.09 to 0.25 [0.02, 0.98]). The use of network meta-analysis was reliable to compare different measures of adherence of complex interventions in short-periods follow-up. Analyses with longer follow-up periods are needed to confirm these results. Different measures of adherence produced similar results. The use of composite measures revealed reliable alternatives to establish a broader and more detailed picture of adherence. Copyright © 2018 Elsevier Inc. All rights reserved.

Statistical adjustment of culture-independent diagnostic tests for trend analysis in the Foodborne Diseases Active Surveillance Network (FoodNet), USA.

PubMed

Gu, Weidong; Dutta, Vikrant; Patrick, Mary; Bruce, Beau B; Geissler, Aimee; Huang, Jennifer; Fitzgerald, Collette; Henao, Olga

2018-03-19

Culture-independent diagnostic tests (CIDTs) are increasingly used to diagnose Campylobacter infection in the Foodborne Diseases Active Surveillance Network (FoodNet). Because CIDTs have different performance characteristics compared with culture, which has been used historically and is still used to diagnose campylobacteriosis, adjustment of cases diagnosed by CIDT is needed to compare with culture-confirmed cases for monitoring incidence trends. We identified the necessary parameters for CIDT adjustment using culture as the gold standard, and derived formulas to calculate positive predictive values (PPVs). We conducted a literature review and meta-analysis to examine the variability in CIDT performance and Campylobacter prevalence applicable to FoodNet sites. We then developed a Monte Carlo method to estimate test-type and site-specific PPVs with their associated uncertainties. The uncertainty in our estimated PPVs was largely derived from uncertainty about the specificity of CIDTs and low prevalence of Campylobacter in tested samples. Stable CIDT-adjusted incidences of Campylobacter cases from 2012 to 2015 were observed compared with a decline in culture-confirmed incidence. We highlight the lack of data on the total numbers of tested samples as one of main limitations for CIDT adjustment. Our results demonstrate the importance of adjusting CIDTs for understanding trends in Campylobacter incidence in FoodNet.

Systemic analysis of genome-wide expression profiles identified potential therapeutic targets of demethylation drugs for glioblastoma.

PubMed

Ning, Tongbo; Cui, Hao; Sun, Feng; Zou, Jidian

2017-09-05

Glioblastoma represents one of the most aggressive malignant brain tumors with high morbidity and motility. Demethylation drugs have been developed for its treatment with little efficacy has been observed. The purpose of this study was to screen therapeutic targets of demethylation drugs or bioactive molecules for glioblastoma through systemic bioinformatics analysis. We firstly downloaded genome-wide expression profiles from the Gene Expression Omnibus (GEO) and conducted the primary analysis through R software, mainly including preprocessing of raw microarray data, transformation between probe ID and gene symbol and identification of differential expression genes (DEGs). Secondly, functional enrichment analysis was conducted via the Database for Annotation, Visualization and Integrated Discovery (DAVID) to explore biological processes involved in the development of glioblastoma. Thirdly, we constructed protein-protein interaction (PPI) network of interested genes and conducted cross analysis for multi datasets to obtain potential therapeutic targets for glioblastoma. Finally, we further confirmed the therapeutic targets through real-time RT-PCR. As a result, biological processes that related to cancer development, amino metabolism, immune response and etc. were found to be significantly enriched in genes that differential expression in glioblastoma and regulated by 5'aza-dC. Besides, network and cross analysis identified ACAT2, UFC1 and CYB5R1 as novel therapeutic targets of demethylation drugs which also confirmed by real time RT-PCR. In conclusions, our study identified several biological processes and genes that involved in the development of glioblastoma and regulated by 5'aza-dC, which would be helpful for the treatment of glioblastoma. Copyright © 2017 Elsevier B.V. All rights reserved.

Enhanced reconstruction of weighted networks from strengths and degrees

NASA Astrophysics Data System (ADS)

Mastrandrea, Rossana; Squartini, Tiziano; Fagiolo, Giorgio; Garlaschelli, Diego

2014-04-01

Network topology plays a key role in many phenomena, from the spreading of diseases to that of financial crises. Whenever the whole structure of a network is unknown, one must resort to reconstruction methods that identify the least biased ensemble of networks consistent with the partial information available. A challenging case, frequently encountered due to privacy issues in the analysis of interbank flows and Big Data, is when there is only local (node-specific) aggregate information available. For binary networks, the relevant ensemble is one where the degree (number of links) of each node is constrained to its observed value. However, for weighted networks the problem is much more complicated. While the naïve approach prescribes to constrain the strengths (total link weights) of all nodes, recent counter-intuitive results suggest that in weighted networks the degrees are often more informative than the strengths. This implies that the reconstruction of weighted networks would be significantly enhanced by the specification of both strengths and degrees, a computationally hard and bias-prone procedure. Here we solve this problem by introducing an analytical and unbiased maximum-entropy method that works in the shortest possible time and does not require the explicit generation of reconstructed samples. We consider several real-world examples and show that, while the strengths alone give poor results, the additional knowledge of the degrees yields accurately reconstructed networks. Information-theoretic criteria rigorously confirm that the degree sequence, as soon as it is non-trivial, is irreducible to the strength sequence. Our results have strong implications for the analysis of motifs and communities and whenever the reconstructed ensemble is required as a null model to detect higher-order patterns.

Pathway and network-based analysis of genome-wide association studies and RT-PCR validation in polycystic ovary syndrome

PubMed Central

Shen, Haoran; Liang, Zhou; Zheng, Saihua; Li, Xuelian

2017-01-01

The purpose of this study was to identify promising candidate genes and pathways in polycystic ovary syndrome (PCOS). Microarray dataset GSE345269 obtained from the Gene Expression Omnibus database includes 7 granulosa cell samples from PCOS patients, and 3 normal granulosa cell samples. Differentially expressed genes (DEGs) were screened between PCOS and normal samples. Pathway enrichment analysis was conducted for DEGs using ClueGO and CluePedia plugin of Cytoscape. A Reactome functional interaction (FI) network of the DEGs was built using ReactomeFIViz, and then network modules were extracted, followed by pathway enrichment analysis for the modules. Expression of DEGs in granulosa cell samples was measured using quantitative RT-PCR. A total of 674 DEGs were retained, which were significantly enriched with inflammation and immune-related pathways. Eight modules were extracted from the Reactome FI network. Pathway enrichment analysis revealed significant pathways of each module: module 0, Regulation of RhoA activity and Signaling by Rho GTPases pathways shared ARHGAP4 and ARHGAP9; module 2, GlycoProtein VI-mediated activation cascade pathway was enriched with RHOG; module 3, Thromboxane A2 receptor signaling, Chemokine signaling pathway, CXCR4-mediated signaling events pathways were enriched with LYN, the hub gene of module 3. Results of RT-PCR confirmed the finding of the bioinformatic analysis that ARHGAP4, ARHGAP9, RHOG and LYN were significantly upregulated in PCOS. RhoA-related pathways, GlycoProtein VI-mediated activation cascade pathway, ARHGAP4, ARHGAP9, RHOG and LYN may be involved in the pathogenesis of PCOS. PMID:28949383

Proteins Encoded in Genomic Regions Associated with Immune-Mediated Disease Physically Interact and Suggest Underlying Biology

PubMed Central

Rossin, Elizabeth J.; Lage, Kasper; Raychaudhuri, Soumya; Xavier, Ramnik J.; Tatar, Diana; Benita, Yair

2011-01-01

Genome-wide association studies (GWAS) have defined over 150 genomic regions unequivocally containing variation predisposing to immune-mediated disease. Inferring disease biology from these observations, however, hinges on our ability to discover the molecular processes being perturbed by these risk variants. It has previously been observed that different genes harboring causal mutations for the same Mendelian disease often physically interact. We sought to evaluate the degree to which this is true of genes within strongly associated loci in complex disease. Using sets of loci defined in rheumatoid arthritis (RA) and Crohn's disease (CD) GWAS, we build protein–protein interaction (PPI) networks for genes within associated loci and find abundant physical interactions between protein products of associated genes. We apply multiple permutation approaches to show that these networks are more densely connected than chance expectation. To confirm biological relevance, we show that the components of the networks tend to be expressed in similar tissues relevant to the phenotypes in question, suggesting the network indicates common underlying processes perturbed by risk loci. Furthermore, we show that the RA and CD networks have predictive power by demonstrating that proteins in these networks, not encoded in the confirmed list of disease associated loci, are significantly enriched for association to the phenotypes in question in extended GWAS analysis. Finally, we test our method in 3 non-immune traits to assess its applicability to complex traits in general. We find that genes in loci associated to height and lipid levels assemble into significantly connected networks but did not detect excess connectivity among Type 2 Diabetes (T2D) loci beyond chance. Taken together, our results constitute evidence that, for many of the complex diseases studied here, common genetic associations implicate regions encoding proteins that physically interact in a preferential manner, in line with observations in Mendelian disease. PMID:21249183

The regulatory network analysis of long noncoding RNAs in human colorectal cancer.

PubMed

Zhang, Yuwei; Tao, Yang; Li, Yang; Zhao, Jinshun; Zhang, Lina; Zhang, Xiaohong; Dong, Changzheng; Xie, Yangyang; Dai, Xiaoyu; Zhang, Xinjun; Liao, Qi

2018-05-01

Colorectal cancer (CRC) is among one of the most prevalent and lethiferous diseases worldwide. Long noncoding RNAs (lncRNAs) are commonly accepted to function as a key regulatory factor in human cancer, but the potential regulatory mechanisms of CRC-associated lncRNA are largely obscure. Here, we integrated several expression profiles to obtain 55 differentially expressed (DE) lncRNAs. We first detected lncRNA interactions with transcription factors, microRNAs, mRNAs, and RNA-binding proteins to construct a regulatory network and then create functional enrichment analyses for them using bioinformatics approaches. We found the upregulated genes in the regulatory network are enriched in cell cycle and DNA damage response, while the downregulated genes are enriched in cell differentiation, cellular response, and cell signaling. We then employed module-based methods to mine several intriguing modules from the overall network, which helps to classify the functions of genes more specifically. Next, we confirmed the validity of our network by comparisons with a randomized network using computational method. Finally, we attempted to annotate lncRNA functions based on the regulatory network, which indicated its potential application. Our study of the lncRNA regulatory network provided significant clues to unveil lncRNAs potential regulatory mechanisms in CRC and laid a foundation for further experimental investigation.

New generation of elastic network models.

PubMed

López-Blanco, José Ramón; Chacón, Pablo

2016-04-01

The intrinsic flexibility of proteins and nucleic acids can be grasped from remarkably simple mechanical models of particles connected by springs. In recent decades, Elastic Network Models (ENMs) combined with Normal Model Analysis widely confirmed their ability to predict biologically relevant motions of biomolecules and soon became a popular methodology to reveal large-scale dynamics in multiple structural biology scenarios. The simplicity, robustness, low computational cost, and relatively high accuracy are the reasons behind the success of ENMs. This review focuses on recent advances in the development and application of ENMs, paying particular attention to combinations with experimental data. Successful application scenarios include large macromolecular machines, structural refinement, docking, and evolutionary conservation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Transcriptome Profiling of the Pineapple under Low Temperature to Facilitate Its Breeding for Cold Tolerance

PubMed Central

Chen, Chengjie; Zhang, Yafeng; Xu, Zhiqiang; Luan, Aiping; Mao, Qi; Feng, Junting; Xie, Tao; Gong, Xue; Wang, Xiaoshuang; Chen, Hao; He, Yehua

2016-01-01

The pineapple (Ananas comosus) is cold sensitive. Most cultivars are injured during winter periods, especially in sub-tropical regions. There is a lack of molecular information on the pineapple’s response to cold stress. In this study, high-throughput transcriptome sequencing and gene expression analysis were performed on plantlets of a cold-tolerant genotype of the pineapple cultivar ‘Shenwan’ before and after cold treatment. A total of 1,186 candidate cold responsive genes were identified, and their credibility was confirmed by RT-qPCR. Gene set functional enrichment analysis indicated that genes related to cell wall properties, stomatal closure and ABA and ROS signal transduction play important roles in pineapple cold tolerance. In addition, a protein association network of CORs (cold responsive genes) was predicted, which could serve as an entry point to dissect the complex cold response network. Our study found a series of candidate genes and their association network, which will be helpful to cold stress response studies and pineapple breeding for cold tolerance. PMID:27656892

Interprofessional practice and learning in a youth mental health service: A case study using network analysis.

PubMed

Barnett, Tony; Hoang, Ha; Cross, Merylin; Bridgman, Heather

2015-01-01

Few studies have examined interprofessional practice (IPP) from a mental health service perspective. This study applied a mixed-method approach to examine the IPP and learning occurring in a youth mental health service in Tasmania, Australia. The aims of the study were to investigate the extent to which staff were networked, how collaboratively they practiced and supported student learning, and to elicit the organisation's strengths and opportunities regarding IPP and learning. Six data sets were collected: pre- and post-test readiness for interprofessional learning surveys, Social Network survey, organisational readiness for IPP and learning checklist, "talking wall" role clarification activity, and observations of participants working through a clinical case study. Participants (n = 19) were well-networked and demonstrated a patient-centred approach. Results confirmed participants' positive attitudes to IPP and learning and identified ways to strengthen the organisation's interprofessional capability. This mixed-method approach could assist others to investigate IPP and learning.

Electroencephalography epilepsy classifications using hybrid cuckoo search and neural network

NASA Astrophysics Data System (ADS)

Pratiwi, A. B.; Damayanti, A.; Miswanto

2017-07-01

Epilepsy is a condition that affects the brain and causes repeated seizures. This seizure is episodes that can vary and nearly undetectable to long periods of vigorous shaking or brain contractions. Epilepsy often can be confirmed with an electrocephalography (EEG). Neural Networks has been used in biomedic signal analysis, it has successfully classified the biomedic signal, such as EEG signal. In this paper, a hybrid cuckoo search and neural network are used to recognize EEG signal for epilepsy classifications. The weight of the multilayer perceptron is optimized by the cuckoo search algorithm based on its error. The aim of this methods is making the network faster to obtained the local or global optimal then the process of classification become more accurate. Based on the comparison results with the traditional multilayer perceptron, the hybrid cuckoo search and multilayer perceptron provides better performance in term of error convergence and accuracy. The purpose methods give MSE 0.001 and accuracy 90.0 %.

Unravelling the secret of seed-based gels in water: the nanoscale 3D network formation.

PubMed

Samateh, Malick; Pottackal, Neethu; Manafirasi, Setareh; Vidyasagar, Adiyala; Maldarelli, Charles; John, George

2018-05-09

Chia (Salvia hispanica) and basil (Ocimum basilicum) seeds have the intrinsic ability to form a hydrogel concomitant with moisture-retention, slow releasing capability and proposed health benefits such as curbing diabetes and obesity by delaying digestion process. However, the underlying mode of gelation at nanoscopic level is not clearly explained or explored. The present study elucidates and corroborates the hypothesis that the gelling behavior of such seeds is due to their nanoscale 3D-network formation. The preliminary study revealed the influence of several conditions like polarity, pH and hydrophilicity/hydrophobicity on fiber extrusion from the seeds which leads to gelation. Optical microscopic analysis clearly demonstrated bundles of fibers emanating from the seed coat while in contact with water, and live growth of fibers to form 3D network. Scanning electron microscope (SEM) and transmission electron microscope (TEM) studies confirmed 3D network formation with fiber diameters ranging from 20 to 50 nm.

Anti AIDS drug design with the help of neural networks

NASA Astrophysics Data System (ADS)

Tetko, I. V.; Tanchuk, V. Yu.; Luik, A. I.

1995-04-01

Artificial neural networks were used to analyze and predict the human immunodefiency virus type 1 reverse transcriptase inhibitors. Training and control set included 44 molecules (most of them are well-known substances such as AZT, TIBO, dde, etc.) The biological activities of molecules were taken from literature and rated for two classes: active and inactive compounds according to their values. We used topological indices as molecular parameters. Four most informative parameters (out of 46) were chosen using cluster analysis and original input parameters' estimation procedure and were used to predict activities of both control and new (synthesized in our institute) molecules. We applied pruning network algorithm and network ensembles to obtain the final classifier and avoid chance correlation. The increasing of neural network generalization of the data from the control set was observed, when using the aforementioned methods. The prognosis of new molecules revealed one molecule as possibly active. It was confirmed by further biological tests. The compound was as active as AZT and in order less toxic. The active compound is currently being evaluated in pre clinical trials as possible drug for anti-AIDS therapy.

Social structure of a semi-free ranging group of mandrills (Mandrillus sphinx): a social network analysis.

PubMed

Bret, Céline; Sueur, Cédric; Ngoubangoye, Barthélémy; Verrier, Delphine; Deneubourg, Jean-Louis; Petit, Odile

2013-01-01

The difficulty involved in following mandrills in the wild means that very little is known about social structure in this species. Most studies initially considered mandrill groups to be an aggregation of one-male/multifemale units, with males occupying central positions in a structure similar to those observed in the majority of baboon species. However, a recent study hypothesized that mandrills form stable groups with only two or three permanent males, and that females occupy more central positions than males within these groups. We used social network analysis methods to examine how a semi-free ranging group of 19 mandrills is structured. We recorded all dyads of individuals that were in contact as a measure of association. The betweenness and the eigenvector centrality for each individual were calculated and correlated to kinship, age and dominance. Finally, we performed a resilience analysis by simulating the removal of individuals displaying the highest betweenness and eigenvector centrality values. We found that related dyads were more frequently associated than unrelated dyads. Moreover, our results showed that the cumulative distribution of individual betweenness and eigenvector centrality followed a power function, which is characteristic of scale-free networks. This property showed that some group members, mostly females, occupied a highly central position. Finally, the resilience analysis showed that the removal of the two most central females split the network into small subgroups and increased the network diameter. Critically, this study confirms that females appear to occupy more central positions than males in mandrill groups. Consequently, these females appear to be crucial for group cohesion and probably play a pivotal role in this species.

Social Structure of a Semi-Free Ranging Group of Mandrills (Mandrillus sphinx): A Social Network Analysis

PubMed Central

Bret, Céline; Sueur, Cédric; Ngoubangoye, Barthélémy; Verrier, Delphine; Deneubourg, Jean-Louis; Petit, Odile

2013-01-01

The difficulty involved in following mandrills in the wild means that very little is known about social structure in this species. Most studies initially considered mandrill groups to be an aggregation of one-male/multifemale units, with males occupying central positions in a structure similar to those observed in the majority of baboon species. However, a recent study hypothesized that mandrills form stable groups with only two or three permanent males, and that females occupy more central positions than males within these groups. We used social network analysis methods to examine how a semi-free ranging group of 19 mandrills is structured. We recorded all dyads of individuals that were in contact as a measure of association. The betweenness and the eigenvector centrality for each individual were calculated and correlated to kinship, age and dominance. Finally, we performed a resilience analysis by simulating the removal of individuals displaying the highest betweenness and eigenvector centrality values. We found that related dyads were more frequently associated than unrelated dyads. Moreover, our results showed that the cumulative distribution of individual betweenness and eigenvector centrality followed a power function, which is characteristic of scale-free networks. This property showed that some group members, mostly females, occupied a highly central position. Finally, the resilience analysis showed that the removal of the two most central females split the network into small subgroups and increased the network diameter. Critically, this study confirms that females appear to occupy more central positions than males in mandrill groups. Consequently, these females appear to be crucial for group cohesion and probably play a pivotal role in this species. PMID:24340074

Networks in a Large-Scale Phylogenetic Analysis: Reconstructing Evolutionary History of Asparagales (Lilianae) Based on Four Plastid Genes

PubMed Central

Chase, Mark W.; Kim, Joo-Hwan

2013-01-01

Phylogenetic analysis aims to produce a bifurcating tree, which disregards conflicting signals and displays only those that are present in a large proportion of the data. However, any character (or tree) conflict in a dataset allows the exploration of support for various evolutionary hypotheses. Although data-display network approaches exist, biologists cannot easily and routinely use them to compute rooted phylogenetic networks on real datasets containing hundreds of taxa. Here, we constructed an original neighbour-net for a large dataset of Asparagales to highlight the aspects of the resulting network that will be important for interpreting phylogeny. The analyses were largely conducted with new data collected for the same loci as in previous studies, but from different species accessions and greater sampling in many cases than in published analyses. The network tree summarised the majority data pattern in the characters of plastid sequences before tree building, which largely confirmed the currently recognised phylogenetic relationships. Most conflicting signals are at the base of each group along the Asparagales backbone, which helps us to establish the expectancy and advance our understanding of some difficult taxa relationships and their phylogeny. The network method should play a greater role in phylogenetic analyses than it has in the past. To advance the understanding of evolutionary history of the largest order of monocots Asparagales, absolute diversification times were estimated for family-level clades using relaxed molecular clock analyses. PMID:23544071

The influence of lifestyle on cardiovascular risk factors. Analysis using a neural network.

PubMed

Gueli, Nicoló; Piccirillo, Gianfanco; Troisi, Giovanni; Cicconetti, Paolo; Meloni, Fortunato; Ettorre, Evaristo; Verico, Paola; D'Arcangelo, Enzo; Cacciafesta, Mauro

2005-01-01

The cardiovascular pathologies are the most common causes of death in the elderly patient. To single out the main risk factors in order to effectively prevent the onset of the disease, the authors experimented a special computerized tool, the neural network, that works out a mathematical relation that can obtain certain data (defined as output) as a function of other data (defined as input). Data were processed from a sample of 276 subjects of both sexes aged 26-69 years old. The output data were: high/low cholesterolemia, HDL cholesterol, triglyceridemia with respect to an established cut-off; the input data were: sex, age, build, weight, married/single, number of children, number of cigarettes smoked/day, amount of wine and number of cups of coffee. We conclude that: (i) a relationship exists, deduced from a neural network, between a set of input variables and a dichotomous output variable; (ii) this relationship can be expressed as a mathematical function; (iii) a neural network, having learned the data on a sufficiently large population, can provide valid predictive data for a single individual with a high probability (up to 93.33%) that the response it gives is correct. In this study, such a result is found for two of the three cardiovascular risk indicators considered (cholesterol and triglycerides); (iv) the repetition of the neural network analysis of the cases in question after a "pruning" operation provided a somewhat less good performance; (v) a statistical analysis conducted on those same cases has confirmed the existence of a strong relationship between the input and the output variables. Therefore the neural network is a valid instrument for providing predictive in a single subject on cardiovascular pathology risks.

Sustained modelling ability of artificial neural networks in the analysis of two pharmaceuticals (dextropropoxyphene and dipyrone) present in unequal concentrations.

PubMed

Cámara, María S; Ferroni, Félix M; De Zan, Mercedes; Goicoechea, Héctor C

2003-07-01

An improvement is presented on the simultaneous determination of two active ingredients present in unequal concentrations in injections. The analysis was carried out with spectrophotometric data and non-linear multivariate calibration methods, in particular artificial neural networks (ANNs). The presence of non-linearities caused by the major analyte concentrations which deviate from Beer's law was confirmed by plotting actual vs. predicted concentrations, and observing curvatures in the residuals for the estimated concentrations with linear methods. Mixtures of dextropropoxyphene and dipyrone have been analysed by using linear and non-linear partial least-squares (PLS and NPLSs) and ANNs. Notwithstanding the high degree of spectral overlap and the occurrence of non-linearities, rapid and simultaneous analysis has been achieved, with reasonably good accuracy and precision. A commercial sample was analysed by using the present methodology, and the obtained results show reasonably good agreement with those obtained by using high-performance liquid chromatography (HPLC) and a UV-spectrophotometric comparative methods.

Network meta-analysis of first- and second-generation protease inhibitors for chronic hepatitis C genotype 1: efficacy based on RVR and SVR 24.

PubMed

Borba, Helena H; Wiens, Astrid; Steimbach, Laiza M; Perlin, Cassio M; Tonin, Fernanda S; Pedroso, Maria L A; Fernandez-Llimos, Fernando; Pontarolo, Roberto

2017-01-01

This study aimed to compare the efficacy among direct-acting antiviral agents (first and second-generation direct-acting antiviral agents (DAAs)) with placebo and with standard dual therapy (pegylated interferon + ribavirin (Peg-IFN + RBV)) in terms of rapid virologic response (RVR) and sustained virologic response (SVR) in chronic hepatitis C genotype 1 treatment. We performed a systematic review of randomized controlled trials (RCTs) in MEDLINE, International Pharmaceutical Abstracts, Cochrane Library, SCIELO, and Scopus and conducted a network meta-analysis to compare the efficacy of boceprevir (BOC), daclatasvir (DCV), grazoprevir, simeprevir (SMV) and telaprevir (TVR), in treatment-naive and treatment-experienced patients. Sixteen studies encompassing 7171 patients were analysed. Associations between DAAs therapies (IFN-free regimens) could not be addressed since no common comparator was found in the RCTs among these associations and the other agents included in the present analysis. All agents were more efficacious than placebo or Peg-IFN + RBV in terms of RVR, while only BOC and SMV showed statistically significant superiority for the SVR outcome when compared to placebo or standard dual therapy. No significant differences between the DAAs were observed. The analysis prioritized treatment with DCV for both efficacy outcomes. Node-splitting analysis showed that our networks are robust (p > 0.05). The superiority of DAAs over placebo or standard dual therapy with Peg-IFN + RBV was confirmed, indicating the greater efficacy of DCV. This study is the first network meta-analysis that included RVR as an outcome in the evaluation of these agents via indirect comparison. Further investigation should be carried out addressing safety and tolerability outcomes.

Reconstruction of the genome-scale co-expression network for the Hippo signaling pathway in colorectal cancer.

PubMed

Dehghanian, Fariba; Hojati, Zohreh; Hosseinkhan, Nazanin; Mousavian, Zaynab; Masoudi-Nejad, Ali

2018-05-26

The Hippo signaling pathway (HSP) has been identified as an essential and complex signaling pathway for tumor suppression that coordinates proliferation, differentiation, cell death, cell growth and stemness. In the present study, we conducted a genome-scale co-expression analysis to reconstruct the HSP in colorectal cancer (CRC). Five key modules were detected through network clustering, and a detailed discussion of two modules containing respectively 18 and 13 over and down-regulated members of HSP was provided. Our results suggest new potential regulatory factors in the HSP. The detected modules also suggest novel genes contributing to CRC. Moreover, differential expression analysis confirmed the differential expression pattern of HSP members and new suggested regulatory factors between tumor and normal samples. These findings can further reveal the importance of HSP in CRC. Copyright © 2018 Elsevier Ltd. All rights reserved.

Impact of Bounded Noise and Rewiring on the Formation and Instability of Spiral Waves in a Small-World Network of Hodgkin-Huxley Neurons.

PubMed

Yao, Yuangen; Deng, Haiyou; Ma, Chengzhang; Yi, Ming; Ma, Jun

2017-01-01

Spiral waves are observed in the chemical, physical and biological systems, and the emergence of spiral waves in cardiac tissue is linked to some diseases such as heart ventricular fibrillation and epilepsy; thus it has importance in theoretical studies and potential medical applications. Noise is inevitable in neuronal systems and can change the electrical activities of neuron in different ways. Many previous theoretical studies about the impacts of noise on spiral waves focus an unbounded Gaussian noise and even colored noise. In this paper, the impacts of bounded noise and rewiring of network on the formation and instability of spiral waves are discussed in small-world (SW) network of Hodgkin-Huxley (HH) neurons through numerical simulations, and possible statistical analysis will be carried out. Firstly, we present SW network of HH neurons subjected to bounded noise. Then, it is numerically demonstrated that bounded noise with proper intensity σ, amplitude A, or frequency f can facilitate the formation of spiral waves when rewiring probability p is below certain thresholds. In other words, bounded noise-induced resonant behavior can occur in the SW network of neurons. In addition, rewiring probability p always impairs spiral waves, while spiral waves are confirmed to be robust for small p, thus shortcut-induced phase transition of spiral wave with the increase of p is induced. Furthermore, statistical factors of synchronization are calculated to discern the phase transition of spatial pattern, and it is confirmed that larger factor of synchronization is approached with increasing of rewiring probability p, and the stability of spiral wave is destroyed.

Meta-Analysis of Tumor Stem-Like Breast Cancer Cells Using Gene Set and Network Analysis

PubMed Central

Lee, Won Jun; Kim, Sang Cheol; Yoon, Jung-Ho; Yoon, Sang Jun; Lim, Johan; Kim, You-Sun; Kwon, Sung Won; Park, Jeong Hill

2016-01-01

Generally, cancer stem cells have epithelial-to-mesenchymal-transition characteristics and other aggressive properties that cause metastasis. However, there have been no confident markers for the identification of cancer stem cells and comparative methods examining adherent and sphere cells are widely used to investigate mechanism underlying cancer stem cells, because sphere cells have been known to maintain cancer stem cell characteristics. In this study, we conducted a meta-analysis that combined gene expression profiles from several studies that utilized tumorsphere technology to investigate tumor stem-like breast cancer cells. We used our own gene expression profiles along with the three different gene expression profiles from the Gene Expression Omnibus, which we combined using the ComBat method, and obtained significant gene sets using the gene set analysis of our datasets and the combined dataset. This experiment focused on four gene sets such as cytokine-cytokine receptor interaction that demonstrated significance in both datasets. Our observations demonstrated that among the genes of four significant gene sets, six genes were consistently up-regulated and satisfied the p-value of < 0.05, and our network analysis showed high connectivity in five genes. From these results, we established CXCR4, CXCL1 and HMGCS1, the intersecting genes of the datasets with high connectivity and p-value of < 0.05, as significant genes in the identification of cancer stem cells. Additional experiment using quantitative reverse transcription-polymerase chain reaction showed significant up-regulation in MCF-7 derived sphere cells and confirmed the importance of these three genes. Taken together, using meta-analysis that combines gene set and network analysis, we suggested CXCR4, CXCL1 and HMGCS1 as candidates involved in tumor stem-like breast cancer cells. Distinct from other meta-analysis, by using gene set analysis, we selected possible markers which can explain the biological mechanisms and suggested network analysis as an additional criterion for selecting candidates. PMID:26870956

Improved automatic adjustment of density and contrast in FCR system using neural network

NASA Astrophysics Data System (ADS)

Takeo, Hideya; Nakajima, Nobuyoshi; Ishida, Masamitsu; Kato, Hisatoyo

1994-05-01

FCR system has an automatic adjustment of image density and contrast by analyzing the histogram of image data in the radiation field. Advanced image recognition methods proposed in this paper can improve the automatic adjustment performance, in which neural network technology is used. There are two methods. Both methods are basically used 3-layer neural network with back propagation. The image data are directly input to the input-layer in one method and the histogram data is input in the other method. The former is effective to the imaging menu such as shoulder joint in which the position of interest region occupied on the histogram changes by difference of positioning and the latter is effective to the imaging menu such as chest-pediatrics in which the histogram shape changes by difference of positioning. We experimentally confirm the validity of these methods (about the automatic adjustment performance) as compared with the conventional histogram analysis methods.

Dissecting the expression relationships between RNA-binding proteins and their cognate targets in eukaryotic post-transcriptional regulatory networks.

PubMed

Nishtala, Sneha; Neelamraju, Yaseswini; Janga, Sarath Chandra

2016-05-10

RNA-binding proteins (RBPs) are pivotal in orchestrating several steps in the metabolism of RNA in eukaryotes thereby controlling an extensive network of RBP-RNA interactions. Here, we employed CLIP (cross-linking immunoprecipitation)-seq datasets for 60 human RBPs and RIP-ChIP (RNP immunoprecipitation-microarray) data for 69 yeast RBPs to construct a network of genome-wide RBP- target RNA interactions for each RBP. We show in humans that majority (~78%) of the RBPs are strongly associated with their target transcripts at transcript level while ~95% of the studied RBPs were also found to be strongly associated with expression levels of target transcripts when protein expression levels of RBPs were employed. At transcript level, RBP - RNA interaction data for the yeast genome, exhibited a strong association for 63% of the RBPs, confirming the association to be conserved across large phylogenetic distances. Analysis to uncover the features contributing to these associations revealed the number of target transcripts and length of the selected protein-coding transcript of an RBP at the transcript level while intensity of the CLIP signal, number of RNA-Binding domains, location of the binding site on the transcript, to be significant at the protein level. Our analysis will contribute to improved modelling and prediction of post-transcriptional networks.

Dissecting the expression relationships between RNA-binding proteins and their cognate targets in eukaryotic post-transcriptional regulatory networks

NASA Astrophysics Data System (ADS)

Nishtala, Sneha; Neelamraju, Yaseswini; Janga, Sarath Chandra

2016-05-01

RNA-binding proteins (RBPs) are pivotal in orchestrating several steps in the metabolism of RNA in eukaryotes thereby controlling an extensive network of RBP-RNA interactions. Here, we employed CLIP (cross-linking immunoprecipitation)-seq datasets for 60 human RBPs and RIP-ChIP (RNP immunoprecipitation-microarray) data for 69 yeast RBPs to construct a network of genome-wide RBP- target RNA interactions for each RBP. We show in humans that majority (~78%) of the RBPs are strongly associated with their target transcripts at transcript level while ~95% of the studied RBPs were also found to be strongly associated with expression levels of target transcripts when protein expression levels of RBPs were employed. At transcript level, RBP - RNA interaction data for the yeast genome, exhibited a strong association for 63% of the RBPs, confirming the association to be conserved across large phylogenetic distances. Analysis to uncover the features contributing to these associations revealed the number of target transcripts and length of the selected protein-coding transcript of an RBP at the transcript level while intensity of the CLIP signal, number of RNA-Binding domains, location of the binding site on the transcript, to be significant at the protein level. Our analysis will contribute to improved modelling and prediction of post-transcriptional networks.

Identification, Comparison, and Validation of Robust Rumen Microbial Biomarkers for Methane Emissions Using Diverse Bos Taurus Breeds and Basal Diets

PubMed Central

Auffret, Marc D.; Stewart, Robert; Dewhurst, Richard J.; Duthie, Carol-Anne; Rooke, John A.; Wallace, Robert J.; Freeman, Tom C.; Snelling, Timothy J.; Watson, Mick; Roehe, Rainer

2018-01-01

Previous shotgun metagenomic analyses of ruminal digesta identified some microbial information that might be useful as biomarkers to select cattle that emit less methane (CH4), which is a potent greenhouse gas. It is known that methane production (g/kgDMI) and to an extent the microbial community is heritable and therefore biomarkers can offer a method of selecting cattle for low methane emitting phenotypes. In this study a wider range of Bos Taurus cattle, varying in breed and diet, was investigated to determine microbial communities and genetic markers associated with high/low CH4 emissions. Digesta samples were taken from 50 beef cattle, comprising four cattle breeds, receiving two basal diets containing different proportions of concentrate and also including feed additives (nitrate or lipid), that may influence methane emissions. A combination of partial least square analysis and network analysis enabled the identification of the most significant and robust biomarkers of CH4 emissions (VIP > 0.8) across diets and breeds when comparing all potential biomarkers together. Genes associated with the hydrogenotrophic methanogenesis pathway converting carbon dioxide to methane, provided the dominant biomarkers of CH4 emissions and methanogens were the microbial populations most closely correlated with CH4 emissions and identified by metagenomics. Moreover, these genes grouped together as confirmed by network analysis for each independent experiment and when combined. Finally, the genes involved in the methane synthesis pathway explained a higher proportion of variation in CH4 emissions by PLS analysis compared to phylogenetic parameters or functional genes. These results confirmed the reproducibility of the analysis and the advantage to use these genes as robust biomarkers of CH4 emissions. Volatile fatty acid concentrations and ratios were significantly correlated with CH4, but these factors were not identified as robust enough for predictive purposes. Moreover, the methanotrophic Methylomonas genus was found to be negatively correlated with CH4. Finally, this study confirmed the importance of using robust and applicable biomarkers from the microbiome as a proxy of CH4 emissions across diverse production systems and environments. PMID:29375511

Identification, Comparison, and Validation of Robust Rumen Microbial Biomarkers for Methane Emissions Using Diverse Bos Taurus Breeds and Basal Diets.

PubMed

Auffret, Marc D; Stewart, Robert; Dewhurst, Richard J; Duthie, Carol-Anne; Rooke, John A; Wallace, Robert J; Freeman, Tom C; Snelling, Timothy J; Watson, Mick; Roehe, Rainer

2017-01-01

Previous shotgun metagenomic analyses of ruminal digesta identified some microbial information that might be useful as biomarkers to select cattle that emit less methane (CH 4 ), which is a potent greenhouse gas. It is known that methane production (g/kgDMI) and to an extent the microbial community is heritable and therefore biomarkers can offer a method of selecting cattle for low methane emitting phenotypes. In this study a wider range of Bos Taurus cattle, varying in breed and diet, was investigated to determine microbial communities and genetic markers associated with high/low CH 4 emissions. Digesta samples were taken from 50 beef cattle, comprising four cattle breeds, receiving two basal diets containing different proportions of concentrate and also including feed additives (nitrate or lipid), that may influence methane emissions. A combination of partial least square analysis and network analysis enabled the identification of the most significant and robust biomarkers of CH 4 emissions (VIP > 0.8) across diets and breeds when comparing all potential biomarkers together. Genes associated with the hydrogenotrophic methanogenesis pathway converting carbon dioxide to methane, provided the dominant biomarkers of CH 4 emissions and methanogens were the microbial populations most closely correlated with CH 4 emissions and identified by metagenomics. Moreover, these genes grouped together as confirmed by network analysis for each independent experiment and when combined. Finally, the genes involved in the methane synthesis pathway explained a higher proportion of variation in CH 4 emissions by PLS analysis compared to phylogenetic parameters or functional genes. These results confirmed the reproducibility of the analysis and the advantage to use these genes as robust biomarkers of CH 4 emissions. Volatile fatty acid concentrations and ratios were significantly correlated with CH 4 , but these factors were not identified as robust enough for predictive purposes. Moreover, the methanotrophic Methylomonas genus was found to be negatively correlated with CH 4 . Finally, this study confirmed the importance of using robust and applicable biomarkers from the microbiome as a proxy of CH 4 emissions across diverse production systems and environments.

Neurons from the adult human dentate nucleus: neural networks in the neuron classification.

PubMed

Grbatinić, Ivan; Marić, Dušica L; Milošević, Nebojša T

2015-04-07

Topological (central vs. border neuron type) and morphological classification of adult human dentate nucleus neurons according to their quantified histomorphological properties using neural networks on real and virtual neuron samples. In the real sample 53.1% and 14.1% of central and border neurons, respectively, are classified correctly with total of 32.8% of misclassified neurons. The most important result present 62.2% of misclassified neurons in border neurons group which is even greater than number of correctly classified neurons (37.8%) in that group, showing obvious failure of network to classify neurons correctly based on computational parameters used in our study. On the virtual sample 97.3% of misclassified neurons in border neurons group which is much greater than number of correctly classified neurons (2.7%) in that group, again confirms obvious failure of network to classify neurons correctly. Statistical analysis shows that there is no statistically significant difference in between central and border neurons for each measured parameter (p>0.05). Total of 96.74% neurons are morphologically classified correctly by neural networks and each one belongs to one of the four histomorphological types: (a) neurons with small soma and short dendrites, (b) neurons with small soma and long dendrites, (c) neuron with large soma and short dendrites, (d) neurons with large soma and long dendrites. Statistical analysis supports these results (p<0.05). Human dentate nucleus neurons can be classified in four neuron types according to their quantitative histomorphological properties. These neuron types consist of two neuron sets, small and large ones with respect to their perykarions with subtypes differing in dendrite length i.e. neurons with short vs. long dendrites. Besides confirmation of neuron classification on small and large ones, already shown in literature, we found two new subtypes i.e. neurons with small soma and long dendrites and with large soma and short dendrites. These neurons are most probably equally distributed throughout the dentate nucleus as no significant difference in their topological distribution is observed. Copyright © 2015 Elsevier Ltd. All rights reserved.

Moving from theory to practice: A participatory social network mapping approach to address unmet need for family planning in Benin.

PubMed

Igras, Susan; Diakité, Mariam; Lundgren, Rebecka

2017-07-01

In West Africa, social factors influence whether couples with unmet need for family planning act on birth-spacing desires. Tékponon Jikuagou is testing a social network-based intervention to reduce social barriers by diffusing new ideas. Individuals and groups judged socially influential by their communities provide entrée to networks. A participatory social network mapping methodology was designed to identify these diffusion actors. Analysis of monitoring data, in-depth interviews, and evaluation reports assessed the methodology's acceptability to communities and staff and whether it produced valid, reliable data to identify influential individuals and groups who diffuse new ideas through their networks. Results indicated the methodology's acceptability. Communities were actively and equitably engaged. Staff appreciated its ability to yield timely, actionable information. The mapping methodology also provided valid and reliable information by enabling communities to identify highly connected and influential network actors. Consistent with social network theory, this methodology resulted in the selection of informal groups and individuals in both informal and formal positions. In-depth interview data suggest these actors were diffusing new ideas, further confirming their influence/connectivity. The participatory methodology generated insider knowledge of who has social influence, challenging commonly held assumptions. Collecting and displaying information fostered staff and community learning, laying groundwork for social change.

The meaning and validation of social support networks for close family of persons with advanced cancer.

PubMed

Sjolander, Catarina; Ahlstrom, Gerd

2012-09-17

To strengthen the mental well-being of close family of persons newly diagnosed as having cancer, it is necessary to acquire a greater understanding of their experiences of social support networks, so as to better assess what resources are available to them from such networks and what professional measures are required. The main aim of the present study was to explore the meaning of these networks for close family of adult persons in the early stage of treatment for advanced lung or gastrointestinal cancer. An additional aim was to validate the study's empirical findings by means of the Finfgeld-Connett conceptual model for social support. The intention was to investigate whether these findings were in accordance with previous research in nursing. Seventeen family members with a relative who 8-14 weeks earlier had been diagnosed as having lung or gastrointestinal cancer were interviewed. The data were subjected to qualitative latent content analysis and validated by means of identifying antecedents and critical attributes. The meaning or main attribute of the social support network was expressed by the theme Confirmation through togetherness, based on six subthemes covering emotional and, to a lesser extent, instrumental support. Confirmation through togetherness derived principally from information, understanding, encouragement, involvement and spiritual community. Three subthemes were identified as the antecedents to social support: Need of support, Desire for a deeper relationship with relatives, Network to turn to. Social support involves reciprocal exchange of verbal and non-verbal information provided mainly by lay persons. The study provides knowledge of the antecedents and attributes of social support networks, particularly from the perspective of close family of adult persons with advanced lung or gastrointestinal cancer. There is a need for measurement instruments that could encourage nurses and other health-care professionals to focus on family members' personal networks as a way to strengthen their mental health. There is also a need for further clarification of the meaning of social support versus caring during the whole illness trajectory of cancer from the family members' perspective.

The meaning and validation of social support networks for close family of persons with advanced cancer

PubMed Central

2012-01-01

Background To strengthen the mental well-being of close family of persons newly diagnosed as having cancer, it is necessary to acquire a greater understanding of their experiences of social support networks, so as to better assess what resources are available to them from such networks and what professional measures are required. The main aim of the present study was to explore the meaning of these networks for close family of adult persons in the early stage of treatment for advanced lung or gastrointestinal cancer. An additional aim was to validate the study’s empirical findings by means of the Finfgeld-Connett conceptual model for social support. The intention was to investigate whether these findings were in accordance with previous research in nursing. Methods Seventeen family members with a relative who 8–14 weeks earlier had been diagnosed as having lung or gastrointestinal cancer were interviewed. The data were subjected to qualitative latent content analysis and validated by means of identifying antecedents and critical attributes. Results The meaning or main attribute of the social support network was expressed by the theme Confirmation through togetherness, based on six subthemes covering emotional and, to a lesser extent, instrumental support. Confirmation through togetherness derived principally from information, understanding, encouragement, involvement and spiritual community. Three subthemes were identified as the antecedents to social support: Need of support, Desire for a deeper relationship with relatives, Network to turn to. Social support involves reciprocal exchange of verbal and non-verbal information provided mainly by lay persons. Conclusions The study provides knowledge of the antecedents and attributes of social support networks, particularly from the perspective of close family of adult persons with advanced lung or gastrointestinal cancer. There is a need for measurement instruments that could encourage nurses and other health-care professionals to focus on family members’ personal networks as a way to strengthen their mental health. There is also a need for further clarification of the meaning of social support versus caring during the whole illness trajectory of cancer from the family members’ perspective. PMID:22978508

Pathway and network-based analysis of genome-wide association studies and RT-PCR validation in polycystic ovary syndrome.

PubMed

Shen, Haoran; Liang, Zhou; Zheng, Saihua; Li, Xuelian

2017-11-01

The purpose of this study was to identify promising candidate genes and pathways in polycystic ovary syndrome (PCOS). Microarray dataset GSE345269 obtained from the Gene Expression Omnibus database includes 7 granulosa cell samples from PCOS patients, and 3 normal granulosa cell samples. Differentially expressed genes (DEGs) were screened between PCOS and normal samples. Pathway enrichment analysis was conducted for DEGs using ClueGO and CluePedia plugin of Cytoscape. A Reactome functional interaction (FI) network of the DEGs was built using ReactomeFIViz, and then network modules were extracted, followed by pathway enrichment analysis for the modules. Expression of DEGs in granulosa cell samples was measured using quantitative RT-PCR. A total of 674 DEGs were retained, which were significantly enriched with inflammation and immune-related pathways. Eight modules were extracted from the Reactome FI network. Pathway enrichment analysis revealed significant pathways of each module: module 0, Regulation of RhoA activity and Signaling by Rho GTPases pathways shared ARHGAP4 and ARHGAP9; module 2, GlycoProtein VI-mediated activation cascade pathway was enriched with RHOG; module 3, Thromboxane A2 receptor signaling, Chemokine signaling pathway, CXCR4-mediated signaling events pathways were enriched with LYN, the hub gene of module 3. Results of RT-PCR confirmed the finding of the bioinformatic analysis that ARHGAP4, ARHGAP9, RHOG and LYN were significantly upregulated in PCOS. RhoA-related pathways, GlycoProtein VI-mediated activation cascade pathway, ARHGAP4, ARHGAP9, RHOG and LYN may be involved in the pathogenesis of PCOS.

Experimental Modal Analysis and Dynaic Strain Fiber Bragg Gratings for Structural Health Monitoring of Composite Aerospace Structures

NASA Astrophysics Data System (ADS)

Panopoulou, A.; Fransen, S.; Gomez Molinero, V.; Kostopoulos, V.

2012-07-01

The objective of this work is to develop a new structural health monitoring system for composite aerospace structures based on dynamic response strain measurements and experimental modal analysis techniques. Fibre Bragg Grating (FBG) optical sensors were used for monitoring the dynamic response of the composite structure. The structural dynamic behaviour has been numerically simulated and experimentally verified by means of vibration testing. The hypothesis of all vibration tests was that actual damage in composites reduces their stiffness and produces the same result as mass increase produces. Thus, damage was simulated by slightly varying locally the mass of the structure at different zones. Experimental modal analysis based on the strain responses was conducted and the extracted strain mode shapes were the input for the damage detection expert system. A feed-forward back propagation neural network was the core of the damage detection system. The features-input to the neural network consisted of the strain mode shapes, extracted from the experimental modal analysis. Dedicated training and validation activities were carried out based on the experimental results. The system showed high reliability, confirmed by the ability of the neural network to recognize the size and the position of damage on the structure. The experiments were performed on a real structure i.e. a lightweight antenna sub- reflector, manufactured and tested at EADS CASA ESPACIO. An integrated FBG sensor network, based on the advantage of multiplexing, was mounted on the structure with optimum topology. Numerical simulation of both structures was used as a support tool at all the steps of the work. Potential applications for the proposed system are during ground qualification extensive tests of space structures and during the mission as modal analysis tool on board, being able via the FBG responses to identify a potential failure.

An integrated molecular dynamics, principal component analysis and residue interaction network approach reveals the impact of M184V mutation on HIV reverse transcriptase resistance to lamivudine.

PubMed

Bhakat, Soumendranath; Martin, Alberto J M; Soliman, Mahmoud E S

2014-08-01

The emergence of different drug resistant strains of HIV-1 reverse transcriptase (HIV RT) remains of prime interest in relation to viral pathogenesis as well as drug development. Amongst those mutations, M184V was found to cause a complete loss of ligand fitness. In this study, we report the first account of the molecular impact of M184V mutation on HIV RT resistance to 3TC (lamivudine) using an integrated computational approach. This involved molecular dynamics simulation, binding free energy analysis, principle component analysis (PCA) and residue interaction networks (RINs). Results clearly confirmed that M184V mutation leads to steric conflict between 3TC and the beta branched side chain of valine, decreases the ligand (3TC) binding affinity by ∼7 kcal mol(-1) when compared to the wild type, changes the overall conformational landscape of the protein and distorts the native enzyme residue-residue interaction network. The comprehensive molecular insight gained from this study should be of great importance in understanding drug resistance against HIV RT as well as assisting in the design of novel reverse transcriptase inhibitors with high ligand efficacy on resistant strains.

Initial deployment of the cardiogenic gene regulatory network in the basal chordate, Ciona intestinalis.

PubMed

Woznica, Arielle; Haeussler, Maximilian; Starobinska, Ella; Jemmett, Jessica; Li, Younan; Mount, David; Davidson, Brad

2012-08-01

The complex, partially redundant gene regulatory architecture underlying vertebrate heart formation has been difficult to characterize. Here, we dissect the primary cardiac gene regulatory network in the invertebrate chordate, Ciona intestinalis. The Ciona heart progenitor lineage is first specified by Fibroblast Growth Factor/Map Kinase (FGF/MapK) activation of the transcription factor Ets1/2 (Ets). Through microarray analysis of sorted heart progenitor cells, we identified the complete set of primary genes upregulated by FGF/Ets shortly after heart progenitor emergence. Combinatorial sequence analysis of these co-regulated genes generated a hypothetical regulatory code consisting of Ets binding sites associated with a specific co-motif, ATTA. Through extensive reporter analysis, we confirmed the functional importance of the ATTA co-motif in primary heart progenitor gene regulation. We then used the Ets/ATTA combination motif to successfully predict a number of additional heart progenitor gene regulatory elements, including an intronic element driving expression of the core conserved cardiac transcription factor, GATAa. This work significantly advances our understanding of the Ciona heart gene network. Furthermore, this work has begun to elucidate the precise regulatory architecture underlying the conserved, primary role of FGF/Ets in chordate heart lineage specification. Copyright © 2012 Elsevier Inc. All rights reserved.

Calcium and Vitamin D Supplementation for Prevention of Preeclampsia: A Systematic Review and Network Meta-Analysis

PubMed Central

Khaing, Win; Vallibhakara, Sakda Arj-Ong; Tantrakul, Visasiri; Vallibhakara, Orawin; Rattanasiri, Sasivimol; McEvoy, Mark; Attia, John; Thakkinstian, Ammarin

2017-01-01

Vitamin D supplementation effects with or without calcium in pregnancy for reducing risk of preeclampsia and gestational or pregnancy induced hypertension are controversial. Literature was systematically searched in Medline, Scopus and Cochrane databases from inception to July 2017. Only randomized controlled trials (RCTs) in English were selected if they had any pair of interventions (calcium, vitamin D, both, or placebo). Systematic review with two-step network-meta-analysis was used to indirectly estimate supplementary effects. Twenty-seven RCTs with 28,000 women were eligible. A direct meta-analysis suggested that calcium, vitamin D, and calcium plus vitamin D could lower risk of preeclampsia when compared to placebo with the pooled risk ratios (RRs) of 0.54 (0.41, 0.70), 0.47 (0.24, 0.89) and 0.50 (0.32, 0.78), respectively. Results of network meta-analysis were similar with the corresponding RRs of 0.49 (0.35, 0.69), 0.43 (0.17, 1.11), and 0.57 (0.30, 1.10), respectively. None of the controls were significant. Efficacy of supplementation, which was ranked by surface under cumulative ranking probabilities, were: vitamin D (47.4%), calcium (31.6%) and calcium plus vitamin D (19.6%), respectively. Calcium supplementation may be used for prevention for preeclampsia. Vitamin D might also worked well but further large scale RCTs are warranted to confirm our findings. PMID:29057843

Calcium and Vitamin D Supplementation for Prevention of Preeclampsia: A Systematic Review and Network Meta-Analysis.

PubMed

Khaing, Win; Vallibhakara, Sakda Arj-Ong; Tantrakul, Visasiri; Vallibhakara, Orawin; Rattanasiri, Sasivimol; McEvoy, Mark; Attia, John; Thakkinstian, Ammarin

2017-10-18

Vitamin D supplementation effects with or without calcium in pregnancy for reducing risk of preeclampsia and gestational or pregnancy induced hypertension are controversial. Literature was systematically searched in Medline, Scopus and Cochrane databases from inception to July 2017. Only randomized controlled trials (RCTs) in English were selected if they had any pair of interventions (calcium, vitamin D, both, or placebo). Systematic review with two-step network-meta-analysis was used to indirectly estimate supplementary effects. Twenty-seven RCTs with 28,000 women were eligible. A direct meta-analysis suggested that calcium, vitamin D, and calcium plus vitamin D could lower risk of preeclampsia when compared to placebo with the pooled risk ratios (RRs) of 0.54 (0.41, 0.70), 0.47 (0.24, 0.89) and 0.50 (0.32, 0.78), respectively. Results of network meta-analysis were similar with the corresponding RRs of 0.49 (0.35, 0.69), 0.43 (0.17, 1.11), and 0.57 (0.30, 1.10), respectively. None of the controls were significant. Efficacy of supplementation, which was ranked by surface under cumulative ranking probabilities, were: vitamin D (47.4%), calcium (31.6%) and calcium plus vitamin D (19.6%), respectively. Calcium supplementation may be used for prevention for preeclampsia. Vitamin D might also worked well but further large scale RCTs are warranted to confirm our findings.

Characterizing the evolution of climate networks

NASA Astrophysics Data System (ADS)

Tupikina, L.; Rehfeld, K.; Molkenthin, N.; Stolbova, V.; Marwan, N.; Kurths, J.

2014-06-01

Complex network theory has been successfully applied to understand the structural and functional topology of many dynamical systems from nature, society and technology. Many properties of these systems change over time, and, consequently, networks reconstructed from them will, too. However, although static and temporally changing networks have been studied extensively, methods to quantify their robustness as they evolve in time are lacking. In this paper we develop a theory to investigate how networks are changing within time based on the quantitative analysis of dissimilarities in the network structure. Our main result is the common component evolution function (CCEF) which characterizes network development over time. To test our approach we apply it to several model systems, Erdős-Rényi networks, analytically derived flow-based networks, and transient simulations from the START model for which we control the change of single parameters over time. Then we construct annual climate networks from NCEP/NCAR reanalysis data for the Asian monsoon domain for the time period of 1970-2011 CE and use the CCEF to characterize the temporal evolution in this region. While this real-world CCEF displays a high degree of network persistence over large time lags, there are distinct time periods when common links break down. This phasing of these events coincides with years of strong El Niño/Southern Oscillation phenomena, confirming previous studies. The proposed method can be applied for any type of evolving network where the link but not the node set is changing, and may be particularly useful to characterize nonstationary evolving systems using complex networks.

Altered anatomical patterns of depression in relation to antidepressant treatment: Evidence from a pattern recognition analysis on the topological organization of brain networks.

PubMed

Qin, Jiaolong; Wei, Maobin; Liu, Haiyan; Chen, Jianhuai; Yan, Rui; Yao, Zhijian; Lu, Qing

2015-07-15

Accumulated evidence has illuminated the topological infrastructure of major depressive disorder (MDD). However, the changes of topological properties of anatomical brain networks in remitted major depressive disorder patients (rMDD) remain an open question. The present study provides an exploratory examination of pattern changes among current major depressive disorder patients (cMDD), rMDD patients and healthy controls (HC) by means of a pattern recognition analysis. Twenty-eight cMDD patients (age range: 22-54, mean age: 39.57), 15 rMDD patients (age range: 23-53, mean age: 38.40) and 30 HC (23-54, mean age: 35.57) were enrolled. For each subject, we computed five kinds of weighted white matter (WM) networks via employing five physiological parameters (i.e. fractional anisotropy, mean diffusivity, λ1, λ2 and λ3) and then calculated three network measures of these weighted networks. We treated these measures as features and fed into a feature selection mechanism to choose the most discriminative features for linear support vector machine (SVM) classifiers. Linear SVM could excellently distinguish the three groups with the 100% classification accuracy of recognizing cMDD/rMDD from HC, and 97.67% classification accuracy of recognizing cMDD from rMDD. The further pattern analysis found two types of discriminative patterns among cMDD, rMDD and HC. (i) Compared with HC, both cMDD and rMDD exhibited the similar deficit patterns of node strength primarily involving the salience network (SN), default mode network (DMN) and frontoparietal network (FPN). (ii) Compared with cMDD and rMDD showed the altered pattern of intra-communicability within DMN and inter-communicability between DMN and the other sub-networks including the visual recognition network (VRN) and SN. The present study had a limited sample size and a lack of larger independent data set to validate the methods and confirm the findings. These findings implied that the impairment of MDD was closely associated with the alterations of connections within SN, DMN and FPN, whereas the remission of MDD was benefitted from the network compensatory of intra-communication within DMN and inter-communication between DMN and the other sub-networks (i.e., VRN and SN). Copyright © 2015 Elsevier B.V. All rights reserved.

Biomarker microRNAs for prostate cancer metastasis: screened with a network vulnerability analysis model.

PubMed

Lin, Yuxin; Chen, Feifei; Shen, Li; Tang, Xiaoyu; Du, Cui; Sun, Zhandong; Ding, Huijie; Chen, Jiajia; Shen, Bairong

2018-05-21

Prostate cancer (PCa) is a fatal malignant tumor among males in the world and the metastasis is a leading cause for PCa death. Biomarkers are therefore urgently needed to detect PCa metastatic signature at the early time. MicroRNAs are small non-coding RNAs with the potential to be biomarkers for disease prediction. In addition, computer-aided biomarker discovery is now becoming an attractive paradigm for precision diagnosis and prognosis of complex diseases. In this study, we identified key microRNAs as biomarkers for predicting PCa metastasis based on network vulnerability analysis. We first extracted microRNAs and mRNAs that were differentially expressed between primary PCa and metastatic PCa (MPCa) samples. Then we constructed the MPCa-specific microRNA-mRNA network and screened microRNA biomarkers by a novel bioinformatics model. The model emphasized the characterization of systems stability changes and the network vulnerability with three measurements, i.e. the structurally single-line regulation, the functional importance of microRNA targets and the percentage of transcription factor genes in microRNA unique targets. With this model, we identified five microRNAs as putative biomarkers for PCa metastasis. Among them, miR-101-3p and miR-145-5p have been previously reported as biomarkers for PCa metastasis and the remaining three, i.e. miR-204-5p, miR-198 and miR-152, were screened as novel biomarkers for PCa metastasis. The results were further confirmed by the assessment of their predictive power and biological function analysis. Five microRNAs were identified as candidate biomarkers for predicting PCa metastasis based on our network vulnerability analysis model. The prediction performance, literature exploration and functional enrichment analysis convinced our findings. This novel bioinformatics model could be applied to biomarker discovery for other complex diseases.

Systems Genetics Analysis of GWAS reveals Novel Associations between Key Biological Processes and Coronary Artery Disease

PubMed Central

Ghosh, Sujoy; Vivar, Juan; Nelson, Christopher P; Willenborg, Christina; Segrè, Ayellet V; Mäkinen, Ville-Petteri; Nikpay, Majid; Erdmann, Jeannette; Blankenberg, Stefan; O'Donnell, Christopher; März, Winfried; Laaksonen, Reijo; Stewart, Alexandre FR; Epstein, Stephen E; Shah, Svati H; Granger, Christopher B; Hazen, Stanley L; Kathiresan, Sekar; Reilly, Muredach P; Yang, Xia; Quertermous, Thomas; Samani, Nilesh J; Schunkert, Heribert; Assimes, Themistocles L; McPherson, Ruth

2016-01-01

Objective Genome-wide association (GWA) studies have identified multiple genetic variants affecting the risk of coronary artery disease (CAD). However, individually these explain only a small fraction of the heritability of CAD and for most, the causal biological mechanisms remain unclear. We sought to obtain further insights into potential causal processes of CAD by integrating large-scale GWA data with expertly curated databases of core human pathways and functional networks. Approaches and Results Employing pathways (gene sets) from Reactome, we carried out a two-stage gene set enrichment analysis strategy. From a meta-analyzed discovery cohort of 7 CADGWAS data sets (9,889 cases/11,089 controls), nominally significant gene-sets were tested for replication in a meta-analysis of 9 additional studies (15,502 cases/55,730 controls) from the CARDIoGRAM Consortium. A total of 32 of 639 Reactome pathways tested showed convincing association with CAD (replication p<0.05). These pathways resided in 9 of 21 core biological processes represented in Reactome, and included pathways relevant to extracellular matrix integrity, innate immunity, axon guidance, and signaling by PDRF, NOTCH, and the TGF-β/SMAD receptor complex. Many of these pathways had strengths of association comparable to those observed in lipid transport pathways. Network analysis of unique genes within the replicated pathways further revealed several interconnected functional and topologically interacting modules representing novel associations (e.g. semaphorin regulated axonal guidance pathway) besides confirming known processes (lipid metabolism). The connectivity in the observed networks was statistically significant compared to random networks (p<0.001). Network centrality analysis (‘degree’ and ‘betweenness’) further identified genes (e.g. NCAM1, FYN, FURIN etc.) likely to play critical roles in the maintenance and functioning of several of the replicated pathways. Conclusions These findings provide novel insights into how genetic variation, interpreted in the context of biological processes and functional interactions among genes, may help define the genetic architecture of CAD. PMID:25977570

Dynamics of coupled mode solitons in bursting neural networks

NASA Astrophysics Data System (ADS)

Nfor, N. Oma; Ghomsi, P. Guemkam; Moukam Kakmeni, F. M.

2018-02-01

Using an electrically coupled chain of Hindmarsh-Rose neural models, we analytically derived the nonlinearly coupled complex Ginzburg-Landau equations. This is realized by superimposing the lower and upper cutoff modes of wave propagation and by employing the multiple scale expansions in the semidiscrete approximation. We explore the modified Hirota method to analytically obtain the bright-bright pulse soliton solutions of our nonlinearly coupled equations. With these bright solitons as initial conditions of our numerical scheme, and knowing that electrical signals are the basis of information transfer in the nervous system, it is found that prior to collisions at the boundaries of the network, neural information is purely conveyed by bisolitons at lower cutoff mode. After collision, the bisolitons are completely annihilated and neural information is now relayed by the upper cutoff mode via the propagation of plane waves. It is also shown that the linear gain of the system is inextricably linked to the complex physiological mechanisms of ion mobility, since the speeds and spatial profiles of the coupled nerve impulses vary with the gain. A linear stability analysis performed on the coupled system mainly confirms the instability of plane waves in the neural network, with a glaring example of the transition of weak plane waves into a dark soliton and then static kinks. Numerical simulations have confirmed the annihilation phenomenon subsequent to collision in neural systems. They equally showed that the symmetry breaking of the pulse solution of the system leaves in the network static internal modes, sometime referred to as Goldstone modes.

Dynamics of coupled mode solitons in bursting neural networks.

PubMed

Nfor, N Oma; Ghomsi, P Guemkam; Moukam Kakmeni, F M

2018-02-01

Using an electrically coupled chain of Hindmarsh-Rose neural models, we analytically derived the nonlinearly coupled complex Ginzburg-Landau equations. This is realized by superimposing the lower and upper cutoff modes of wave propagation and by employing the multiple scale expansions in the semidiscrete approximation. We explore the modified Hirota method to analytically obtain the bright-bright pulse soliton solutions of our nonlinearly coupled equations. With these bright solitons as initial conditions of our numerical scheme, and knowing that electrical signals are the basis of information transfer in the nervous system, it is found that prior to collisions at the boundaries of the network, neural information is purely conveyed by bisolitons at lower cutoff mode. After collision, the bisolitons are completely annihilated and neural information is now relayed by the upper cutoff mode via the propagation of plane waves. It is also shown that the linear gain of the system is inextricably linked to the complex physiological mechanisms of ion mobility, since the speeds and spatial profiles of the coupled nerve impulses vary with the gain. A linear stability analysis performed on the coupled system mainly confirms the instability of plane waves in the neural network, with a glaring example of the transition of weak plane waves into a dark soliton and then static kinks. Numerical simulations have confirmed the annihilation phenomenon subsequent to collision in neural systems. They equally showed that the symmetry breaking of the pulse solution of the system leaves in the network static internal modes, sometime referred to as Goldstone modes.

Modeling, Simulation and Analysis of Public Key Infrastructure

NASA Technical Reports Server (NTRS)

Liu, Yuan-Kwei; Tuey, Richard; Ma, Paul (Technical Monitor)

1998-01-01

Security is an essential part of network communication. The advances in cryptography have provided solutions to many of the network security requirements. Public Key Infrastructure (PKI) is the foundation of the cryptography applications. The main objective of this research is to design a model to simulate a reliable, scalable, manageable, and high-performance public key infrastructure. We build a model to simulate the NASA public key infrastructure by using SimProcess and MatLab Software. The simulation is from top level all the way down to the computation needed for encryption, decryption, digital signature, and secure web server. The application of secure web server could be utilized in wireless communications. The results of the simulation are analyzed and confirmed by using queueing theory.

Sexual network analysis of a gonorrhoea outbreak

PubMed Central

De, P; Singh, A; Wong, T; Yacoub, W; Jolly, A

2004-01-01

Objectives: Sexual partnerships can be viewed as networks in order to study disease transmission. We examined the transmission of Neisseria gonorrhoeae in a localised outbreak in Alberta, Canada, using measures of network centrality to determine the association between risk of infection of network members and their position within the sexual network. We also compared risk in smaller disconnected components with a large network centred on a social venue. Methods: During the investigation of the outbreak, epidemiological data were collected on gonorrhoea cases and their sexual contacts from STI surveillance records. In addition to traditional contact tracing information, subjects were interviewed about social venues they attended in the past year where casual sexual partnering may have occurred. Sexual networks were constructed by linking together named partners. Univariate comparisons of individual network member characteristics and algebraic measures of network centrality were completed. Results: The sexual networks consisted of 182 individuals, of whom 107 were index cases with laboratory confirmed gonorrhoea and 75 partners of index cases. People who had significantly higher information centrality within each of their local networks were found to have patronised a popular motel bar in the main town in the region (p = 0.05). When the social interaction through the bar was considered, a large network of 89 individuals was constructed that joined all eight of the largest local networks. Moreover, several networks from different communities were linked by individuals who served as bridge populations as a result of their sexual partnering. Conclusion: Asking clients about particular social venues emphasised the importance of location in disease transmission. Network measures of centrality, particularly information centrality, allowed the identification of key individuals through whom infection could be channelled into local networks. Such individuals would be ideal targets for increased interventions. PMID:15295126

Construct Validation of Wenger's Support Network Typology.

PubMed

Szabo, Agnes; Stephens, Christine; Allen, Joanne; Alpass, Fiona

2016-10-07

The study aimed to validate Wenger's empirically derived support network typology of responses to the Practitioner Assessment of Network Type (PANT) in an older New Zealander population. The configuration of network types was tested across ethnic groups and in the total sample. Data (N = 872, Mage = 67 years, SDage = 1.56 years) from the 2006 wave of the New Zealand Health, Work and Retirement study were analyzed using latent profile analysis. In addition, demographic differences among the emerging profiles were tested. Competing models were evaluated based on a range of fit criteria, which supported a five-profile solution. The "locally integrated," "community-focused," "local self-contained," "private-restricted," and "friend- and family-dependent" network types were identified as latent profiles underlying the data. There were no differences between Māori and non-Māori in final profile configurations. However, Māori were more likely to report integrated network types. Findings confirm the validity of Wenger's network types. However, the level to which participants endorse accessibility of family, frequency of interactions, and community engagement can be influenced by sample and contextual characteristics. Future research using the PANT items should empirically verify and derive the social support network types, rather than use a predefined scoring system. © The Author 2016. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Time Series Analysis of the Bacillus subtilis Sporulation Network Reveals Low Dimensional Chaotic Dynamics.

PubMed

Lecca, Paola; Mura, Ivan; Re, Angela; Barker, Gary C; Ihekwaba, Adaoha E C

2016-01-01

Chaotic behavior refers to a behavior which, albeit irregular, is generated by an underlying deterministic process. Therefore, a chaotic behavior is potentially controllable. This possibility becomes practically amenable especially when chaos is shown to be low-dimensional, i.e., to be attributable to a small fraction of the total systems components. In this case, indeed, including the major drivers of chaos in a system into the modeling approach allows us to improve predictability of the systems dynamics. Here, we analyzed the numerical simulations of an accurate ordinary differential equation model of the gene network regulating sporulation initiation in Bacillus subtilis to explore whether the non-linearity underlying time series data is due to low-dimensional chaos. Low-dimensional chaos is expectedly common in systems with few degrees of freedom, but rare in systems with many degrees of freedom such as the B. subtilis sporulation network. The estimation of a number of indices, which reflect the chaotic nature of a system, indicates that the dynamics of this network is affected by deterministic chaos. The neat separation between the indices obtained from the time series simulated from the model and those obtained from time series generated by Gaussian white and colored noise confirmed that the B. subtilis sporulation network dynamics is affected by low dimensional chaos rather than by noise. Furthermore, our analysis identifies the principal driver of the networks chaotic dynamics to be sporulation initiation phosphotransferase B (Spo0B). We then analyzed the parameters and the phase space of the system to characterize the instability points of the network dynamics, and, in turn, to identify the ranges of values of Spo0B and of the other drivers of the chaotic dynamics, for which the whole system is highly sensitive to minimal perturbation. In summary, we described an unappreciated source of complexity in the B. subtilis sporulation network by gathering evidence for the chaotic behavior of the system, and by suggesting candidate molecules driving chaos in the system. The results of our chaos analysis can increase our understanding of the intricacies of the regulatory network under analysis, and suggest experimental work to refine our behavior of the mechanisms underlying B. subtilis sporulation initiation control.

Experimental verification of a radiofrequency power model for Wi-Fi technology.

PubMed

Fang, Minyu; Malone, David

2010-04-01

When assessing the power emitted from a Wi-Fi network, it has been observed that these networks operate at a relatively low duty cycle. In this paper, we extend a recently introduced model of emitted power in Wi-Fi networks to cover conditions where devices do not always have packets to transmit. We present experimental results to validate the original model and its extension by developing approximate, but practical, testbed measurement techniques. The accuracy of the models is confirmed, with small relative errors: less than 5-10%. Moreover, we confirm that the greatest power is emitted when the network is saturated with traffic. Using this, we give a simple technique to quickly estimate power output based on traffic levels and give examples showing how this might be used in practice to predict current or future power output from a Wi-Fi network.

Different propagation speeds of recalled sequences in plastic spiking neural networks

NASA Astrophysics Data System (ADS)

Huang, Xuhui; Zheng, Zhigang; Hu, Gang; Wu, Si; Rasch, Malte J.

2015-03-01

Neural networks can generate spatiotemporal patterns of spike activity. Sequential activity learning and retrieval have been observed in many brain areas, and e.g. is crucial for coding of episodic memory in the hippocampus or generating temporal patterns during song production in birds. In a recent study, a sequential activity pattern was directly entrained onto the neural activity of the primary visual cortex (V1) of rats and subsequently successfully recalled by a local and transient trigger. It was observed that the speed of activity propagation in coordinates of the retinotopically organized neural tissue was constant during retrieval regardless how the speed of light stimulation sweeping across the visual field during training was varied. It is well known that spike-timing dependent plasticity (STDP) is a potential mechanism for embedding temporal sequences into neural network activity. How training and retrieval speeds relate to each other and how network and learning parameters influence retrieval speeds, however, is not well described. We here theoretically analyze sequential activity learning and retrieval in a recurrent neural network with realistic synaptic short-term dynamics and STDP. Testing multiple STDP rules, we confirm that sequence learning can be achieved by STDP. However, we found that a multiplicative nearest-neighbor (NN) weight update rule generated weight distributions and recall activities that best matched the experiments in V1. Using network simulations and mean-field analysis, we further investigated the learning mechanisms and the influence of network parameters on recall speeds. Our analysis suggests that a multiplicative STDP rule with dominant NN spike interaction might be implemented in V1 since recall speed was almost constant in an NMDA-dominant regime. Interestingly, in an AMPA-dominant regime, neural circuits might exhibit recall speeds that instead follow the change in stimulus speeds. This prediction could be tested in experiments.

Which Surgical Treatment for Open Tibial Shaft Fractures Results in the Fewest Reoperations? A Network Meta-analysis.

PubMed

Foote, Clary J; Guyatt, Gordon H; Vignesh, K Nithin; Mundi, Raman; Chaudhry, Harman; Heels-Ansdell, Diane; Thabane, Lehana; Tornetta, Paul; Bhandari, Mohit

2015-07-01

Open tibial shaft fractures are one of the most devastating orthopaedic injuries. Surgical treatment options include reamed or unreamed nailing, plating, Ender nails, Ilizarov fixation, and external fixation. Using a network meta-analysis allows comparison and facilitates pooling of a diverse population of randomized trials across these approaches in ways that a traditional meta-analysis does not. Our aim was to perform a network meta-analysis using evidence from randomized trials on the relative effect of alternative approaches on the risk of unplanned reoperation after open fractures of the tibial diaphysis. Our secondary study endpoints included malunion, deep infection, and superficial infection. A network meta-analysis allows for simultaneous consideration of the relative effectiveness of multiple treatment alternatives. To do this on the subject of surgical treatments for open tibial fractures, we began with systematic searches of databases (including EMBASE and MEDLINE) and performed hand searches of orthopaedic journals, bibliographies, abstracts from orthopaedic conferences, and orthopaedic textbooks, for all relevant material published between 1980 and 2013. Two authors independently screened abstracts and manuscripts and extracted the data, three evaluated the risk of bias in individual studies, and two applied Grading of Recommendation Assessment, Development and Evaluation (GRADE) criteria to bodies of evidence. We included all randomized and quasirandomized trials comparing two (or more) surgical treatment options for open tibial shaft fractures in predominantly (ie, > 80%) adult patients. We calculated pooled estimates for all direct comparisons and conducted a network meta-analysis combining direct and indirect evidence for all 15 comparisons between six stabilization strategies. Fourteen trials published between 1989 and November 2011 met our inclusion criteria; the trials comprised a total of 1279 patients surgically treated for open tibial shaft fractures. Moderate confidence evidence showed that unreamed nailing may reduce the likelihood of reoperation compared with external fixation (network odds ratio [OR], 0.38; 95% CI, 0.23-0.62; p < 0.05), although not necessarily compared with reamed nailing (direct OR, 0.74; 95% CI, 0.45-1.24; p = 0.25). Only low- or very low-quality evidence informed the primary outcome for other treatment comparisons, such as those involving internal plate fixation, Ilizarov external fixation, and Ender nailing. Method ranking based on reoperation data showed that unreamed nailing had the highest probability of being the best treatment, followed by reamed nailing, external fixation, and plate fixation. CIs around pooled estimates of malunion and infection risk were very wide, and therefore no conclusive results could be made based on these data. Current evidence suggests that intramedullary nailing may be superior to other fixation strategies for open tibial shaft fractures. Use of unreamed nails over reamed nails also may be advantageous in the setting of open fractures, but this remains to be confirmed. Unfortunately, these conclusions are based on trials that have had high risk of bias and poor precision. Larger and higher-quality head-to-head randomized controlled trials are required to confirm these conclusions and better inform clinical decision-making. Level I, therapeutic study.

Molecular networks discriminating mouse bladder responses to intravesical bacillus Calmette-Guerin (BCG), LPS, and TNF-α

PubMed Central

Saban, Marcia R; O'Donnell, Michael A; Hurst, Robert E; Wu, Xue-Ru; Simpson, Cindy; Dozmorov, Igor; Davis, Carole; Saban, Ricardo

2008-01-01

Background Despite being a mainstay for treating superficial bladder carcinoma and a promising agent for interstitial cystitis, the precise mechanism of Bacillus Calmette-Guerin (BCG) remains poorly understood. It is particularly unclear whether BCG is capable of altering gene expression in the bladder target organ beyond its well-recognized pro-inflammatory effects and how this relates to its therapeutic efficacy. The objective of this study was to determine differentially expressed genes in the mouse bladder following chronic intravesical BCG therapy and to compare the results to non-specific pro inflammatory stimuli (LPS and TNF-α). For this purpose, C57BL/6 female mice received four weekly instillations of BCG, LPS, or TNF-α. Seven days after the last instillation, the urothelium along with the submucosa was removed from detrusor muscle and the RNA was extracted from both layers for cDNA array experiments. Microarray results were normalized by a robust regression analysis and only genes with an expression above a conditional threshold of 0.001 (3SD above background) were selected for analysis. Next, genes presenting a 3-fold ratio in regard to the control group were entered in Ingenuity Pathway Analysis (IPA) for a comparative analysis in order to determine genes specifically regulated by BCG, TNF-α, and LPS. In addition, the transcriptome was precipitated with an antibody against RNA polymerase II and real-time polymerase chain reaction assay (Q-PCR) was used to confirm some of the BCG-specific transcripts. Results Molecular networks of treatment-specific genes generated several hypotheses regarding the mode of action of BCG. BCG-specific genes involved small GTPases and BCG-specific networks overlapped with the following canonical signaling pathways: axonal guidance, B cell receptor, aryl hydrocarbon receptor, IL-6, PPAR, Wnt/β-catenin, and cAMP. In addition, a specific detrusor network expressed a high degree of overlap with the development of the lymphatic system. Interestingly, TNF-α-specific networks overlapped with the following canonical signaling pathways: PPAR, death receptor, and apoptosis. Finally, LPS-specific networks overlapped with the LPS/IL-1 mediated inhibition of RXR. Because NF-kappaB occupied a central position in several networks, we further determined whether this transcription factor was part of the responses to BCG. Electrophoretic mobility shift assays confirmed the participation of NF-kappaB in the mouse bladder responses to BCG. In addition, BCG treatment of a human urothelial cancer cell line (J82) also increased the binding activity of NF-kappaB, as determined by precipitation of the chromatin by a NF-kappaB-p65 antibody and Q-PCR of genes bearing a NF-kappaB consensus sequence. Next, we tested the hypothesis of whether small GTPases such as LRG-47 are involved in the uptake of BCG by the bladder urothelium. Conclusion As expected, BCG treatment induces the transcription of genes belonging to common pro-inflammatory networks. However, BCG also induces unique genes belonging to molecular networks involved in axonal guidance and lymphatic system development within the bladder target organ. In addition, NF-kappaB seems to play a predominant role in the bladder responses to BCG therapy. Finally, in intact urothelium, BCG-GFP internalizes in LRG-47-positive vesicles. These results provide a molecular framework for the further study of the involvement of immune and nervous systems in the bladder responses to BCG therapy. PMID:18267009

The relationship between node degree and dissipation rate in networks of diffusively coupled oscillators and its significance for pancreatic beta cells.

PubMed

Gosak, Marko; Stožer, Andraž; Markovič, Rene; Dolenšek, Jurij; Marhl, Marko; Rupnik, Marjan Slak; Perc, Matjaž

2015-07-01

Self-sustained oscillatory dynamics is a motion along a stable limit cycle in the phase space, and it arises in a wide variety of mechanical, electrical, and biological systems. Typically, oscillations are due to a balance between energy dissipation and generation. Their stability depends on the properties of the attractor, in particular, its dissipative characteristics, which in turn determine the flexibility of a given dynamical system. In a network of oscillators, the coupling additionally contributes to the dissipation, and hence affects the robustness of the oscillatory solution. Here, we therefore investigate how a heterogeneous network structure affects the dissipation rate of individual oscillators. First, we show that in a network of diffusively coupled oscillators, the dissipation is a linearly decreasing function of the node degree, and we demonstrate this numerically by calculating the average divergence of coupled Hopf oscillators. Subsequently, we use recordings of intracellular calcium dynamics in pancreatic beta cells in mouse acute tissue slices and the corresponding functional connectivity networks for an experimental verification of the presented theory. We use methods of nonlinear time series analysis to reconstruct the phase space and calculate the sum of Lyapunov exponents. Our analysis reveals a clear tendency of cells with a higher degree, that is, more interconnected cells, having more negative values of divergence, thus confirming our theoretical predictions. We discuss these findings in the context of energetic aspects of signaling in beta cells and potential risks for pathological changes in the tissue.

Does network topology influence systemic risk contribution? A perspective from the industry indices in Chinese stock market.

PubMed

Long, Haiming; Zhang, Ji; Tang, Nengyu

2017-01-01

This study considers the effect of an industry's network topology on its systemic risk contribution to the stock market using data from the CSI 300 two-tier industry indices from the Chinese stock market. We first measure industry's conditional-value-at-risk (CoVaR) and the systemic risk contribution (ΔCoVaR) using the fitted time-varying t-copula function. The network of the stock industry is established based on dynamic conditional correlations with the minimum spanning tree. Then, we investigate the connection characteristics and topology of the network. Finally, we utilize seemingly unrelated regression estimation (SUR) of panel data to analyze the relationship between network topology of the stock industry and the industry's systemic risk contribution. The results show that the systemic risk contribution of small-scale industries such as real estate, food and beverage, software services, and durable goods and clothing, is higher than that of large-scale industries, such as banking, insurance and energy. Industries with large betweenness centrality, closeness centrality, and clustering coefficient and small node occupancy layer are associated with greater systemic risk contribution. In addition, further analysis using a threshold model confirms that the results are robust.

Finding pathway-modulating genes from a novel Ontology Fingerprint-derived gene network.

PubMed

Qin, Tingting; Matmati, Nabil; Tsoi, Lam C; Mohanty, Bidyut K; Gao, Nan; Tang, Jijun; Lawson, Andrew B; Hannun, Yusuf A; Zheng, W Jim

2014-10-01

To enhance our knowledge regarding biological pathway regulation, we took an integrated approach, using the biomedical literature, ontologies, network analyses and experimental investigation to infer novel genes that could modulate biological pathways. We first constructed a novel gene network via a pairwise comparison of all yeast genes' Ontology Fingerprints--a set of Gene Ontology terms overrepresented in the PubMed abstracts linked to a gene along with those terms' corresponding enrichment P-values. The network was further refined using a Bayesian hierarchical model to identify novel genes that could potentially influence the pathway activities. We applied this method to the sphingolipid pathway in yeast and found that many top-ranked genes indeed displayed altered sphingolipid pathway functions, initially measured by their sensitivity to myriocin, an inhibitor of de novo sphingolipid biosynthesis. Further experiments confirmed the modulation of the sphingolipid pathway by one of these genes, PFA4, encoding a palmitoyl transferase. Comparative analysis showed that few of these novel genes could be discovered by other existing methods. Our novel gene network provides a unique and comprehensive resource to study pathway modulations and systems biology in general. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Finding pathway-modulating genes from a novel Ontology Fingerprint-derived gene network

PubMed Central

Qin, Tingting; Matmati, Nabil; Tsoi, Lam C.; Mohanty, Bidyut K.; Gao, Nan; Tang, Jijun; Lawson, Andrew B.; Hannun, Yusuf A.; Zheng, W. Jim

2014-01-01

To enhance our knowledge regarding biological pathway regulation, we took an integrated approach, using the biomedical literature, ontologies, network analyses and experimental investigation to infer novel genes that could modulate biological pathways. We first constructed a novel gene network via a pairwise comparison of all yeast genes’ Ontology Fingerprints—a set of Gene Ontology terms overrepresented in the PubMed abstracts linked to a gene along with those terms’ corresponding enrichment P-values. The network was further refined using a Bayesian hierarchical model to identify novel genes that could potentially influence the pathway activities. We applied this method to the sphingolipid pathway in yeast and found that many top-ranked genes indeed displayed altered sphingolipid pathway functions, initially measured by their sensitivity to myriocin, an inhibitor of de novo sphingolipid biosynthesis. Further experiments confirmed the modulation of the sphingolipid pathway by one of these genes, PFA4, encoding a palmitoyl transferase. Comparative analysis showed that few of these novel genes could be discovered by other existing methods. Our novel gene network provides a unique and comprehensive resource to study pathway modulations and systems biology in general. PMID:25063300

Morphine Regulated Synaptic Networks Revealed by Integrated Proteomics and Network Analysis*

PubMed Central

Stockton, Steven D.; Gomes, Ivone; Liu, Tong; Moraje, Chandrakala; Hipólito, Lucia; Jones, Matthew R.; Ma'ayan, Avi; Morón, Jose A.; Li, Hong; Devi, Lakshmi A.

2015-01-01

Despite its efficacy, the use of morphine for the treatment of chronic pain remains limited because of the rapid development of tolerance, dependence and ultimately addiction. These undesired effects are thought to be because of alterations in synaptic transmission and neuroplasticity within the reward circuitry including the striatum. In this study we used subcellular fractionation and quantitative proteomics combined with computational approaches to investigate the morphine-induced protein profile changes at the striatal postsynaptic density. Over 2,600 proteins were identified by mass spectrometry analysis of subcellular fractions enriched in postsynaptic density associated proteins from saline or morphine-treated striata. Among these, the levels of 34 proteins were differentially altered in response to morphine. These include proteins involved in G-protein coupled receptor signaling, regulation of transcription and translation, chaperones, and protein degradation pathways. The altered expression levels of several of these proteins was validated by Western blotting analysis. Using Genes2Fans software suite we connected the differentially expressed proteins with proteins identified within the known background protein-protein interaction network. This led to the generation of a network consisting of 116 proteins with 40 significant intermediates. To validate this, we confirmed the presence of three proteins predicted to be significant intermediates: caspase-3, receptor-interacting serine/threonine protein kinase 3 and NEDD4 (an E3-ubiquitin ligase identified as a neural precursor cell expressed developmentally down-regulated protein 4). Because this morphine-regulated network predicted alterations in proteasomal degradation, we examined the global ubiquitination state of postsynaptic density proteins and found it to be substantially altered. Together, these findings suggest a role for protein degradation and for the ubiquitin/proteasomal system in the etiology of opiate dependence and addiction. PMID:26149443

Social Network Map: Some Further Refinements on Administration.

ERIC Educational Resources Information Center

Tracy, Elizabeth M.; Abell, Neil

1994-01-01

Notes that social network mapping techniques have been advanced as means of assessing social and environmental resources. Addresses issue of convergent construct validity, correlations among dimensions of perceived social support as measured by social network data with other standardized social support instruments. Findings confirm that structural…

Detecting trends in academic research from a citation network using network representation learning

PubMed Central

Mori, Junichiro; Ochi, Masanao; Sakata, Ichiro

2018-01-01

Several network features and information retrieval methods have been proposed to elucidate the structure of citation networks and to detect important nodes. However, it is difficult to retrieve information related to trends in an academic field and to detect cutting-edge areas from the citation network. In this paper, we propose a novel framework that detects the trend as the growth direction of a citation network using network representation learning(NRL). We presume that the linear growth of citation network in latent space obtained by NRL is the result of the iterative edge additional process of a citation network. On APS datasets and papers of some domains of the Web of Science, we confirm the existence of trends by observing that an academic field grows in a specific direction linearly in latent space. Next, we calculate each node’s degree of trend-following as an indicator called the intrinsic publication year (IPY). As a result, there is a correlation between the indicator and the number of future citations. Furthermore, a word frequently used in the abstracts of cutting-edge papers (high-IPY paper) is likely to be used often in future publications. These results confirm the validity of the detected trend for predicting citation network growth. PMID:29782521

Strong-motion observations of the M 7.8 Gorkha, Nepal, earthquake sequence and development of the N-shake strong-motion network

USGS Publications Warehouse

Dixit, Amod; Ringler, Adam; Sumy, Danielle F.; Cochran, Elizabeth S.; Hough, Susan E.; Martin, Stacey; Gibbons, Steven; Luetgert, James H.; Galetzka, John; Shrestha, Surya; Rajaure, Sudhir; McNamara, Daniel E.

2015-01-01

We present and describe strong-motion data observations from the 2015 M 7.8 Gorkha, Nepal, earthquake sequence collected using existing and new Quake-Catcher Network (QCN) and U.S. Geological Survey NetQuakes sensors located in the Kathmandu Valley. A comparison of QCN data with waveforms recorded by a conventional strong-motion (NetQuakes) instrument validates the QCN data. We present preliminary analysis of spectral accelerations, and peak ground acceleration and velocity for earthquakes up to M 7.3 from the QCN stations, as well as preliminary analysis of the mainshock recording from the NetQuakes station. We show that mainshock peak accelerations were lower than expected and conclude the Kathmandu Valley experienced a pervasively nonlinear response during the mainshock. Phase picks from the QCN and NetQuakes data are also used to improve aftershock locations. This study confirms the utility of QCN instruments to contribute to ground-motion investigations and aftershock response in regions where conventional instrumentation and open-access seismic data are limited. Initial pilot installations of QCN instruments in 2014 are now being expanded to create the Nepal–Shaking Hazard Assessment for Kathmandu and its Environment (N-SHAKE) network.

Solutions to Improve Road Circulation in the Pitesti City Based on Analysis-Diagnostics of Road Traffic

NASA Astrophysics Data System (ADS)

Vîlcan, A.; Neagu, E.; Badarau Suster, H.; Boroiu, A. A.

2017-10-01

Road traffic congestion has become a daily phenomenon in the central area of Pitesti in the peak traffic periods. In order to achieve the mobility plan of Pitesti, an important stage is the diagnostic analysis of the road traffic. For this purpose, the urban road network was formalized through a graph containing the most important 40 intersections and traffic measurements were made at all these intersections and on the main roads connecting the peri-urban area. The data obtained by traffic macrosimulation confirmed the overloading of the street network during peak traffic hours and the analyzes made for various road traffic organization scenarios have shown that there are sustainable solutions for urban mobility only if the road network is fundamentally reconfigured (a belt outside the city and a median ring). Thus, the necessity of realizing the road passage in the Prundu neighbourhood and the finishing of the city belt by realizing the “detour West” of the city is argued. The importance of the work is that it brings scientific arguments for the realization of these road infrastructure projects, integrated in the urban mobility plan, which will base the development strategy of the Pitesti municipality.

Enhanced method of fast re-routing with load balancing in software-defined networks

NASA Astrophysics Data System (ADS)

Lemeshko, Oleksandr; Yeremenko, Oleksandra

2017-11-01

A two-level method of fast re-routing with load balancing in a software-defined network (SDN) is proposed. The novelty of the method consists, firstly, in the introduction of a two-level hierarchy of calculating the routing variables responsible for the formation of the primary and backup paths, and secondly, in ensuring a balanced load of the communication links of the network, which meets the requirements of the traffic engineering concept. The method provides implementation of link, node, path, and bandwidth protection schemes for fast re-routing in SDN. The separation in accordance with the interaction prediction principle along two hierarchical levels of the calculation functions of the primary (lower level) and backup (upper level) routes allowed to abandon the initial sufficiently large and nonlinear optimization problem by transiting to the iterative solution of linear optimization problems of half the dimension. The analysis of the proposed method confirmed its efficiency and effectiveness in terms of obtaining optimal solutions for ensuring balanced load of communication links and implementing the required network element protection schemes for fast re-routing in SDN.

Macroscopic self-oscillations and aging transition in a network of synaptically coupled quadratic integrate-and-fire neurons.

PubMed

Ratas, Irmantas; Pyragas, Kestutis

2016-09-01

We analyze the dynamics of a large network of coupled quadratic integrate-and-fire neurons, which represent the canonical model for class I neurons near the spiking threshold. The network is heterogeneous in that it includes both inherently spiking and excitable neurons. The coupling is global via synapses that take into account the finite width of synaptic pulses. Using a recently developed reduction method based on the Lorentzian ansatz, we derive a closed system of equations for the neuron's firing rate and the mean membrane potential, which are exact in the infinite-size limit. The bifurcation analysis of the reduced equations reveals a rich scenario of asymptotic behavior, the most interesting of which is the macroscopic limit-cycle oscillations. It is shown that the finite width of synaptic pulses is a necessary condition for the existence of such oscillations. The robustness of the oscillations against aging damage, which transforms spiking neurons into nonspiking neurons, is analyzed. The validity of the reduced equations is confirmed by comparing their solutions with the solutions of microscopic equations for the finite-size networks.

Combined metabolomic and correlation networks analyses reveal fumarase insufficiency altered amino acid metabolism.

PubMed

Hou, Entai; Li, Xian; Liu, Zerong; Zhang, Fuchang; Tian, Zhongmin

2018-04-01

Fumarase catalyzes the interconversion of fumarate and l-malate in the tricarboxylic acid cycle. Fumarase insufficiencies were associated with increased levels of fumarate, decreased levels of malate and exacerbated salt-induced hypertension. To gain insights into the metabolism profiles induced by fumarase insufficiency and identify key regulatory metabolites, we applied a GC-MS based metabolomics platform coupled with a network approach to analyze fumarase insufficient human umbilical vein endothelial cells (HUVEC) and negative controls. A total of 24 altered metabolites involved in seven metabolic pathways were identified as significantly altered, and enriched for the biological module of amino acids metabolism. In addition, Pearson correlation network analysis revealed that fumaric acid, l-malic acid, l-aspartic acid, glycine and l-glutamic acid were hub metabolites according to Pagerank based on their three centrality indices. Alanine aminotransferase and glutamate dehydrogenase activities increased significantly in fumarase deficiency HUVEC. These results confirmed that fumarase insufficiency altered amino acid metabolism. The combination of metabolomics and network methods would provide another perspective on expounding the molecular mechanism at metabolomics level. Copyright © 2017 John Wiley & Sons, Ltd.

Metabolic Brain Network Analysis of Hypothyroidism Symptom Based on [18F]FDG-PET of Rats.

PubMed

Wan, Hongkai; Tan, Ziyu; Zheng, Qiang; Yu, Jing

2018-03-12

Recent researches have demonstrated the value of using 2-deoxy-2-[ 18 F]fluoro-D-glucose ([ 18 F]FDG) positron emission tomography (PET) imaging to reveal the hypothyroidism-related damages in local brain regions. However, the influence of hypothyroidism on the entire brain network is barely studied. This study focuses on the application of graph theory on analyzing functional brain networks of the hypothyroidism symptom. For both the hypothyroidism and the control groups of Wistar rats, the functional brain networks were constructed by thresholding the glucose metabolism correlation matrices of 58 brain regions. The network topological properties (including the small-world properties and the nodal centralities) were calculated and compared between the two groups. We found that the rat brains, like human brains, have typical properties of the small-world network in both the hypothyroidism and the control groups. However, the hypothyroidism group demonstrated lower global efficiency and decreased local cliquishness of the brain network, indicating hypothyroidism-related impairment to the brain network. The hypothyroidism group also has decreased nodal centrality in the left posterior hippocampus, the right hypothalamus, pituitary, pons, and medulla. This observation accorded with the hypothyroidism-related functional disorder of hypothalamus-pituitary-thyroid (HPT) feedback regulation mechanism. Our research quantitatively confirms that hypothyroidism hampers brain cognitive function by causing impairment to the brain network of glucose metabolism. This study reveals the feasibility and validity of applying graph theory method to preclinical [ 18 F]FDG-PET images and facilitates future study on human subjects.

Estimation of Psychophysical Thresholds Based on Neural Network Analysis of DPOAE Input/Output Functions

NASA Astrophysics Data System (ADS)

Naghibolhosseini, Maryam; Long, Glenis

2011-11-01

The distortion product otoacoustic emission (DPOAE) input/output (I/O) function may provide a potential tool for evaluating cochlear compression. Hearing loss causes an increase in the level of the sound that is just audible for the person, which affects the cochlea compression and thus the dynamic range of hearing. Although the slope of the I/O function is highly variable when the total DPOAE is used, separating the nonlinear-generator component from the reflection component reduces this variability. We separated the two components using least squares fit (LSF) analysis of logarithmic sweeping tones, and confirmed that the separated generator component provides more consistent I/O functions than the total DPOAE. In this paper we estimated the slope of the I/O functions of the generator components at different sound levels using LSF analysis. An artificial neural network (ANN) was used to estimate psychophysical thresholds using the estimated slopes of the I/O functions. DPOAE I/O functions determined in this way may help to estimate hearing thresholds and cochlear health.

The COBATEST network: a platform to perform monitoring and evaluation of HIV community-based testing practices in Europe and conduct operational research.

PubMed

Fernàndez-López, L; Reyes-Urueña, J; Agustí, C; Kustec, T; Klavs, I; Casabona, C

2016-01-01

The European project "HIV community-based testing practices in Europe" (HIV-COBATEST) has contributed to the establishment of a network of community-based voluntary counselling and testing services (CBVCTs) that monitors and evaluates HIV testing activity in the communities. The objective of this paper is to describe the data that have been collected during 2014 by the COBATEST network in order to provide an insight into testing activity of CBVCTs in Europe. Members of the CBVCT network share common instruments for data collection and data entry. The network has a common database that allows global data analysis and comparison between different centres. In 2014, 40 CBVCTs of 18 European countries were participating in the network, and, from those, 20 CBVCTs were using the common COBATEST data collection tools. In these 20 CBVCTs, a total of 9266 HIV screening tests were performed on 8554 people, of which 1.58% (135/8554) were reactive and 51.1% (69/135) confirmed positive. Five cases were false positives, and 84.1% (58/69) of the confirmed positive cases were linked to care. Most of the tested individuals were men (70.8%), between 21 and 35 years of age (57.6%) and natives (67.1%). A higher proportion of men who had sex with men (MSM) (38.8%; 3267/8554) were tested compared to heterosexual men (27.7%) and women (23.5%). Rapid blood test was used in 78.5% of the cases and mostly performed in CBVCT offices (88.3%). Among sex workers (SWs), the percentage of reactive screening tests was particularly high (4.0%), especially among male SWs (7.7%) as compared to other risk groups, such as MSM (3.1%). The COBATEST network contributes to the availability of standardized information about the activity and impact of CBVCT centres in Europe. This information and standardized tools can help improve these services and inform decision-makers to better contextualize these interventions within their national HIV-prevention programmes.

Effect of horizontal displacements due to ocean tide loading on the determination of polar motion and UT1

NASA Astrophysics Data System (ADS)

Scherneck, Hans-Georg; Haas, Rüdiger

We show the influence of horizontal displacements due to ocean tide loading on the determination of polar motion and UT1 (PMU) on the daily and subdaily timescale. So called ‘virtual PMU variations’ due to modelling errors of ocean tide loading are predicted for geodetic Very Long Baseline Interferometry (VLBI) networks. This leads to errors of subdaily determination of PMU. The predicted effects are confirmed by the analysis of geodetic VLBI observations.

Integration opportunities for HIV and family planning services in Addis Ababa, Ethiopia: an organizational network analysis

PubMed Central

2014-01-01

Background Public health resources are often deployed in developing countries by foreign governments, national governments, civil society and the private health clinics, but seldom in ways that are coordinated within a particular community or population. The lack of coordination results in inefficiencies and suboptimal results. Organizational network analysis can reveal how organizations interact with each other and provide insights into means of realizing better public health results from the resources already deployed. Our objective in this study was to identify the missed opportunities for the integration of HIV care and family planning services and to inform future network strengthening. Methods In two sub-cities of Addis Ababa, we identified each organization providing either HIV care or family planning services. We interviewed representatives of each of them about exchanges of clients with each of the others. With network analysis, we identified network characteristics in each sub-city network, such as referral density and centrality; and gaps in the referral patterns. The results were shared with representatives from the organizations. Results The two networks were of similar size (25 and 26 organizations) and had referral densities of 0.115 and 0.155 out of a possible range from 0 (none) to 1.0 (all possible connections). Two organizations in one sub-city did not refer HIV clients to a family planning organization. One organization in one sub-city and seven in the other offered few HIV services and did not refer clients to any other HIV service provider. Representatives from the networks confirmed the results reflected their experience and expressed an interest in establishing more links between organizations. Conclusions Because of organizations not working together, women in the two sub-cities were at risk of not receiving needed family planning or HIV care services. Facilitating referrals among a few organizations that are most often working in isolation could remediate the problem, but the overall referral densities suggests that improved connections throughout might benefit conditions in addition to HIV and family planning that need service integration. PMID:24438522

Integration opportunities for HIV and family planning services in Addis Ababa, Ethiopia: an organizational network analysis.

PubMed

Thomas, James C; Reynolds, Heidi; Bevc, Christine; Tsegaye, Ademe

2014-01-18

Public health resources are often deployed in developing countries by foreign governments, national governments, civil society and the private health clinics, but seldom in ways that are coordinated within a particular community or population. The lack of coordination results in inefficiencies and suboptimal results. Organizational network analysis can reveal how organizations interact with each other and provide insights into means of realizing better public health results from the resources already deployed. Our objective in this study was to identify the missed opportunities for the integration of HIV care and family planning services and to inform future network strengthening. In two sub-cities of Addis Ababa, we identified each organization providing either HIV care or family planning services. We interviewed representatives of each of them about exchanges of clients with each of the others. With network analysis, we identified network characteristics in each sub-city network, such as referral density and centrality; and gaps in the referral patterns. The results were shared with representatives from the organizations. The two networks were of similar size (25 and 26 organizations) and had referral densities of 0.115 and 0.155 out of a possible range from 0 (none) to 1.0 (all possible connections). Two organizations in one sub-city did not refer HIV clients to a family planning organization. One organization in one sub-city and seven in the other offered few HIV services and did not refer clients to any other HIV service provider. Representatives from the networks confirmed the results reflected their experience and expressed an interest in establishing more links between organizations. Because of organizations not working together, women in the two sub-cities were at risk of not receiving needed family planning or HIV care services. Facilitating referrals among a few organizations that are most often working in isolation could remediate the problem, but the overall referral densities suggests that improved connections throughout might benefit conditions in addition to HIV and family planning that need service integration.

Attractor controllability of Boolean networks by flipping a subset of their nodes

NASA Astrophysics Data System (ADS)

Rafimanzelat, Mohammad Reza; Bahrami, Fariba

2018-04-01

The controllability analysis of Boolean networks (BNs), as models of biomolecular regulatory networks, has drawn the attention of researchers in recent years. In this paper, we aim at governing the steady-state behavior of BNs using an intervention method which can easily be applied to most real system, which can be modeled as BNs, particularly to biomolecular regulatory networks. To this end, we introduce the concept of attractor controllability of a BN by flipping a subset of its nodes, as the possibility of making a BN converge from any of its attractors to any other one, by one-time flipping members of a subset of BN nodes. Our approach is based on the algebraic state-space representation of BNs using semi-tensor product of matrices. After introducing some new matrix tools, we use them to derive necessary and sufficient conditions for the attractor controllability of BNs. A forward search algorithm is then suggested to identify the minimal perturbation set for attractor controllability of a BN. Next, a lower bound is derived for the cardinality of this set. Two new indices are also proposed for quantifying the attractor controllability of a BN and the influence of each network variable on the attractor controllability of the network and the relationship between them is revealed. Finally, we confirm the efficiency of the proposed approach by applying it to the BN models of some real biomolecular networks.

Inferring and analysis of social networks using RFID check-in data in China

PubMed Central

Liu, Tao; Liu, Shouyin; Ge, Shuangkui

2017-01-01

Social networks play an important role in our daily lives. However, social ties are rather elusive to quantify, especially for large groups of subjects over prolonged periods of time. In this work, we first propose a methodology for extracting social ties from long spatio-temporal data streams, where the subjects are 17,795 undergraduates from a university of China and the data streams are the 9,147,106 time-stamped RFID check-in records left behind by them during one academic year. By several metrics mentioned below, we then analyze the structure of the social network. Our results center around three main observations. First, we characterize the global structure of the network, and we confirm the small-world phenomenon on a global scale. Second, we find that the network shows clear community structure. And we observe that younger students at lower levels tend to form large communities, while students at higher levels mostly form smaller communities. Third, we characterize the assortativity patterns by studying the basic demographic and network properties of users. We observe clear degree assortativity on a global scale. Furthermore, we find a strong effect of grade and school on tie formation preference, but we do not find any strong region homophily. Our research may help us to elucidate the structural characteristics and the preference of the formation of social ties in college students’ social network. PMID:28570586

Identification of neuron-related genes for cell therapy of neurological disorders by network analysis.

PubMed

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.

The strong ground motion in Mexico City: array and borehole data analysis.

NASA Astrophysics Data System (ADS)

Roullé, A.; Chávez-García, F. J.

2003-04-01

Site response at Mexico City has been intensively studied for the last 15 years, since the disastrous 1985 earthquakes. After those events, more than 100 accelerographs were installed, and their data have been extremely useful in quantifying amplification and in the subsequent upgrading of the building code. However, detailed analysis of the wavefield has been hampered by the lack of absolute time in the records and the large spacing between stations in terms of dominant wavelengths. In 2001, thanks to the support of CONACYT, Mexico, a new dense accelerographic network was installed in the lake bed zone of Mexico City. The entire network, including an existing network of 3 surface and 2 borehole stations operated by CENAPRED, consists in 12 surface and 4 borehole stations (at 30, 102 and 50 meters). Each station has a 18 bits recorder and a GPS receiver so that the complete network is a 3D array with absolute time. The main objective of this array is to provide data that can help us to better understand the wavefield that propagates in Mexico City during large earthquakes. Last year, a small event of magnitude 6.0 was partially recorded by 6 of the 12 surface stations and all the borehole stations. We analysed the surface data using different array processing techniques such as f-k methods and MUSIC algorithm and the borehole ones using a cross-correlation method. For periods inferior to the site resonance period, the soft clay layer with very low propagation velocities (less than 500 m/s) and a possible multipathing rule the wavefield pattern. For the large period range, the dominant surface wave comes from the epicentral direction and propagates with a quicker velocity (more than 1500 m/s) that corresponds to the velocity of deep layers. The analysis of borehole data shows the presence of different quick wavetrains in the short period range that could correspond to the first harmonic modes of Rayleigh waves. To complete this study, four others events recorded in 1994 by a temporal dense network installed in the firm rock zone of Mexico City were analysed using the same techniques. The results confirm the presence of a diffracting zone south of the valley. These results confirm the hypothesis of a possible interaction between the soft clay layers resonance and diffracted wavetrains of Rayleigh waves to explain both the amplification and the long duration of strong ground motion in Mexico City.

Metabolic pathway analysis and kinetic studies for production of nattokinase in Bacillus subtilis.

PubMed

Unrean, Pornkamol; Nguyen, Nhung H A

2013-01-01

We have constructed a reaction network model of Bacillus subtilis. The model was analyzed using a pathway analysis tool called elementary mode analysis (EMA). The analysis tool was used to study the network capabilities and the possible effects of altered culturing conditions on the production of a fibrinolytic enzyme, nattokinase (NK) by B. subtilis. Based on all existing metabolic pathways, the maximum theoretical yield for NK synthesis in B. subtilis under different substrates and oxygen availability was predicted and the optimal culturing condition for NK production was identified. To confirm model predictions, experiments were conducted by testing these culture conditions for their influence on NK activity. The optimal culturing conditions were then applied to batch fermentation, resulting in high NK activity. The EMA approach was also applied for engineering B. subtilis metabolism towards the most efficient pathway for NK synthesis by identifying target genes for deletion and overexpression that enable the cell to produce NK at the maximum theoretical yield. The consistency between experiments and model predictions proves the feasibility of EMA being used to rationally design culture conditions and genetic manipulations for the efficient production of desired products.

Systems Genetics Analysis of Genome-Wide Association Study Reveals Novel Associations Between Key Biological Processes and Coronary Artery Disease.

PubMed

Ghosh, Sujoy; Vivar, Juan; Nelson, Christopher P; Willenborg, Christina; Segrè, Ayellet V; Mäkinen, Ville-Petteri; Nikpay, Majid; Erdmann, Jeannette; Blankenberg, Stefan; O'Donnell, Christopher; März, Winfried; Laaksonen, Reijo; Stewart, Alexandre F R; Epstein, Stephen E; Shah, Svati H; Granger, Christopher B; Hazen, Stanley L; Kathiresan, Sekar; Reilly, Muredach P; Yang, Xia; Quertermous, Thomas; Samani, Nilesh J; Schunkert, Heribert; Assimes, Themistocles L; McPherson, Ruth

2015-07-01

Genome-wide association studies have identified multiple genetic variants affecting the risk of coronary artery disease (CAD). However, individually these explain only a small fraction of the heritability of CAD and for most, the causal biological mechanisms remain unclear. We sought to obtain further insights into potential causal processes of CAD by integrating large-scale GWA data with expertly curated databases of core human pathways and functional networks. Using pathways (gene sets) from Reactome, we carried out a 2-stage gene set enrichment analysis strategy. From a meta-analyzed discovery cohort of 7 CAD genome-wide association study data sets (9889 cases/11 089 controls), nominally significant gene sets were tested for replication in a meta-analysis of 9 additional studies (15 502 cases/55 730 controls) from the Coronary ARtery DIsease Genome wide Replication and Meta-analysis (CARDIoGRAM) Consortium. A total of 32 of 639 Reactome pathways tested showed convincing association with CAD (replication P<0.05). These pathways resided in 9 of 21 core biological processes represented in Reactome, and included pathways relevant to extracellular matrix (ECM) integrity, innate immunity, axon guidance, and signaling by PDRF (platelet-derived growth factor), NOTCH, and the transforming growth factor-β/SMAD receptor complex. Many of these pathways had strengths of association comparable to those observed in lipid transport pathways. Network analysis of unique genes within the replicated pathways further revealed several interconnected functional and topologically interacting modules representing novel associations (eg, semaphoring-regulated axonal guidance pathway) besides confirming known processes (lipid metabolism). The connectivity in the observed networks was statistically significant compared with random networks (P<0.001). Network centrality analysis (degree and betweenness) further identified genes (eg, NCAM1, FYN, FURIN, etc) likely to play critical roles in the maintenance and functioning of several of the replicated pathways. These findings provide novel insights into how genetic variation, interpreted in the context of biological processes and functional interactions among genes, may help define the genetic architecture of CAD. © 2015 American Heart Association, Inc.

Starch functionalized biodegradable semi-IPN as a pH-tunable controlled release platform for memantine.

PubMed

Ganguly, Sayan; Maity, Tushar; Mondal, Subhadip; Das, Poushali; Das, Narayan C

2017-02-01

Sequentially prepared semi-interpenetrating polymer network (semi-IPN) has been developed here via Michael type addition of acrylic acid (AA) and 2-acrylamido-2-methylpropane sulfonic acid (AMPS) on to starch. The semi-IPN hydrogel have proficiency in fast water imbibition towards gel network and swelling tunable character with pH alteration in ambient condition. The synthesized gel has been characterized by Fourier transformed infrared spectroscopy (FTIR) to confirm Michael type grafting of monomers on to starch. The surface morphology, observed from Scanning Electron Microscopy (SEM) exhibited corrugated rough surface on hydrogel which enhances the fast water uptake feature by anomalous Fickian case II diffusion mechanism. Grafting reaction also improves its thermal stability which has been confirmed by thermogravimetric analysis (TGA). Biodegradation study with hen egg lysozyme medium reveals the accelerated enzymatic scission of the starch backbone and progressive mass loss. Degradation of the hydrogel around 60% of its primary mass has been observed within 7days. The physicochemical characterizations of this hydrogel suggest this as a promising pH-tunable, biodegradable candidate for control drug delivery vehicle. Copyright © 2016 Elsevier B.V. All rights reserved.

Statistical analysis of the pulse-coupled synchronization strategy for wireless sensor networks

PubMed Central

Wang, Yongqiang; Núñez, Felipe; Doyle, Francis J.

2013-01-01

Pulse-coupled synchronization is attracting increased attention in the sensor network community. Yet its properties have not been fully investigated. Using statistical analysis, we prove analytically that by controlling the number of connections at each node, synchronization can be guaranteed for generally pulse-coupled oscillators even in the presence of a refractory period. The approach does not require the initial phases to reside in half an oscillation cycle, which improves existing results. We also find that a refractory period can be strategically included to reduce idle listening at nearly no sacrifice to the synchronization probability. Given that reduced idle listening leads to higher energy efficiency in the synchronization process, the strategically added refractory period makes the synchronization scheme appealing to cheap sensor nodes, where energy is a precious system resource. We also analyzed the pulse-coupled synchronization in the presence of unreliable communication links and obtained similar results. QualNet experimental results are given to confirm the effectiveness of the theoretical predictions. PMID:24324322

Construction and validation of a scale of assessment of self-care behaviours anticipatory to creation of arteriovenous fistula.

PubMed

Sousa, Clemente Neves; Figueiredo, Maria Henriqueta; Dias, Vanessa Filipa; Teles, Paulo; Apóstolo, João Luís

2015-12-01

We developed a scale to assess the self-care behaviours developed by patients with end-stage renal disease to preserve the vascular network prior to construction of arteriovenous fistula. The possibility of creation of an arteriovenous fistula depends on the existence of an arterial and venous network in good condition, namely the size and elasticity of the vessels. It is essential to teach the person to develop self-care behaviours for the preservation of the vascular network, regardless of the modality of dialysis selected. Methodological study. The scale was developed based on clinical experience and research conducted by the researcher in the area of the vascular access for haemodialysis. The content of the scale was judged by two panels of experts for content validity. The revised version of the scale was administered to a convenience sample of 90 patients with end-stage renal disease. In the statistical analysis, we used the Cronbach's alpha, the Kaiser-Meyer-Olkin and scree plot and the principal component analysis with varimax rotation. A principal component analysis confirmed the univariate structure of the scale (KMO = 0·759, Bartlett's sphericity test-approximate χ(2) 142·201, p < 0·000). Cronbach's α is 0·831, varying between 0·711-0·879. This scale revealed properties that allow its use to assess the patients self-care behaviours regarding the preservation of the vascular network. This scale can be used to evaluate educational programmes for the development of self-care behaviours in the preservation of vascular network. This scale can identify not only the patients that are able to take care of their vascular network but also the proportion of patients who are not able to do it, that need to be educated. © 2015 John Wiley & Sons Ltd.

The Human Blood Metabolome-Transcriptome Interface

PubMed Central

Schramm, Katharina; Adamski, Jerzy; Gieger, Christian; Herder, Christian; Carstensen, Maren; Peters, Annette; Rathmann, Wolfgang; Roden, Michael; Strauch, Konstantin; Suhre, Karsten; Kastenmüller, Gabi; Prokisch, Holger; Theis, Fabian J.

2015-01-01

Biological systems consist of multiple organizational levels all densely interacting with each other to ensure function and flexibility of the system. Simultaneous analysis of cross-sectional multi-omics data from large population studies is a powerful tool to comprehensively characterize the underlying molecular mechanisms on a physiological scale. In this study, we systematically analyzed the relationship between fasting serum metabolomics and whole blood transcriptomics data from 712 individuals of the German KORA F4 cohort. Correlation-based analysis identified 1,109 significant associations between 522 transcripts and 114 metabolites summarized in an integrated network, the ‘human blood metabolome-transcriptome interface’ (BMTI). Bidirectional causality analysis using Mendelian randomization did not yield any statistically significant causal associations between transcripts and metabolites. A knowledge-based interpretation and integration with a genome-scale human metabolic reconstruction revealed systematic signatures of signaling, transport and metabolic processes, i.e. metabolic reactions mainly belonging to lipid, energy and amino acid metabolism. Moreover, the construction of a network based on functional categories illustrated the cross-talk between the biological layers at a pathway level. Using a transcription factor binding site enrichment analysis, this pathway cross-talk was further confirmed at a regulatory level. Finally, we demonstrated how the constructed networks can be used to gain novel insights into molecular mechanisms associated to intermediate clinical traits. Overall, our results demonstrate the utility of a multi-omics integrative approach to understand the molecular mechanisms underlying both normal physiology and disease. PMID:26086077

Substrates of metacognition on perception and metacognition on higher-order cognition relate to different subsystems of the mentalizing network.

PubMed

Valk, Sofie L; Bernhardt, Boris C; Böckler, Anne; Kanske, Philipp; Singer, Tania

2016-10-01

Humans have the ability to reflect upon their perception, thoughts, and actions, known as metacognition (MC). The brain basis of MC is incompletely understood, and it is debated whether MC on different processes is subserved by common or divergent networks. We combined behavioral phenotyping with multi-modal neuroimaging to investigate whether structural substrates of individual differences in MC on higher-order cognition (MC-C) are dissociable from those underlying MC on perceptual accuracy (MC-P). Motivated by conceptual work suggesting a link between MC and cognitive perspective taking, we furthermore tested for overlaps between MC substrates and mentalizing networks. In a large sample of healthy adults, individual differences in MC-C and MC-P did not correlate. MRI-based cortical thickness mapping revealed a structural basis of this independence, by showing that individual differences in MC-P related to right prefrontal cortical thickness, while MC-C scores correlated with measures in lateral prefrontal, temporo-parietal, and posterior midline regions. Surface-based superficial white matter diffusivity analysis revealed substrates resembling those seen for cortical thickness, confirming the divergence of both MC faculties using an independent imaging marker. Despite their specificity, substrates of MC-C and MC-P fell clearly within networks known to participate in mentalizing, confirmed by task-based fMRI in the same subjects, previous meta-analytical findings, and ad-hoc Neurosynth-based meta-analyses. Our integrative multi-method approach indicates domain-specific substrates of MC; despite their divergence, these nevertheless likely rely on component processes mediated by circuits also involved in mentalizing. Hum Brain Mapp 37:3388-3399, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Detecting recurrent gene mutation in interaction network context using multi-scale graph diffusion.

PubMed

Babaei, Sepideh; Hulsman, Marc; Reinders, Marcel; de Ridder, Jeroen

2013-01-23

Delineating the molecular drivers of cancer, i.e. determining cancer genes and the pathways which they deregulate, is an important challenge in cancer research. In this study, we aim to identify pathways of frequently mutated genes by exploiting their network neighborhood encoded in the protein-protein interaction network. To this end, we introduce a multi-scale diffusion kernel and apply it to a large collection of murine retroviral insertional mutagenesis data. The diffusion strength plays the role of scale parameter, determining the size of the network neighborhood that is taken into account. As a result, in addition to detecting genes with frequent mutations in their genomic vicinity, we find genes that harbor frequent mutations in their interaction network context. We identify densely connected components of known and putatively novel cancer genes and demonstrate that they are strongly enriched for cancer related pathways across the diffusion scales. Moreover, the mutations in the clusters exhibit a significant pattern of mutual exclusion, supporting the conjecture that such genes are functionally linked. Using multi-scale diffusion kernel, various infrequently mutated genes are found to harbor significant numbers of mutations in their interaction network neighborhood. Many of them are well-known cancer genes. The results demonstrate the importance of defining recurrent mutations while taking into account the interaction network context. Importantly, the putative cancer genes and networks detected in this study are found to be significant at different diffusion scales, confirming the necessity of a multi-scale analysis.

Network inference reveals novel connections in pathways regulating growth and defense in the yeast salt response.

PubMed

MacGilvray, Matthew E; Shishkova, Evgenia; Chasman, Deborah; Place, Michael; Gitter, Anthony; Coon, Joshua J; Gasch, Audrey P

2018-05-01

Cells respond to stressful conditions by coordinating a complex, multi-faceted response that spans many levels of physiology. Much of the response is coordinated by changes in protein phosphorylation. Although the regulators of transcriptome changes during stress are well characterized in Saccharomyces cerevisiae, the upstream regulatory network controlling protein phosphorylation is less well dissected. Here, we developed a computational approach to infer the signaling network that regulates phosphorylation changes in response to salt stress. We developed an approach to link predicted regulators to groups of likely co-regulated phospho-peptides responding to stress, thereby creating new edges in a background protein interaction network. We then use integer linear programming (ILP) to integrate wild type and mutant phospho-proteomic data and predict the network controlling stress-activated phospho-proteomic changes. The network we inferred predicted new regulatory connections between stress-activated and growth-regulating pathways and suggested mechanisms coordinating metabolism, cell-cycle progression, and growth during stress. We confirmed several network predictions with co-immunoprecipitations coupled with mass-spectrometry protein identification and mutant phospho-proteomic analysis. Results show that the cAMP-phosphodiesterase Pde2 physically interacts with many stress-regulated transcription factors targeted by PKA, and that reduced phosphorylation of those factors during stress requires the Rck2 kinase that we show physically interacts with Pde2. Together, our work shows how a high-quality computational network model can facilitate discovery of new pathway interactions during osmotic stress.

Networks of volatility spillovers among stock markets

NASA Astrophysics Data System (ADS)

Baumöhl, Eduard; Kočenda, Evžen; Lyócsa, Štefan; Výrost, Tomáš

2018-01-01

In our network analysis of 40 developed, emerging and frontier stock markets during the 2006-2014 period, we describe and model volatility spillovers during both the global financial crisis and tranquil periods. The resulting market interconnectedness is depicted by fitting a spatial model incorporating several exogenous characteristics. We document the presence of significant temporal proximity effects between markets and somewhat weaker temporal effects with regard to the US equity market - volatility spillovers decrease when markets are characterized by greater temporal proximity. Volatility spillovers also present a high degree of interconnectedness, which is measured by high spatial autocorrelation. This finding is confirmed by spatial regression models showing that indirect effects are much stronger than direct effects; i.e., market-related changes in 'neighboring' markets (within a network) affect volatility spillovers more than changes in the given market alone, suggesting that spatial effects simply cannot be ignored when modeling stock market relationships. Our results also link spillovers of escalating magnitude with increasing market size, market liquidity and economic openness.

Transforming Big Data into cancer-relevant insight: An initial, multi-tier approach to assess reproducibility and relevance

PubMed Central

2016-01-01

The Cancer Target Discovery and Development (CTD2) Network was established to accelerate the transformation of “Big Data” into novel pharmacological targets, lead compounds, and biomarkers for rapid translation into improved patient outcomes. It rapidly became clear in this collaborative network that a key central issue was to define what constitutes sufficient computational or experimental evidence to support a biologically or clinically relevant finding. This manuscript represents a first attempt to delineate the challenges of supporting and confirming discoveries arising from the systematic analysis of large-scale data resources in a collaborative work environment and to provide a framework that would begin a community discussion to resolve these challenges. The Network implemented a multi-Tier framework designed to substantiate the biological and biomedical relevance as well as the reproducibility of data and insights resulting from its collaborative activities. The same approach can be used by the broad scientific community to drive development of novel therapeutic and biomarker strategies for cancer. PMID:27401613

Construction of an integrated gene regulatory network link to stress-related immune system in cattle.

PubMed

Behdani, Elham; Bakhtiarizadeh, Mohammad Reza

2017-10-01

The immune system is an important biological system that is negatively impacted by stress. This study constructed an integrated regulatory network to enhance our understanding of the regulatory gene network used in the stress-related immune system. Module inference was used to construct modules of co-expressed genes with bovine leukocyte RNA-Seq data. Transcription factors (TFs) were then assigned to these modules using Lemon-Tree algorithms. In addition, the TFs assigned to each module were confirmed using the promoter analysis and protein-protein interactions data. Therefore, our integrated method identified three TFs which include one TF that is previously known to be involved in immune response (MYBL2) and two TFs (E2F8 and FOXS1) that had not been recognized previously and were identified for the first time in this study as novel regulatory candidates in immune response. This study provides valuable insights on the regulatory programs of genes involved in the stress-related immune system.

Network reconstruction of platelet metabolism identifies metabolic signature for aspirin resistance

NASA Astrophysics Data System (ADS)

Thomas, Alex; Rahmanian, Sorena; Bordbar, Aarash; Palsson, Bernhard Ø.; Jamshidi, Neema

2014-01-01

Recently there has not been a systematic, objective assessment of the metabolic capabilities of the human platelet. A manually curated, functionally tested, and validated biochemical reaction network of platelet metabolism, iAT-PLT-636, was reconstructed using 33 proteomic datasets and 354 literature references. The network contains enzymes mapping to 403 diseases and 231 FDA approved drugs, alluding to an expansive scope of biochemical transformations that may affect or be affected by disease processes in multiple organ systems. The effect of aspirin (ASA) resistance on platelet metabolism was evaluated using constraint-based modeling, which revealed a redirection of glycolytic, fatty acid, and nucleotide metabolism reaction fluxes in order to accommodate eicosanoid synthesis and reactive oxygen species stress. These results were confirmed with independent proteomic data. The construction and availability of iAT-PLT-636 should stimulate further data-driven, systems analysis of platelet metabolism towards the understanding of pathophysiological conditions including, but not strictly limited to, coagulopathies.

Closed loop supply chain network design with fuzzy tactical decisions

NASA Astrophysics Data System (ADS)

Sherafati, Mahtab; Bashiri, Mahdi

2016-09-01

One of the most strategic and the most significant decisions in supply chain management is reconfiguration of the structure and design of the supply chain network. In this paper, a closed loop supply chain network design model is presented to select the best tactical and strategic decision levels simultaneously considering the appropriate transportation mode in activated links. The strategic decisions are made for a long term; thus, it is more satisfactory and more appropriate when the decision variables are considered uncertain and fuzzy, because it is more flexible and near to the real world. This paper is the first research which considers fuzzy decision variables in the supply chain network design model. Moreover, in this study a new fuzzy optimization approach is proposed to solve a supply chain network design problem with fuzzy tactical decision variables. Finally, the proposed approach and model are verified using several numerical examples. The comparison of the results with other existing approaches confirms efficiency of the proposed approach. Moreover the results confirms that by considering the vagueness of tactical decisions some properties of the supply chain network will be improved.

The Use of RNA Sequencing and Correlation Network Analysis to Study Potential Regulators of Crabapple Leaf Color Transformation.

PubMed

Yang, Tuo; Li, Keting; Hao, Suxiao; Zhang, Jie; Song, Tingting; Tian, Ji; Yao, Yuncong

2018-05-01

Anthocyanins are plant pigments that contribute to the color of leaves, flowers and fruits, and that are beneficial to human health in the form of dietary antioxidants. The study of a transformable crabapple cultivar, 'India magic', which has red buds and green mature leaves, using mRNA profiling of four leaf developmental stages, allowed us to characterize molecular mechanisms regulating red color formation in early leaf development and the subsequent rapid down-regulation of anthocyanin biosynthesis. This analysis of differential gene expression during leaf development revealed that ethylene signaling-responsive genes are up-regulated during leaf pigmentation. Genes in the ethylene response factor (ERF), SPL, NAC, WRKY and MADS-box transcription factor (TF) families were identified in two weighted gene co-expression network analysis (WGCNA) modules as having a close relationship to anthocyanin accumulation. Analyses of network hub genes indicated that SPL TFs are located in central positions within anthocyanin-related modules. Furthermore, cis-motif and yeast one-hybrid assays suggested that several anthocyanin biosynthetic or regulatory genes are potential targets of SPL8 and SPL13B. Transient silencing of these two genes confirmed that they play a role in co-ordinating anthocyanin biosynthesis and crabapple leaf development. We present a high-resolution method for identifying regulatory modules associated with leaf pigmentation, which provides a platform for functional genomic studies of anthocyanin biosynthesis.

Meditation-related activations are modulated by the practices needed to obtain it and by the expertise: an ALE meta-analysis study

PubMed Central

Tomasino, Barbara; Fregona, Sara; Skrap, Miran; Fabbro, Franco

2013-01-01

The brain network governing meditation has been studied using a variety of meditation practices and techniques practices eliciting different cognitive processes (e.g., silence, attention to own body, sense of joy, mantras, etc.). It is very possible that different practices of meditation are subserved by largely, if not entirely, disparate brain networks. This assumption was tested by conducting an activation likelihood estimation (ALE) meta-analysis of meditation neuroimaging studies, which assessed 150 activation foci from 24 experiments. Different ALE meta-analyses were carried out. One involved the subsets of studies involving meditation induced through exercising focused attention (FA). The network included clusters bilaterally in the medial gyrus, the left superior parietal lobe, the left insula and the right supramarginal gyrus (SMG). A second analysis addressed the studies involving meditation states induced by chanting or by repetition of words or phrases, known as “mantra.” This type of practice elicited a cluster of activity in the right SMG, the SMA bilaterally and the left postcentral gyrus. Furthermore, the last analyses addressed the effect of meditation experience (i.e., short- vs. long-term meditators). We found that frontal activation was present for short-term, as compared with long-term experience meditators, confirming that experts are better enabled to sustain attentional focus, rather recruiting the right SMG and concentrating on aspects involving disembodiment. PMID:23316154

Identification and comparison of aberrant key regulatory networks in breast, colon, liver, lung, and stomach cancers through methylome database analysis.

PubMed

Kim, Byungtak; Kang, Seongeun; Jeong, Gookjoo; Park, Sung-Bin; Kim, Sun Jung

2014-01-01

Aberrant methylation of specific CpG sites at the promoter is widely responsible for genesis and development of various cancer types. Even though the microarray-based methylome analyzing techniques have contributed to the elucidation of the methylation change at the genome-wide level, the identification of key methylation markers or top regulatory networks appearing common in highly incident cancers through comparison analysis is still limited. In this study, we in silico performed the genome-wide methylation analysis on each 10 sets of normal and cancer pairs of five tissues: breast, colon, liver, lung, and stomach. The methylation array covers 27,578 CpG sites, corresponding to 14,495 genes, and significantly hypermethylated or hypomethylated genes in the cancer were collected (FDR adjusted p-value <0.05; methylation difference >0.3). Analysis of the dataset confirmed the methylation of previously known methylation markers and further identified novel methylation markers, such as GPX2, CLDN15, and KL. Cluster analysis using the methylome dataset resulted in a diagram with a bipartite mode distinguishing cancer cells from normal cells regardless of tissue types. The analysis further revealed that breast cancer was closest with lung cancer, whereas it was farthest from colon cancer. Pathway analysis identified that either the "cancer" related network or the "cancer" related bio-function appeared as the highest confidence in all the five cancers, whereas each cancer type represents its tissue-specific gene sets. Our results contribute toward understanding the essential abnormal epigenetic pathways involved in carcinogenesis. Further, the novel methylation markers could be applied to establish markers for cancer prognosis.

A common functional neural network for overt production of speech and gesture.

PubMed

Marstaller, L; Burianová, H

2015-01-22

The perception of co-speech gestures, i.e., hand movements that co-occur with speech, has been investigated by several studies. The results show that the perception of co-speech gestures engages a core set of frontal, temporal, and parietal areas. However, no study has yet investigated the neural processes underlying the production of co-speech gestures. Specifically, it remains an open question whether Broca's area is central to the coordination of speech and gestures as has been suggested previously. The objective of this study was to use functional magnetic resonance imaging to (i) investigate the regional activations underlying overt production of speech, gestures, and co-speech gestures, and (ii) examine functional connectivity with Broca's area. We hypothesized that co-speech gesture production would activate frontal, temporal, and parietal regions that are similar to areas previously found during co-speech gesture perception and that both speech and gesture as well as co-speech gesture production would engage a neural network connected to Broca's area. Whole-brain analysis confirmed our hypothesis and showed that co-speech gesturing did engage brain areas that form part of networks known to subserve language and gesture. Functional connectivity analysis further revealed a functional network connected to Broca's area that is common to speech, gesture, and co-speech gesture production. This network consists of brain areas that play essential roles in motor control, suggesting that the coordination of speech and gesture is mediated by a shared motor control network. Our findings thus lend support to the idea that speech can influence co-speech gesture production on a motoric level. Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

Resting-state brain networks in patients with Parkinson's disease and impulse control disorders.

PubMed

Tessitore, Alessandro; Santangelo, Gabriella; De Micco, Rosa; Giordano, Alfonso; Raimo, Simona; Amboni, Marianna; Esposito, Fabrizio; Barone, Paolo; Tedeschi, Gioacchino; Vitale, Carmine

2017-09-01

To investigate intrinsic neural networks connectivity changes in Parkinson's disease (PD) patients with and without impulse control disorders (ICD). Fifteen patients with PD with ICD (ICD+), 15 patients with PD without ICD (ICD-) and 24 age and sex-matched healthy controls (HC) were enrolled in the study. To identify patients with and without ICD and/or punding, we used the Minnesota Impulsive Disorders Interview (MIDI) and a clinical interview based on diagnostic criteria for each symptom. All patients underwent a detailed neuropsychological evaluation. Whole brain structural and functional imaging was performed on a 3T GE MR scanner. Statistical analysis of functional data was completed using BrainVoyager QX software. Voxel-based morphometry (VBM) was used to test whether between-group differences in resting-state connectivity were related to structural abnormalities. The presence of ICD symptoms was associated with an increased connectivity within the salience and default-mode networks, as well as with a decreased connectivity within the central executive network (p < .05 corrected). ICD severity was correlated with both salience and default mode networks connectivity changes only in the ICD+ group. VBM analysis did not reveal any statistically significant differences in local grey matter volume between ICD+ and ICD- patients and between all patients and HC (p < .05. FWE). The presence of a disrupted connectivity within the three core neurocognitive networks may be considered as a potential neural correlate of ICD presence in patients with PD. Our findings provide additional insights into the mechanisms underlying ICD in PD, confirming the crucial role of an abnormal prefrontal-limbic-striatal homeostasis in their development. Copyright © 2017 Elsevier Ltd. All rights reserved.

Vulnerability of countries to food-production crises propagating in the virtual water trade network

NASA Astrophysics Data System (ADS)

Tamea, S.; Laio, F.; Ridolfi, L.

2015-12-01

In recent years, the international trade of food and agricultural commodities has undergone a marked increase of exchanged volumes and an expansion of the trade network. This globalization of trade has both positive and negative effects, but the interconnectedness and external dependency of countries generate complex dynamics which are often difficult to understand and model. In this study we consider the volume of water used for the production of agricultural commodities, virtually exchanged among countries through commodity trade, i.e. the virtual water trade. Then, we set up a parsimonious mechanistic model describing the propagation, into the global trade network, of food-production crises generated locally by a social, economic or environmental event (such as war, economic crisis, drought, pest). The model, accounting for the network structure and the virtual water balance of all countries, bases on rules derived from observed virtual water flows and on data-based and statistically verified assumption. It is also tested on real case studies that prove its capability to capture the main features of crises propagation. The model is then employed as the basis for the development of an index of country vulnerability, measuring the exposure of countries to crises propagating in the virtual water trade network. Results of the analysis are discussed within the context of socio-economic and environmental conditions of countries, showing that not only water-scarce, but also wealthy and globalized countries, are among the most vulnerable to external crises. The temporal analysis for the period 1986-2011 reveals that the global average vulnerability has strongly increased over time, confirming the increased exposure of countries to external crises which may occur in the virtual water trade network.

Widespread receptivity to neuropeptide PDF throughout the neuronal circadian clock network of Drosophila revealed by real-time cyclic AMP imaging.

PubMed

Shafer, Orie T; Kim, Dong Jo; Dunbar-Yaffe, Richard; Nikolaev, Viacheslav O; Lohse, Martin J; Taghert, Paul H

2008-04-24

The neuropeptide PDF is released by sixteen clock neurons in Drosophila and helps maintain circadian activity rhythms by coordinating a network of approximately 150 neuronal clocks. Whether PDF acts directly on elements of this neural network remains unknown. We address this question by adapting Epac1-camps, a genetically encoded cAMP FRET sensor, for use in the living brain. We find that a subset of the PDF-expressing neurons respond to PDF with long-lasting cAMP increases and confirm that such responses require the PDF receptor. In contrast, an unrelated Drosophila neuropeptide, DH31, stimulates large cAMP increases in all PDF-expressing clock neurons. Thus, the network of approximately 150 clock neurons displays widespread, though not uniform, PDF receptivity. This work introduces a sensitive means of measuring cAMP changes in a living brain with subcellular resolution. Specifically, it experimentally confirms the longstanding hypothesis that PDF is a direct modulator of most neurons in the Drosophila clock network.

Alginate-hydroxypropylcellulose hydrogel microbeads for alkaline phosphatase encapsulation.

PubMed

Karewicz, A; Zasada, K; Bielska, D; Douglas, T E L; Jansen, J A; Leeuwenburgh, S C G; Nowakowska, M

2014-01-01

There is a growing interest in using proteins as therapeutics agents. Unfortunately, they suffer from limited stability and bioavailability. We aimed to develop a new delivery system for proteins. ALP, a model protein, was successfully encapsulated in the physically cross-linked sodium alginate/hydroxypropylcellulose (ALG-HPC) hydrogel microparticles. The obtained objects had regular, spherical shape and a diameter of ∼4 µm, as confirmed by optical microscopy and SEM analysis. The properties of the obtained microbeads could be controlled by temperature and additional coating or crosslinking procedures. The slow, sustained release of ALP in its active form with no initial burst effect was observed for chitosan-coated microspheres at pH = 7.4 and 37 °C. Activity of ALP released from ALG/HPC microspheres was confirmed by the occurance of effectively induced mineralization. SEM and AFM images revealed formation of the interpenetrated three-dimensional network of mineral, originating from the microbeads' surfaces. FTIR and XRD analyses confirmed formation of hydroxyapatite.

Validation of a Social Networks and Support Measurement Tool for Use in International Aging Research: The International Mobility in Aging Study.

PubMed

Ahmed, Tamer; Belanger, Emmanuelle; Vafaei, Afshin; Koné, Georges K; Alvarado, Beatriz; Béland, François; Zunzunegui, Maria Victoria

2018-03-01

The purpose of this study was to develop and validate a new instrument to assess social networks and social support (IMIAS-SNSS) for different types of social ties in an international sample of older adults. The study sample included n = 1995 community dwelling older people aged between 65 and 74 years from the baseline of the longitudinal International Mobility in Aging Study (IMIAS). In order to measure social networks for each type of social tie, participants were asked about the number of contacts, the number of contacts they see at least once a month or have a very good relationship with, or speak with at least once a month. For social support, participants had to rate the level of social support provided by the four types of contacts for five Likert scale items. Confirmatory Factor Analysis was conducted to determine the goodness of fit of the measurement models. Satisfactory goodness-of-fit indices confirmed the satisfactory factorial structure of the IMIAS-SNSS instrument. Reliability coefficients were 0.80, 0.81, 0.85, and 0.88 for friends, children, family, and partner models, respectively. The models were confirmed by CFA for each type of social tie. Moreover, IMIAS-SNSS detected gender differences in the older adult populations of IMIAS. These results provide evidence supporting that IMIAS-SNSS is a psychometrically sound instrument and of its validity and reliability for international populations of older adults.

Analysis of the status of pre-release cracks in prestressed concrete structures using long-gauge sensors

NASA Astrophysics Data System (ADS)

Abdel-Jaber, H.; Glisic, B.

2015-02-01

Prestressed structures experience limited tensile stresses in concrete, which limits or completely eliminates the occurrence of cracks. However, in some cases, large tensile stresses can develop during the early age of the concrete due to thermal gradients and shrinkage effects. Such stresses can cause early-age cracks, termed ‘pre-release cracks’, which occur prior to the transfer of the prestressing force. When the prestressing force is applied to the cross-section, it is assumed that partial or full closure of the cracks occurs by virtue of the force transfer through the cracked cross-section. Verification of the closure of the cracks after the application of the prestressing force is important as it can either confirm continued structural integrity or indicate and approximate reduced structural capacity. Structural health monitoring (SHM) can be used for this purpose. This paper researches an SHM method that can be applied to prestressed beam structures to assess the condition of pre-release cracks. The sensor network used in this method consists of parallel long-gauge fiber optic strain sensors embedded in the concrete cross-sections at various locations. The same network is used for damage detection, i.e. detection and characterization of the pre-release cracks, and for monitoring the prestress force transfer. The method is validated on a real structure, a curved continuous girder. Results from the analysis confirm the safety and integrity of the structure. The method and its application are presented in this paper.

An Energy-Aware Hybrid ARQ Scheme with Multi-ACKs for Data Sensing Wireless Sensor Networks.

PubMed

Zhang, Jinhuan; Long, Jun

2017-06-12

Wireless sensor networks (WSNs) are one of the important supporting technologies of edge computing. In WSNs, reliable communications are essential for most applications due to the unreliability of wireless links. In addition, network lifetime is also an important performance metric and needs to be considered in many WSN studies. In the paper, an energy-aware hybrid Automatic Repeat-reQuest protocol (ARQ) scheme is proposed to ensure energy efficiency under the guarantee of network transmission reliability. In the scheme, the source node sends data packets continuously with the correct window size and it does not need to wait for the acknowledgement (ACK) confirmation for each data packet. When the destination receives K data packets, it will return multiple copies of one ACK for confirmation to avoid ACK packet loss. The energy consumption of each node in flat circle network applying the proposed scheme is statistical analyzed and the cases under which it is more energy efficiency than the original scheme is discussed. Moreover, how to select parameters of the scheme is addressed to extend the network lifetime under the constraint of the network reliability. In addition, the energy efficiency of the proposed schemes is evaluated. Simulation results are presented to demonstrate that a node energy consumption reduction could be gained and the network lifetime is prolonged.

Limit of a nonpreferential attachment multitype network model

NASA Astrophysics Data System (ADS)

Shang, Yilun

2017-02-01

Here, we deal with a model of multitype network with nonpreferential attachment growth. The connection between two nodes depends asymmetrically on their types, reflecting the implication of time order in temporal networks. Based upon graph limit theory, we analytically determined the limit of the network model characterized by a kernel, in the sense that the number of copies of any fixed subgraph converges when network size tends to infinity. The results are confirmed by extensive simulations. Our work thus provides a theoretical framework for quantitatively understanding grown temporal complex networks as a whole.

Comparative efficacy of disease-modifying therapies for patients with relapsing remitting multiple sclerosis: Systematic review and network meta-analysis.

PubMed

Fogarty, Emer; Schmitz, Susanne; Tubridy, Niall; Walsh, Cathal; Barry, Michael

2016-09-01

Randomised studies have demonstrated efficacy of disease-modifying therapies in relapsing remitting multiple sclerosis (RRMS). However it is unclear how the magnitude of treatment efficacy varies across all currently available therapies. To perform a systematic review and network meta-analysis to evaluate the comparative efficacy of available therapies in reducing relapses and disability progression in RRMS. A systematic review identified 28 randomised, placebo-controlled and direct comparative trials. A network meta-analysis was conducted within a Bayesian framework to estimate comparative annualised relapse rates (ARR) and risks of disability progression (defined by both a 3-month, and 6-month confirmation interval). Potential sources of treatment-effect modification from study-level covariates and baseline risk were evaluated through meta-regression methods. The Surface Under the Cumulative RAnking curve (SUCRA) method was used to provide a ranking of treatments for each outcome. The magnitude of ARR reduction varied between 15-36% for all interferon-beta products, glatiramer acetate and teriflunomide, and from 50 to 69% for alemtuzumab, dimethyl fumarate, fingolimod and natalizumab. The risk of disability progression (3-month) was reduced by 19-28% with interferon-beta products, glatiramer acetate, fingolimod and teriflunomide, by 38-45% for pegylated interferon-beta, dimethyl fumarate and natalizumab and by 68% with alemtuzumab. Broadly similar estimates for the risk of disability progression (6-month), with the exception of interferon-beta-1b 250mcg which was much more efficacious based on this definition. Alemtuzumab and natalizumab had the highest SUCRA scores (97% and 95% respectively) for ARR, while ranking for disability progression varied depending on the definition of the outcome. Interferon-beta-1b 250mcg ranked among the most efficacious treatments for disability progression confirmed after six months (92%) and among the least efficacious when the outcome was confirmed after three months (30%). No significant modification of relative treatment effects was identified from study-level covariates or baseline risk. Compared with placebo, clear reductions in ARR with disease-modifying therapies were accompanied by more uncertain changes in disability progression. The magnitude of the reduction and the uncertainty associated with treatment effects varied between DMTs. While natalizumab and alemtuzumab demonstrated consistently high ranking across outcomes, with older interferon-beta and glatiramer acetate products ranking lowest, variation in disability progression definitions lead to variation in the relative ranking of treatments. Rigorously conducted comparative studies are required to fully evaluate the comparative treatment effects of disease modifying therapies for RRMS. Copyright © 2016 Elsevier B.V. All rights reserved.

Mining induced seismic event on an inactive fault in view of local surface and in mine underground networksS

NASA Astrophysics Data System (ADS)

Rudzinski, Lukasz; Lizurek, Grzegorz; Plesiewicz, Beata

2014-05-01

On 19th March 2013 tremor shook the surface of Polkowice town were "Rudna" mine is located. This event of ML=4.2 was third most powerful seismic event recorded in Legnica Głogów Copper District (LGCD). Citizens of the area reported that felt tremors were bigger and last longer than any other ones felt in last couple years. The event was studied with use of two different networks: underground network of "Rudna" mine and surface local network run by IGF PAS (LUMINEOS network). The first one is composed of 32 vertical seismometers at mining level, except 5 sensors placed in elevator shafts, seismometers location depth varies from 300 down to 1000 meters below surface. The seismometers used in this network are vertical short period Willmore MkII and MkIII sensors, with the frequency band from 1Hz to 100Hz. At the beginning of 2013th the local surface network of the Institute of Geophysics Polish Academy of Sciences (IGF PAS) with acronym LUMINEOS was installed under agreement with KGHM SA and "Rudna" mine officials. This network at the moment of the March 19th 2013 event was composed of 4 short-period one-second triaxial seismometers LE-3D/1s manufactured by Lenartz Electronics. Analysis of spectral parameters of the records from in mine seismic system and surface LUMINEOS network along with broadband station KSP record were carried out. Location of the event was close to the Rudna Główna fault zone, the nodal planes orientations determined with two different approaches were almost parallel to the strike of the fault. The mechanism solutions were also obtained in form of Full Moment Tensor inversion from P wave amplitude pulses of underground records and waveform inversion of surface network seismograms. Final results of the seismic analysis along with macroseismic survey and observed effects from the destroyed part of the mining panel indicate that the mechanism of the event was thrust faulting on inactive tectonic fault. The results confirm that the fault zones are the areas of higher risk, even in case of carefully taken mining operations.

How Useful Is Electroencephalography in the Diagnosis of Autism Spectrum Disorders and the Delineation of Subtypes: A Systematic Review

PubMed Central

Gurau, Oana; Bosl, William J.; Newton, Charles R.

2017-01-01

Autism spectrum disorders (ASD) are thought to be associated with abnormal neural connectivity. Presently, neural connectivity is a theoretical construct that cannot be easily measured. Research in network science and time series analysis suggests that neural network structure, a marker of neural activity, can be measured with electroencephalography (EEG). EEG can be quantified by different methods of analysis to potentially detect brain abnormalities. The aim of this review is to examine evidence for the utility of three methods of EEG signal analysis in the ASD diagnosis and subtype delineation. We conducted a review of literature in which 40 studies were identified and classified according to the principal method of EEG analysis in three categories: functional connectivity analysis, spectral power analysis, and information dynamics. All studies identified significant differences between ASD patients and non-ASD subjects. However, due to high heterogeneity in the results, generalizations could not be inferred and none of the methods alone are currently useful as a new diagnostic tool. The lack of studies prevented the analysis of these methods as tools for ASD subtypes delineation. These results confirm EEG abnormalities in ASD, but as yet not sufficient to help in the diagnosis. Future research with larger samples and more robust study designs could allow for higher sensitivity and consistency in characterizing ASD, paving the way for developing new means of diagnosis. PMID:28747892

The apoptotic microtubule network preserves plasma membrane integrity during the execution phase of apoptosis.

PubMed

Sánchez-Alcázar, José A; Rodríguez-Hernández, Angeles; Cordero, Mario D; Fernández-Ayala, Daniel J M; Brea-Calvo, Gloria; Garcia, Katherina; Navas, Plácido

2007-07-01

It has recently been shown that the microtubule cytoskeleton is reformed during the execution phase of apoptosis. We demonstrate that this microtubule reformation occurs in many cell types and under different apoptotic stimuli. We confirm that the apoptotic microtubule network possesses a novel organization, whose nucleation appears independent of conventional gamma-tubulin ring complex containing structures. Our analysis suggests that microtubules are closely associated with the plasma membrane, forming a cortical ring or cellular "cocoon". Concomitantly other components of the cytoskeleton, such as actin and cytokeratins disassemble. We found that colchicine-mediated disruption of apoptotic microtubule network results in enhanced plasma membrane permeability and secondary necrosis, suggesting that the reformation of a microtubule cytoskeleton plays an important role in preserving plasma membrane integrity during apoptosis. Significantly, cells induced to enter apoptosis in the presence of the pan-caspase inhibitor z-VAD, nevertheless form microtubule-like structures suggesting that microtubule formation is not dependent on caspase activation. In contrast we found that treatment with EGTA-AM, an intracellular calcium chelator, prevents apoptotic microtubule network formation, suggesting that intracellular calcium may play an essential role in the microtubule reformation. We propose that apoptotic microtubule network is required to maintain plasma membrane integrity during the execution phase of apoptosis.

Role of Structural Asymmetry in Controlling Drop Spacing in Microfluidic Ladder Networks

NASA Astrophysics Data System (ADS)

Wang, William; Maddala, Jeevan; Vanapalli, Siva; Rengasamy, Raghunathan

2012-02-01

Manipulation of drop spacing is crucial to many processes in microfluidic devices including drop coalescence, detection and storage. Microfluidic ladder networks ---where two droplet-carrying parallel channels are connected by narrow bypass channels through which the motion of drops is forbidden---have been proposed as a means to control relative separation between pairs of drops. Prior studies in microfluidic ladder networks with vertical bypasses, which possess fore-aft structural symmetry, have revealed that pairs of drops can only undergo reduction in drop spacing at the ladder exit. We investigate the dynamics of drops in microfluidic ladder networks with both vertical and slanted bypasses. Our analytical results indicate that unlike symmetric ladder networks, structural asymmetry introduced by a single slanted bypass can be used to modulate the relative spacing between drops, enabling them to contract, synchronize, expand or even flip at the ladder exit. Our experiments confirm all the behaviors predicted by theory. Numerical analysis further shows that ladders containing several identical bypasses can only linearly transform the input drop spacing. Finally, we find that ladders with specific combinations of vertical and slanted bypasses can generate non-linear transformation of input drop spacing, despite the absence of drop decision-making events at the bypass junctions.

Phage-bacteria infection networks: From nestedness to modularity

NASA Astrophysics Data System (ADS)

Flores, Cesar O.; Valverde, Sergi; Weitz, Joshua S.

2013-03-01

Bacteriophages (viruses that infect bacteria) are the most abundant biological life-forms on Earth. However, very little is known regarding the structure of phage-bacteria infections. In a recent study we re-evaluated 38 prior studies and demonstrated that phage-bacteria infection networks tend to be statistically nested in small scale communities (Flores et al 2011). Nestedness is consistent with a hierarchy of infection and resistance within phages and bacteria, respectively. However, we predicted that at large scales, phage-bacteria infection networks should be typified by a modular structure. We evaluate and confirm this hypothesis using the most extensive study of phage-bacteria infections (Moebus and Nattkemper 1981). In this study, cross-infections were evaluated between 215 marine phages and 286 marine bacteria. We develop a novel multi-scale network analysis and find that the Moebus and Nattkemper (1981) study, is highly modular (at the whole network scale), yet also exhibits nestedness and modularity at the within-module scale. We examine the role of geography in driving these modular patterns and find evidence that phage-bacteria interactions can exhibit strong similarity despite large distances between sites. CFG acknowledges the support of CONACyT Foundation. JSW holds a Career Award at the Scientific Interface from the Burroughs Wellcome Fund and acknowledges the support of the James S. McDonnell Foundation

Does network topology influence systemic risk contribution? A perspective from the industry indices in Chinese stock market

PubMed Central

Long, Haiming; Tang, Nengyu

2017-01-01

This study considers the effect of an industry’s network topology on its systemic risk contribution to the stock market using data from the CSI 300 two-tier industry indices from the Chinese stock market. We first measure industry’s conditional-value-at-risk (CoVaR) and the systemic risk contribution (ΔCoVaR) using the fitted time-varying t-copula function. The network of the stock industry is established based on dynamic conditional correlations with the minimum spanning tree. Then, we investigate the connection characteristics and topology of the network. Finally, we utilize seemingly unrelated regression estimation (SUR) of panel data to analyze the relationship between network topology of the stock industry and the industry’s systemic risk contribution. The results show that the systemic risk contribution of small-scale industries such as real estate, food and beverage, software services, and durable goods and clothing, is higher than that of large-scale industries, such as banking, insurance and energy. Industries with large betweenness centrality, closeness centrality, and clustering coefficient and small node occupancy layer are associated with greater systemic risk contribution. In addition, further analysis using a threshold model confirms that the results are robust. PMID:28683130

Graph theoretical analysis of EEG functional connectivity during music perception.

PubMed

Wu, Junjie; Zhang, Junsong; Liu, Chu; Liu, Dongwei; Ding, Xiaojun; Zhou, Changle

2012-11-05

The present study evaluated the effect of music on large-scale structure of functional brain networks using graph theoretical concepts. While most studies on music perception used Western music as an acoustic stimulus, Guqin music, representative of Eastern music, was selected for this experiment to increase our knowledge of music perception. Electroencephalography (EEG) was recorded from non-musician volunteers in three conditions: Guqin music, noise and silence backgrounds. Phase coherence was calculated in the alpha band and between all pairs of EEG channels to construct correlation matrices. Each resulting matrix was converted into a weighted graph using a threshold, and two network measures: the clustering coefficient and characteristic path length were calculated. Music perception was found to display a higher level mean phase coherence. Over the whole range of thresholds, the clustering coefficient was larger while listening to music, whereas the path length was smaller. Networks in music background still had a shorter characteristic path length even after the correction for differences in mean synchronization level among background conditions. This topological change indicated a more optimal structure under music perception. Thus, prominent small-world properties are confirmed in functional brain networks. Furthermore, music perception shows an increase of functional connectivity and an enhancement of small-world network organizations. Copyright © 2012 Elsevier B.V. All rights reserved.

Melanoma Spheroid Formation Involves Laminin-Associated Vasculogenic Mimicry

PubMed Central

Larson, Allison R.; Lee, Chung-Wei; Lezcano, Cecilia; Zhan, Qian; Huang, John; Fischer, Andrew H.; Murphy, George F.

2015-01-01

Melanoma is a tumor where virulence is conferred on transition from flat (radial) to three-dimensional (tumorigenic) growth. Virulence of tumorigenic growth is governed by numerous attributes, including presence of self-renewing stem-like cells and related formation of patterned networks associated with the melanoma mitogen, laminin, a phenomenon known as vasculogenic mimicry. Vasculogenic mimicry is posited to contribute to melanoma perfusion and nutrition in vivo; we hypothesized that it may also play a role in stem cell–driven spheroid formation in vitro. Using a model of melanoma in vitro tumorigenesis, laminin-associated networks developed in association with three-dimensional melanoma spheroids. Real-time PCR analysis of laminin subunits showed that spheroids formed from anchorage-independent melanoma cells expressed increased α4 and β1 laminin chains and α4 laminin expression was confirmed by in situ hybridization. Association of laminin networks with melanoma stem cell–associated nestin and vascular endothelial growth factor receptor-1 also was documented. Moreover, knockdown of nestin gene expression impaired laminin expression and network formation within spheroids. Laminin networks were remarkably similar to those observed in melanoma xenografts in mice and to those seen in patient melanomas. These data indicate that vasculogenic mimicry–like laminin networks, in addition to their genesis in vivo, are integral to the extracellular architecture of melanoma spheroids in vitro, where they may serve as stimulatory scaffolds to support three-dimensional growth. PMID:24332013

Mucosal Barrier Injury Laboratory-Confirmed Bloodstream Infections (MBI-LCBI): Descriptive Analysis of Data Reported to National Healthcare Safety Network (NHSN), 2013.

PubMed

Epstein, Lauren; See, Isaac; Edwards, Jonathan R; Magill, Shelley S; Thompson, Nicola D

2016-01-01

OBJECTIVES To determine the impact of mucosal barrier injury laboratory-confirmed bloodstream infections (MBI-LCBIs) on central-line-associated bloodstream infection (CLABSI) rates during the first year of MBI-LCBI reporting to the National Healthcare Safety Network (NHSN) DESIGN Descriptive analysis of 2013 NHSN data SETTING Selected inpatient locations in acute care hospitals METHODS A descriptive analysis of MBI-LCBI cases was performed. CLABSI rates per 1,000 central-line days were calculated with and without the inclusion of MBI-LCBIs in the subset of locations reporting ≥1 MBI-LCBI, and in all locations (regardless of MBI-LCBI reporting) to determine rate differences overall and by location type. RESULTS From 418 locations in 252 acute care hospitals reporting ≥1 MBI-LCBIs, 3,162 CLABSIs were reported; 1,415 (44.7%) met the MBI-LCBI definition. Among these locations, removing MBI-LCBI from the CLABSI rate determination produced the greatest CLABSI rate decreases in oncology (49%) and ward locations (45%). Among all locations reporting CLABSI data, including those reporting no MBI-LCBIs, removing MBI-LCBI reduced rates by 8%. Here, the greatest decrease was in oncology locations (38% decrease); decreases in other locations ranged from 1.2% to 4.2%. CONCLUSIONS An understanding of the potential impact of removing MBI-LCBIs from CLABSI data is needed to accurately interpret CLABSI trends over time and to inform changes to state and federal reporting programs. Whereas the MBI-LCBI definition may have a large impact on CLABSI rates in locations where patients with certain clinical conditions are cared for, the impact of MBI-LCBIs on overall CLABSI rates across inpatient locations appears to be more modest. Infect. Control Hosp. Epidemiol. 2015;37(1):2-7.

A managed clinical network for cardiac services: set-up, operation and impact on patient care.

PubMed

Stc Hamilton, Karen E; Sullivan, Frank M; Donnan, Peter T; Taylor, Rex; Ikenwilo, Divine; Scott, Anthony; Baker, Chris; Wyke, Sally

2005-01-01

To investigate the set up and operation of a Managed Clinical Network for cardiac services and assess its impact on patient care. This single case study used process evaluation with observational before and after comparison of indicators of quality of care and costs. The study was conducted in Dumfries and Galloway, Scotland and used a three-level framework. Process evaluation of the network set-up and operation through a documentary review of minutes; guidelines and protocols; transcripts of fourteen semi-structured interviews with health service personnel including senior managers, general practitioners, nurses, cardiologists and members of the public. Outcome evaluation of the impact of the network through interrupted time series analysis of clinical data of 202 patients aged less than 76 years admitted to hospital with a confirmed myocardial infarction one-year pre and one-year post, the establishment of the network. The main outcome measures were differences between indicators of quality of care targeted by network protocols. Economic evaluation of the transaction costs of the set-up and operation of the network and the resource costs of the clinical care of the 202 myocardial infarction patients from the time of hospital admission to 6 months post discharge through interrupted time series analysis. The outcome measure was different in National Health Service resource use. Despite early difficulties, the network was successful in bringing together clinicians, patients and managers to redesign services, exhibiting most features of good network management. The role of the energetic lead clinician was crucial, but the network took time to develop and 'bed down'. Its primary "modus operand" was the development of a myocardial infarction pathway and associated protocols. Of sixteen clinical care indicators, two improved significantly following the launch of the network and nine showed improvements, which were not statistically significant. There was no difference in resource use. The Managed Clinical Network made a difference to ways of working, particularly in breaching traditional boundaries and involving the public, and made modest changes in patient care. However, it required a two-year "set-up" period. Managed clinical networks are complex initiatives with an increasing profile in health care policy. This study suggests that they require energetic leadership and improvements are likely to be slow and incremental.

A managed clinical network for cardiac services: set-up, operation and impact on patient care

PubMed Central

E StC Hamilton, Karen; M Sullivan, Frank; T Donnan, Peter; Taylor, Rex; Ikenwilo, Divine; Scott, Anthony; Baker, Chris; Wyke, Sally

2005-01-01

Abstract Purpose To investigate the set up and operation of a Managed Clinical Network for cardiac services and assess its impact on patient care. Methods This single case study used process evaluation with observational before and after comparison of indicators of quality of care and costs. The study was conducted in Dumfries and Galloway, Scotland and used a three-level framework. Process evaluation of the network set-up and operation through a documentary review of minutes; guidelines and protocols; transcripts of fourteen semi-structured interviews with health service personnel including senior managers, general practitioners, nurses, cardiologists and members of the public. Outcome evaluation of the impact of the network through interrupted time series analysis of clinical data of 202 patients aged less than 76 years admitted to hospital with a confirmed myocardial infarction one-year pre and one-year post, the establishment of the network. The main outcome measures were differences between indicators of quality of care targeted by network protocols. Economic evaluation of the transaction costs of the set-up and operation of the network and the resource costs of the clinical care of the 202 myocardial infarction patients from the time of hospital admission to 6 months post discharge through interrupted time series analysis. The outcome measure was different in National Health Service resource use. Results Despite early difficulties, the network was successful in bringing together clinicians, patients and managers to redesign services, exhibiting most features of good network management. The role of the energetic lead clinician was crucial, but the network took time to develop and ‘bed down’. Its primary “modus operand” was the development of a myocardial infarction pathway and associated protocols. Of sixteen clinical care indicators, two improved significantly following the launch of the network and nine showed improvements, which were not statistically significant. There was no difference in resource use. Discussion and conclusions The Managed Clinical Network made a difference to ways of working, particularly in breaching traditional boundaries and involving the public, and made modest changes in patient care. However, it required a two-year “set-up” period. Managed clinical networks are complex initiatives with an increasing profile in health care policy. This study suggests that they require energetic leadership and improvements are likely to be slow and incremental. PMID:16773161

Impact of removing mucosal barrier injury laboratory-confirmed bloodstream infections from central line-associated bloodstream infection rates in the National Healthcare Safety Network, 2014.

PubMed

See, Isaac; Soe, Minn M; Epstein, Lauren; Edwards, Jonathan R; Magill, Shelley S; Thompson, Nicola D

2017-03-01

Central line-associated bloodstream infection (CLABSI) event data reported to the National Healthcare Safety Network from 2014, the first year of required use of the mucosal barrier injury laboratory-confirmed bloodstream infection (MBI-LCBI) definition, were analyzed to assess the impact of removing MBI-LCBI events from CLABSI rates. CLABSI rates decreased significantly in some location types after removing MBI-LCBI events, and MBI-LCBI events will be removed from publicly reported CLABSI rates. Published by Elsevier Inc.

Corporate funding and ideological polarization about climate change

PubMed Central

Farrell, Justin

2016-01-01

Drawing on large-scale computational data and methods, this research demonstrates how polarization efforts are influenced by a patterned network of political and financial actors. These dynamics, which have been notoriously difficult to quantify, are illustrated here with a computational analysis of climate change politics in the United States. The comprehensive data include all individual and organizational actors in the climate change countermovement (164 organizations), as well as all written and verbal texts produced by this network between 1993–2013 (40,785 texts, more than 39 million words). Two main findings emerge. First, that organizations with corporate funding were more likely to have written and disseminated texts meant to polarize the climate change issue. Second, and more importantly, that corporate funding influences the actual thematic content of these polarization efforts, and the discursive prevalence of that thematic content over time. These findings provide new, and comprehensive, confirmation of dynamics long thought to be at the root of climate change politics and discourse. Beyond the specifics of climate change, this paper has important implications for understanding ideological polarization more generally, and the increasing role of private funding in determining why certain polarizing themes are created and amplified. Lastly, the paper suggests that future studies build on the novel approach taken here that integrates large-scale textual analysis with social networks. PMID:26598653

Ensemble transcript interaction networks: a case study on Alzheimer's disease.

PubMed

Armañanzas, Rubén; Larrañaga, Pedro; Bielza, Concha

2012-10-01

Systems biology techniques are a topic of recent interest within the neurological field. Computational intelligence (CI) addresses this holistic perspective by means of consensus or ensemble techniques ultimately capable of uncovering new and relevant findings. In this paper, we propose the application of a CI approach based on ensemble Bayesian network classifiers and multivariate feature subset selection to induce probabilistic dependences that could match or unveil biological relationships. The research focuses on the analysis of high-throughput Alzheimer's disease (AD) transcript profiling. The analysis is conducted from two perspectives. First, we compare the expression profiles of hippocampus subregion entorhinal cortex (EC) samples of AD patients and controls. Second, we use the ensemble approach to study four types of samples: EC and dentate gyrus (DG) samples from both patients and controls. Results disclose transcript interaction networks with remarkable structures and genes not directly related to AD by previous studies. The ensemble is able to identify a variety of transcripts that play key roles in other neurological pathologies. Classical statistical assessment by means of non-parametric tests confirms the relevance of the majority of the transcripts. The ensemble approach pinpoints key metabolic mechanisms that could lead to new findings in the pathogenesis and development of AD. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Corporate funding and ideological polarization about climate change.

PubMed

Farrell, Justin

2016-01-05

Drawing on large-scale computational data and methods, this research demonstrates how polarization efforts are influenced by a patterned network of political and financial actors. These dynamics, which have been notoriously difficult to quantify, are illustrated here with a computational analysis of climate change politics in the United States. The comprehensive data include all individual and organizational actors in the climate change countermovement (164 organizations), as well as all written and verbal texts produced by this network between 1993-2013 (40,785 texts, more than 39 million words). Two main findings emerge. First, that organizations with corporate funding were more likely to have written and disseminated texts meant to polarize the climate change issue. Second, and more importantly, that corporate funding influences the actual thematic content of these polarization efforts, and the discursive prevalence of that thematic content over time. These findings provide new, and comprehensive, confirmation of dynamics long thought to be at the root of climate change politics and discourse. Beyond the specifics of climate change, this paper has important implications for understanding ideological polarization more generally, and the increasing role of private funding in determining why certain polarizing themes are created and amplified. Lastly, the paper suggests that future studies build on the novel approach taken here that integrates large-scale textual analysis with social networks.

Computational and experimental analysis identified 6-diazo-5-oxonorleucine as a potential agent for treating infection by Plasmodium falciparum.

PubMed

Plaimas, Kitiporn; Wang, Yulin; Rotimi, Solomon O; Olasehinde, Grace; Fatumo, Segun; Lanzer, Michael; Adebiyi, Ezekiel; König, Rainer

2013-12-01

Plasmodium falciparum (PF) is the most severe malaria parasite. It is developing resistance quickly to existing drugs making it indispensable to discover new drugs. Effective drugs have been discovered targeting metabolic enzymes of the parasite. In order to predict new drug targets, computational methods can be used employing database information of metabolism. Using this data, we performed recently a computational network analysis of metabolism of PF. We analyzed the topology of the network to find reactions which are sensitive against perturbations, i.e., when a single enzyme is blocked by drugs. We now used a refined network comprising also the host enzymes which led to a refined set of the five targets glutamyl-tRNA (gln) amidotransferase, hydroxyethylthiazole kinase, deoxyribose-phophate aldolase, pseudouridylate synthase, and deoxyhypusine synthase. It was shown elsewhere that glutamyl-tRNA (gln) amidotransferase of other microorganisms can be inhibited by 6-diazo-5-oxonorleucine. Performing a half maximal inhibitory concentration (IC50) assay, we showed, that 6-diazo-5-oxonorleucine is also severely affecting viability of PF in blood plasma of the human host. We confirmed this by an in vivo study observing Plasmodium berghei infected mice. Copyright © 2013 Elsevier B.V. All rights reserved.

Primary health care service delivery networks for the prevention and management of type 2 diabetes: using social network methods to describe interorganisational collaboration in a rural setting.

PubMed

McDonald, Julie; Jayasuriya, Rohan; Harris, Mark Fort

2011-01-01

Adults with type 2 diabetes or with behavioural risk factors require comprehensive and well coordinated responses from a range of health care providers who often work in different organisational settings. This study examines three types of collaborative links between organisations involved in a rural setting. Social network methods were employed using survey data on three types of links, and data was collected from a purposive sample of 17 organisations representing the major provider types. The analysis included a mix of unconfirmed and confirmed links, and network measures. General practices were the most influential provider group in initiating referrals, and they referred to the broadest range of organisations in the network. Team care arrangements formed a small part of the general practice referral network. They were used more for access to private sector allied health care providers and less for sharing care with public sector health services. Involvement in joint programs/activities was limited to public and non-government sector services, with no participation from the private sector. The patterns of interactions suggest that informal referral networks provide access to services and coordination of care for individual patients with diabetes. Two population subgroups would benefit from more proactive approaches to ensure equitable access to services and coordination of care across organisational boundaries: people with more complex health care needs and people at risk of developing diabetes.

Geometry and the onset of rigidity in a disordered network

NASA Astrophysics Data System (ADS)

Vermeulen, Mathijs F. J.; Bose, Anwesha; Storm, Cornelis; Ellenbroek, Wouter G.

2017-11-01

Disordered spring networks that are undercoordinated may abruptly rigidify when sufficient strain is applied. Since the deformation in response to applied strain does not change the generic quantifiers of network architecture, the number of nodes and the number of bonds between them, this rigidity transition must have a geometric origin. Naive, degree-of-freedom-based mechanical analyses such as the Maxwell-Calladine count or the pebble game algorithm overlook such geometric rigidity transitions and offer no means of predicting or characterizing them. We apply tools that were developed for the topological analysis of zero modes and states of self-stress on regular lattices to two-dimensional random spring networks and demonstrate that the onset of rigidity, at a finite simple shear strain γ★, coincides with the appearance of a single state of self-stress, accompanied by a single floppy mode. The process conserves the topologically invariant difference between the number of zero modes and the number of states of self-stress but imparts a finite shear modulus to the spring network. Beyond the critical shear, the network acquires a highly anisotropic elastic modulus, resisting further deformation most strongly in the direction of the rigidifying shear. We confirm previously reported critical scaling of the corresponding differential shear modulus. In the subcritical regime, a singular value decomposition of the network's compatibility matrix foreshadows the onset of rigidity by way of a continuously vanishing singular value corresponding to the nascent state of self-stress.

Movement pattern recognition in basketball free-throw shooting.

PubMed

Schmidt, Andrea

2012-04-01

The purpose of the present study was to analyze the movement patterns of free-throw shooters in basketball at different skill levels. There were two points of interest. First, to explore what information can be drawn from the movement pattern and second, to examine the methodological possibilities of pattern analysis. To this end, several qualitative and quantitative methods were employed. The resulting data were converged in a triangulation. Using a special kind of ANN named Dynamically Controlled Networks (DyCoN), a 'complex feature' consisting of several isolated features (angle displacements and velocities of the articulations of the kinematic chain) was calculated. This 'complex feature' was displayed by a trajectory combining several neurons of the network, reflecting the devolution of the twelve angle measures over the time course of each shooting action. In further network analyses individual characteristics were detected, as well as movement phases. Throwing patterns were successfully classified and the stability and variability of the realized pattern were established. The movement patterns found were clearly individually shaped as well as formed by the skill level. The triangulation confirmed the individual movement organizations. Finally, a high stability of the network methods was documented. Copyright © 2012. Published by Elsevier B.V.

Do cancer proteins really interact strongly in the human protein-protein interaction network?

PubMed

Xia, Junfeng; Sun, Jingchun; Jia, Peilin; Zhao, Zhongming

2011-06-01

Protein-protein interaction (PPI) network analysis has been widely applied in the investigation of the mechanisms of diseases, especially cancer. Recent studies revealed that cancer proteins tend to interact more strongly than other categories of proteins, even essential proteins, in the human interactome. However, it remains unclear whether this observation was introduced by the bias towards more cancer studies in humans. Here, we examined this important issue by uniquely comparing network characteristics of cancer proteins with three other sets of proteins in four organisms, three of which (fly, worm, and yeast) whose interactomes are essentially not biased towards cancer or other diseases. We confirmed that cancer proteins had stronger connectivity, shorter distance, and larger betweenness centrality than non-cancer disease proteins, essential proteins, and control proteins. Our statistical evaluation indicated that such observations were overall unlikely attributed to random events. Considering the large size and high quality of the PPI data in the four organisms, the conclusion that cancer proteins interact strongly in the PPI networks is reliable and robust. This conclusion suggests that perturbation of cancer proteins might cause major changes of cellular systems and result in abnormal cell function leading to cancer. © 2011 Elsevier Ltd. All rights reserved.

Do cancer proteins really interact strongly in the human protein-protein interaction network?

PubMed Central

Xia, Junfeng; Sun, Jingchun; Jia, Peilin; Zhao, Zhongming

2011-01-01

Protein-protein interaction (PPI) network analysis has been widely applied in the investigation of the mechanisms of diseases, especially cancer. Recent studies revealed that cancer proteins tend to interact more strongly than other categories of proteins, even essential proteins, in the human interactome. However, it remains unclear whether this observation was introduced by the bias towards more cancer studies in humans. Here, we examined this important issue by uniquely comparing network characteristics of cancer proteins with three other sets of proteins in four organisms, three of which (fly, worm, and yeast) whose interactomes are essentially not biased towards cancer or other diseases. We confirmed that cancer proteins had stronger connectivity, shorter distance, and larger betweenness centrality than non-cancer disease proteins, essential proteins, and control proteins. Our statistical evaluation indicated that such observations were overall unlikely attributed to random events. Considering the large size and high quality of the PPI data in the four organisms, the conclusion that cancer proteins interact strongly in the PPI networks is reliable and robust. This conclusion suggests that perturbation of cancer proteins might cause major changes of cellular systems and result in abnormal cell function leading to cancer. PMID:21666777

Grammatical Analysis as a Distributed Neurobiological Function

PubMed Central

Bozic, Mirjana; Fonteneau, Elisabeth; Su, Li; Marslen-Wilson, William D

2015-01-01

Language processing engages large-scale functional networks in both hemispheres. Although it is widely accepted that left perisylvian regions have a key role in supporting complex grammatical computations, patient data suggest that some aspects of grammatical processing could be supported bilaterally. We investigated the distribution and the nature of grammatical computations across language processing networks by comparing two types of combinatorial grammatical sequences—inflectionally complex words and minimal phrases—and contrasting them with grammatically simple words. Novel multivariate analyses revealed that they engage a coalition of separable subsystems: inflected forms triggered left-lateralized activation, dissociable into dorsal processes supporting morphophonological parsing and ventral, lexically driven morphosyntactic processes. In contrast, simple phrases activated a consistently bilateral pattern of temporal regions, overlapping with inflectional activations in L middle temporal gyrus. These data confirm the role of the left-lateralized frontotemporal network in supporting complex grammatical computations. Critically, they also point to the capacity of bilateral temporal regions to support simple, linear grammatical computations. This is consistent with a dual neurobiological framework where phylogenetically older bihemispheric systems form part of the network that supports language function in the modern human, and where significant capacities for language comprehension remain intact even following severe left hemisphere damage. PMID:25421880

Identifying Supervisory Control and Data Acquisition (SCADA) Systems on a Network via Remote Reconnaissance

DTIC Science & Technology

2006-09-01

and other forms of Internet research were used to confirm the relevance of the OUI to control system reconnaissance. I assigned a subjective level...equipment 4 = Confirmed with Internet research 5 = Suspected with Internet research OUI Organization Confidence Notes 00:00:0A Omron Tateisi...IP Vendor List 2 = Cross Referenced with IANA port list 3 = Confirmed with lab equipment 4 = Confirmed with Internet research 5 = Suspected with

Systematic identification of an integrative network module during senescence from time-series gene expression.

PubMed

Park, Chihyun; Yun, So Jeong; Ryu, Sung Jin; Lee, Soyoung; Lee, Young-Sam; Yoon, Youngmi; Park, Sang Chul

2017-03-15

Cellular senescence irreversibly arrests growth of human diploid cells. In addition, recent studies have indicated that senescence is a multi-step evolving process related to important complex biological processes. Most studies analyzed only the genes and their functions representing each senescence phase without considering gene-level interactions and continuously perturbed genes. It is necessary to reveal the genotypic mechanism inferred by affected genes and their interaction underlying the senescence process. We suggested a novel computational approach to identify an integrative network which profiles an underlying genotypic signature from time-series gene expression data. The relatively perturbed genes were selected for each time point based on the proposed scoring measure denominated as perturbation scores. Then, the selected genes were integrated with protein-protein interactions to construct time point specific network. From these constructed networks, the conserved edges across time point were extracted for the common network and statistical test was performed to demonstrate that the network could explain the phenotypic alteration. As a result, it was confirmed that the difference of average perturbation scores of common networks at both two time points could explain the phenotypic alteration. We also performed functional enrichment on the common network and identified high association with phenotypic alteration. Remarkably, we observed that the identified cell cycle specific common network played an important role in replicative senescence as a key regulator. Heretofore, the network analysis from time series gene expression data has been focused on what topological structure was changed over time point. Conversely, we focused on the conserved structure but its context was changed in course of time and showed it was available to explain the phenotypic changes. We expect that the proposed method will help to elucidate the biological mechanism unrevealed by the existing approaches.

Top-Down Network Effective Connectivity in Abstinent Substance Dependent Individuals

PubMed Central

Regner, Michael F.; Saenz, Naomi; Maharajh, Keeran; Yamamoto, Dorothy J.; Mohl, Brianne; Wylie, Korey; Tregellas, Jason; Tanabe, Jody

2016-01-01

Objective We hypothesized that compared to healthy controls, long-term abstinent substance dependent individuals (SDI) will differ in their effective connectivity between large-scale brain networks and demonstrate increased directional information from executive control to interoception-, reward-, and habit-related networks. In addition, using graph theory to compare network efficiencies we predicted decreased small-worldness in SDI compared to controls. Methods 50 SDI and 50 controls of similar sex and age completed psychological surveys and resting state fMRI. fMRI results were analyzed using group independent component analysis; 14 networks-of-interest (NOI) were selected using template matching to a canonical set of resting state networks. The number, direction, and strength of connections between NOI were analyzed with Granger Causality. Within-group thresholds were p<0.005 using a bootstrap permutation. Between group thresholds were p<0.05, FDR-corrected for multiple comparisons. NOI were correlated with behavioral measures, and group-level graph theory measures were compared. Results Compared to controls, SDI showed significantly greater Granger causal connectivity from right executive control network (RECN) to dorsal default mode network (dDMN) and from dDMN to basal ganglia network (BGN). RECN was negatively correlated with impulsivity, behavioral approach, and negative affect; dDMN was positively correlated with impulsivity. Among the 14 NOI, SDI showed greater bidirectional connectivity; controls showed more unidirectional connectivity. SDI demonstrated greater global efficiency and lower local efficiency. Conclusions Increased effective connectivity in long-term abstinent drug users may reflect improved cognitive control over habit and reward processes. Higher global and lower local efficiency across all networks in SDI compared to controls may reflect connectivity changes associated with drug dependence or remission and requires future, longitudinal studies to confirm. PMID:27776135

Inheritance Patterns in Citation Networks Reveal Scientific Memes

NASA Astrophysics Data System (ADS)

Kuhn, Tobias; Perc, Matjaž; Helbing, Dirk

2014-10-01

Memes are the cultural equivalent of genes that spread across human culture by means of imitation. What makes a meme and what distinguishes it from other forms of information, however, is still poorly understood. Our analysis of memes in the scientific literature reveals that they are governed by a surprisingly simple relationship between frequency of occurrence and the degree to which they propagate along the citation graph. We propose a simple formalization of this pattern and validate it with data from close to 50 million publication records from the Web of Science, PubMed Central, and the American Physical Society. Evaluations relying on human annotators, citation network randomizations, and comparisons with several alternative approaches confirm that our formula is accurate and effective, without a dependence on linguistic or ontological knowledge and without the application of arbitrary thresholds or filters.

Analysis of papaya cell wall-related genes during fruit ripening indicates a central role of polygalacturonases during pulp softening.

PubMed

Fabi, João Paulo; Broetto, Sabrina Garcia; da Silva, Sarah Lígia Garcia Leme; Zhong, Silin; Lajolo, Franco Maria; do Nascimento, João Roberto Oliveira

2014-01-01

Papaya (Carica papaya L.) is a climacteric fleshy fruit that undergoes dramatic changes during ripening, most noticeably a severe pulp softening. However, little is known regarding the genetics of the cell wall metabolism in papayas. The present work describes the identification and characterization of genes related to pulp softening. We used gene expression profiling to analyze the correlations and co-expression networks of cell wall-related genes, and the results suggest that papaya pulp softening is accomplished by the interactions of multiple glycoside hydrolases. The polygalacturonase cpPG1 appeared to play a central role in the network and was further studied. The transient expression of cpPG1 in papaya results in pulp softening and leaf necrosis in the absence of ethylene action and confirms its role in papaya fruit ripening.

The LCOGT NEO Follow-up Network

NASA Astrophysics Data System (ADS)

Lister, Tim; Greenstreet, Sarah; Gomez, Edward; Christensen, Eric J.; Larson, Stephen M.

2016-10-01

The LCOGT NEO Follow-up Network is using the telescopes of the Las Cumbres Observatory Global Telescope Network (LCOGT) and a web-based target selection, scheduling and data reduction system to confirm NEO candidates and characterize radar-targeted known NEOs. Starting in July 2014, the LCOGT NEO Follow-up Network has observed over 3,500 targets and reported more than 16,000 astrometric and photometric measurements to the Minor Planet Center (MPC).The LCOGT NEO Follow-up Network's main aims are to perform confirming follow-up of the large number of NEO candidates and to perform characterization measurements of radar targets to obtain light curves and rotation rates. The NEO candidates come from the NEO surveys such as Catalina, PanSTARRS, ATLAS, NEOWISE and others. In particular, we are targeting objects in the Southern Hemisphere, where the LCOGT NEO Follow-up Network is the largest resource for NEO observations.LCOGT has completed the first phase of the deployment with the installation and commissioning of the nine 1-meter telescopes at McDonald Observatory (Texas), Cerro Tololo (Chile), SAAO (South Africa) and Siding Spring Observatory (Australia). The telescope network has been fully operational since 2014 May, and observations are being executed remotely and robotically. Future expansion to a site at Ali Observatory, Tibet is planned for 2017-2018.We have developed web-based software called NEOexchange which automatically downloads and aggregates NEO candidates from the Minor Planet Center's NEO Confirmation Page, the Arecibo and Goldstone radar target lists and the NASA ARM list. NEOexchange allows the planning and scheduling of observations on the LCOGT Telescope Network and the tracking of the resulting blocks and generated data. We have recently extended the NEOexchange software to include automated data reduction to re-compute the astrometric solution, determine the photometric zeropoint and find moving objects and present these results to the user via the website.We will present results from the LCOGT NEO Follow-up Network and from the development of the NEOexchange software which is used to schedule, analyze and report observations taken with the LCOGT Network.

Network analysis of knowledge and practices regarding sexual and reproductive health: a study among adolescent street girls in Kinshasa (DRC).

PubMed

Vallès, Xavier; Lusala, Patrick Lunzayiladio; Devalière, Hortense; Metsia-Thiam, Marie-Michele; Aguilar, Daniel; Cheyron, Anne-Laure; Cannet, Didier

2017-02-01

The aim of the study was to ascertain the influence of knowledge and interventions in sexual and reproductive health and contraception practices among adolescent street girls from Kinshasa, Democratic Republic of the Congo. A cross-sectional study was carried out among street girls between 12 and 21 years of age. A standardised questionnaire was used, encompassing socio-demographic data and knowledge and practices regarding sexual and reproductive health. A network analysis was carried out. The study comprised 293 street girls. The mean age was 17.1 years (range 12-21 years) and the mean time spent living on the streets was 3.9 years (range 0-15 years). Commercial sex was reported by 78.5% (95% confidence interval [CI] 73.3%, 83.2%) as the main source of their income. During their last sexual intercourse, 44.0% (95%CI 38.1%, 50.4%) had not used a condom; 29.3% (95%CI 23.3%, 35.9%) had used hormonal contraception. Previous pregnancy was reported by 62.5% (95%CI 56.7%, 68.3%) and current pregnancy by 12.3% (95%CI 8.8%, 17.2%); 24.5% of previous pregnancies ended in voluntary termination, with a higher rate among the youngest street girls (12-15 years, 50.0%; p = 0.01). Time spent living on the streets was independently associated with pregnancy (odds ratio 1.2; 95%CI 1.1, 1.4). Practices and outcomes (previous or current pregnancy) were poorly correlated with knowledge about sexual and reproductive health. The network analysis confirmed the poor influence of exposure to intervention activities on sexual and reproductive health practices and outcomes, but did confirm a centrality effect of knowledge about HIV/AIDS. Street girls in Kinshasa are extremely vulnerable with regard to their sexual and reproductive health, especially the youngest street girls. Behavioural and biomedical interventions have had limited influence. Structural and societal changes are necessary to positively impact street girls' sexual and reproductive health. Knowledge about HIV/AIDS than about risk of pregnancy had a greater influence on sexual and reproductive health practices.

Effortful control and resting state networks: A longitudinal EEG study.

PubMed

Knyazev, Gennady G; Savostyanov, Alexander N; Bocharov, Andrey V; Slobodskaya, Helena R; Bairova, Nadezhda B; Tamozhnikov, Sergey S; Stepanova, Valentina V

2017-03-27

Resting state networks' (RSNs) architecture is well delineated in mature brain, but our understanding of their development remains limited. Particularly, there are few longitudinal studies. Besides, all existing evidence is obtained using functional magnetic resonance imaging (fMRI) and there are no data on electrophysiological correlates of RSN maturation. We acquired three yearly waves of resting state EEG data in 80 children between 7 and 9years and in 55 adults. Children's parents filled in the Effortful Control (EC) scale. Seed-based oscillatory power envelope correlation in conjunction with beamformer spatial filtering was used to obtain electrophysiological signatures of the default mode network (DMN) and two task-positive networks (TPN). In line with existing fMRI evidence, both cross-sectional comparison with adults and longitudinal analysis showed that the general pattern of maturation consisted in an increase in long-distance connections with posterior cortical regions and a decrease in short connections within prefrontal cortical areas. Latent growth curve analysis showed that EC scores were predicted by a linear increase over time in DMN integrity in alpha band and an increase in the segregation between DMN and TPN in beta band. These data confirm the neural basis of observed in fMRI research maturation-related changes and show that integrity of the DMN and sufficient level of segregation between DMN and TPN is a prerequisite for appropriate attentional and behavioral control. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

Interaction Network of Proteins Associated with Human Cytomegalovirus IE2-p86 Protein during Infection: A Proteomic Analysis

PubMed Central

Du, Guixin; Stinski, Mark F.

2013-01-01

Human cytomegalovirus protein IE2-p86 exerts its functions through interaction with other viral and cellular proteins. To further delineate its protein interaction network, we generated a recombinant virus expressing SG-tagged IE2-p86 and used tandem affinity purification coupled with mass spectrometry. A total of 9 viral proteins and 75 cellular proteins were found to associate with IE2-p86 protein during the first 48 hours of infection. The protein profile at 8, 24, and 48 h post infection revealed that UL84 tightly associated with IE2-p86, and more viral and cellular proteins came into association with IE2-p86 with the progression of virus infection. A computational analysis of the protein-protein interaction network indicated that all of the 9 viral proteins and most of the cellular proteins identified in the study are interconnected to varying degrees. Of the cellular proteins that were confirmed to associate with IE2-p86 by immunoprecipitation, C1QBP was further shown to be upregulated by HCMV infection and colocalized with IE2-p86, UL84 and UL44 in the virus replication compartment of the nucleus. The IE2-p86 interactome network demonstrated the temporal development of stable and abundant protein complexes that associate with IE2-p86 and provided a framework to benefit future studies of various protein complexes during HCMV infection. PMID:24358118

Discovering Preferential Patterns in Sectoral Trade Networks

PubMed Central

Cingolani, Isabella; Piccardi, Carlo; Tajoli, Lucia

2015-01-01

We analyze the patterns of import/export bilateral relations, with the aim of assessing the relevance and shape of “preferentiality” in countries’ trade decisions. Preferentiality here is defined as the tendency to concentrate trade on one or few partners. With this purpose, we adopt a systemic approach through the use of the tools of complex network analysis. In particular, we apply a pattern detection approach based on community and pseudocommunity analysis, in order to highlight the groups of countries within which most of members’ trade occur. The method is applied to two intra-industry trade networks consisting of 221 countries, relative to the low-tech “Textiles and Textile Articles” and the high-tech “Electronics” sectors for the year 2006, to look at the structure of world trade before the start of the international financial crisis. It turns out that the two networks display some similarities and some differences in preferential trade patterns: they both include few significant communities that define narrow sets of countries trading with each other as preferential destinations markets or supply sources, and they are characterized by the presence of similar hierarchical structures, led by the largest economies. But there are also distinctive features due to the characteristics of the industries examined, in which the organization of production and the destination markets are different. Overall, the extent of preferentiality and partner selection at the sector level confirm the relevance of international trade costs still today, inducing countries to seek the highest efficiency in their trade patterns. PMID:26485163

Discovering Preferential Patterns in Sectoral Trade Networks.

PubMed

Cingolani, Isabella; Piccardi, Carlo; Tajoli, Lucia

2015-01-01

We analyze the patterns of import/export bilateral relations, with the aim of assessing the relevance and shape of "preferentiality" in countries' trade decisions. Preferentiality here is defined as the tendency to concentrate trade on one or few partners. With this purpose, we adopt a systemic approach through the use of the tools of complex network analysis. In particular, we apply a pattern detection approach based on community and pseudocommunity analysis, in order to highlight the groups of countries within which most of members' trade occur. The method is applied to two intra-industry trade networks consisting of 221 countries, relative to the low-tech "Textiles and Textile Articles" and the high-tech "Electronics" sectors for the year 2006, to look at the structure of world trade before the start of the international financial crisis. It turns out that the two networks display some similarities and some differences in preferential trade patterns: they both include few significant communities that define narrow sets of countries trading with each other as preferential destinations markets or supply sources, and they are characterized by the presence of similar hierarchical structures, led by the largest economies. But there are also distinctive features due to the characteristics of the industries examined, in which the organization of production and the destination markets are different. Overall, the extent of preferentiality and partner selection at the sector level confirm the relevance of international trade costs still today, inducing countries to seek the highest efficiency in their trade patterns.

New Nanomedicine Approaches Using Gold-thioguanine Nanoconjugates as Metallo-ligands

PubMed Central

Sleightholm, Lee; Zambre, Ajit; Chanda, Nripen; Afrasiabi, Zahra; Katti, Kattesh; Kannan, Raghuraman

2011-01-01

Gold-thioguanine nanoconjugates (AuNP-TG) of size 3–4 nm were synthesized and the ratio between gold and 6-Thioguanine (TG) was estimated as ~1:1.5 using a cyanide digestion method and confirmed by flame atomic absorption spectroscopic analysis. AuNP-TG constructs showed high in vitro stability under different pH conditions and biologically relevant solutions for a period of 24 hours. Reaction of AuNP-TG with europium or platinum salts resulted in the formation of organized self-assembled metallo-networks. PMID:21709763

A probabilistic analysis of electrical equipment vulnerability to carbon fibers

NASA Technical Reports Server (NTRS)

Elber, W.

1980-01-01

The statistical problems of airborne carbon fibers falling onto electrical circuits were idealized and analyzed. The probability of making contact between randomly oriented finite length fibers and sets of parallel conductors with various spacings and lengths was developed theoretically. The probability of multiple fibers joining to bridge a single gap between conductors, or forming continuous networks is included. From these theoretical considerations, practical statistical analyses to assess the likelihood of causing electrical malfunctions was produced. The statistics obtained were confirmed by comparison with results of controlled experiments.

Dynamic reconfiguration of human brain functional networks through neurofeedback.

PubMed

Haller, Sven; Kopel, Rotem; Jhooti, Permi; Haas, Tanja; Scharnowski, Frank; Lovblad, Karl-Olof; Scheffler, Klaus; Van De Ville, Dimitri

2013-11-01

Recent fMRI studies demonstrated that functional connectivity is altered following cognitive tasks (e.g., learning) or due to various neurological disorders. We tested whether real-time fMRI-based neurofeedback can be a tool to voluntarily reconfigure brain network interactions. To disentangle learning-related from regulation-related effects, we first trained participants to voluntarily regulate activity in the auditory cortex (training phase) and subsequently asked participants to exert learned voluntary self-regulation in the absence of feedback (transfer phase without learning). Using independent component analysis (ICA), we found network reconfigurations (increases in functional network connectivity) during the neurofeedback training phase between the auditory target region and (1) the auditory pathway; (2) visual regions related to visual feedback processing; (3) insula related to introspection and self-regulation and (4) working memory and high-level visual attention areas related to cognitive effort. Interestingly, the auditory target region was identified as the hub of the reconfigured functional networks without a-priori assumptions. During the transfer phase, we again found specific functional connectivity reconfiguration between auditory and attention network confirming the specific effect of self-regulation on functional connectivity. Functional connectivity to working memory related networks was no longer altered consistent with the absent demand on working memory. We demonstrate that neurofeedback learning is mediated by widespread changes in functional connectivity. In contrast, applying learned self-regulation involves more limited and specific network changes in an auditory setup intended as a model for tinnitus. Hence, neurofeedback training might be used to promote recovery from neurological disorders that are linked to abnormal patterns of brain connectivity. Copyright © 2013 Elsevier Inc. All rights reserved.

The effect of time synchronization of wireless sensors on the modal analysis of structures

NASA Astrophysics Data System (ADS)

Krishnamurthy, V.; Fowler, K.; Sazonov, E.

2008-10-01

Driven by the need to reduce the installation cost and maintenance cost of structural health monitoring (SHM) systems, wireless sensor networks (WSNs) are becoming increasingly popular. Perfect time synchronization amongst the wireless sensors is a key factor enabling the use of low-cost, low-power WSNs for structural health monitoring applications based on output-only modal analysis of structures. In this paper we present a theoretical framework for analysis of the impact created by time delays in the measured system response on the reconstruction of mode shapes using the popular frequency domain decomposition (FDD) technique. This methodology directly estimates the change in mode shape values based on sensor synchronicity. We confirm the proposed theoretical model by experimental validation in modal identification experiments performed on an aluminum beam. The experimental validation was performed using a wireless intelligent sensor and actuator network (WISAN) which allows for close time synchronization between sensors (0.6-10 µs in the tested configuration) and guarantees lossless data delivery under normal conditions. The experimental results closely match theoretical predictions and show that even very small delays in output response impact the mode shapes.

An option for measuring maternal mortality in developing countries: a survey using community informants.

PubMed

Qomariyah, Siti Nurul; Braunholtz, David; Achadi, Endang L; Witten, Karen H; Pambudi, Eko Setyo; Anggondowati, Trisari; Latief, Kamaluddin; Graham, Wendy J

2010-11-17

The maternal mortality ratio (MMR) remains high in most developing countries. Local, recent estimates of MMR are needed to motivate policymakers and evaluate interventions. But, estimating MMR, in the absence of vital registration systems, is difficult. This paper describes an efficient approach using village informant networks to capture maternal death cases (Maternal Deaths from Informants/Maternal Death Follow on Review or MADE-IN/MADE-FOR) developed to address this gap, and examines its validity and efficiency. MADE-IN used two village informant networks - heads of neighbourhood units (RTs) and health volunteers (Kaders). Informants were invited to attend separate network meetings - through the village head (for the RT) and through health centre for the kaders. Attached to the letter was a form with written instructions requesting informants list deaths of women of reproductive age (WRA) in the village during the previous two years. At a 'listing meeting' the informants' understanding on the form was checked, informants could correct their forms, and then collectively agreed a consolidated list. MADE-FOR consisted of visits relatives of likely pregnancy related deaths (PRDs) identified from MADE-IN, to confirm the PRD status and gather information about the cause of death. Capture-recapture (CRC) analysis enabled estimation of coverage rates of the two networks, and of total PRDs. The RT network identified a higher proportion of PRDs than the kaders (estimated 0.85 vs. 0.71), but the latter was easier and cheaper to access. Assigned PRD status amongst identified WRA deaths was more accurate for the kader network, and seemingly for more recent deaths, and for deaths from rural areas. Assuming information on live births from an existing source to calculate the MMR, MADE-IN/MADE-FOR cost only $0.1 (US) per women-year risk of exposure, substantially cheaper than alternatives. This study shows that reliable local, recent estimates of MMR can be obtained relatively cheaply using two independent informant networks to identify cases. Neither network captured all PRDs, but capture-recapture analysis allowed self-calibration. However, it requires careful avoidance of false-positives, and matching of cases identified by both networks, which was achieved by the home visit.

Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data.

PubMed

Alakwaa, Fadhl M; Chaudhary, Kumardeep; Garmire, Lana X

2018-01-05

Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+), and 67 negative estrogen receptor (ER-) to test the accuracies of feed-forward networks, a deep learning (DL) framework, as well as six widely used machine learning models, namely random forest (RF), support vector machines (SVM), recursive partitioning and regression trees (RPART), linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), and generalized boosted models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER- patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted P-value <0.05) that cannot be discovered by other machine learning methods. Among them, protein digestion and absorption and ATP-binding cassette (ABC) transporters pathways are also confirmed in integrated analysis between metabolomics and gene expression data in these samples. In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accuracy (AUC = 0.93) and better revelation of disease biology. We encourage the adoption of feed-forward networks based deep learning method in the metabolomics research community for classification.

What causes childhood stunting among children of San Vicente, Guatemala: Employing complimentary, system-analysis approaches.

PubMed

Voth-Gaeddert, Lee E; Stoker, Matthew; Cornell, Devin; Oerther, Daniel B

2018-04-01

Guatemala has the sixth worst stunting rate with 48% of children under five years of age classified as stunted according to World Health Organization standards. This study utilizes two different yet complimentary system-analysis approaches to analyze correlations among environmental and demographic variables, environmental enteric dysfunction (EED), and child height-for-age (stunting metric) in Guatemala. Two descriptive models constructed around applicable environmental and demographic factors on child height-for-age and EED were analyzed using Network Analysis (NA) and Structural Equation Modeling (SEM). Data from two populations of children between the age of three months and five years were used. The first population (n = 2103) was drawn from the Food for Peace Baseline Survey conducted by the US Agency for International Development (USAID) in 2012, and the second population (n = 372) was drawn from an independent survey conducted by the San Vicente Health Center in 2016. The results from the NA of the height-for-age model confirmed pathogen exposure, nutrition, and prenatal health as important, and the results from the NA of the EED model confirmed water source, water treatment, and type of sanitation as important. The results from the SEM of the height-for-age model confirmed a statistically significant correlation among child height-for-age and child-mother interaction (-0.092, p = 0.076) while the SEM of the EED model confirmed the statistically significant correlation among EED and type of water treatment (-0.115, p = 0.013). Our approach supports important efforts to understand the complex set of factors associated with child stunting among communities sharing similarities with San Vicente. Copyright © 2018 Elsevier GmbH. All rights reserved.

Evolving network with different edges

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sun Jie; Department of Mathematics and Computer Science, Clarkson University, Potsdam, New York 13699; Ge Yizhi

2007-10-15

We propose a scale-free network similar to Barabasi-Albert networks but with two different types of edges. This model is based on the idea that in many cases there are more than one kind of link in a network and when a new node enters the network both old nodes and different kinds of links compete to obtain it. The degree distribution of both the total degree and the degree of each type of edge is analyzed and found to be scale-free. Simulations are shown to confirm these results.

Detecting Large-Scale Brain Networks Using EEG: Impact of Electrode Density, Head Modeling and Source Localization

PubMed Central

Liu, Quanying; Ganzetti, Marco; Wenderoth, Nicole; Mantini, Dante

2018-01-01

Resting state networks (RSNs) in the human brain were recently detected using high-density electroencephalography (hdEEG). This was done by using an advanced analysis workflow to estimate neural signals in the cortex and to assess functional connectivity (FC) between distant cortical regions. FC analyses were conducted either using temporal (tICA) or spatial independent component analysis (sICA). Notably, EEG-RSNs obtained with sICA were very similar to RSNs retrieved with sICA from functional magnetic resonance imaging data. It still remains to be clarified, however, what technological aspects of hdEEG acquisition and analysis primarily influence this correspondence. Here we examined to what extent the detection of EEG-RSN maps by sICA depends on the electrode density, the accuracy of the head model, and the source localization algorithm employed. Our analyses revealed that the collection of EEG data using a high-density montage is crucial for RSN detection by sICA, but also the use of appropriate methods for head modeling and source localization have a substantial effect on RSN reconstruction. Overall, our results confirm the potential of hdEEG for mapping the functional architecture of the human brain, and highlight at the same time the interplay between acquisition technology and innovative solutions in data analysis. PMID:29551969

A systematic atlas of chaperome deregulation topologies across the human cancer landscape

PubMed Central

Sverchkova, Angelina

2018-01-01

Proteome balance is safeguarded by the proteostasis network (PN), an intricately regulated network of conserved processes that evolved to maintain native function of the diverse ensemble of protein species, ensuring cellular and organismal health. Proteostasis imbalances and collapse are implicated in a spectrum of human diseases, from neurodegeneration to cancer. The characteristics of PN disease alterations however have not been assessed in a systematic way. Since the chaperome is among the central components of the PN, we focused on the chaperome in our study by utilizing a curated functional ontology of the human chaperome that we connect in a high-confidence physical protein-protein interaction network. Challenged by the lack of a systems-level understanding of proteostasis alterations in the heterogeneous spectrum of human cancers, we assessed gene expression across more than 10,000 patient biopsies covering 22 solid cancers. We derived a novel customized Meta-PCA dimension reduction approach yielding M-scores as quantitative indicators of disease expression changes to condense the complexity of cancer transcriptomics datasets into quantitative functional network topographies. We confirm upregulation of the HSP90 family and also highlight HSP60s, Prefoldins, HSP100s, ER- and mitochondria-specific chaperones as pan-cancer enriched. Our analysis also reveals a surprisingly consistent strong downregulation of small heat shock proteins (sHSPs) and we stratify two cancer groups based on the preferential upregulation of ATP-dependent chaperones. Strikingly, our analyses highlight similarities between stem cell and cancer proteostasis, and diametrically opposed chaperome deregulation between cancers and neurodegenerative diseases. We developed a web-based Proteostasis Profiler tool (Pro2) enabling intuitive analysis and visual exploration of proteostasis disease alterations using gene expression data. Our study showcases a comprehensive profiling of chaperome shifts in human cancers and sets the stage for a systematic global analysis of PN alterations across the human diseasome towards novel hypotheses for therapeutic network re-adjustment in proteostasis disorders. PMID:29293508

A systematic atlas of chaperome deregulation topologies across the human cancer landscape.

PubMed

Hadizadeh Esfahani, Ali; Sverchkova, Angelina; Saez-Rodriguez, Julio; Schuppert, Andreas A; Brehme, Marc

2018-01-01

Proteome balance is safeguarded by the proteostasis network (PN), an intricately regulated network of conserved processes that evolved to maintain native function of the diverse ensemble of protein species, ensuring cellular and organismal health. Proteostasis imbalances and collapse are implicated in a spectrum of human diseases, from neurodegeneration to cancer. The characteristics of PN disease alterations however have not been assessed in a systematic way. Since the chaperome is among the central components of the PN, we focused on the chaperome in our study by utilizing a curated functional ontology of the human chaperome that we connect in a high-confidence physical protein-protein interaction network. Challenged by the lack of a systems-level understanding of proteostasis alterations in the heterogeneous spectrum of human cancers, we assessed gene expression across more than 10,000 patient biopsies covering 22 solid cancers. We derived a novel customized Meta-PCA dimension reduction approach yielding M-scores as quantitative indicators of disease expression changes to condense the complexity of cancer transcriptomics datasets into quantitative functional network topographies. We confirm upregulation of the HSP90 family and also highlight HSP60s, Prefoldins, HSP100s, ER- and mitochondria-specific chaperones as pan-cancer enriched. Our analysis also reveals a surprisingly consistent strong downregulation of small heat shock proteins (sHSPs) and we stratify two cancer groups based on the preferential upregulation of ATP-dependent chaperones. Strikingly, our analyses highlight similarities between stem cell and cancer proteostasis, and diametrically opposed chaperome deregulation between cancers and neurodegenerative diseases. We developed a web-based Proteostasis Profiler tool (Pro2) enabling intuitive analysis and visual exploration of proteostasis disease alterations using gene expression data. Our study showcases a comprehensive profiling of chaperome shifts in human cancers and sets the stage for a systematic global analysis of PN alterations across the human diseasome towards novel hypotheses for therapeutic network re-adjustment in proteostasis disorders.

Mathematical Model of a Telomerase Transcriptional Regulatory Network Developed by Cell-Based Screening: Analysis of Inhibitor Effects and Telomerase Expression Mechanisms

PubMed Central

Bilsland, Alan E.; Stevenson, Katrina; Liu, Yu; Hoare, Stacey; Cairney, Claire J.; Roffey, Jon; Keith, W. Nicol

2014-01-01

Cancer cells depend on transcription of telomerase reverse transcriptase (TERT). Many transcription factors affect TERT, though regulation occurs in context of a broader network. Network effects on telomerase regulation have not been investigated, though deeper understanding of TERT transcription requires a systems view. However, control over individual interactions in complex networks is not easily achievable. Mathematical modelling provides an attractive approach for analysis of complex systems and some models may prove useful in systems pharmacology approaches to drug discovery. In this report, we used transfection screening to test interactions among 14 TERT regulatory transcription factors and their respective promoters in ovarian cancer cells. The results were used to generate a network model of TERT transcription and to implement a dynamic Boolean model whose steady states were analysed. Modelled effects of signal transduction inhibitors successfully predicted TERT repression by Src-family inhibitor SU6656 and lack of repression by ERK inhibitor FR180204, results confirmed by RT-QPCR analysis of endogenous TERT expression in treated cells. Modelled effects of GSK3 inhibitor 6-bromoindirubin-3′-oxime (BIO) predicted unstable TERT repression dependent on noise and expression of JUN, corresponding with observations from a previous study. MYC expression is critical in TERT activation in the model, consistent with its well known function in endogenous TERT regulation. Loss of MYC caused complete TERT suppression in our model, substantially rescued only by co-suppression of AR. Interestingly expression was easily rescued under modelled Ets-factor gain of function, as occurs in TERT promoter mutation. RNAi targeting AR, JUN, MXD1, SP3, or TP53, showed that AR suppression does rescue endogenous TERT expression following MYC knockdown in these cells and SP3 or TP53 siRNA also cause partial recovery. The model therefore successfully predicted several aspects of TERT regulation including previously unknown mechanisms. An extrapolation suggests that a dominant stimulatory system may programme TERT for transcriptional stability. PMID:24550717

Whole-exome sequencing in obsessive-compulsive disorder identifies rare mutations in immunological and neurodevelopmental pathways

PubMed Central

Cappi, C; Brentani, H; Lima, L; Sanders, S J; Zai, G; Diniz, B J; Reis, V N S; Hounie, A G; Conceição do Rosário, M; Mariani, D; Requena, G L; Puga, R; Souza-Duran, F L; Shavitt, R G; Pauls, D L; Miguel, E C; Fernandez, T V

2016-01-01

Studies of rare genetic variation have identified molecular pathways conferring risk for developmental neuropsychiatric disorders. To date, no published whole-exome sequencing studies have been reported in obsessive-compulsive disorder (OCD). We sequenced all the genome coding regions in 20 sporadic OCD cases and their unaffected parents to identify rare de novo (DN) single-nucleotide variants (SNVs). The primary aim of this pilot study was to determine whether DN variation contributes to OCD risk. To this aim, we evaluated whether there is an elevated rate of DN mutations in OCD, which would justify this approach toward gene discovery in larger studies of the disorder. Furthermore, to explore functional molecular correlations among genes with nonsynonymous DN SNVs in OCD probands, a protein–protein interaction (PPI) network was generated based on databases of direct molecular interactions. We applied Degree-Aware Disease Gene Prioritization (DADA) to rank the PPI network genes based on their relatedness to a set of OCD candidate genes from two OCD genome-wide association studies (Stewart et al., 2013; Mattheisen et al., 2014). In addition, we performed a pathway analysis with genes from the PPI network. The rate of DN SNVs in OCD was 2.51 × 10−8 per base per generation, significantly higher than a previous estimated rate in unaffected subjects using the same sequencing platform and analytic pipeline. Several genes harboring DN SNVs in OCD were highly interconnected in the PPI network and ranked high in the DADA analysis. Nearly all the DN SNVs in this study are in genes expressed in the human brain, and a pathway analysis revealed enrichment in immunological and central nervous system functioning and development. The results of this pilot study indicate that further investigation of DN variation in larger OCD cohorts is warranted to identify specific risk genes and to confirm our preliminary finding with regard to PPI network enrichment for particular biological pathways and functions. PMID:27023170

Detection and analysis of a transient energy burst with beamforming of multiple teleseismic phases

NASA Astrophysics Data System (ADS)

Retailleau, Lise; Landès, Matthieu; Gualtieri, Lucia; Shapiro, Nikolai M.; Campillo, Michel; Roux, Philippe; Guilbert, Jocelyn

2018-01-01

Seismological detection methods are traditionally based on picking techniques. These methods cannot be used to analyse emergent signals where the arrivals cannot be picked. Here, we detect and locate seismic events by applying a beamforming method that combines multiple body-wave phases to USArray data. This method explores the consistency and characteristic behaviour of teleseismic body waves that are recorded by a large-scale, still dense, seismic network. We perform time-slowness analysis of the signals and correlate this with the time-slowness equivalent of the different body-wave phases predicted by a global traveltime calculator, to determine the occurrence of an event with no a priori information about it. We apply this method continuously to one year of data to analyse the different events that generate signals reaching the USArray network. In particular, we analyse in detail a low-frequency secondary microseismic event that occurred on 2010 February 1. This event, that lasted 1 d, has a narrow frequency band around 0.1 Hz, and it occurred at a distance of 150° to the USArray network, South of Australia. We show that the most energetic phase observed is the PKPab phase. Direct amplitude analysis of regional seismograms confirms the occurrence of this event. We compare the seismic observations with models of the spectral density of the pressure field generated by the interferences between oceanic waves. We attribute the observed signals to a storm-generated microseismic event that occurred along the South East Indian Ridge.

Genome-Wide Analysis of Long Noncoding RNA (lncRNA) Expression in Hepatoblastoma Tissues

PubMed Central

Xue, Ping; Cui, Ximao; Li, Kai; Zheng, Shan; He, Xianghuo; Dong, Kuiran

2014-01-01

Long noncoding RNAs (lncRNAs) have crucial roles in cancer biology. We performed a genome-wide analysis of lncRNA expression in hepatoblastoma tissues to identify novel targets for further study of hepatoblastoma. Hepatoblastoma and normal liver tissue samples were obtained from hepatoblastoma patients. The genome-wide analysis of lncRNA expression in these tissues was performed using a 4×180 K lncRNA microarray and Sureprint G3 Human lncRNA Chips. Quantitative RT-PCR (qRT-PCR) was performed to confirm these results. The differential expressions of lncRNAs and mRNAs were identified through fold-change filtering. Gene Ontology (GO) and pathway analyses were performed using the standard enrichment computation method. Associations between lncRNAs and adjacent protein-coding genes were determined through complex transcriptional loci analysis. We found that 2736 lncRNAs were differentially expressed in hepatoblastoma tissues. Among these, 1757 lncRNAs were upregulated more than two-fold relative to normal tissues and 979 lncRNAs were downregulated. Moreover, in hepatoblastoma there were 420 matched lncRNA-mRNA pairs for 120 differentially expressed lncRNAs, and 167 differentially expressed mRNAs. The co-expression network analysis predicted 252 network nodes and 420 connections between 120 lncRNAs and 132 coding genes. Within this co-expression network, 369 pairs were positive, and 51 pairs were negative. Lastly, qRT-PCR data verified six upregulated and downregulated lncRNAs in hepatoblastoma, plus endothelial cell-specific molecule 1 (ESM1) mRNA. Our results demonstrated that expression of these aberrant lncRNAs could respond to hepatoblastoma development. Further study of these lncRNAs could provide useful insight into hepatoblastoma biology. PMID:24465615

Co-concentration effect of silane with natural extract on biodegradable polymeric films for food packaging.

PubMed

Bashir, Anbreen; Jabeen, Sehrish; Gull, Nafisa; Islam, Atif; Sultan, Misbah; Ghaffar, Abdul; Khan, Shahzad Maqsood; Iqbal, Sadia Sagar; Jamil, Tahir

2018-01-01

Novel biodegradable films were prepared by blending guar gum, chitosan and poly (vinyl alcohol) having mint (ME) and grapefruit peel (GE) extracts and crosslinked with nontoxic tetraethoxysilane (TEOS). The co-concentration effect of TEOS with natural extracts on the films was studied. FTIR analysis confirmed the presence of incorporated components and the developed interactions among the polymer chains. The surface morphology of the films by SEM showed the hydrophilic character due to porous network structure. The films having both ME and GE with maximum amount of crosslinker (100μL), showed maximum swelling (58g/g) and stability while the optical properties showed increased protection against UV light. This film sample showed compact network structure which enhanced the ultimate tensile strength (40.03MPa) and elongation at break (104.8%). ME/GE conferred the antioxidant properties determined by radical scavenging activity and total phenolic contents (TPC) as ME films have greater TPC compared to GE films. The soil burial test exhibited the degradation of films rapidly (6days) confirming their strong microbial activity in soil. The lower water vapour transmission rate and water vapour permeability showed better shelf life; hence, these biodegradable films are environmental friendly and have potential for food and other packaging. Copyright © 2017 Elsevier B.V. All rights reserved.

An Integrative Structural Health Monitoring System for the Local/Global Responses of a Large-Scale Irregular Building under Construction

PubMed Central

Park, Hyo Seon; Shin, Yunah; Choi, Se Woon; Kim, Yousok

2013-01-01

In this study, a practical and integrative SHM system was developed and applied to a large-scale irregular building under construction, where many challenging issues exist. In the proposed sensor network, customized energy-efficient wireless sensing units (sensor nodes, repeater nodes, and master nodes) were employed and comprehensive communications from the sensor node to the remote monitoring server were conducted through wireless communications. The long-term (13-month) monitoring results recorded from a large number of sensors (75 vibrating wire strain gauges, 10 inclinometers, and three laser displacement sensors) indicated that the construction event exhibiting the largest influence on structural behavior was the removal of bents that were temporarily installed to support the free end of the cantilevered members during their construction. The safety of each member could be confirmed based on the quantitative evaluation of each response. Furthermore, it was also confirmed that the relation between these responses (i.e., deflection, strain, and inclination) can provide information about the global behavior of structures induced from specific events. Analysis of the measurement results demonstrates the proposed sensor network system is capable of automatic and real-time monitoring and can be applied and utilized for both the safety evaluation and precise implementation of buildings under construction. PMID:23860317

Automatic detection and recognition of traffic signs in stereo images based on features and probabilistic neural networks

NASA Astrophysics Data System (ADS)

Sheng, Yehua; Zhang, Ka; Ye, Chun; Liang, Cheng; Li, Jian

2008-04-01

Considering the problem of automatic traffic sign detection and recognition in stereo images captured under motion conditions, a new algorithm for traffic sign detection and recognition based on features and probabilistic neural networks (PNN) is proposed in this paper. Firstly, global statistical color features of left image are computed based on statistics theory. Then for red, yellow and blue traffic signs, left image is segmented to three binary images by self-adaptive color segmentation method. Secondly, gray-value projection and shape analysis are used to confirm traffic sign regions in left image. Then stereo image matching is used to locate the homonymy traffic signs in right image. Thirdly, self-adaptive image segmentation is used to extract binary inner core shapes of detected traffic signs. One-dimensional feature vectors of inner core shapes are computed by central projection transformation. Fourthly, these vectors are input to the trained probabilistic neural networks for traffic sign recognition. Lastly, recognition results in left image are compared with recognition results in right image. If results in stereo images are identical, these results are confirmed as final recognition results. The new algorithm is applied to 220 real images of natural scenes taken by the vehicle-borne mobile photogrammetry system in Nanjing at different time. Experimental results show a detection and recognition rate of over 92%. So the algorithm is not only simple, but also reliable and high-speed on real traffic sign detection and recognition. Furthermore, it can obtain geometrical information of traffic signs at the same time of recognizing their types.

Gaussian Graphical Models Identify Networks of Dietary Intake in a German Adult Population.

PubMed

Iqbal, Khalid; Buijsse, Brian; Wirth, Janine; Schulze, Matthias B; Floegel, Anna; Boeing, Heiner

2016-03-01

Data-reduction methods such as principal component analysis are often used to derive dietary patterns. However, such methods do not assess how foods are consumed in relation to each other. Gaussian graphical models (GGMs) are a set of novel methods that can address this issue. We sought to apply GGMs to derive sex-specific dietary intake networks representing consumption patterns in a German adult population. Dietary intake data from 10,780 men and 16,340 women of the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort were cross-sectionally analyzed to construct dietary intake networks. Food intake for each participant was estimated using a 148-item food-frequency questionnaire that captured the intake of 49 food groups. GGMs were applied to log-transformed intakes (grams per day) of 49 food groups to construct sex-specific food networks. Semiparametric Gaussian copula graphical models (SGCGMs) were used to confirm GGM results. In men, GGMs identified 1 major dietary network that consisted of intakes of red meat, processed meat, cooked vegetables, sauces, potatoes, cabbage, poultry, legumes, mushrooms, soup, and whole-grain and refined breads. For women, a similar network was identified with the addition of fried potatoes. Other identified networks consisted of dairy products and sweet food groups. SGCGMs yielded results comparable to those of GGMs. GGMs are a powerful exploratory method that can be used to construct dietary networks representing dietary intake patterns that reveal how foods are consumed in relation to each other. GGMs indicated an apparent major role of red meat intake in a consumption pattern in the studied population. In the future, identified networks might be transformed into pattern scores for investigating their associations with health outcomes. © 2016 American Society for Nutrition.

Vulnerability of animal trade networks to the spread of infectious diseases: a methodological approach applied to evaluation and emergency control strategies in cattle, France, 2005.

PubMed

Rautureau, S; Dufour, B; Durand, B

2011-04-01

Besides farming, trade of livestock is a major component of agricultural economy. However, the networks generated by live animal movements are the major support for the propagation of infectious agents between farms, and their structure strongly affects how fast a disease may spread. Structural characteristics may thus be indicators of network vulnerability to the spread of infectious disease. The method proposed here is based upon the analysis of specific subnetworks: the giant strongly connected components (GSCs). Their existence, size and geographic extent are used to assess network vulnerability. Their disappearance when targeted nodes are removed allows studying how network vulnerability may be controlled under emergency conditions. The method was applied to the cattle trade network in France, 2005. Giant strongly connected components were present and widely spread all over the country in yearly, monthly and weekly networks. Among several tested approaches, the most efficient way to make GSCs disappear was based on the ranking of nodes by decreasing betweenness centrality (the proportion of shortest paths between nodes on which a specific node lies). Giant strongly connected components disappearance was obtained after removal of <1% of network nodes. Under emergency conditions, suspending animal trade activities in a small subset of holdings may thus allow to control the spread of an infectious disease through the animal trade network. Nodes representing markets and dealers were widely affected by these simulated control measures. This confirms their importance as 'hubs' for infectious diseases spread. Besides emergency conditions, specific sensitization and preventive measures should be dedicated to this population. © 2010 Blackwell Verlag GmbH.

Integrative network analysis unveils convergent molecular pathways in Parkinson's disease and diabetes.

PubMed

Santiago, Jose A; Potashkin, Judith A

2013-01-01

Shared dysregulated pathways may contribute to Parkinson's disease and type 2 diabetes, chronic diseases that afflict millions of people worldwide. Despite the evidence provided by epidemiological and gene profiling studies, the molecular and functional networks implicated in both diseases, have not been fully explored. In this study, we used an integrated network approach to investigate the extent to which Parkinson's disease and type 2 diabetes are linked at the molecular level. Using a random walk algorithm within the human functional linkage network we identified a molecular cluster of 478 neighboring genes closely associated with confirmed Parkinson's disease and type 2 diabetes genes. Biological and functional analysis identified the protein serine-threonine kinase activity, MAPK cascade, activation of the immune response, and insulin receptor and lipid signaling as convergent pathways. Integration of results from microarrays studies identified a blood signature comprising seven genes whose expression is dysregulated in Parkinson's disease and type 2 diabetes. Among this group of genes, is the amyloid precursor protein (APP), previously associated with neurodegeneration and insulin regulation. Quantification of RNA from whole blood of 192 samples from two independent clinical trials, the Harvard Biomarker Study (HBS) and the Prognostic Biomarker Study (PROBE), revealed that expression of APP is significantly upregulated in Parkinson's disease patients compared to healthy controls. Assessment of biomarker performance revealed that expression of APP could distinguish Parkinson's disease from healthy individuals with a diagnostic accuracy of 80% in both cohorts of patients. These results provide the first evidence that Parkinson's disease and diabetes are strongly linked at the molecular level and that shared molecular networks provide an additional source for identifying highly sensitive biomarkers. Further, these results suggest for the first time that increased expression of APP in blood may modulate the neurodegenerative phenotype in type 2 diabetes patients.

Is neonatal neurological damage in the delivery room avoidable? Experience of 33 levels I and II maternity units of a French perinatal network.

PubMed

Dupuis, O; Dupont, C; Gaucherand, P; Rudigoz, R-C; Fernandez, M P; Peigne, E; Labaune, J M

2007-09-01

To determine the frequency of avoidable neonatal neurological damage. We carried out a retrospective study from January 1st to December 31st 2003, including all children transferred from a level I or II maternity unit for suspected neurological damage (SND). Only cases confirmed by a persistent abnormality on clinical examination, EEG, transfontanelle ultrasound scan, CT scan or cerebral MRI were retained. Each case was studied in detail by an expert committee and classified as "avoidable", "unavoidable" or "of indeterminate avoidability." The management of "avoidable" cases was analysed to identify potentially avoidable factors (PAFs): not taking into account a major risk factor (PAF1), diagnostic errors (PAF2), suboptimal decision to delivery interval (PAF3) and mechanical complications (PAF4). In total, 77 children were transferred for SND; two cases were excluded (inaccessible medical files). Forty of the 75 cases of SND included were confirmed: 29 were "avoidable", 8 were "unavoidable" and 3 were "of indeterminate avoidability". Analysis of the 29 avoidable cases identified 39 PAFs: 18 PAF1, 5 PAF2, 10 PAF3 and 6 PAF4. Five had no classifiable PAF (0 death), 11 children had one type of PAF (one death), 11 children had two types of PAF (3 deaths), 2 had three types of PAF (2 deaths). Three quarters of the confirmed cases of neurological damage occurring in levels I and II maternity units of the Aurore network in 2003 were avoidable. Five out of six cases resulting in early death involved several potentially avoidable factors.

Functional segregation of the human cingulate cortex is confirmed by functional connectivity based neuroanatomical parcellation.

PubMed

Yu, Chunshui; Zhou, Yuan; Liu, Yong; Jiang, Tianzi; Dong, Haiwei; Zhang, Yunting; Walter, Martin

2011-02-14

The four-region model with 7 specified subregions represents a theoretical construct of functionally segregated divisions of the cingulate cortex based on integrated neurobiological assessments. Under this framework, we aimed to investigate the functional specialization of the human cingulate cortex by analyzing the resting-state functional connectivity (FC) of each subregion from a network perspective. In 20 healthy subjects we systematically investigated the FC patterns of the bilateral subgenual (sACC) and pregenual (pACC) anterior cingulate cortices, anterior (aMCC) and posterior (pMCC) midcingulate cortices, dorsal (dPCC) and ventral (vPCC) posterior cingulate cortices and retrosplenial cortices (RSC). We found that each cingulate subregion was specifically integrated in the predescribed functional networks and showed anti-correlated resting-state fluctuations. The sACC and pACC were involved in an affective network and anti-correlated with the sensorimotor and cognitive networks, while the pACC also correlated with the default-mode network and anti-correlated with the visual network. In the midcingulate cortex, however, the aMCC was correlated with the cognitive and sensorimotor networks and anti-correlated with the visual, affective and default-mode networks, whereas the pMCC only correlated with the sensorimotor network and anti-correlated with the cognitive and visual networks. The dPCC and vPCC involved in the default-mode network and anti-correlated with the sensorimotor, cognitive and visual networks, in contrast, the RSC was mainly correlated with the PCC and thalamus. Based on a strong hypothesis driven approach of anatomical partitions of the cingulate cortex, we could confirm their segregation in terms of functional neuroanatomy, as suggested earlier by task studies or exploratory multi-seed investigations. Copyright © 2010 Elsevier Inc. All rights reserved.

Mechanical, dielectric, and physicochemical properties of impregnating resin based on unsaturated polyesterimides

NASA Astrophysics Data System (ADS)

Fetouhi, Louiza; Petitgas, Benoit; Dantras, Eric; Martinez-Vega, Juan

2017-10-01

This work aims to characterize the dielectric and the mechanical properties of a resin based on an unsaturated polyesterimide diluted in methacrylate reactive diluents used in the impregnation of rotating machines. The broadband dielectric spectrometry and the dynamic mechanical analysis were used to quantify the changes in dielectric and mechanical properties of the network PEI resin, as a function of temperature and frequency. The network characterizations highlight the presence of two main relaxations, α and α', confirmed by the differential scanning calorimetry analysis, showing the complexity of the chemical composition of this resin. The dielectric spectroscopy shows a significant increase in the dielectric values due to an increase of the material conductivity, while the mechanical spectroscopy shows an important decrease of the polymer rigidity and viscosity expressed by an important decrease in the storage modulus. The PEI resin shows a high reactivity when it is submitted in successive heating ramps, which involves in a post-cross-linking reaction. Contribution to the topical issue "Electrical Engineering Symposium (SGE 2016)", edited by Adel Razek

Structured plant metabolomics for the simultaneous exploration of multiple factors.

PubMed

Vasilev, Nikolay; Boccard, Julien; Lang, Gerhard; Grömping, Ulrike; Fischer, Rainer; Goepfert, Simon; Rudaz, Serge; Schillberg, Stefan

2016-11-17

Multiple factors act simultaneously on plants to establish complex interaction networks involving nutrients, elicitors and metabolites. Metabolomics offers a better understanding of complex biological systems, but evaluating the simultaneous impact of different parameters on metabolic pathways that have many components is a challenging task. We therefore developed a novel approach that combines experimental design, untargeted metabolic profiling based on multiple chromatography systems and ionization modes, and multiblock data analysis, facilitating the systematic analysis of metabolic changes in plants caused by different factors acting at the same time. Using this method, target geraniol compounds produced in transgenic tobacco cell cultures were grouped into clusters based on their response to different factors. We hypothesized that our novel approach may provide more robust data for process optimization in plant cell cultures producing any target secondary metabolite, based on the simultaneous exploration of multiple factors rather than varying one factor each time. The suitability of our approach was verified by confirming several previously reported examples of elicitor-metabolite crosstalk. However, unravelling all factor-metabolite networks remains challenging because it requires the identification of all biochemically significant metabolites in the metabolomics dataset.

A 40 GHz fully integrated circuit with a vector network analyzer and a coplanar-line-based detection area for circulating tumor cell analysis using 65 nm CMOS technology

NASA Astrophysics Data System (ADS)

Nakanishi, Taiki; Matsunaga, Maya; Kobayashi, Atsuki; Nakazato, Kazuo; Niitsu, Kiichi

2018-03-01

A 40-GHz fully integrated CMOS-based circuit for circulating tumor cells (CTC) analysis, consisting of an on-chip vector network analyzer (VNA) and a highly sensitive coplanar-line-based detection area is presented in this paper. In this work, we introduce a fully integrated architecture that eliminates unwanted parasitic effects. The proposed analyzer was designed using 65 nm CMOS technology, and SPICE and MWS simulations were used to validate its operation. The simulation confirmed that the proposed circuit can measure S-parameter shifts resulting from the addition of various types of tumor cells to the detection area, the data of which are provided in a previous study: the |S 21| values for HepG2, A549, and HEC-1-A cells are -0.683, -0.580, and -0.623 dB, respectively. Additionally, the measurement demonstrated an S-parameters reduction of -25.7% when a silicone resin was put on the circuit. Hence, the proposed system is expected to contribute to cancer diagnosis.

Performance Analysis of Control Signal Transmission Technique for Cognitive Radios in Dynamic Spectrum Access Networks

NASA Astrophysics Data System (ADS)

Sakata, Ren; Tomioka, Tazuko; Kobayashi, Takahiro

When cognitive radio (CR) systems dynamically use the frequency band, a control signal is necessary to indicate which carrier frequencies are currently available in the network. In order to keep efficient spectrum utilization, this control signal also should be transmitted based on the channel conditions. If transmitters dynamically select carrier frequencies, receivers have to receive control signals without knowledge of their carrier frequencies. To enable such transmission and reception, this paper proposes a novel scheme called DCPT (Differential Code Parallel Transmission). With DCPT, receivers can receive low-rate information with no knowledge of the carrier frequencies. The transmitter transmits two signals whose carrier frequencies are spaced by a predefined value. The absolute values of the carrier frequencies can be varied. When the receiver acquires the DCPT signal, it multiplies the signal by a frequency-shifted version of the signal; this yields a DC component that represents the data signal which is then demodulated. The performance was evaluated by means of numerical analysis and computer simulation. We confirmed that DCPT operates successfully even under severe interference if its parameters are appropriately configured.

Artificial neural network model of the relationship between Betula pollen and meteorological factors in Szczecin (Poland)

NASA Astrophysics Data System (ADS)

Puc, Małgorzata

2012-03-01

Birch pollen is one of the main causes of allergy during spring and early summer in northern and central Europe. The aim of this study was to create a forecast model that can accurately predict daily average concentrations of Betula sp. pollen grains in the atmosphere of Szczecin, Poland. In order to achieve this, a novel data analysis technique—artificial neural networks (ANN)—was used. Sampling was carried out using a volumetric spore trap of the Hirst design in Szczecin during 2003-2009. Spearman's rank correlation analysis revealed that humidity had a strong negative correlation with Betula pollen concentrations. Significant positive correlations were observed for maximum temperature, average temperature, minimum temperature and precipitation. The ANN resulted in multilayer perceptrons 366 8: 2928-7-1:1, time series prediction was of quite high accuracy (SD Ratio between 0.3 and 0.5, R > 0.85). Direct comparison of the observed and calculated values confirmed good performance of the model and its ability to recreate most of the variation.

Biosorption of Acid Black 172 and Congo Red from aqueous solution by nonviable Penicillium YW 01: kinetic study, equilibrium isotherm and artificial neural network modeling.

PubMed

Yang, Yuyi; Wang, Guan; Wang, Bing; Li, Zeli; Jia, Xiaoming; Zhou, Qifa; Zhao, Yuhua

2011-01-01

The main objective of this work was to investigate the biosorption performance of nonviable Penicillium YW 01 biomass for removal of Acid Black 172 metal-complex dye (AB) and Congo Red (CR) in solutions. Maximum biosorption capacities of 225.38 and 411.53 mg g(-1) under initial dye concentration of 800 mg L(-1), pH 3.0 and 40 °C conditions were observed for AB and CR, respectively. Biosorption data were successfully described with Langmuir isotherm and the pseudo-second-order kinetic model. The Weber-Morris model analysis indicated that intraparticle diffusion was the limiting step for biosorption of AB and CR onto biosorbent. Analysis based on the artificial neural network and genetic algorithms hybrid model indicated that initial dye concentration and temperature appeared to be the most influential parameters for biosorption process of AB and CR onto biosorbent, respectively. Characterization of the biosorbent and possible dye-biosorbent interaction were confirmed by Fourier transform infrared spectroscopy and scanning electron microscopy. Copyright © 2010 Elsevier Ltd. All rights reserved.

How does investment in research training affect the development of research networks and collaborations?

PubMed

Paina, Ligia; Ssengooba, Freddie; Waswa, Douglas; M'imunya, James M; Bennett, Sara

2013-05-20

Whether and how research training programs contribute to research network development is underexplored. The Fogarty International Center (FIC) has supported overseas research training programs for over two decades. FIC programs could provide an entry point in the development of research networks and collaborations. We examine whether FIC's investment in research training contributed to the development of networks and collaborations in two countries with longstanding FIC investments - Uganda and Kenya - and the factors which facilitated this process. As part of two case studies at Uganda's Makerere University and Kenya's University of Nairobi, we conducted 53 semi-structured in-depth interviews and nine focus group discussions. To expand on our case study findings, we conducted a focused bibliometric analysis on two purposively selected topic areas to examine scientific productivity and used online network illustration tools to examine the resulting network structures. FIC support made important contributions to network development. Respondents from both Uganda and Kenya confirmed that FIC programs consistently provided trainees with networking skills and exposure to research collaborations, primarily within the institutions implementing FIC programs. In both countries, networks struggled with inclusiveness, particularly in HIV/AIDS research. Ugandan respondents perceived their networks to be more cohesive than Kenyan respondents did. Network cohesiveness was positively correlated with the magnitude and longevity of FIC's programs. Support from FIC grants to local and regional research network development and networking opportunities, such as conferences, was rare. Synergies between FIC programs and research grants helped to solidify and maintain research collaborations. Networks developed where FIC's programs focused on a particular institution, there was a critical mass of trainees with similar interests, and investments for network development were available from early implementation. Networks were less likely to emerge where FIC efforts were thinly scattered across multiple institutions. The availability of complementary research grants created opportunities for researchers to collaborate in grant writing, research implementation, and publications. FIC experiences in Uganda and Kenya showcase the important role of research training programs in creating and sustaining research networks. FIC programs should consider including support to research networks more systematically in their capacity development agenda.

Resource utilization and cost of influenza requiring hospitalization in Canadian adults: A study from the serious outcomes surveillance network of the Canadian Immunization Research Network.

PubMed

Ng, Carita; Ye, Lingyun; Noorduyn, Stephen G; Hux, Margaret; Thommes, Edward; Goeree, Ron; Ambrose, Ardith; Andrew, Melissa K; Hatchette, Todd; Boivin, Guy; Bowie, William; ElSherif, May; Green, Karen; Johnstone, Jennie; Katz, Kevin; Leblanc, Jason; Loeb, Mark; MacKinnon-Cameron, Donna; McCarthy, Anne; McElhaney, Janet; McGeer, Allison; Poirier, Andre; Powis, Jeff; Richardson, David; Sharma, Rohita; Semret, Makeda; Smith, Stephanie; Smyth, Daniel; Stiver, Grant; Trottier, Sylvie; Valiquette, Louis; Webster, Duncan; McNeil, Shelly A

2018-03-01

Consideration of cost determinants is crucial to inform delivery of public vaccination programs. To estimate the average total cost of laboratory-confirmed influenza requiring hospitalization in Canadians prior to, during, and 30 days following discharge. To analyze effects of patient/disease characteristics, treatment, and regional differences in costs. Study utilized previously recorded clinical characteristics, resource use, and outcomes of laboratory-confirmed influenza patients admitted to hospitals in the Serious Outcomes Surveillance (SOS), Canadian Immunization Research Network (CIRN), from 2010/11 to 2012/13. Unit costs including hospital overheads were linked to inpatient/outpatient resource utilization before and after admissions. Dataset included 2943 adult admissions to 17 SOS Network hospitals and 24 Toronto Invasive Bacterial Disease Network hospitals. Mean age was 69.5 years. Average hospital stay was 10.8 days (95% CI: 10.3, 11.3), general ward stays were 9.4 days (95% CI: 9.0, 9.8), and ICU stays were 9.8 days (95% CI: 8.6, 11.1) for the 14% of patients admitted to the ICU. Average cost per case was $14 612 CAD (95% CI: $13 852, $15 372) including $133 (95% CI: $116, $150) for medical care prior to admission, $14 031 (95% CI: $13 295, $14 768) during initial hospital stay, $447 (95% CI: $271, $624) post-discharge, including readmission within 30 days. The cost of laboratory-confirmed influenza was higher than previous estimates, driven mostly by length of stay and analyzing only laboratory-confirmed influenza cases. The true per-patient cost of influenza-related hospitalization has been underestimated, and prevention programs should be evaluated in this context. © 2017 The Authors. Influenza and Other Respiratory Viruses Published by John Wiley & Sons Ltd.

Statistical Mechanical Analysis of Online Learning with Weight Normalization in Single Layer Perceptron

NASA Astrophysics Data System (ADS)

Yoshida, Yuki; Karakida, Ryo; Okada, Masato; Amari, Shun-ichi

2017-04-01

Weight normalization, a newly proposed optimization method for neural networks by Salimans and Kingma (2016), decomposes the weight vector of a neural network into a radial length and a direction vector, and the decomposed parameters follow their steepest descent update. They reported that learning with the weight normalization achieves better converging speed in several tasks including image recognition and reinforcement learning than learning with the conventional parameterization. However, it remains theoretically uncovered how the weight normalization improves the converging speed. In this study, we applied a statistical mechanical technique to analyze on-line learning in single layer linear and nonlinear perceptrons with weight normalization. By deriving order parameters of the learning dynamics, we confirmed quantitatively that weight normalization realizes fast converging speed by automatically tuning the effective learning rate, regardless of the nonlinearity of the neural network. This property is realized when the initial value of the radial length is near the global minimum; therefore, our theory suggests that it is important to choose the initial value of the radial length appropriately when using weight normalization.

Construction of diagnosis system and gene regulatory networks based on microarray analysis.

PubMed

Hong, Chun-Fu; Chen, Ying-Chen; Chen, Wei-Chun; Tu, Keng-Chang; Tsai, Meng-Hsiun; Chan, Yung-Kuan; Yu, Shyr Shen

2018-05-01

A microarray analysis generally contains expression data of thousands of genes, but most of them are irrelevant to the disease of interest, making analyzing the genes concerning specific diseases complicated. Therefore, filtering out a few essential genes as well as their regulatory networks is critical, and a disease can be easily diagnosed just depending on the expression profiles of a few critical genes. In this study, a target gene screening (TGS) system, which is a microarray-based information system that integrates F-statistics, pattern recognition matching, a two-layer K-means classifier, a Parameter Detection Genetic Algorithm (PDGA), a genetic-based gene selector (GBG selector) and the association rule, was developed to screen out a small subset of genes that can discriminate malignant stages of cancers. During the first stage, F-statistic, pattern recognition matching, and a two-layer K-means classifier were applied in the system to filter out the 20 critical genes most relevant to ovarian cancer from 9600 genes, and the PDGA was used to decide the fittest values of the parameters for these critical genes. Among the 20 critical genes, 15 are associated with cancer progression. In the second stage, we further employed a GBG selector and the association rule to screen out seven target gene sets, each with only four to six genes, and each of which can precisely identify the malignancy stage of ovarian cancer based on their expression profiles. We further deduced the gene regulatory networks of the 20 critical genes by applying the Pearson correlation coefficient to evaluate the correlationship between the expression of each gene at the same stages and at different stages. Correlationships between gene pairs were calculated, and then, three regulatory networks were deduced. Their correlationships were further confirmed by the Ingenuity pathway analysis. The prognostic significances of the genes identified via regulatory networks were examined using online tools, and most represented biomarker candidates. In summary, our proposed system provides a new strategy to identify critical genes or biomarkers, as well as their regulatory networks, from microarray data. Copyright © 2018. Published by Elsevier Inc.

Transcriptional Networks in Single Perivascular Cells Sorted from Human Adipose Tissue Reveal a Hierarchy of Mesenchymal Stem Cells.

PubMed

Hardy, W Reef; Moldovan, Nicanor I; Moldovan, Leni; Livak, Kenneth J; Datta, Krishna; Goswami, Chirayu; Corselli, Mirko; Traktuev, Dmitry O; Murray, Iain R; Péault, Bruno; March, Keith

2017-05-01

Adipose tissue is a rich source of multipotent mesenchymal stem-like cells, located in the perivascular niche. Based on their surface markers, these have been assigned to two main categories: CD31 - /CD45 - /CD34 + /CD146 - cells (adventitial stromal/stem cells [ASCs]) and CD31 - /CD45 - /CD34 - /CD146 + cells (pericytes [PCs]). These populations display heterogeneity of unknown significance. We hypothesized that aldehyde dehydrogenase (ALDH) activity, a functional marker of primitivity, could help to better define ASC and PC subclasses. To this end, the stromal vascular fraction from a human lipoaspirate was simultaneously stained with fluorescent antibodies to CD31, CD45, CD34, and CD146 antigens and the ALDH substrate Aldefluor, then sorted by fluorescence-activated cell sorting. Individual ASCs (n = 67) and PCs (n = 73) selected from the extremities of the ALDH-staining spectrum were transcriptionally profiled by Fluidigm single-cell quantitative polymerase chain reaction for a predefined set (n = 429) of marker genes. To these single-cell data, we applied differential expression and principal component and clustering analysis, as well as an original gene coexpression network reconstruction algorithm. Despite the stochasticity at the single-cell level, covariation of gene expression analysis yielded multiple network connectivity parameters suggesting that these perivascular progenitor cell subclasses possess the following order of maturity: (a) ALDH br ASC (most primitive); (b) ALDH dim ASC; (c) ALDH br PC; (d) ALDH dim PC (least primitive). This order was independently supported by specific combinations of class-specific expressed genes and further confirmed by the analysis of associated signaling pathways. In conclusion, single-cell transcriptional analysis of four populations isolated from fat by surface markers and enzyme activity suggests a developmental hierarchy among perivascular mesenchymal stem cells supported by markers and coexpression networks. Stem Cells 2017;35:1273-1289. © 2017 AlphaMed Press.

Applying artificial neural networks to predict communication risks in the emergency department.

PubMed

Bagnasco, Annamaria; Siri, Anna; Aleo, Giuseppe; Rocco, Gennaro; Sasso, Loredana

2015-10-01

To describe the utility of artificial neural networks in predicting communication risks. In health care, effective communication reduces the risk of error. Therefore, it is important to identify the predictive factors of effective communication. Non-technical skills are needed to achieve effective communication. This study explores how artificial neural networks can be applied to predict the risk of communication failures in emergency departments. A multicentre observational study. Data were collected between March-May 2011 by observing the communication interactions of 840 nurses with their patients during their routine activities in emergency departments. The tools used for our observation were a questionnaire to collect personal and descriptive data, level of training and experience and Guilbert's observation grid, applying the Situation-Background-Assessment-Recommendation technique to communication in emergency departments. A total of 840 observations were made on the nurses working in the emergency departments. Based on Guilbert's observation grid, the output variables is likely to influence the risk of communication failure were 'terminology'; 'listening'; 'attention' and 'clarity', whereas nurses' personal characteristics were used as input variables in the artificial neural network model. A model based on the multilayer perceptron topology was developed and trained. The receiver operator characteristic analysis confirmed that the artificial neural network model correctly predicted the performance of more than 80% of the communication failures. The application of the artificial neural network model could offer a valid tool to forecast and prevent harmful communication errors in the emergency department. © 2015 John Wiley & Sons Ltd.

Multiscale Aspects of Generation of High-Gamma Activity during Seizures in Human Neocortex123

PubMed Central

Marcuccilli, Charles J.; Ben-Mabrouk, Faiza; Lew, Sean M.; Goodman, Robert R.; McKhann, Guy M.; Frim, David M.; Kohrman, Michael H.; Schevon, Catherine A.; van Drongelen, Wim

2016-01-01

High-gamma (HG; 80-150 Hz) activity in macroscopic clinical records is considered a marker for critical brain regions involved in seizure initiation; it is correlated with pathological multiunit firing during neocortical seizures in the seizure core, an area identified by correlated multiunit spiking and low frequency seizure activity. However, the effects of the spatiotemporal dynamics of seizure on HG power generation are not well understood. Here, we studied HG generation and propagation, using a three-step, multiscale signal analysis and modeling approach. First, we analyzed concurrent neuronal and microscopic network HG activity in neocortical slices from seven intractable epilepsy patients. We found HG activity in these networks, especially when neurons displayed paroxysmal depolarization shifts and network activity was highly synchronized. Second, we examined HG activity acquired with microelectrode arrays recorded during human seizures (n = 8). We confirmed the presence of synchronized HG power across microelectrode records and the macroscale, both specifically associated with the core region of the seizure. Third, we used volume conduction-based modeling to relate HG activity and network synchrony at different network scales. We showed that local HG oscillations require high levels of synchrony to cross scales, and that this requirement is met at the microscopic scale, but not within macroscopic networks. Instead, we present evidence that HG power at the macroscale may result from harmonics of ongoing seizure activity. Ictal HG power marks the seizure core, but the generating mechanism can differ across spatial scales. PMID:27257623

Cluster analysis of phytoplankton data collected from the National Stream Quality Accounting Network in the Tennessee River basin, 1974-81

USGS Publications Warehouse

Stephens, D.W.; Wangsgard, J.B.

1988-01-01

A computer program, Numerical Taxonomy System of Multivariate Statistical Programs (NTSYS), was used with interfacing software to perform cluster analyses of phytoplankton data stored in the biological files of the U.S. Geological Survey. The NTSYS software performs various types of statistical analyses and is capable of handling a large matrix of data. Cluster analyses were done on phytoplankton data collected from 1974 to 1981 at four national Stream Quality Accounting Network stations in the Tennessee River basin. Analysis of the changes in clusters of phytoplankton genera indicated possible changes in the water quality of the French Broad River near Knoxville, Tennessee. At this station, the most common diatom groups indicated a shift in dominant forms with some of the less common diatoms being replaced by green and blue-green algae. There was a reduction in genera variability between 1974-77 and 1979-81 sampling periods. Statistical analysis of chloride and dissolved solids confirmed that concentrations of these substances were smaller in 1974-77 than in 1979-81. At Pickwick Landing Dam, the furthest downstream station used in the study, there was an increase in the number of genera of ' rare ' organisms with time. The appearance of two groups of green and blue-green algae indicated that an increase in temperature or nutrient concentrations occurred from 1974 to 1981, but this could not be confirmed using available water quality data. Associations of genera forming the phytoplankton communities at three stations on the Tennessee River were found to be seasonal. Nodal analysis of combined data from all four stations used in the study did not identify any seasonal or temporal patterns during 1974-81. Cluster analysis using the NYSYS programs was effective in reducing the large phytoplankton data set to a manageable size and provided considerable insight into the structure of phytoplankton communities in the Tennessee River basin. Problems encountered using cluster analysis were the subjectivity introduced in the definition of meaningful clusters, and the lack of taxonomic identification to the species level. (Author 's abstract)

Midfrontal Theta and Posterior Parietal Alpha Band Oscillations Support Conflict Resolution in a Masked Affective Priming Task.

PubMed

Jiang, Jun; Bailey, Kira; Xiao, Xiao

2018-01-01

Past attempts to characterize the neural mechanisms of affective priming have conceptualized it in terms of classic cognitive conflict, but have not examined the neural oscillatory mechanisms of subliminal affective priming. Using behavioral and electroencephalogram (EEG) time frequency (TF) analysis, the current study examines the oscillatory dynamics of unconsciously triggered conflict in an emotional facial expressions version of the masked affective priming task. The results demonstrate that the power dynamics of conflict are characterized by increased midfrontal theta activity and suppressed parieto-occipital alpha activity. Across-subject and within-trial correlation analyses further confirmed this pattern. Phase synchrony and Granger causality analyses (GCAs) revealed that the fronto-parietal network was involved in unconscious conflict detection and resolution. Our findings support a response conflict account of affective priming, and reveal the role of the fronto-parietal network in unconscious conflict control.

Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing

NASA Astrophysics Data System (ADS)

Shao, Haidong; Jiang, Hongkai; Zhang, Haizhou; Duan, Wenjing; Liang, Tianchen; Wu, Shuaipeng

2018-02-01

The vibration signals collected from rolling bearing are usually complex and non-stationary with heavy background noise. Therefore, it is a great challenge to efficiently learn the representative fault features of the collected vibration signals. In this paper, a novel method called improved convolutional deep belief network (CDBN) with compressed sensing (CS) is developed for feature learning and fault diagnosis of rolling bearing. Firstly, CS is adopted for reducing the vibration data amount to improve analysis efficiency. Secondly, a new CDBN model is constructed with Gaussian visible units to enhance the feature learning ability for the compressed data. Finally, exponential moving average (EMA) technique is employed to improve the generalization performance of the constructed deep model. The developed method is applied to analyze the experimental rolling bearing vibration signals. The results confirm that the developed method is more effective than the traditional methods.

Analysis of Papaya Cell Wall-Related Genes during Fruit Ripening Indicates a Central Role of Polygalacturonases during Pulp Softening

PubMed Central

Fabi, João Paulo; Broetto, Sabrina Garcia; da Silva, Sarah Lígia Garcia Leme; Zhong, Silin; Lajolo, Franco Maria; do Nascimento, João Roberto Oliveira

2014-01-01

Papaya (Carica papaya L.) is a climacteric fleshy fruit that undergoes dramatic changes during ripening, most noticeably a severe pulp softening. However, little is known regarding the genetics of the cell wall metabolism in papayas. The present work describes the identification and characterization of genes related to pulp softening. We used gene expression profiling to analyze the correlations and co-expression networks of cell wall-related genes, and the results suggest that papaya pulp softening is accomplished by the interactions of multiple glycoside hydrolases. The polygalacturonase cpPG1 appeared to play a central role in the network and was further studied. The transient expression of cpPG1 in papaya results in pulp softening and leaf necrosis in the absence of ethylene action and confirms its role in papaya fruit ripening. PMID:25162506

Attributing Crop Production in the United States Using Artificial Neural Network

NASA Astrophysics Data System (ADS)

Ma, Y.; Zhang, Z.; Pan, B.

2017-12-01

Crop production plays key role in supporting life, economy and shaping environment. It is on one hand influenced by natural factors including precipitation, temperature, energy, and on the other hand shaped by the investment of fertilizers, pesticides and human power. Successful attributing of crop production to different factors can help optimize resources and improve productivity. Based on the meteorological records from National Center for Environmental Prediction and state-wise crop production related data provided by the United States Department of Agriculture Economic Research Service, an artificial neural network was constructed to connect crop production with precipitation and temperature anormlies, capital input, labor input, energy input, pesticide consumption and fertilizer consumption. Sensitivity analysis were carried out to attribute their specific influence on crop production for each grid. Results confirmed that the listed factors can generally determine the crop production. Different state response differently to the pertubation of predictands. Their spatial distribution is visulized and discussed.

Construction of flame retardant nanocoating on ramie fabric via layer-by-layer assembly of carbon nanotube and ammonium polyphosphate.

PubMed

Zhang, Tao; Yan, Hongqiang; Peng, Mao; Wang, Lili; Ding, Hongliang; Fang, Zhengping

2013-04-07

A new flame retardant nanocoating has been constructed by the alternate adsorption of polyelectrolyte amino-functionalized multiwall carbon nanotube (MWNT-NH2) and ammonium polyphosphate (APP) onto flexible and porous ramie fabric. Scanning electron microscopy indicates that the adsorbed carbon nanotube coating is a randomly oriented and overlapped network structure, which is a promising candidate for flame retardancy applications. Attenuated total reflection Fourier transform infrared spectroscopy and energy-dispersive X-ray analysis confirm that the APP is successfully incorporated into the multilayers sequentially. Assessment of the thermal and flammability properties for the pristine and nanocoated ramie fabrics shows that the thermal stability, flame retardancy and residual char are enhanced as the concentration of MWNT-NH2 suspension and number of deposition cycles increases. The enhancements are mostly attributed to the barrier effect of intumescent network structure, which is composed of MWNT-NH2 and the absorbed APP.

Acral melanoma detection using a convolutional neural network for dermoscopy images.

PubMed

Yu, Chanki; Yang, Sejung; Kim, Wonoh; Jung, Jinwoong; Chung, Kee-Yang; Lee, Sang Wook; Oh, Byungho

2018-01-01

Acral melanoma is the most common type of melanoma in Asians, and usually results in a poor prognosis due to late diagnosis. We applied a convolutional neural network to dermoscopy images of acral melanoma and benign nevi on the hands and feet and evaluated its usefulness for the early diagnosis of these conditions. A total of 724 dermoscopy images comprising acral melanoma (350 images from 81 patients) and benign nevi (374 images from 194 patients), and confirmed by histopathological examination, were analyzed in this study. To perform the 2-fold cross validation, we split them into two mutually exclusive subsets: half of the total image dataset was selected for training and the rest for testing, and we calculated the accuracy of diagnosis comparing it with the dermatologist's and non-expert's evaluation. The accuracy (percentage of true positive and true negative from all images) of the convolutional neural network was 83.51% and 80.23%, which was higher than the non-expert's evaluation (67.84%, 62.71%) and close to that of the expert (81.08%, 81.64%). Moreover, the convolutional neural network showed area-under-the-curve values like 0.8, 0.84 and Youden's index like 0.6795, 0.6073, which were similar score with the expert. Although further data analysis is necessary to improve their accuracy, convolutional neural networks would be helpful to detect acral melanoma from dermoscopy images of the hands and feet.

Efficient room temperature hydrogen sensor based on UV-activated ZnO nano-network

NASA Astrophysics Data System (ADS)

Kumar, Mohit; Kumar, Rahul; Rajamani, Saravanan; Ranwa, Sapana; Fanetti, Mattia; Valant, Matjaz; Kumar, Mahesh

2017-09-01

Room temperature hydrogen sensors were fabricated from Au embedded ZnO nano-networks using a 30 mW GaN ultraviolet LED. The Au-decorated ZnO nano-networks were deposited on a SiO2/Si substrate by a chemical vapour deposition process. X-ray diffraction (XRD) spectrum analysis revealed a hexagonal wurtzite structure of ZnO and presence of Au. The ZnO nanoparticles were interconnected, forming nano-network structures. Au nanoparticles were uniformly distributed on ZnO surfaces, as confirmed by FESEM imaging. Interdigitated electrodes (IDEs) were fabricated on the ZnO nano-networks using optical lithography. Sensor performances were measured with and without UV illumination, at room temperate, with concentrations of hydrogen varying from 5 ppm to 1%. The sensor response was found to be ˜21.5% under UV illumination and 0% without UV at room temperature for low hydrogen concentration of 5 ppm. The UV-photoactivated mode enhanced the adsorption of photo-induced O- and O2- ions, and the d-band electron transition from the Au nanoparticles to ZnO—which increased the chemisorbed reaction between hydrogen and oxygen. The sensor response was also measured at 150 °C (without UV illumination) and found to be ˜18% at 5 ppm. Energy efficient low cost hydrogen sensors can be designed and fabricated with the combination of GaN UV LEDs and ZnO nanostructures.

Disentangling the neural mechanisms involved in Hinduism- and Buddhism-related meditations.

PubMed

Tomasino, Barbara; Chiesa, Alberto; Fabbro, Franco

2014-10-01

The most diffuse forms of meditation derive from Hinduism and Buddhism spiritual traditions. Different cognitive processes are set in place to reach these meditation states. According to an historical-philological hypothesis (Wynne, 2009) the two forms of meditation could be disentangled. While mindfulness is the focus of Buddhist meditation reached by focusing sustained attention on the body, on breathing and on the content of the thoughts, reaching an ineffable state of nothigness accompanied by a loss of sense of self and duality (Samadhi) is the main focus of Hinduism-inspired meditation. It is possible that these different practices activate separate brain networks. We tested this hypothesis by conducting an activation likelihood estimation (ALE) meta-analysis of functional magnetic resonance imaging (fMRI) studies. The network related to Buddhism-inspired meditation (16 experiments, 263 subjects, and 96 activation foci) included activations in some frontal lobe structures associated with executive attention, possibly confirming the fundamental role of mindfulness shared by many Buddhist meditations. By contrast, the network related to Hinduism-inspired meditation (8 experiments, 54 activation foci and 66 subjects) triggered a left lateralized network of areas including the postcentral gyrus, the superior parietal lobe, the hippocampus and the right middle cingulate cortex. The dissociation between anterior and posterior networks support the notion that different meditation styles and traditions are characterized by different patterns of neural activation. Copyright © 2014. Published by Elsevier Inc.

What Drives Nurses' Blended e-Learning Continuance Intention?

ERIC Educational Resources Information Center

Cheng, Yung-Ming

2014-01-01

This study's purpose was to synthesize the user network (including subjective norm and network externality), task-technology fit (TTF), and expectation-confirmation model (ECM) to explain nurses' intention to continue using the blended electronic learning (e-learning) system within medical institutions. A total of 450 questionnaires were…

Emergence of Rich-Club Topology and Coordinated Dynamics in Development of Hippocampal Functional Networks In Vitro

PubMed Central

Charlesworth, Paul; Kitzbichler, Manfred G.; Paulsen, Ole

2015-01-01

Recent studies demonstrated that the anatomical network of the human brain shows a “rich-club” organization. This complex topological feature implies that highly connected regions, hubs of the large-scale brain network, are more densely interconnected with each other than expected by chance. Rich-club nodes were traversed by a majority of short paths between peripheral regions, underlining their potential importance for efficient global exchange of information between functionally specialized areas of the brain. Network hubs have also been described at the microscale of brain connectivity (so-called “hub neurons”). Their role in shaping synchronous dynamics and forming microcircuit wiring during development, however, is not yet fully understood. The present study aimed to investigate the role of hubs during network development, using multi-electrode arrays and functional connectivity analysis during spontaneous multi-unit activity (MUA) of dissociated primary mouse hippocampal neurons. Over the first 4 weeks in vitro, functional connectivity significantly increased in strength, density, and size, with mature networks demonstrating a robust modular and small-world topology. As expected by a “rich-get-richer” growth rule of network evolution, MUA graphs were found to form rich-clubs at an early stage in development (14 DIV). Later on, rich-club nodes were a consistent topological feature of MUA graphs, demonstrating high nodal strength, efficiency, and centrality. Rich-club nodes were also found to be crucial for MUA dynamics. They often served as broker of spontaneous activity flow, confirming that hub nodes and rich-clubs may play an important role in coordinating functional dynamics at the microcircuit level. PMID:25855164

Implementation of WirelessHART in the NS-2 Simulator and Validation of Its Correctness

PubMed Central

Zand, Pouria; Mathews, Emi; Havinga, Paul; Stojanovski, Spase; Sisinni, Emiliano; Ferrari, Paolo

2014-01-01

One of the first standards in the wireless sensor networks domain, WirelessHART (HART (Highway Addressable Remote Transducer)), was introduced to address industrial process automation and control requirements. This standard can be used as a reference point to evaluate other wireless protocols in the domain of industrial monitoring and control. This makes it worthwhile to set up a reliable WirelessHART simulator in order to achieve that reference point in a relatively easy manner. Moreover, it offers an alternative to expensive testbeds for testing and evaluating the performance of WirelessHART. This paper explains our implementation of WirelessHART in the NS-2 network simulator. According to our knowledge, this is the first implementation that supports the WirelessHART network manager, as well as the whole stack (all OSI (Open Systems Interconnection model) layers) of the WirelessHART standard. It also explains our effort to validate the correctness of our implementation, namely through the validation of the implementation of the WirelessHART stack protocol and of the network manager. We use sniffed traffic from a real WirelessHART testbed installed in the Idrolab plant for these validations. This confirms the validity of our simulator. Empirical analysis shows that the simulated results are nearly comparable to the results obtained from real networks. We also demonstrate the versatility and usability of our implementation by providing some further evaluation results in diverse scenarios. For example, we evaluate the performance of the WirelessHART network by applying incremental interference in a multi-hop network. PMID:24841245

Human-computer interaction reflected in the design of user interfaces for general practitioners.

PubMed

Stoicu-Tivadar, Lacramioara; Stoicu-Tivadar, Vasile

2006-01-01

To address the problem of properly built health information systems in general practice as an important issue for their approval and use in clinical practice. We present how a national general practitioner (GP) network was built, put in practice and several results of its activity seen from the clinician's and the software application team's points of view. We used a multi-level incremental development appropriate for the conditions of the required information system. After the development of the first version of the software components (based on rapid prototyping) of the sentinel network, a questionnaire addressed the needs and improvements required by the health professionals. Based on the answers, the functionality of the system and the interface were improved regarding the real needs expressed by the end-users. The network is functional and the collected data from the network are being processed using statistical methods. The academic software team developed a GP application that is well received by the GPs in the network, as resulted from the survey and discussions during the training period. As an added confirmation, several GPs outside the network enrolled after seeing the software at work. Another confirmation that we did a good job was that after the final presentation of the results of the project a representative from the Romanian Society for Cardiology expressed the wish of this society to access the data yielded by the network.

Engineering anastomosis between living capillary networks and endothelial cell-lined microfluidic channels.

PubMed

Wang, Xiaolin; Phan, Duc T T; Sobrino, Agua; George, Steven C; Hughes, Christopher C W; Lee, Abraham P

2016-01-21

This paper reports a method for generating an intact and perfusable microvascular network that connects to microfluidic channels without appreciable leakage. This platform incorporates different stages of vascular development including vasculogenesis, endothelial cell (EC) lining, sprouting angiogenesis, and anastomosis in sequential order. After formation of a capillary network inside the tissue chamber via vasculogenesis, the adjacent microfluidic channels are lined with a monolayer of ECs, which then serve as the high-pressure input ("artery") and low pressure output ("vein") conduits. To promote a tight interconnection between the artery/vein and the capillary network, sprouting angiogenesis is induced, which promotes anastomosis of the vasculature inside the tissue chamber with the EC lining along the microfluidic channels. Flow of fluorescent microparticles confirms the perfusability of the lumenized microvascular network, and minimal leakage of 70 kDa FITC-dextran confirms physiologic tightness of the EC junctions and completeness of the interconnections between artery/vein and the capillary network. This versatile device design and its robust construction methodology establish a physiological transport model of interconnected perfused vessels from artery to vascularized tissue to vein. The system has utility in a wide range of organ-on-a-chip applications as it enables the physiological vascular interconnection of multiple on-chip tissue constructs that can serve as disease models for drug screening.

The LCO Follow-up and Characterization Network and AgentNEO Citizen Science Project

NASA Astrophysics Data System (ADS)

Lister, Tim; Greenstreet, Sarah; Gomez, Edward; Christensen, Eric J.; Larson, Stephen M.

2017-10-01

The LCO NEO Follow-up Network is using the telescopes of the Las Cumbres Observatory (LCO) and a web-based target selection, scheduling and data reduction system to confirm NEO candidates and characterize radar-targeted known NEOs. Starting in July 2014, the LCO NEO Follow-up Network has observed over 4,500 targets and reported more than 25,000 astrometric and photometric measurements to the Minor Planet Center.The LCO NEO Follow-up Network's main aims are to perform confirming follow-up of the large number of NEO candidates and to perform characterization measurements of radar targets to obtain light curves and rotation rates. The NEO candidates come from the NEO surveys such as Catalina, PanSTARRS, ATLAS, NEOWISE and others. In particular, we are targeting objects in the Southern Hemisphere, where the LCO NEO Follow-up Network is the largest resource for NEO observations.The first phase of the LCO Network comprises nine 1-meter and seven 0.4-meter telescopes at site at McDonald Observatory (Texas), Cerro Tololo (Chile), SAAO (South Africa) and Siding Spring Observatory (Australia). The network has been fully operational since 2014 May, and observations are being executed remotely and robotically. Additional 0.4-meter telescopes will be deployed in 2017 and 2x1-meter telescopes for a site at Ali Observatory, Tibet are planned for 2018-2019.We have developed web-based software called NEOexchange which automatically downloads and aggregates NEO candidates from the Minor Planet Center's NEO Confirmation Page, the Arecibo and Goldstone radar target lists and the NASA lists. NEOexchange allows the planning and scheduling of observations on the LCO Telescope Network and the tracking of the resulting blocks and generated data. We have extended the NEOexchange software to include automated scheduling and moving object detection, with the results presented to the user via the website.We will present results from the LCO NEO Follow-up Network and from the development of the NEOexchange software which is used to schedule, analyze and report observations taken with the LCO Network. In addition, we describe a Citizen Science project, AgentNEO, which uses LCO data to allow the public to find and learn about asteroids.

The "handwriting brain": a meta-analysis of neuroimaging studies of motor versus orthographic processes.

PubMed

Planton, Samuel; Jucla, Mélanie; Roux, Franck-Emmanuel; Démonet, Jean-François

2013-01-01

Handwriting is a modality of language production whose cerebral substrates remain poorly known although the existence of specific regions is postulated. The description of brain damaged patients with agraphia and, more recently, several neuroimaging studies suggest the involvement of different brain regions. However, results vary with the methodological choices made and may not always discriminate between "writing-specific" and motor or linguistic processes shared with other abilities. We used the "Activation Likelihood Estimate" (ALE) meta-analytical method to identify the cerebral network of areas commonly activated during handwriting in 18 neuroimaging studies published in the literature. Included contrasts were also classified according to the control tasks used, whether non-specific motor/output-control or linguistic/input-control. These data were included in two secondary meta-analyses in order to reveal the functional role of the different areas of this network. An extensive, mainly left-hemisphere network of 12 cortical and sub-cortical areas was obtained; three of which were considered as primarily writing-specific (left superior frontal sulcus/middle frontal gyrus area, left intraparietal sulcus/superior parietal area, right cerebellum) while others related rather to non-specific motor (primary motor and sensorimotor cortex, supplementary motor area, thalamus and putamen) or linguistic processes (ventral premotor cortex, posterior/inferior temporal cortex). This meta-analysis provides a description of the cerebral network of handwriting as revealed by various types of neuroimaging experiments and confirms the crucial involvement of the left frontal and superior parietal regions. These findings provide new insights into cognitive processes involved in handwriting and their cerebral substrates. Copyright © 2013 Elsevier Ltd. All rights reserved.

Asymmetry of cortical decline in subtypes of primary progressive aphasia.

PubMed

Rogalski, Emily; Cobia, Derin; Martersteck, Adam; Rademaker, Alfred; Wieneke, Christina; Weintraub, Sandra; Mesulam, M-Marsel

2014-09-23

The aim of this study was to provide quantitative measures of changes in cortical atrophy over a 2-year period associated with 3 subtypes of primary progressive aphasia (PPA) using whole-brain vertex-wise and region-of-interest (ROI) neuroimaging methods. The purpose was to quantitate disease progression, establish an empirical basis for clinical expectations, and provide outcome measures for therapeutic trials. Changes in cortical thickness and volume loss as well as neuropsychological performance were assessed at baseline and 2-year follow-up in 26 patients who fulfilled criteria for logopenic (8 patients), agrammatic (10 patients), and semantic (8 patients) PPA subtypes. Whole-brain vertex-wise and ROI imaging analysis were conducted using the FreeSurfer longitudinal pipeline. Clinical deficits and cortical atrophy patterns showed distinct patterns of change among the subtypes over 2 years. Results confirmed that progression for each of the 3 subtypes showed left greater than right hemisphere asymmetry. An ROI analysis also revealed that progression was greater within, rather than outside, the language network. Preferential neurodegeneration of the left hemisphere language network is a common denominator for all 3 PPA subtypes, even as the disease progresses. Using a focal cortical language network ROI as an outcome measure of disease progression appears to be more sensitive than whole-brain or ventricular volume measures of change and may be helpful for designing future clinical trials in PPA. © 2014 American Academy of Neurology.

Asymmetry of cortical decline in subtypes of primary progressive aphasia

PubMed Central

Cobia, Derin; Martersteck, Adam; Rademaker, Alfred; Wieneke, Christina; Weintraub, Sandra; Mesulam, M.-Marsel

2014-01-01

Objective: The aim of this study was to provide quantitative measures of changes in cortical atrophy over a 2-year period associated with 3 subtypes of primary progressive aphasia (PPA) using whole-brain vertex-wise and region-of-interest (ROI) neuroimaging methods. The purpose was to quantitate disease progression, establish an empirical basis for clinical expectations, and provide outcome measures for therapeutic trials. Methods: Changes in cortical thickness and volume loss as well as neuropsychological performance were assessed at baseline and 2-year follow-up in 26 patients who fulfilled criteria for logopenic (8 patients), agrammatic (10 patients), and semantic (8 patients) PPA subtypes. Whole-brain vertex-wise and ROI imaging analysis were conducted using the FreeSurfer longitudinal pipeline. Results: Clinical deficits and cortical atrophy patterns showed distinct patterns of change among the subtypes over 2 years. Results confirmed that progression for each of the 3 subtypes showed left greater than right hemisphere asymmetry. An ROI analysis also revealed that progression was greater within, rather than outside, the language network. Conclusions: Preferential neurodegeneration of the left hemisphere language network is a common denominator for all 3 PPA subtypes, even as the disease progresses. Using a focal cortical language network ROI as an outcome measure of disease progression appears to be more sensitive than whole-brain or ventricular volume measures of change and may be helpful for designing future clinical trials in PPA. PMID:25165386

Trends in surface ozone over Europe, 1978-1990

NASA Technical Reports Server (NTRS)

Low, Pak Sum; Kelly, P. Michael; Davies, Trevor D.

1994-01-01

It has been suggested that surface ozone concentrations in rural areas of Europe have been increasing at a rate of 1 to 3 percent per year over the past two to three decades, presumably due to human influences (Feister and Warmbt, 1987; Bojkov, 1988; Penkett, 1989). Recently, we have analyzed surface ozone data from 20 European stations of differing character (remote, rural, suburban and urban) for a common period of 1978-1988 (Low et al., 1992). It was found that there were pronounced annual and seasonal variations in the linear trends in different areas, and there was no dominant region-wide trend. In spring and, most notably, summer, stations on the maritime fringe of the network generally exhibited negative trends whilst those located further into the continental interior exhibited positive trends. In winter, most of the stations in the network exhibited positive trends. Relatively few of these trends were statistically significant. This paper updates our earlier analysis by extending the data sets of the network up to the year 1990. The spatial variations in surface ozone trends over the extended period 1978-1990 are examined and discussed in comparison to the 1978-1988 patterns. The update confirms the overall conclusions of the earlier analysis, specifically that caution should be exercised in interpreting the results of trend analyses based on station data representative of a limited period of time and/or geographical area.

A Novel Hybrid Yeast-Human Network Analysis Reveals an Essential Role for FNBP1L in Antibacterial Autophagy1

PubMed Central

Huett, Alan; Ng, Aylwin; Cao, Zhifang; Kuballa, Petric; Komatsu, Masaaki; Daly, Mark J.; Podolsky, Daniel K.; Xavier, Ramnik J.

2009-01-01

Autophagy is a conserved cellular process required for the removal of defective organelles, protein aggregates, and intracellular pathogens. We used a network analysis strategy to identify novel human autophagy components based upon the yeast interactome centered on the core yeast autophagy proteins. This revealed the potential involvement of 14 novel mammalian genes in autophagy, several of which have known or predicted roles in membrane organization or dynamics. We selected one of these membrane interactors, FNBP1L (formin binding protein 1-like), an F-BAR-containing protein (also termed Toca-1), for further study based upon a predicted interaction with ATG3. We confirmed the FNBP1L/ATG3 interaction biochemically and mapped the FNBP1L domains responsible. Using a functional RNA interference approach, we determined that FNBP1L is essential for autophagy of the intracellular pathogen Salmonella enterica serovar Typhimurium and show that the autophagy process serves to restrict the growth of intracellular bacteria. However, FNBP1L appears dispensable for other forms of autophagy induced by serum starvation or rapamycin. We present a model where FNBP1L is essential for autophagy of intracellular pathogens and identify FNBP1L as a differentially used molecule in specific autophagic contexts. By using network biology to derive functional biological information, we demonstrate the utility of integrated genomics to novel molecule discovery in autophagy. PMID:19342671

Combinatorial explosion in model gene networks

NASA Astrophysics Data System (ADS)

Edwards, R.; Glass, L.

2000-09-01

The explosive growth in knowledge of the genome of humans and other organisms leaves open the question of how the functioning of genes in interacting networks is coordinated for orderly activity. One approach to this problem is to study mathematical properties of abstract network models that capture the logical structures of gene networks. The principal issue is to understand how particular patterns of activity can result from particular network structures, and what types of behavior are possible. We study idealized models in which the logical structure of the network is explicitly represented by Boolean functions that can be represented by directed graphs on n-cubes, but which are continuous in time and described by differential equations, rather than being updated synchronously via a discrete clock. The equations are piecewise linear, which allows significant analysis and facilitates rapid integration along trajectories. We first give a combinatorial solution to the question of how many distinct logical structures exist for n-dimensional networks, showing that the number increases very rapidly with n. We then outline analytic methods that can be used to establish the existence, stability and periods of periodic orbits corresponding to particular cycles on the n-cube. We use these methods to confirm the existence of limit cycles discovered in a sample of a million randomly generated structures of networks of 4 genes. Even with only 4 genes, at least several hundred different patterns of stable periodic behavior are possible, many of them surprisingly complex. We discuss ways of further classifying these periodic behaviors, showing that small mutations (reversal of one or a few edges on the n-cube) need not destroy the stability of a limit cycle. Although these networks are very simple as models of gene networks, their mathematical transparency reveals relationships between structure and behavior, they suggest that the possibilities for orderly dynamics in such networks are extremely rich and they offer novel ways to think about how mutations can alter dynamics.

Combinatorial explosion in model gene networks.

PubMed

Edwards, R.; Glass, L.

2000-09-01

The explosive growth in knowledge of the genome of humans and other organisms leaves open the question of how the functioning of genes in interacting networks is coordinated for orderly activity. One approach to this problem is to study mathematical properties of abstract network models that capture the logical structures of gene networks. The principal issue is to understand how particular patterns of activity can result from particular network structures, and what types of behavior are possible. We study idealized models in which the logical structure of the network is explicitly represented by Boolean functions that can be represented by directed graphs on n-cubes, but which are continuous in time and described by differential equations, rather than being updated synchronously via a discrete clock. The equations are piecewise linear, which allows significant analysis and facilitates rapid integration along trajectories. We first give a combinatorial solution to the question of how many distinct logical structures exist for n-dimensional networks, showing that the number increases very rapidly with n. We then outline analytic methods that can be used to establish the existence, stability and periods of periodic orbits corresponding to particular cycles on the n-cube. We use these methods to confirm the existence of limit cycles discovered in a sample of a million randomly generated structures of networks of 4 genes. Even with only 4 genes, at least several hundred different patterns of stable periodic behavior are possible, many of them surprisingly complex. We discuss ways of further classifying these periodic behaviors, showing that small mutations (reversal of one or a few edges on the n-cube) need not destroy the stability of a limit cycle. Although these networks are very simple as models of gene networks, their mathematical transparency reveals relationships between structure and behavior, they suggest that the possibilities for orderly dynamics in such networks are extremely rich and they offer novel ways to think about how mutations can alter dynamics. (c) 2000 American Institute of Physics.

Identification of Stimulated Sites Using Artificial Neural Networks Based on Transcranial Magnetic Stimulation-Elicited Motor Evoked Potentials and Finger Forces

NASA Astrophysics Data System (ADS)

Fukuda, Hiroshi; Odagaki, Masato; Hiwaki, Osamu

Motor evoked potentials (MEPs) elicited by transcranial magnetic stimulation (TMS) over the primary motor cortex (M1) vary in their amplitude from trial to trial. To investigate the functions of motor cortex by TMS, it is necessary to confirm the causal relationship between stimulated sites and variable MEPs. We created artificial neural networks to classify sets of variable MEP signals and finger forces into the corresponding stimulated sites. We conducted TMS at three different positions over M1 and measured MEPs of hand and forearm muscles and forces of the index finger in four subjects. We estimated the sites within motor cortex stimulated by TMS based on cortical columnar structure and nerve excitation properties. Finally, we tried to classify the various MEPs and finger forces into three groups using artificial neural networks. MEPs and finger forces varied from trial to trial, even if the stimulating coil was fixed on the subject's head. Our proposed neural network was able to identify the MEPs and finger forces with the corresponding stimulated sites in M1. We proposed the artificial neural networks to confirm the TMS-stimulated sites using various MEPs and evoked finger forces.

Structural and optical properties of Sb65Se35-xGex thin films

NASA Astrophysics Data System (ADS)

Saleh, S. A.; Al-Hajry, A.; Ali, H. M.

2011-07-01

Sb65Se35-xGex (x=0-20 at.%) thin films, prepared by the electron beam evaporation technique on ultrasonically cleaned glass substrates at 300 K, were investigated. The amorphous structure of the thin films was confirmed by x-ray diffraction analysis. The structure was deduced from the Raman spectra measured for all germanium contents in the Sb-Se-Ge matrix. The absorption coefficient (α) of the films was determined by optical transmission measurements. The compositional dependence of the optical band gap is discussed in light of topological and chemical ordered network models.

Correlation based networks of equity returns sampled at different time horizons

NASA Astrophysics Data System (ADS)

Tumminello, M.; di Matteo, T.; Aste, T.; Mantegna, R. N.

2007-01-01

We investigate the planar maximally filtered graphs of the portfolio of the 300 most capitalized stocks traded at the New York Stock Exchange during the time period 2001 2003. Topological properties such as the average length of shortest paths, the betweenness and the degree are computed on different planar maximally filtered graphs generated by sampling the returns at different time horizons ranging from 5 min up to one trading day. This analysis confirms that the selected stocks compose a hierarchical system progressively structuring as the sampling time horizon increases. Finally, a cluster formation, associated to economic sectors, is quantitatively investigated.

Thermomagnetic processing of liquid-crystalline epoxy resins and their mechanical characterization using nanoindentation.

PubMed

Li, Yuzhan; Rios, Orlando; Kessler, Michael R

2014-11-12

A thermomagnetic processing method was used to produce a biphenyl-based liquid-crystalline epoxy resin (LCER) with oriented liquid-crystalline (LC) domains. The orientation of the LCER was confirmed and quantified using two-dimensional X-ray diffraction. The effect of molecular alignment on the mechanical and thermomechanical properties of the LCER was investigated using nanoindentation and thermomechanical analysis, respectively. The effect of the orientation on the fracture behavior was also examined. The results showed that macroscopic orientation of the LC domains was achieved, resulting in an epoxy network with an anisotropic modulus, hardness, creep behavior, and thermal expansion.

Heterogeneity of link weight and the evolution of cooperation

NASA Astrophysics Data System (ADS)

Iwata, Manabu; Akiyama, Eizo

2016-04-01

In this paper, we investigate the effect of heterogeneity of link weight, heterogeneity of the frequency or amount of interactions among individuals, on the evolution of cooperation. Based on an analysis of the evolutionary prisoner's dilemma game on a weighted one-dimensional lattice network with intra-individual heterogeneity, we confirm that moderate level of link-weight heterogeneity can facilitate cooperation. Furthermore, we identify two key mechanisms by which link-weight heterogeneity promotes the evolution of cooperation: mechanisms for spread and maintenance of cooperation. We also derive the corresponding conditions under which the mechanisms can work through evolutionary dynamics.

Grammatical analysis as a distributed neurobiological function.

PubMed

Bozic, Mirjana; Fonteneau, Elisabeth; Su, Li; Marslen-Wilson, William D

2015-03-01

Language processing engages large-scale functional networks in both hemispheres. Although it is widely accepted that left perisylvian regions have a key role in supporting complex grammatical computations, patient data suggest that some aspects of grammatical processing could be supported bilaterally. We investigated the distribution and the nature of grammatical computations across language processing networks by comparing two types of combinatorial grammatical sequences--inflectionally complex words and minimal phrases--and contrasting them with grammatically simple words. Novel multivariate analyses revealed that they engage a coalition of separable subsystems: inflected forms triggered left-lateralized activation, dissociable into dorsal processes supporting morphophonological parsing and ventral, lexically driven morphosyntactic processes. In contrast, simple phrases activated a consistently bilateral pattern of temporal regions, overlapping with inflectional activations in L middle temporal gyrus. These data confirm the role of the left-lateralized frontotemporal network in supporting complex grammatical computations. Critically, they also point to the capacity of bilateral temporal regions to support simple, linear grammatical computations. This is consistent with a dual neurobiological framework where phylogenetically older bihemispheric systems form part of the network that supports language function in the modern human, and where significant capacities for language comprehension remain intact even following severe left hemisphere damage. Copyright © 2014 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

System design and implementation of digital-image processing using computational grids

NASA Astrophysics Data System (ADS)

Shen, Zhanfeng; Luo, Jiancheng; Zhou, Chenghu; Huang, Guangyu; Ma, Weifeng; Ming, Dongping

2005-06-01

As a special type of digital image, remotely sensed images are playing increasingly important roles in our daily lives. Because of the enormous amounts of data involved, and the difficulties of data processing and transfer, an important issue for current computer and geo-science experts is developing internet technology to implement rapid remotely sensed image processing. Computational grids are able to solve this problem effectively. These networks of computer workstations enable the sharing of data and resources, and are used by computer experts to solve imbalances of network resources and lopsided usage. In China, computational grids combined with spatial-information-processing technology have formed a new technology: namely, spatial-information grids. In the field of remotely sensed images, spatial-information grids work more effectively for network computing, data processing, resource sharing, task cooperation and so on. This paper focuses mainly on the application of computational grids to digital-image processing. Firstly, we describe the architecture of digital-image processing on the basis of computational grids, its implementation is then discussed in detail with respect to the technology of middleware. The whole network-based intelligent image-processing system is evaluated on the basis of the experimental analysis of remotely sensed image-processing tasks; the results confirm the feasibility of the application of computational grids to digital-image processing.

A Network Pharmacology Approach to Determine Active Compounds and Action Mechanisms of Ge-Gen-Qin-Lian Decoction for Treatment of Type 2 Diabetes

PubMed Central

Li, Huiying; Zhao, Linhua; Zhang, Bo; Jiang, Yuyu; Wang, Xu; Guo, Yun; Liu, Hongxing; Li, Shao; Tong, Xiaolin

2014-01-01

Traditional Chinese medicine (TCM) herbal formulae can be valuable therapeutic strategies and drug discovery resources. However, the active ingredients and action mechanisms of most TCM formulae remain unclear. Therefore, the identification of potent ingredients and their actions is a major challenge in TCM research. In this study, we used a network pharmacology approach we previously developed to help determine the potential antidiabetic ingredients from the traditional Ge-Gen-Qin-Lian decoction (GGQLD) formula. We predicted the target profiles of all available GGQLD ingredients to infer the active ingredients by clustering the target profile of ingredients with FDA-approved antidiabetic drugs. We also applied network target analysis to evaluate the links between herbal ingredients and pharmacological actions to help explain the action mechanisms of GGQLD. According to the predicted results, we confirmed that a novel antidiabetic ingredient from Puerariae Lobatae radix (Ge-Gen), 4-Hydroxymephenytoin, increased the insulin secretion in RIN-5F cells and improved insulin resistance in 3T3-L1 adipocytes. The network pharmacology strategy used here provided a powerful means for identifying bioactive ingredients and mechanisms of action for TCM herbal formulae, including Ge-Gen-Qin-Lian decoction. PMID:24527048

Infection with Pathogens Transmitted Commonly Through Food and the Effect of Increasing Use of Culture-Independent Diagnostic Tests on Surveillance--Foodborne Diseases Active Surveillance Network, 10 U.S. Sites, 2012-2015.

PubMed

Huang, Jennifer Y; Henao, Olga L; Griffin, Patricia M; Vugia, Duc J; Cronquist, Alicia B; Hurd, Sharon; Tobin-D'Angelo, Melissa; Ryan, Patricia; Smith, Kirk; Lathrop, Sarah; Zansky, Shelley; Cieslak, Paul R; Dunn, John; Holt, Kristin G; Wolpert, Beverly J; Patrick, Mary E

2016-04-15

To evaluate progress toward prevention of enteric and foodborne illnesses in the United States, the Foodborne Diseases Active Surveillance Network (FoodNet) monitors the incidence of laboratory-confirmed infections caused by nine pathogens transmitted commonly through food in 10 U.S. sites. This report summarizes preliminary 2015 data and describes trends since 2012. In 2015, FoodNet reported 20,107 confirmed cases (defined as culture-confirmed bacterial infections and laboratory-confirmed parasitic infections), 4,531 hospitalizations, and 77 deaths. FoodNet also received reports of 3,112 positive culture-independent diagnostic tests (CIDTs) without culture-confirmation, a number that has markedly increased since 2012. Diagnostic testing practices for enteric pathogens are rapidly moving away from culture-based methods. The continued shift from culture-based methods to CIDTs that do not produce the isolates needed to distinguish between strains and subtypes affects the interpretation of public health surveillance data and ability to monitor progress toward prevention efforts. Expanded case definitions and strategies for obtaining bacterial isolates are crucial during this transition period.

Dysplastic spondylolysis is caused by mutations in the diastrophic dysplasia sulfate transporter gene

PubMed Central

Cai, Tao; Yang, Liu; Cai, Wanshi; Guo, Sen; Yu, Ping; Li, Jinchen; Hu, Xueyu; Yan, Ming; Shao, Qianzhi; Jin, Yan; Sun, Zhong Sheng; Luo, Zhuo-Jing

2015-01-01

Spondylolysis is a fracture in part of the vertebra with a reported prevalence of about 3–6% in the general population. Genetic etiology of this disorder remains unknown. The present study was aimed at identifying genomic mutations in patients with dysplastic spondylolysis as well as the potential pathogenesis of the abnormalities. Whole-exome sequencing and functional analysis were performed for patients with spondylolysis. We identified a novel heterozygous mutation (c.2286A > T; p.D673V) in the sulfate transporter gene SLC26A2 in five affected subjects of a Chinese family. Two additional mutations (e.g., c.1922A > G; p.H641R and g.18654T > C in the intron 1) in the gene were identified by screening a cohort of 30 unrelated patients with the disease. In situ hybridization analysis showed that SLC26A2 is abundantly expressed in the lumbosacral spine of the mouse embryo at day 14.5. Sulfate uptake activities in CHO cells transfected with mutant SLC26A2 were dramatically reduced compared with the wild type, confirming the pathogenicity of the two missense mutations. Further analysis of the gene–disease network revealed a convergent pathogenic network for the development of lumbosacral spine. To our knowledge, our findings provide the first identification of autosomal dominant SLC26A2 mutations in patients with dysplastic spondylolysis, suggesting a new clinical entity in the pathogenesis of chondrodysplasia involving lumbosacral spine. The analysis of the gene–disease network may shed new light on the study of patients with dysplastic spondylolysis and spondylolisthesis as well as high-risk individuals who are asymptomatic. PMID:26077908

Anthrax: Diagnosis

MedlinePlus

... Laboratory Testing Confirming Anthrax Through the Laboratory Response Network FAQs Information for Specific Groups Laboratory Professionals Collecting Specimens Recommended Specimens Worker Safety ...

Provision of QoS for Multimedia Services in IEEE 802.11 Wireless Network

DTIC Science & Technology

2006-10-01

Provision of QoS for Multimedia Services in IEEE 802.11 Wireless Network. In Dynamic Communications Management (pp. 10-1 – 10-16). Meeting Proceedings...mechanisms have been used for managing a limited bandwidth link within the IPv6 military narrowband network. The detailed description of these...confirms that implemented video rate adaptation mechanism enables improvement of qaulity of video transfer. Provision of QoS for Multimedia Services in

Application of artificial neural networks to gaming

NASA Astrophysics Data System (ADS)

Baba, Norio; Kita, Tomio; Oda, Kazuhiro

1995-04-01

Recently, neural network technology has been applied to various actual problems. It has succeeded in producing a large number of intelligent systems. In this article, we suggest that it could be applied to the field of gaming. In particular, we suggest that the neural network model could be used to mimic players' characters. Several computer simulation results using a computer gaming system which is a modified version of the COMMONS GAME confirm our idea.

Mining Gene Regulatory Networks by Neural Modeling of Expression Time-Series.

PubMed

Rubiolo, Mariano; Milone, Diego H; Stegmayer, Georgina

2015-01-01

Discovering gene regulatory networks from data is one of the most studied topics in recent years. Neural networks can be successfully used to infer an underlying gene network by modeling expression profiles as times series. This work proposes a novel method based on a pool of neural networks for obtaining a gene regulatory network from a gene expression dataset. They are used for modeling each possible interaction between pairs of genes in the dataset, and a set of mining rules is applied to accurately detect the subjacent relations among genes. The results obtained on artificial and real datasets confirm the method effectiveness for discovering regulatory networks from a proper modeling of the temporal dynamics of gene expression profiles.

Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks.

PubMed

Onaga, Tomokatsu; Gleeson, James P; Masuda, Naoki

2017-09-08

Social contact networks underlying epidemic processes in humans and animals are highly dynamic. The spreading of infections on such temporal networks can differ dramatically from spreading on static networks. We theoretically investigate the effects of concurrency, the number of neighbors that a node has at a given time point, on the epidemic threshold in the stochastic susceptible-infected-susceptible dynamics on temporal network models. We show that network dynamics can suppress epidemics (i.e., yield a higher epidemic threshold) when the node's concurrency is low, but can also enhance epidemics when the concurrency is high. We analytically determine different phases of this concurrency-induced transition, and confirm our results with numerical simulations.

Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks

NASA Astrophysics Data System (ADS)

Onaga, Tomokatsu; Gleeson, James P.; Masuda, Naoki

2017-09-01

Social contact networks underlying epidemic processes in humans and animals are highly dynamic. The spreading of infections on such temporal networks can differ dramatically from spreading on static networks. We theoretically investigate the effects of concurrency, the number of neighbors that a node has at a given time point, on the epidemic threshold in the stochastic susceptible-infected-susceptible dynamics on temporal network models. We show that network dynamics can suppress epidemics (i.e., yield a higher epidemic threshold) when the node's concurrency is low, but can also enhance epidemics when the concurrency is high. We analytically determine different phases of this concurrency-induced transition, and confirm our results with numerical simulations.

Analysis of genomic rearrangements, horizontal gene transfer and role of plasmids in the evolution of industrial important Thermus species.

PubMed

Kumwenda, Benjamin; Litthauer, Derek; Reva, Oleg

2014-09-25

Bacteria of genus Thermus inhabit both man-made and natural thermal environments. Several Thermus species have shown biotechnological potential such as reduction of heavy metals which is essential for eradication of heavy metal pollution; removing of organic contaminants in water; opening clogged pipes, controlling global warming among many others. Enzymes from thermophilic bacteria have exhibited higher activity and stability than synthetic or enzymes from mesophilic organisms. Using Meiothermus silvanus DSM 9946 as a reference genome, high level of coordinated rearrangements has been observed in extremely thermophilic Thermus that may imply existence of yet unknown evolutionary forces controlling adaptive re-organization of whole genomes of thermo-extremophiles. However, no remarkable differences were observed across species on distribution of functionally related genes on the chromosome suggesting constraints imposed by metabolic networks. The metabolic network exhibit evolutionary pressures similar to levels of rearrangements as measured by the cross-clustering index. Using stratigraphic analysis of donor-recipient, intensive gene exchanges were observed from Meiothermus species and some unknown sources to Thermus species confirming a well established DNA uptake mechanism as previously proposed. Global genome rearrangements were found to play an important role in the evolution of Thermus bacteria at both genomic and metabolic network levels. Relatively higher level of rearrangements was observed in extremely thermophilic Thermus strains in comparison to the thermo-tolerant Thermus scotoductus. Rearrangements did not significantly disrupt operons and functionally related genes. Thermus species appeared to have a developed capability for acquiring DNA through horizontal gene transfer as shown by the donor-recipient stratigraphic analysis.

Effects of milling media on the fabrication of melt-derived bioactive glass powder for biomaterial application

NASA Astrophysics Data System (ADS)

Ibrahim, Nurul Farhana; Mohamad, Hasmaliza; Noor, Siti Noor Fazliah Mohd

2016-12-01

The present work aims to study the effects of using different milling media on bioactive glass produced through melt-derived method for biomaterial application. The bioactive glass powder based on SiO2-CaO-Na2O-P2O5 system was fabricated using two different types of milling media which are tungsten carbide (WC) and zirconia (ZrO2) balls. However, in this work, no P2O5 was added in the new composition. XRF analysis indicated that tungsten trioxide (WO3) was observed in glass powder milled using WC balls whereas ZrO2 was observed in glass powder milled using ZrO2 balls. Amorphous structure was detected with no crystalline peak observed through XRD analysis for both glass powders. FTIR analysis confirmed the formation of silica network with the existence of functional groups Si-O-Si (bend), Si-O-Si (tetrahedral) and Si-O-Si (stretch) for both glass powders. The results revealed that there was no significant effect of milling media on amorphous silica network glass structure which shows that WC and zirconia can be used as milling media for bioactive glass fabrication without any contamination. Therefore, the fabricated BG can be tested safely for bioactivity assessment in biological fluids environment.

An economic analysis of the EHAS telemedicine system in Alto Amazonas.

PubMed

Martínez, Andrés; Villarroel, Valentín; Puig-Junoy, Jaume; Seoane, Joaquín; del Pozo, Francisco

2007-01-01

Telemedicine systems providing voice communication and email by radio were installed at seven health centres (HCs) and 32 health posts (HPs) in the Alto Amazonas province of Peru during 2001. A cost analysis was performed to estimate the net effect on direct resource consumption from the perspective of society. Prior to the availability of the EHAS telemedicine system, there was a mean of 11.1 urgent patient referrals per year from the HPs and 14.0 referrals per year from the HCs. After the implementation of telemedicine, patient referrals fell to 2.5 per year from the HPs (P = 0.03) and to 8.4 per year from the HCs (P = 0.17). The net economic effect of the telemedicine programme over a four-year period was clearly positive, amounting to annual net savings of US$320,126 (using a 5% discounting rate). A one-way sensitivity analysis using a range of values for the discounting rate, and the number of urgent referrals, confirms that the programme was efficient (i.e. it made net financial savings) in all cases. From the restricted budgetary perspective of the health network, the results also demonstrate that the additional operational costs (telephone and maintenance) introduced by the telemedicine system were lower than the direct cost-savings produced for the health-care network.

Dim Networks: The Utility of Social Network Analysis for Illuminating Partner Security Force Networks

DTIC Science & Technology

2015-12-01

use of social network analysis (SNA) has allowed the military to map dark networks of terrorist organizations and selectively target key elements...data to improve SC. 14. SUBJECT TERMS social network analysis, dark networks, light networks, dim networks, security cooperation, Southeast Asia...task may already exist. Recently, the use of social network analysis (SNA) has allowed the military to map dark networks of terrorist organizations

Leuconostoc mesenteroides growth in food products: prediction and sensitivity analysis by adaptive-network-based fuzzy inference systems.

PubMed

Wang, Hue-Yu; Wen, Ching-Feng; Chiu, Yu-Hsien; Lee, I-Nong; Kao, Hao-Yun; Lee, I-Chen; Ho, Wen-Hsien

2013-01-01

An adaptive-network-based fuzzy inference system (ANFIS) was compared with an artificial neural network (ANN) in terms of accuracy in predicting the combined effects of temperature (10.5 to 24.5°C), pH level (5.5 to 7.5), sodium chloride level (0.25% to 6.25%) and sodium nitrite level (0 to 200 ppm) on the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. THE ANFIS AND ANN MODELS WERE COMPARED IN TERMS OF SIX STATISTICAL INDICES CALCULATED BY COMPARING THEIR PREDICTION RESULTS WITH ACTUAL DATA: mean absolute percentage error (MAPE), root mean square error (RMSE), standard error of prediction percentage (SEP), bias factor (Bf), accuracy factor (Af), and absolute fraction of variance (R (2)). Graphical plots were also used for model comparison. The learning-based systems obtained encouraging prediction results. Sensitivity analyses of the four environmental factors showed that temperature and, to a lesser extent, NaCl had the most influence on accuracy in predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. The observed effectiveness of ANFIS for modeling microbial kinetic parameters confirms its potential use as a supplemental tool in predictive mycology. Comparisons between growth rates predicted by ANFIS and actual experimental data also confirmed the high accuracy of the Gaussian membership function in ANFIS. Comparisons of the six statistical indices under both aerobic and anaerobic conditions also showed that the ANFIS model was better than all ANN models in predicting the four kinetic parameters. Therefore, the ANFIS model is a valuable tool for quickly predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions.

Leuconostoc Mesenteroides Growth in Food Products: Prediction and Sensitivity Analysis by Adaptive-Network-Based Fuzzy Inference Systems

PubMed Central

Wang, Hue-Yu; Wen, Ching-Feng; Chiu, Yu-Hsien; Lee, I-Nong; Kao, Hao-Yun; Lee, I-Chen; Ho, Wen-Hsien

2013-01-01

Background An adaptive-network-based fuzzy inference system (ANFIS) was compared with an artificial neural network (ANN) in terms of accuracy in predicting the combined effects of temperature (10.5 to 24.5°C), pH level (5.5 to 7.5), sodium chloride level (0.25% to 6.25%) and sodium nitrite level (0 to 200 ppm) on the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. Methods The ANFIS and ANN models were compared in terms of six statistical indices calculated by comparing their prediction results with actual data: mean absolute percentage error (MAPE), root mean square error (RMSE), standard error of prediction percentage (SEP), bias factor (Bf), accuracy factor (Af), and absolute fraction of variance (R 2). Graphical plots were also used for model comparison. Conclusions The learning-based systems obtained encouraging prediction results. Sensitivity analyses of the four environmental factors showed that temperature and, to a lesser extent, NaCl had the most influence on accuracy in predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. The observed effectiveness of ANFIS for modeling microbial kinetic parameters confirms its potential use as a supplemental tool in predictive mycology. Comparisons between growth rates predicted by ANFIS and actual experimental data also confirmed the high accuracy of the Gaussian membership function in ANFIS. Comparisons of the six statistical indices under both aerobic and anaerobic conditions also showed that the ANFIS model was better than all ANN models in predicting the four kinetic parameters. Therefore, the ANFIS model is a valuable tool for quickly predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. PMID:23705023

The autophagy interaction network of the aging model Podospora anserina.

PubMed

Philipp, Oliver; Hamann, Andrea; Osiewacz, Heinz D; Koch, Ina

2017-03-27

Autophagy is a conserved molecular pathway involved in the degradation and recycling of cellular components. It is active either as response to starvation or molecular damage. Evidence is emerging that autophagy plays a key role in the degradation of damaged cellular components and thereby affects aging and lifespan control. In earlier studies, it was found that autophagy in the aging model Podospora anserina acts as a longevity assurance mechanism. However, only little is known about the individual components controlling autophagy in this aging model. Here, we report a biochemical and bioinformatics study to detect the protein-protein interaction (PPI) network of P. anserina combining experimental and theoretical methods. We constructed the PPI network of autophagy in P. anserina based on the corresponding networks of yeast and human. We integrated PaATG8 interaction partners identified in an own yeast two-hybrid analysis using ATG8 of P. anserina as bait. Additionally, we included age-dependent transcriptome data. The resulting network consists of 89 proteins involved in 186 interactions. We applied bioinformatics approaches to analyze the network topology and to prove that the network is not random, but exhibits biologically meaningful properties. We identified hub proteins which play an essential role in the network as well as seven putative sub-pathways, and interactions which are likely to be evolutionary conserved amongst species. We confirmed that autophagy-associated genes are significantly often up-regulated and co-expressed during aging of P. anserina. With the present study, we provide a comprehensive biological network of the autophagy pathway in P. anserina comprising PPI and gene expression data. It is based on computational prediction as well as experimental data. We identified sub-pathways, important hub proteins, and evolutionary conserved interactions. The network clearly illustrates the relation of autophagy to aging processes and enables further specific studies to understand autophagy and aging in P. anserina as well as in other systems.

Thermal Stability and X-ray Attenuation Studies on α-Bi₂O₃, β-Bi₂O₃ and Bi Based Nanocomposites for Radiopaque Fabrics.

PubMed

Jayakumar, Sangeetha; Saravanan, T; Philip, John

2018-06-01

Nanocomposites containing α-Bi2O3, β-Bi2O3 and Bi nanoparticles as nanofillers in vulcanized silicone resin as a matrix are prepared and their diagnostic X-ray attenuation property is studied. The nanocomposites are prepared using a simple solution casting technique, with nanofiller concentration varying from 2-50 wt%. Thermogravimetric analysis and differential scanning calorimetry are performed to study the thermal stability of the nanocomposites. The attenuation property is studied by exposing the nanocomposites containing α-Bi2O3, β-Bi2O3 and Bi nanoparticles to X-rays of energy 30-60 keV. Nanocomposites containing β-Bi2O3 nanoparticles are found to exhibit the highest attenuation than nanocomposites of α-Bi2O3 and Bi nanoparticles of similar concentration. Nanocomposites containing 50 wt% of β-Bi2O3 nanoparticles exhibit an X-ray attenuation of 93, 86, 71, 45 and 10% at an X-ray photon energy of 40, 45, 50, 55 and 59 keV, respectively. Further increase in photon energy is found to saturate the flat panel detector owing to the lower thickness of the nanocomposites. Analysis of high resolution X-ray radiographs of the nanocomposites confirms the uniform distribution of nanofillers in the matrix. Thermal analysis confirms the structural integrity and thermal stability of the nanocomposites. Heat flow curves also confirm the interaction of nanofillers with the matrix, corroborated by a change in the peak position and its endothermic/exothermic nature, corresponding to the phase transition of the nanofillers. It is also interpreted from thermal analysis of nanocomposites that the nanofillers interact with the matrix either by intercalating in the bridging polymer chain of silicone resin network structure or by occupying the interchain space. Thermal analysis of X-ray exposed nanocomposites shows no significant change in heat flow rates, thus, confirming the stability of the nanocomposites. Our study shows that nanocomposites containing β-Bi2O3 nanofiller are potential candidates for radiopaque fabrics which can find application in diagnostic X-ray shielding in mammography, dental scan, etc.

Characteristics of Resting-State Functional Connectivity in Intractable Unilateral Temporal Lobe Epilepsy Patients with Impaired Executive Control Function

PubMed Central

Zhang, Chao; Yang, Hongyu; Qin, Wen; Liu, Chang; Qi, Zhigang; Chen, Nan; Li, Kuncheng

2017-01-01

Executive control function (ECF) deficit is a common complication of temporal lobe epilepsy (TLE). Characteristics of brain network connectivity in TLE with ECF dysfunction are still unknown. The aim of this study was to investigate resting-state functional connectivity (FC) changes in patients with unilateral intractable TLE with impaired ECF. Forty right-handed patients with left TLE confirmed by comprehensive preoperative evaluation and postoperative pathological findings were enrolled. The patients were divided into normal ECF (G1) and decreased ECF (G2) groups according to whether they showed ECF impairment on the Wisconsin Card Sorting Test (WCST). Twenty-three healthy volunteers were recruited as the healthy control (HC) group. All subjects underwent resting-state functional magnetic resonance imaging (rs-fMRI). Group-information-guided independent component analysis (GIG-ICA) was performed to estimate resting-state networks (RSNs) for all subjects. General linear model (GLM) was employed to analyze intra-network FC (p < 0.05, false discovery rate, FDR correction) and inter-network FC (p < 0.05, Bonferroni correction) of RSN among three groups. Pearson correlations between FC and neuropsychological tests were also determined through partial correlation analysis (p < 0.05). Eleven meaningful RSNs were identified from 40 left TLE and 23 HC subjects. Comparison of intra-network FC of all 11 meaningful RSNs did not reveal significant difference among the three groups (p > 0.05, FDR correction). For inter-network analysis, G2 exhibited decreased FC between the executive control network (ECN) and default-mode network (DMN) when compared with G1 (p = 0.000, Bonferroni correction) and HC (p = 0.000, Bonferroni correction). G1 showed no significant difference of FC between ECN and DMN when compared with HC. Furthermore, FC between ECN and DMN had significant negative correlation with perseverative responses (RP), response errors (RE) and perseverative errors (RPE) and had significant positive correlation categories completed (CC) in both G1 and G2 (p < 0.05). No significant difference of Montreal Cognitive Assessment (MoCA) was found between G1 and G2, while intelligence quotient (IQ) testing showed significant difference between G1and G2.There was no correlation between FC and either MoCA or IQ performance. Our findings suggest that ECF impairment in unilateral TLE is not confined to the diseased temporal lobe. Decreased FC between DMN and ECN may be an important characteristic of RSN in intractable unilateral TLE. PMID:29375338

Disentangling the brain networks supporting affective speech comprehension.

PubMed

Hervé, Pierre-Yves; Razafimandimby, Annick; Vigneau, Mathieu; Mazoyer, Bernard; Tzourio-Mazoyer, Nathalie

2012-07-16

Areas involved in social cognition, such as the medial prefrontal cortex (mPFC) and the left temporo-parietal junction (TPJ) appear to be active during the classification of sentences according to emotional criteria (happy, angry or sad, [Beaucousin et al., 2007]). These two regions are frequently co-activated in studies about theory of mind (ToM). To confirm that these regions constitute a coherent network during affective speech comprehension, new event-related functional magnetic resonance imaging data were acquired, using the emotional and grammatical-person sentence classification tasks on a larger sample of 51 participants. The comparison of the emotional and grammatical tasks confirmed the previous findings. Functional connectivity analyses established a clear demarcation between a "Medial" network, including the mPFC and TPJ regions, and a bilateral "Language" network, which gathered inferior frontal and temporal areas. These findings suggest that emotional speech comprehension results from interactions between language, ToM and emotion processing networks. The language network, active during both tasks, would be involved in the extraction of lexical and prosodic emotional cues, while the medial network, active only during the emotional task, would drive the making of inferences about the sentences' emotional content, based on their meanings. The left and right amygdalae displayed a stronger response during the emotional condition, but were seldom correlated with the other regions, and thus formed a third entity. Finally, distinct regions belonging to the Language and Medial networks were found in the left angular gyrus, where these two systems could interface. Copyright © 2012 Elsevier Inc. All rights reserved.

Determining quality of caviar from Caspian Sea based on Raman spectroscopy and using artificial neural networks.

PubMed

Mohamadi Monavar, H; Afseth, N K; Lozano, J; Alimardani, R; Omid, M; Wold, J P

2013-07-15

The purpose of this study was to evaluate the feasibility of Raman spectroscopy for predicting purity of caviars. The 93 wild caviar samples of three different types, namely; Beluga, Asetra and Sevruga were analysed by Raman spectroscopy in the range 1995 cm(-1) to 545 cm(-1). Also, 60 samples from combinations of every two types were examined. The chemical origin of the samples was identified by reference measurements on pure samples. Linear chemometric methods like Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were used for data visualisation and classification which permitted clear distinction between different caviars. Non-linear methods like Artificial Neural Networks (ANN) were used to classify caviar samples. Two different networks were tested in the classification: Probabilistic Neural Network with Radial-Basis Function (PNN) and Multilayer Feed Forward Networks with Back Propagation (BP-NN). In both cases, scores of principal components (PCs) were chosen as input nodes for the input layer in PC-ANN models in order to reduce the redundancy of data and time of training. Leave One Out (LOO) cross validation was applied in order to check the performance of the networks. Results of PCA indicated that, features like type and purity can be used to discriminate different caviar samples. These findings were also supported by LDA with efficiency between 83.77% and 100%. These results were confirmed with the results obtained by developed PC-ANN models, able to classify pure caviar samples with 93.55% and 71.00% accuracy in BP network and PNN, respectively. In comparison, LDA, PNN and BP-NN models for predicting caviar types have 90.3%, 73.1% and 91.4% accuracy. Partial least squares regression (PLSR) models were built under cross validation and tested with different independent data sets, yielding determination coefficients (R(2)) of 0.86, 0.83, 0.92 and 0.91 with root mean square error (RMSE) of validation of 0.32, 0.11, 0.03 and 0.09 for fatty acids of 16.0, 20.5, 22.6 and fat, respectively. Crown Copyright © 2013. Published by Elsevier B.V. All rights reserved.

Bacterial Meningitis Surveillance in the Eastern Mediterranean Region, 2005–2010: Successes and Challenges of a Regional Network

PubMed Central

Teleb, Nadia; Pilishvili, Tamara; Van Beneden, Chris; Ghoneim, Amani; Amjad, Khawaja; Mostafa, Amani; Estighamati, Abdul Reza; Smeo, Mohamed Najib; Barkia, Abdelaziz; ElKhatib, Mutaz; Mujaly, Abdellatif; Ashmony, Hossam; Jassim, Kifah Ahmed; Hajjeh, Rana A.

2018-01-01

Objective To describe epidemiology of bacterial meningitis in the World Health Organization Eastern Mediterranean Region countries and assist in introduction of new bacterial vaccines. Study design A laboratory-based sentinel surveillance was established in 2004, and up to 10 countries joined the network until 2010. Personnel at participating hospitals and national public health laboratories received training in surveillance and laboratory methods and used standard clinical and laboratory-confirmed case definitions. Results Over 22 000 suspected cases of meningitis were reported among children ≤5 years old and >6600 among children >5 years old. In children ≤5 years old, 921 of 13 125 probable cases (7.0%) were culture-confirmed. The most commonly isolated pathogens were S pneumoniae (27% of confirmed cases), N meningitidis (22%), and H influenzae (10%). Among culture-confirmed case-patients with known outcome, case-fatality rate was 7.0% and 12.2% among children ≤5 years old and those >5 years old, respectively. Declining numbers of Haemophilus influenzae type b meningitis cases within 2 years post-Haemophilus influenzae type b conjugate vaccine introduction were observed in Pakistan. Conclusions Bacterial meningitis continues to cause significant morbidity and mortality in the Eastern Mediterranean Region. Surveillance networks for bacterial meningitis ensure that all sites are using standardized methodologies. Surveillance data are useful to monitor impact of various interventions including vaccines, but maintaining data quality requires consistent reporting and regular technical support. PMID:23773590

Globally altered structural brain network topology in grapheme-color synesthesia.

PubMed

Hänggi, Jürgen; Wotruba, Diana; Jäncke, Lutz

2011-04-13

Synesthesia is a perceptual phenomenon in which stimuli in one particular modality elicit a sensation within the same or another sensory modality (e.g., specific graphemes evoke the perception of particular colors). Grapheme-color synesthesia (GCS) has been proposed to arise from abnormal local cross-activation between grapheme and color areas because of their hyperconnectivity. Recently published studies did not confirm such a hyperconnectivity, although morphometric alterations were found in occipitotemporal, parietal, and frontal regions of synesthetes. We used magnetic resonance imaging surface-based morphometry and graph-theoretical network analyses to investigate the topology of structural brain networks in 24 synesthetes and 24 nonsynesthetes. Connectivity matrices were derived from region-wise cortical thickness correlations of 2366 different cortical parcellations across the whole cortex and from 154 more common brain divisions as well. Compared with nonsynesthetes, synesthetes revealed a globally altered structural network topology as reflected by reduced small-worldness, increased clustering, increased degree, and decreased betweenness centrality. Connectivity of the fusiform gyrus (FuG) and intraparietal sulcus (IPS) was changed as well. Hierarchical modularity analysis revealed increased intramodular and intermodular connectivity of the IPS in GCS. However, connectivity differences in the FuG and IPS showed a low specificity because of global changes. We provide first evidence that GCS is rooted in a reduced small-world network organization that is driven by increased clustering suggesting global hyperconnectivity within the synesthetes' brain. Connectivity alterations were widespread and not restricted to the FuG and IPS. Therefore, synesthetic experience might be only one phenotypic manifestation of the globally altered network architecture in GCS.

Interventions for avian influenza A (H5N1) risk management in live bird market networks

PubMed Central

Fournié, Guillaume; Guitian, Javier; Desvaux, Stéphanie; Cuong, Vu Chi; Dung, Do Huu; Pfeiffer, Dirk Udo; Mangtani, Punam; Ghani, Azra C.

2013-01-01

Highly pathogenic avian influenza virus subtype H5N1 is endemic in Asia, with live bird trade as a major disease transmission pathway. A cross-sectional survey was undertaken in northern Vietnam to investigate the structure of the live bird market (LBM) contact network and the implications for virus spread. Based on the movements of traders between LBMs, weighted and directed networks were constructed and used for social network analysis and individual-based modeling. Most LBMs were connected to one another, suggesting that the LBM network may support large-scale disease spread. Because of cross-border trade, it also may promote transboundary virus circulation. However, opportunities for disease control do exist. The implementation of thorough, daily disinfection of the market environment as well as of traders’ vehicles and equipment in only a small number of hubs can disconnect the network dramatically, preventing disease spread. These targeted interventions would be an effective alternative to the current policy of a complete ban of LBMs in some areas. Some LBMs that have been banned still are very active, and they likely have a substantial impact on disease dynamics, exhibiting the highest levels of susceptibility and infectiousness. The number of trader visits to markets, information that can be collected quickly and easily, may be used to identify LBMs suitable for implementing interventions. This would not require prior knowledge of the force of infection, for which laboratory-confirmed surveillance would be necessary. These findings are of particular relevance for policy development in resource-scarce settings. PMID:23650388

Interventions for avian influenza A (H5N1) risk management in live bird market networks.

PubMed

Fournié, Guillaume; Guitian, Javier; Desvaux, Stéphanie; Cuong, Vu Chi; Dung, Do Huu; Pfeiffer, Dirk Udo; Mangtani, Punam; Ghani, Azra C

2013-05-28

Highly pathogenic avian influenza virus subtype H5N1 is endemic in Asia, with live bird trade as a major disease transmission pathway. A cross-sectional survey was undertaken in northern Vietnam to investigate the structure of the live bird market (LBM) contact network and the implications for virus spread. Based on the movements of traders between LBMs, weighted and directed networks were constructed and used for social network analysis and individual-based modeling. Most LBMs were connected to one another, suggesting that the LBM network may support large-scale disease spread. Because of cross-border trade, it also may promote transboundary virus circulation. However, opportunities for disease control do exist. The implementation of thorough, daily disinfection of the market environment as well as of traders' vehicles and equipment in only a small number of hubs can disconnect the network dramatically, preventing disease spread. These targeted interventions would be an effective alternative to the current policy of a complete ban of LBMs in some areas. Some LBMs that have been banned still are very active, and they likely have a substantial impact on disease dynamics, exhibiting the highest levels of susceptibility and infectiousness. The number of trader visits to markets, information that can be collected quickly and easily, may be used to identify LBMs suitable for implementing interventions. This would not require prior knowledge of the force of infection, for which laboratory-confirmed surveillance would be necessary. These findings are of particular relevance for policy development in resource-scarce settings.

Algebraic Topology of Multi-Brain Connectivity Networks Reveals Dissimilarity in Functional Patterns during Spoken Communications

PubMed Central

Tadić, Bosiljka; Andjelković, Miroslav; Boshkoska, Biljana Mileva; Levnajić, Zoran

2016-01-01

Human behaviour in various circumstances mirrors the corresponding brain connectivity patterns, which are suitably represented by functional brain networks. While the objective analysis of these networks by graph theory tools deepened our understanding of brain functions, the multi-brain structures and connections underlying human social behaviour remain largely unexplored. In this study, we analyse the aggregate graph that maps coordination of EEG signals previously recorded during spoken communications in two groups of six listeners and two speakers. Applying an innovative approach based on the algebraic topology of graphs, we analyse higher-order topological complexes consisting of mutually interwoven cliques of a high order to which the identified functional connections organise. Our results reveal that the topological quantifiers provide new suitable measures for differences in the brain activity patterns and inter-brain synchronisation between speakers and listeners. Moreover, the higher topological complexity correlates with the listener’s concentration to the story, confirmed by self-rating, and closeness to the speaker’s brain activity pattern, which is measured by network-to-network distance. The connectivity structures of the frontal and parietal lobe consistently constitute distinct clusters, which extend across the listener’s group. Formally, the topology quantifiers of the multi-brain communities exceed the sum of those of the participating individuals and also reflect the listener’s rated attributes of the speaker and the narrated subject. In the broader context, the presented study exposes the relevance of higher topological structures (besides standard graph measures) for characterising functional brain networks under different stimuli. PMID:27880802

Predicting musically induced emotions from physiological inputs: linear and neural network models.

PubMed

Russo, Frank A; Vempala, Naresh N; Sandstrom, Gillian M

2013-01-01

Listening to music often leads to physiological responses. Do these physiological responses contain sufficient information to infer emotion induced in the listener? The current study explores this question by attempting to predict judgments of "felt" emotion from physiological responses alone using linear and neural network models. We measured five channels of peripheral physiology from 20 participants-heart rate (HR), respiration, galvanic skin response, and activity in corrugator supercilii and zygomaticus major facial muscles. Using valence and arousal (VA) dimensions, participants rated their felt emotion after listening to each of 12 classical music excerpts. After extracting features from the five channels, we examined their correlation with VA ratings, and then performed multiple linear regression to see if a linear relationship between the physiological responses could account for the ratings. Although linear models predicted a significant amount of variance in arousal ratings, they were unable to do so with valence ratings. We then used a neural network to provide a non-linear account of the ratings. The network was trained on the mean ratings of eight of the 12 excerpts and tested on the remainder. Performance of the neural network confirms that physiological responses alone can be used to predict musically induced emotion. The non-linear model derived from the neural network was more accurate than linear models derived from multiple linear regression, particularly along the valence dimension. A secondary analysis allowed us to quantify the relative contributions of inputs to the non-linear model. The study represents a novel approach to understanding the complex relationship between physiological responses and musically induced emotion.

NON-POTENTIAL FIELDS IN THE QUIET SUN NETWORK: EXTREME-ULTRAVIOLET AND MAGNETIC FOOTPOINT OBSERVATIONS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chesny, D. L.; Oluseyi, H. M.; Orange, N. B.

The quiet Sun (QS) magnetic network is known to contain dynamics which are indicative of non-potential fields. Non-potential magnetic fields forming ''S-shaped'' loop arcades can lead to the breakdown of static activity and have only been observed in high temperature X-ray coronal structures—some of which show eruptive behavior. Thus, analysis of this type of atmospheric structuring has been restricted to large-scale coronal fields. Here we provide the first identification of non-potential loop arcades exclusive to the QS supergranulation network. High-resolution Atmospheric Imaging Assembly data from the Solar Dynamics Observatory have allowed for the first observations of fine-scale ''S-shaped'' loop arcadesmore » spanning the network. We have investigated the magnetic footpoint flux evolution of these arcades from Heliospheric and Magnetic Imager data and find evidence of evolving footpoint flux imbalances accompanying the formation of these non-potential fields. The existence of such non-potentiality confirms that magnetic field dynamics leading to the build up of helicity exist at small scales. QS non-potentiality also suggests a self-similar formation process between the QS network and high temperature corona and the existence of self-organized criticality (SOC) in the form of loop-pair reconnection and helicity dissipation. We argue that this type of behavior could lead to eruptive forms of SOC as seen in active region (AR) and X-ray sigmoids if sufficient free magnetic energy is available. QS magnetic network dynamics may be considered as a coronal proxy at supergranular scales, and events confined to the network can even mimic those in coronal ARs.« less

Disentangling the multigenic and pleiotropic nature of molecular function

PubMed Central

2015-01-01

Background Biological processes at the molecular level are usually represented by molecular interaction networks. Function is organised and modularity identified based on network topology, however, this approach often fails to account for the dynamic and multifunctional nature of molecular components. For example, a molecule engaging in spatially or temporally independent functions may be inappropriately clustered into a single functional module. To capture biologically meaningful sets of interacting molecules, we use experimentally defined pathways as spatial/temporal units of molecular activity. Results We defined functional profiles of Saccharomyces cerevisiae based on a minimal set of Gene Ontology terms sufficient to represent each pathway's genes. The Gene Ontology terms were used to annotate 271 pathways, accounting for pathway multi-functionality and gene pleiotropy. Pathways were then arranged into a network, linked by shared functionality. Of the genes in our data set, 44% appeared in multiple pathways performing a diverse set of functions. Linking pathways by overlapping functionality revealed a modular network with energy metabolism forming a sparse centre, surrounded by several denser clusters comprised of regulatory and metabolic pathways. Signalling pathways formed a relatively discrete cluster connected to the centre of the network. Genetic interactions were enriched within the clusters of pathways by a factor of 5.5, confirming the organisation of our pathway network is biologically significant. Conclusions Our representation of molecular function according to pathway relationships enables analysis of gene/protein activity in the context of specific functional roles, as an alternative to typical molecule-centric graph-based methods. The pathway network demonstrates the cooperation of multiple pathways to perform biological processes and organises pathways into functionally related clusters with interdependent outcomes. PMID:26678917

Emergence of rich-club topology and coordinated dynamics in development of hippocampal functional networks in vitro.

PubMed

Schroeter, Manuel S; Charlesworth, Paul; Kitzbichler, Manfred G; Paulsen, Ole; Bullmore, Edward T

2015-04-08

Recent studies demonstrated that the anatomical network of the human brain shows a "rich-club" organization. This complex topological feature implies that highly connected regions, hubs of the large-scale brain network, are more densely interconnected with each other than expected by chance. Rich-club nodes were traversed by a majority of short paths between peripheral regions, underlining their potential importance for efficient global exchange of information between functionally specialized areas of the brain. Network hubs have also been described at the microscale of brain connectivity (so-called "hub neurons"). Their role in shaping synchronous dynamics and forming microcircuit wiring during development, however, is not yet fully understood. The present study aimed to investigate the role of hubs during network development, using multi-electrode arrays and functional connectivity analysis during spontaneous multi-unit activity (MUA) of dissociated primary mouse hippocampal neurons. Over the first 4 weeks in vitro, functional connectivity significantly increased in strength, density, and size, with mature networks demonstrating a robust modular and small-world topology. As expected by a "rich-get-richer" growth rule of network evolution, MUA graphs were found to form rich-clubs at an early stage in development (14 DIV). Later on, rich-club nodes were a consistent topological feature of MUA graphs, demonstrating high nodal strength, efficiency, and centrality. Rich-club nodes were also found to be crucial for MUA dynamics. They often served as broker of spontaneous activity flow, confirming that hub nodes and rich-clubs may play an important role in coordinating functional dynamics at the microcircuit level. Copyright © 2015 the authors 0270-6474/15/355459-12$15.00/0.

Ethnopharmacological implications of quantitative and network analysis for traditional knowledge regarding the medicinal use of animals by indigenous people in Wolchulsan National Park, Korea.

PubMed

Kim, Geun; Kim, Hyun; Song, Mi-Jang

2018-03-01

The purpose of this study was to record, analyze, and identify ethnopharmacological implications for oral traditional knowledge regarding the medicinal use of animals by indigenous people living in Wolchulsan National Park, Korea. Data were collected through interviews, informal meetings, open and group discussions, and observations guided by semi-structured questionnaires. Data were analyzed via quantitative analysis of informant consensus factor and fidelity level, and network analysis, including centrality and clustering analysis. A total of 46 families, 59 genera, and 60 species of animals, as well as 373 methods of usage, were recorded. Fish comprised 31.7% of the total animal species recorded, followed by mammals at 20.0%, arthropods at 18.3%, and mollusks at 11.7%. Of these animals, 48.0% were utilized as food and 46.1% for medicinal use. Quantitative analysis showed that the category with the highest degree of consensus from informants was veterinary ailments (informant consensus factor value, 0.96). This was followed by poisonings (0.93), pains (0.92), genitourinary system disorders (0.91), cuts and wounds (0.89), and other medical conditions. The lowest degree of consensus was for skin diseases and disorders (0.57). There were 8 species of animals with a fidelity level of 100%, after eliminating from the animals analyzed that were mentioned only once. Finally, using network analysis, Gallus gallus domesticus and Gloydius brevicaudus were defined as species with meaningful medicinal use, while lack of vigor and lung diseases were defined as significant ailments in the study area. This study validates that local communities use animals not only for food but also for medicinal purposes as crucial therapeutic measures. Therefore, the conservation of fauna and preservation of traditional knowledge need to be seriously considered to maintain the health and well-being of the local communities. Network analysis clarified the series of ailments for which each animal species is preferentially used and helped confirm the order of priority when prescribing animal components for medicinal use. The traditional knowledge recorded in the present study will provide the basic data to develop new medicines for the bioindustry. Copyright © 2017 Elsevier B.V. All rights reserved.

Genome-Wide Identification of Molecular Pathways and Biomarkers in Response to Arsenic Exposure in Zebrafish Liver

PubMed Central

Xu, Hongyan; Lam, Siew Hong; Shen, Yuan; Gong, Zhiyuan

2013-01-01

Inorganic arsenic is a worldwide metalloid pollutant in environment. Although extensive studies on arsenic-induced toxicity have been conducted using in vivo and in vitro models, the exact molecular mechanism of arsenate toxicity remains elusive. Here, the RNA-SAGE (serial analysis of gene expression) sequencing technology was used to analyse hepatic response to arsenic exposure at the transcriptome level. Based on more than 12 million SAGE tags mapped to zebrafish genes, 1,444 differentially expressed genes (750 up-regulated and 694 down-regulated) were identified from a relatively abundant transcripts (>10 TPM [transcripts per million]) based on minimal two-fold change. By gene ontology analyses, these differentially expressed genes were significantly enriched in several major biological processes including oxidation reduction, translation, iron ion transport, cell redox, homeostasis, etc. Accordingly, the main pathways disturbed include metabolic pathways, proteasome, oxidative phosphorylation, cancer, etc. Ingenity Pathway Analysis further revealed a network with four important upstream factors or hub genes, including Jun, Kras, APoE and Nr2f2. The network indicated apparent molecular events involved in oxidative stress, carcinogenesis, and metabolism. In order to identify potential biomarker genes for arsenic exposure, 27 out of 29 up-regulated transcripts were validated by RT-qPCR analysis in pooled RNA samples. Among these, 14 transcripts were further confirmed for up-regulation by a lower dosage of arsenic in majority of individual zebrafish. Finally, at least four of these genes, frh3 (ferrintin H3), mgst1 (microsomal glutathione S-transferase-like), cmbl (carboxymethylenebutenolidase homolog) and slc40a1 (solute carrier family 40 [iron-regulated transporter], member 1) could be confirmed in individual medaka fish similarly treated by arsenic; thus, these four genes might be robust arsenic biomarkers across species. Thus, our work represents the first comprehensive investigation of molecular mechanism of asenic toxicity and genome-wide search for potential biomarkers for arsenic exposure. PMID:23922661

Screening key candidate genes and pathways involved in insulinoma by microarray analysis.

PubMed

Zhou, Wuhua; Gong, Li; Li, Xuefeng; Wan, Yunyan; Wang, Xiangfei; Li, Huili; Jiang, Bin

2018-06-01

Insulinoma is a rare type tumor and its genetic features remain largely unknown. This study aimed to search for potential key genes and relevant enriched pathways of insulinoma.The gene expression data from GSE73338 were downloaded from Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified between insulinoma tissues and normal pancreas tissues, followed by pathway enrichment analysis, protein-protein interaction (PPI) network construction, and module analysis. The expressions of candidate key genes were validated by quantitative real-time polymerase chain reaction (RT-PCR) in insulinoma tissues.A total of 1632 DEGs were obtained, including 1117 upregulated genes and 514 downregulated genes. Pathway enrichment results showed that upregulated DEGs were significantly implicated in insulin secretion, and downregulated DEGs were mainly enriched in pancreatic secretion. PPI network analysis revealed 7 hub genes with degrees more than 10, including GCG (glucagon), GCGR (glucagon receptor), PLCB1 (phospholipase C, beta 1), CASR (calcium sensing receptor), F2R (coagulation factor II thrombin receptor), GRM1 (glutamate metabotropic receptor 1), and GRM5 (glutamate metabotropic receptor 5). DEGs involved in the significant modules were enriched in calcium signaling pathway, protein ubiquitination, and platelet degranulation. Quantitative RT-PCR data confirmed that the expression trends of these hub genes were similar to the results of bioinformatic analysis.The present study demonstrated that candidate DEGs and enriched pathways were the potential critical molecule events involved in the development of insulinoma, and these findings were useful for better understanding of insulinoma genesis.

Integrative Analysis Reveals Regulatory Programs in Endometriosis

PubMed Central

Yang, Huan; Kang, Kai; Cheng, Chao; Mamillapalli, Ramanaiah; Taylor, Hugh S.

2015-01-01

Endometriosis is a common gynecological disease found in approximately 10% of reproductive-age women. Gene expression analysis has been performed to explore alterations in gene expression associated with endometriosis; however, the underlying transcription factors (TFs) governing such expression changes have not been investigated in a systematic way. In this study, we propose a method to integrate gene expression with TF binding data and protein–protein interactions to construct an integrated regulatory network (IRN) for endometriosis. The IRN has shown that the most regulated gene in endometriosis is RUNX1, which is targeted by 14 of 26 TFs also involved in endometriosis. Using 2 published cohorts, GSE7305 (Hover, n = 20) and GSE7307 (Roth, n = 36) from the Gene Expression Omnibus database, we identified a network of TFs, which bind to target genes that are differentially expressed in endometriosis. Enrichment analysis based on the hypergeometric distribution allowed us to predict the TFs involved in endometriosis (n = 40). This included known TFs such as androgen receptor (AR) and critical factors in the pathology of endometriosis, estrogen receptor α, and estrogen receptor β. We also identified several new ones from which we selected FOXA2 and TFAP2C, and their regulation was confirmed by quantitative real-time polymerase chain reaction and immunohistochemistry (IHC). Further, our analysis revealed that the function of AR and p53 in endometriosis is regulated by posttranscriptional changes and not by differential gene expression. Our integrative analysis provides new insights into the regulatory programs involved in endometriosis. PMID:26134036

Efficient and self-adaptive in-situ learning in multilayer memristor neural networks.

PubMed

Li, Can; Belkin, Daniel; Li, Yunning; Yan, Peng; Hu, Miao; Ge, Ning; Jiang, Hao; Montgomery, Eric; Lin, Peng; Wang, Zhongrui; Song, Wenhao; Strachan, John Paul; Barnell, Mark; Wu, Qing; Williams, R Stanley; Yang, J Joshua; Xia, Qiangfei

2018-06-19

Memristors with tunable resistance states are emerging building blocks of artificial neural networks. However, in situ learning on a large-scale multiple-layer memristor network has yet to be demonstrated because of challenges in device property engineering and circuit integration. Here we monolithically integrate hafnium oxide-based memristors with a foundry-made transistor array into a multiple-layer neural network. We experimentally demonstrate in situ learning capability and achieve competitive classification accuracy on a standard machine learning dataset, which further confirms that the training algorithm allows the network to adapt to hardware imperfections. Our simulation using the experimental parameters suggests that a larger network would further increase the classification accuracy. The memristor neural network is a promising hardware platform for artificial intelligence with high speed-energy efficiency.

Finding influential nodes for integration in brain networks using optimal percolation theory.

PubMed

Del Ferraro, Gino; Moreno, Andrea; Min, Byungjoon; Morone, Flaviano; Pérez-Ramírez, Úrsula; Pérez-Cervera, Laura; Parra, Lucas C; Holodny, Andrei; Canals, Santiago; Makse, Hernán A

2018-06-11

Global integration of information in the brain results from complex interactions of segregated brain networks. Identifying the most influential neuronal populations that efficiently bind these networks is a fundamental problem of systems neuroscience. Here, we apply optimal percolation theory and pharmacogenetic interventions in vivo to predict and subsequently target nodes that are essential for global integration of a memory network in rodents. The theory predicts that integration in the memory network is mediated by a set of low-degree nodes located in the nucleus accumbens. This result is confirmed with pharmacogenetic inactivation of the nucleus accumbens, which eliminates the formation of the memory network, while inactivations of other brain areas leave the network intact. Thus, optimal percolation theory predicts essential nodes in brain networks. This could be used to identify targets of interventions to modulate brain function.

Changing perspectives on community identity and function: A remote sensing and artifactual re-analysis of Barton Ramie, Belize

NASA Astrophysics Data System (ADS)

Weller, Errin Teresa

This dissertation presents the results of the remote sensing and artifact re-analysis of the archaeological site of Barton Ramie, Belize. The site was the focus of Dr. Gordon R. Willey's innovative archaeological program in the Belize River Valley to study ancient Maya settlement, environment, and population in 1954-1956. Through the use of artifact analysis combined with the examination of high-resolution Worldview-1 imagery and a Geographic Information System (GIS)-based spatial analysis, I consider how the inhabitants of Barton Ramie forged community functioning and identity. I focus on the range of intra-site diversity including differential access to labor, goods, land, and the activities evidenced in households and non-domestic structures. Using a community theory framework, emphasizing the many practices that tied the community together, I underscore the variability expressed in architectural elaboration, sumptuary goods, ritual, and specialization. That variability has profound implications for understanding community diversity and economic, social, and ritual functioning. High-resolution panchromatic Worldview-1 satellite imagery successfully detected the remains of Barton Ramie settlement. Surface archaeology has been largely destroyed due to extensive agricultural activities in recent decades. GIS analysis and ground-truthing determined that mound size is the primary factor enabling detection of ancient features. The confirmation of features in an intensively plowed environment has implications including settlement, survey, and population for other disturbed environments. I argue that the Barton Ramie community developed from a complex interaction of networks and practices. These include activities at the household level, articulation between households to form sub-communities (or neighborhoods), and a larger imagined community of the Barton Ramie polity. Individual households articulated to form seven discrete sub-communities, bounded by landscape features and indicated by interaction spheres in my GIS analysis. This analysis confirmed Dr. Willey's original observations on neighborhoods and settlement. Each subcommunity had a local ritual structure to integrate the households and mitigate the clear status differences. These differences are seen in high status households on prized land, using architectural elaboration, sumptuary goods, and ritual to maintain their status. Once Barton Ramie is understood as a heterogeneous polity connected to a wider economic network, it can be placed into the wider political interaction of the Belize Valley.

G-Protein/β-Arrestin-Linked Fluctuating Network of G-Protein-Coupled Receptors for Predicting Drug Efficacy and Bias Using Short-Term Molecular Dynamics Simulation

PubMed Central

Ichikawa, Osamu; Fujimoto, Kazushi; Yamada, Atsushi; Okazaki, Susumu; Yamazaki, Kazuto

2016-01-01

The efficacy and bias of signal transduction induced by a drug at a target protein are closely associated with the benefits and side effects of the drug. In particular, partial agonist activity and G-protein/β-arrestin-biased agonist activity for the G-protein-coupled receptor (GPCR) family, the family with the most target proteins of launched drugs, are key issues in drug discovery. However, designing GPCR drugs with appropriate efficacy and bias is challenging because the dynamic mechanism of signal transduction induced by ligand—receptor interactions is complicated. Here, we identified the G-protein/β-arrestin-linked fluctuating network, which initiates large-scale conformational changes, using sub-microsecond molecular dynamics (MD) simulations of the β2-adrenergic receptor (β2AR) with a diverse collection of ligands and correlation analysis of their G protein/β-arrestin efficacy. The G-protein-linked fluctuating network extends from the ligand-binding site to the G-protein-binding site through the connector region, and the β-arrestin-linked fluctuating network consists of the NPxxY motif and adjacent regions. We confirmed that the averaged values of fluctuation in the fluctuating network detected are good quantitative indexes for explaining G protein/β-arrestin efficacy. These results indicate that short-term MD simulation is a practical method to predict the efficacy and bias of any compound for GPCRs. PMID:27187591

Effective connectivity of brain regions underlying third-party punishment: Functional MRI and Granger causality evidence.

PubMed

Bellucci, Gabriele; Chernyak, Sergey; Hoffman, Morris; Deshpande, Gopikrishna; Dal Monte, Olga; Knutson, Kristine M; Grafman, Jordan; Krueger, Frank

2017-04-01

Third-party punishment (TPP) for norm violations is an essential deterrent in large-scale human societies, and builds on two essential cognitive functions: evaluating legal responsibility and determining appropriate punishment. Despite converging evidence that TPP is mediated by a specific set of brain regions, little is known about their effective connectivity (direction and strength of connections). Applying parametric event-related functional MRI in conjunction with multivariate Granger causality analysis, we asked healthy participants to estimate how much punishment a hypothetical perpetrator deserves for intentionally committing criminal offenses varying in levels of harm. Our results confirmed that TPP legal decisions are based on two domain-general networks: the mentalizing network for evaluating legal responsibility and the central-executive network for determining appropriate punishment. Further, temporal pole (TP) and dorsomedial prefrontal cortex (PFC) emerged as hubs of the mentalizing network, uniquely generating converging output connections to ventromedial PFC, temporo-parietal junction, and posterior cingulate. In particular, dorsomedial PFC received inputs only from TP and both its activation and its connectivity to dorsolateral PFC correlated with degree of punishment. This supports the hypothesis that dorsomedial PFC acts as the driver of the TPP activation pattern, leading to the decision on the appropriate punishment. In conclusion, these results advance our understanding of the organizational elements of the TPP brain networks and provide better insights into the mental states of judges and jurors tasked with blaming and punishing legal wrongs.

How women organize social networks different from men

PubMed Central

Szell, Michael; Thurner, Stefan

2013-01-01

Superpositions of social networks, such as communication, friendship, or trade networks, are called multiplex networks, forming the structural backbone of human societies. Novel datasets now allow quantification and exploration of multiplex networks. Here we study gender-specific differences of a multiplex network from a complete behavioral dataset of an online-game society of about 300,000 players. On the individual level females perform better economically and are less risk-taking than males. Males reciprocate friendship requests from females faster than vice versa and hesitate to reciprocate hostile actions of females. On the network level females have more communication partners, who are less connected than partners of males. We find a strong homophily effect for females and higher clustering coefficients of females in trade and attack networks. Cooperative links between males are under-represented, reflecting competition for resources among males. These results confirm quantitatively that females and males manage their social networks in substantially different ways. PMID:23393616

How women organize social networks different from men.

PubMed

Szell, Michael; Thurner, Stefan

2013-01-01

Superpositions of social networks, such as communication, friendship, or trade networks, are called multiplex networks, forming the structural backbone of human societies. Novel datasets now allow quantification and exploration of multiplex networks. Here we study gender-specific differences of a multiplex network from a complete behavioral dataset of an online-game society of about 300,000 players. On the individual level females perform better economically and are less risk-taking than males. Males reciprocate friendship requests from females faster than vice versa and hesitate to reciprocate hostile actions of females. On the network level females have more communication partners, who are less connected than partners of males. We find a strong homophily effect for females and higher clustering coefficients of females in trade and attack networks. Cooperative links between males are under-represented, reflecting competition for resources among males. These results confirm quantitatively that females and males manage their social networks in substantially different ways.

Modeling the propagation of mobile malware on complex networks

NASA Astrophysics Data System (ADS)

Liu, Wanping; Liu, Chao; Yang, Zheng; Liu, Xiaoyang; Zhang, Yihao; Wei, Zuxue

2016-08-01

In this paper, the spreading behavior of malware across mobile devices is addressed. By introducing complex networks to model mobile networks, which follows the power-law degree distribution, a novel epidemic model for mobile malware propagation is proposed. The spreading threshold that guarantees the dynamics of the model is calculated. Theoretically, the asymptotic stability of the malware-free equilibrium is confirmed when the threshold is below the unity, and the global stability is further proved under some sufficient conditions. The influences of different model parameters as well as the network topology on malware propagation are also analyzed. Our theoretical studies and numerical simulations show that networks with higher heterogeneity conduce to the diffusion of malware, and complex networks with lower power-law exponents benefit malware spreading.

Neural network classification of clinical neurophysiological data for acute care monitoring

NASA Technical Reports Server (NTRS)

Sgro, Joseph

1994-01-01

The purpose of neurophysiological monitoring of the 'acute care' patient is to allow the accurate recognition of changing or deteriorating neurological function as close to the moment of occurrence as possible, thus permitting immediate intervention. Results confirm that: (1) neural networks are able to accurately identify electroencephalogram (EEG) patterns and evoked potential (EP) wave components, and measuring EP waveform latencies and amplitudes; (2) neural networks are able to accurately detect EP and EEG recordings that have been contaminated by noise; (3) the best performance was obtained consistently with the back propagation network for EP and the HONN for EEG's; (4) neural network performed consistently better than other methods evaluated; and (5) neural network EEG and EP analyses are readily performed on multichannel data.

Patient-Specific Deep Architectural Model for ECG Classification

PubMed Central

Luo, Kan; Cuschieri, Alfred

2017-01-01

Heartbeat classification is a crucial step for arrhythmia diagnosis during electrocardiographic (ECG) analysis. The new scenario of wireless body sensor network- (WBSN-) enabled ECG monitoring puts forward a higher-level demand for this traditional ECG analysis task. Previously reported methods mainly addressed this requirement with the applications of a shallow structured classifier and expert-designed features. In this study, modified frequency slice wavelet transform (MFSWT) was firstly employed to produce the time-frequency image for heartbeat signal. Then the deep learning (DL) method was performed for the heartbeat classification. Here, we proposed a novel model incorporating automatic feature abstraction and a deep neural network (DNN) classifier. Features were automatically abstracted by the stacked denoising auto-encoder (SDA) from the transferred time-frequency image. DNN classifier was constructed by an encoder layer of SDA and a softmax layer. In addition, a deterministic patient-specific heartbeat classifier was achieved by fine-tuning on heartbeat samples, which included a small subset of individual samples. The performance of the proposed model was evaluated on the MIT-BIH arrhythmia database. Results showed that an overall accuracy of 97.5% was achieved using the proposed model, confirming that the proposed DNN model is a powerful tool for heartbeat pattern recognition. PMID:29065597

High frequency electromagnetic reflection loss performance of substituted Sr-hexaferrite nanoparticles/SWCNTs/epoxy nanocomposite

NASA Astrophysics Data System (ADS)

Gordani, Gholam Reza; Ghasemi, Ali; saidi, Ali

2015-10-01

In this study, the electromagnetic properties of a novel nanocomposite material made of substituted Sr-hexaferrite nanoparticles and different percentage of single walled carbon nanotube have been studied. The structural, magnetic and electromagnetic properties of samples were studied as a function of volume percentage of SWCNTs by X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, transmission electron microscopy, vibrating sample magnetometer and vector network analysis. Well suitable crystallinity of hexaferrite nanoparticles was confirmed by XRD patterns. TEM and FESEM micrographs were shown the good homogenity and high level of dispersivity of SWCNTs and Sr-hexaferrite nanoparticles in nanocomposite samples. The VSM results shown that with increasing in amount of CNTs (0-6 vol%), the saturation of magnetization decreased up to 11 emu/g for nanocomposite sample contains of 6 vol% of SWCNTs. The vector network analysis results show that the maximum value of reflection loss was -36.4 dB at the frequency of 11 GHz with an absorption bandwidth of more than 4 GHz (<-20 dB). The results indicate that, this nanocomposite material with appropriate amount of SWCNTs hold great promise for microwave device applications.

Highly Reliable PON Optical Splitters for Optical Access Networks in Outside Environments

NASA Astrophysics Data System (ADS)

Watanabe, Hiroshi; Araki, Noriyuki; Fujimoto, Hisashi

Broadband optical access services are spreading throughout the world, and the number of fiber to the home (FTTH) subscribers is increasing rapidly. Telecom operators are constructing passive optical networks (PONs) to provide optical access services. Externally installed optical splitters for PONs are very important passive devices in optical access networks, and they must provide satisfactory performance as outdoor plant over long periods. Therefore, we calculate the failure rate of optical access networks and assign a failure rate to the optical splitters in optical access networks. The maximum cumulative failure rate of 1 × 8 optical splitters was calculated as 0.025 for an optical access fiber length of 2.1km and a 20-year operating lifetime. We examined planar lightwave circuit (PLC) type optical splitters for use as outside plant in terms of their optical characteristics and environmental reliability. We confirmed that PLC type optical splitters have sufficient optical performance for a PON splitter and sufficient reliability as outside plant in accordance with ITU-T standard values. We estimated the lifetimes of three kinds of PLC type optical splitters by using accelerated aging tests. The estimated failure rate of these splitters installed in optical access networks was below the target value for the cumulative failure rate, and we confirmed that they have sufficient reliability to maintain the quality of the network service. We developed 1 × 8 optical splitter modules with plug and socket type optical connectors and optical fiber cords for optical aerial closures designed for use as outside plant. These technologies make it easy to install optical splitters in an aerial optical closure. The optical splitter modules have sufficient optical performance levels for PONs because the insertion loss at the commercially used wavelengths of 1.31 and 1.55µm is less than the criterion established by ITU-T Recommendation G.671 for optical splitters. We performed a temperature cycling test, and a low temperature storage and damp heat test to confirm the long-term reliability of these modules. They exhibited sufficient reliability as regards heat and moisture because the maximum loss change was less than 0.3dB.

Conducting polymer networks synthesized by photopolymerization-induced phase separation

NASA Astrophysics Data System (ADS)

Yamashita, Yuki; Komori, Kana; Murata, Tasuku; Nakanishi, Hideyuki; Norisuye, Tomohisa; Yamao, Takeshi; Tran-Cong-Miyata, Qui

2018-03-01

Polymer mixtures composed of double networks of a polystyrene derivative (PSAF) and poly(methyl methacrylate) (PMMA) were alternatively synthesized by using ultraviolet (UV) and visible (Vis) light. The PSAF networks were generated by UV irradiation to photodimerize the anthracene (A) moieties labeled on the PSAF chains, whereas PMMA networks were produced by photopolymerization of methyl methacrylate (MMA) monomer and the cross-link reaction using ethylene glycol dimethacrylate (EGDMA) under Vis light irradiation. It was found that phase separation process of these networks can be independently induced and promptly controlled by using UV and Vis light. The characteristic length scale distribution of the resulting co-continuous morphology can be well regulated by the UV and Vis light intensity. In order to confirm and utilize the connectivity of the bicontinuous morphology observed by confocal microscopy, a very small amount, 0.1 wt%, of multi-walled carbon nanotubes (MWCNTs) was introduced into the mixture and the current-voltage (I-V) relationship was subsequently examined. Preliminary data show that MWCNTs are preferentially dispersed in the PSAF-rich continuous domains and the whole mixture became electrically conducting, confirming the connectivity of the observed bi-continuous morphology. The experimental data obtained in this study reveal a promising method to design various scaffolds for conducting soft matter taking advantages of photopolymerization-induced phase separation.

Liposuction Preserves the Morphological Integrity of the Microvascular Network: Flow Cytometry and Confocal Microscopy Evidence in a Controlled Study.

PubMed

Bertheuil, Nicolas; Chaput, Benoit; Berger-Müller, Sandra; Ménard, Cédric; Mourcin, Frédéric; Watier, Eric; Grolleau, Jean-Louis; Garrido, Ignacio; Tarte, Karin; Sensébé, Luc; Varin, Audrey

2016-05-01

Liposuction is a very popular technique in plastic surgery that allows for the taking adipose tissue (AT) on large surfaces with little risk of morbidity. Although liposuction was previously shown to preserve large perforator vessels, little is known about the effects of liposuction on the microvasculature network. The aim of this study was to analyze the effect of liposuction on the preservation of microvessels at tissue and cellular levels by flow cytometry and confocal microscopy following abdominoplasty procedure. Percentage of endothelial cells in AT from liposuction and en bloc AT was determined by multicolor flow cytometry. Moreover, vessel density and adipocyte content were analyzed in situ in 3 different types of AT (en bloc, from liposuction, and residual AT after liposuction) by confocal microscopy. Flow cytometric analysis showed that en bloc AT contained 30.6% ± 12.9% and AT from liposuction 21.6% ± 9.9% of endothelial cells (CD31(pos)/CD45(neg)/CD235a(neg)/CD11b(neg)) (P = .009). Moreover, analysis of paired AT from the same patients (n = 5) confirmed a lower percentage of endothelial cells in AT from liposuction compared to en bloc AT (17.7% ± 4.5% vs 21.9% ± 3.3%, P = .031). Likewise, confocal microscopy showed that en bloc AT contained 8.2% ± 6.3%, AT from liposuction only 1.6% ± 1.0% (P < .0001), and AT after liposuction 8.9% ± 4.1% (P = .111) of CD31(pos) vessels. Conversely, adipocyte content was 39.5% ± 14.5% in the en bloc AT, 45% ± 18.4% in AT from liposuction (P = .390), and 18.8 ± 14.8% in AT after liposuction (P = .011). For the first time, we demonstrate that liposuction preserves the microvascular network. Indeed, a low percentage of endothelial cells was found in AT from liposuction and we confirm the persistence of microvessels in the tissue after liposuction. © 2015 The American Society for Aesthetic Plastic Surgery, Inc. Reprints and permission: journals.permissions@oup.com.

The identification of key genes and pathways in hepatocellular carcinoma by bioinformatics analysis of high-throughput data.

PubMed

Zhang, Chaoyang; Peng, Li; Zhang, Yaqin; Liu, Zhaoyang; Li, Wenling; Chen, Shilian; Li, Guancheng

2017-06-01

Liver cancer is a serious threat to public health and has fairly complicated pathogenesis. Therefore, the identification of key genes and pathways is of much importance for clarifying molecular mechanism of hepatocellular carcinoma (HCC) initiation and progression. HCC-associated gene expression dataset was downloaded from Gene Expression Omnibus database. Statistical software R was used for significance analysis of differentially expressed genes (DEGs) between liver cancer samples and normal samples. Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, based on R software, were applied for the identification of pathways in which DEGs significantly enriched. Cytoscape software was for the construction of protein-protein interaction (PPI) network and module analysis to find the hub genes and key pathways. Finally, weighted correlation network analysis (WGCNA) was conducted to further screen critical gene modules with similar expression pattern and explore their biological significance. Significance analysis identified 1230 DEGs with fold change >2, including 632 significantly down-regulated DEGs and 598 significantly up-regulated DEGs. GO term enrichment analysis suggested that up-regulated DEG significantly enriched in immune response, cell adhesion, cell migration, type I interferon signaling pathway, and cell proliferation, and the down-regulated DEG mainly enriched in response to endoplasmic reticulum stress and endoplasmic reticulum unfolded protein response. KEGG pathway analysis found DEGs significantly enriched in five pathways including complement and coagulation cascades, focal adhesion, ECM-receptor interaction, antigen processing and presentation, and protein processing in endoplasmic reticulum. The top 10 hub genes in HCC were separately GMPS, ACACA, ALB, TGFB1, KRAS, ERBB2, BCL2, EGFR, STAT3, and CD8A, which resulted from PPI network. The top 3 gene interaction modules in PPI network enriched in immune response, organ development, and response to other organism, respectively. WGCNA revealed that the confirmed eight gene modules significantly enriched in monooxygenase and oxidoreductase activity, response to endoplasmic reticulum stress, type I interferon signaling pathway, processing, presentation and binding of peptide antigen, cellular response to cadmium and zinc ion, cell locomotion and differentiation, ribonucleoprotein complex and RNA processing, and immune system process, respectively. In conclusion, we identified some key genes and pathways closely related with HCC initiation and progression by a series of bioinformatics analysis on DEGs. These screened genes and pathways provided for a more detailed molecular mechanism underlying HCC occurrence and progression, holding promise for acting as biomarkers and potential therapeutic targets.

Spreading dynamics of a SIQRS epidemic model on scale-free networks

NASA Astrophysics Data System (ADS)

Li, Tao; Wang, Yuanmei; Guan, Zhi-Hong

2014-03-01

In order to investigate the influence of heterogeneity of the underlying networks and quarantine strategy on epidemic spreading, a SIQRS epidemic model on the scale-free networks is presented. Using the mean field theory the spreading dynamics of the virus is analyzed. The spreading critical threshold and equilibria are derived. Theoretical results indicate that the critical threshold value is significantly dependent on the topology of the underlying networks and quarantine rate. The existence of equilibria is determined by threshold value. The stability of disease-free equilibrium and the permanence of the disease are proved. Numerical simulations confirmed the analytical results.

General Dynamics of Topology and Traffic on Weighted Technological Networks

NASA Astrophysics Data System (ADS)

Wang, Wen-Xu; Wang, Bing-Hong; Hu, Bo; Yan, Gang; Ou, Qing

2005-05-01

For most technical networks, the interplay of dynamics, traffic, and topology is assumed crucial to their evolution. In this Letter, we propose a traffic-driven evolution model of weighted technological networks. By introducing a general strength-coupling mechanism under which the traffic and topology mutually interact, the model gives power-law distributions of degree, weight, and strength, as confirmed in many real networks. Particularly, depending on a parameter W that controls the total weight growth of the system, the nontrivial clustering coefficient C, degree assortativity coefficient r, and degree-strength correlation are all consistent with empirical evidence.

A Predictive Model of the Oxygen and Heme Regulatory Network in Yeast

PubMed Central

Kundaje, Anshul; Xin, Xiantong; Lan, Changgui; Lianoglou, Steve; Zhou, Mei; Zhang, Li; Leslie, Christina

2008-01-01

Deciphering gene regulatory mechanisms through the analysis of high-throughput expression data is a challenging computational problem. Previous computational studies have used large expression datasets in order to resolve fine patterns of coexpression, producing clusters or modules of potentially coregulated genes. These methods typically examine promoter sequence information, such as DNA motifs or transcription factor occupancy data, in a separate step after clustering. We needed an alternative and more integrative approach to study the oxygen regulatory network in Saccharomyces cerevisiae using a small dataset of perturbation experiments. Mechanisms of oxygen sensing and regulation underlie many physiological and pathological processes, and only a handful of oxygen regulators have been identified in previous studies. We used a new machine learning algorithm called MEDUSA to uncover detailed information about the oxygen regulatory network using genome-wide expression changes in response to perturbations in the levels of oxygen, heme, Hap1, and Co2+. MEDUSA integrates mRNA expression, promoter sequence, and ChIP-chip occupancy data to learn a model that accurately predicts the differential expression of target genes in held-out data. We used a novel margin-based score to extract significant condition-specific regulators and assemble a global map of the oxygen sensing and regulatory network. This network includes both known oxygen and heme regulators, such as Hap1, Mga2, Hap4, and Upc2, as well as many new candidate regulators. MEDUSA also identified many DNA motifs that are consistent with previous experimentally identified transcription factor binding sites. Because MEDUSA's regulatory program associates regulators to target genes through their promoter sequences, we directly tested the predicted regulators for OLE1, a gene specifically induced under hypoxia, by experimental analysis of the activity of its promoter. In each case, deletion of the candidate regulator resulted in the predicted effect on promoter activity, confirming that several novel regulators identified by MEDUSA are indeed involved in oxygen regulation. MEDUSA can reveal important information from a small dataset and generate testable hypotheses for further experimental analysis. Supplemental data are included. PMID:19008939

Operational resilience: concepts, design and analysis

NASA Astrophysics Data System (ADS)

Ganin, Alexander A.; Massaro, Emanuele; Gutfraind, Alexander; Steen, Nicolas; Keisler, Jeffrey M.; Kott, Alexander; Mangoubi, Rami; Linkov, Igor

2016-01-01

Building resilience into today’s complex infrastructures is critical to the daily functioning of society and its ability to withstand and recover from natural disasters, epidemics, and cyber-threats. This study proposes quantitative measures that capture and implement the definition of engineering resilience advanced by the National Academy of Sciences. The approach is applicable across physical, information, and social domains. It evaluates the critical functionality, defined as a performance function of time set by the stakeholders. Critical functionality is a source of valuable information, such as the integrated system resilience over a time interval, and its robustness. The paper demonstrates the formulation on two classes of models: 1) multi-level directed acyclic graphs, and 2) interdependent coupled networks. For both models synthetic case studies are used to explore trends. For the first class, the approach is also applied to the Linux operating system. Results indicate that desired resilience and robustness levels are achievable by trading off different design parameters, such as redundancy, node recovery time, and backup supply available. The nonlinear relationship between network parameters and resilience levels confirms the utility of the proposed approach, which is of benefit to analysts and designers of complex systems and networks.

Electron Heat Flux in Pressure Balance Structures at Ulysses

NASA Technical Reports Server (NTRS)

Yamauchi, Yohei; Suess, Steven T.; Sakurai, Takashi; Whitaker, Ann F. (Technical Monitor)

2001-01-01

Pressure balance structures (PBSs) are a common feature in the high-latitude solar wind near solar minimum. Rom previous studies, PBSs are believed to be remnants of coronal plumes and be related to network activity such as magnetic reconnection in the photosphere. We investigated the magnetic structures of the PBSs, applying a minimum variance analysis to Ulysses/Magnetometer data. At 2001 AGU Spring meeting, we reported that PBSs have structures like current sheets or plasmoids, and suggested that they are associated with network activity at the base of polar plumes. In this paper, we have analyzed high-energy electron data at Ulysses/SWOOPS to see whether bi-directional electron flow exists and confirm the conclusions more precisely. As a result, although most events show a typical flux directed away from the Sun, we have obtained evidence that some PBSs show bi-directional electron flux and others show an isotropic distribution of electron pitch angles. The evidence shows that plasmoids are flowing away from the Sun, changing their flow direction dynamically in a way not caused by Alfven waves. From this, we have concluded that PBSs are generated due to network activity at the base of polar plumes and their magnetic structures axe current sheets or plasmoids.

Operational resilience: concepts, design and analysis

PubMed Central

Ganin, Alexander A.; Massaro, Emanuele; Gutfraind, Alexander; Steen, Nicolas; Keisler, Jeffrey M.; Kott, Alexander; Mangoubi, Rami; Linkov, Igor

2016-01-01

Building resilience into today’s complex infrastructures is critical to the daily functioning of society and its ability to withstand and recover from natural disasters, epidemics, and cyber-threats. This study proposes quantitative measures that capture and implement the definition of engineering resilience advanced by the National Academy of Sciences. The approach is applicable across physical, information, and social domains. It evaluates the critical functionality, defined as a performance function of time set by the stakeholders. Critical functionality is a source of valuable information, such as the integrated system resilience over a time interval, and its robustness. The paper demonstrates the formulation on two classes of models: 1) multi-level directed acyclic graphs, and 2) interdependent coupled networks. For both models synthetic case studies are used to explore trends. For the first class, the approach is also applied to the Linux operating system. Results indicate that desired resilience and robustness levels are achievable by trading off different design parameters, such as redundancy, node recovery time, and backup supply available. The nonlinear relationship between network parameters and resilience levels confirms the utility of the proposed approach, which is of benefit to analysts and designers of complex systems and networks. PMID:26782180

A network pharmacology study of Sendeng-4, a Mongolian medicine.

PubMed

Zi, Tian; Yu, Dong

2015-02-01

We collected the data on the Sendeng-4 chemical composition corresponding targets through the literature and from DrugBank, SuperTarget, TTD (Therapeutic Targets Database) and other databases and the relevant signaling pathways from the KEGG (Kyoto Encyclopedia of Genes and Genomes) database and established models of the chemical composition-target network and chemical composition-target-disease network using Cytoscape software, the analysis indicated that the chemical composition had at least nine different types of targets that acted together to exert effects on the diseases, suggesting a "multi-component, multi-target" feature of the traditional Mongolian medicine. We also employed the rat model of rheumatoid arthritis induced by Collgen Type II to validate the key targets of the chemical components of Sendeng-4, and three of the key targets were validated through laboratory experiments, further confirming the anti-inflammatory effects of Sendeng-4. In all, this study predicted the active ingredients and targets of Sendeng-4, and explored its mechanism of action, which provided new strategies and methods for further research and development of Sendeng-4 and other traditional Mongolian medicines as well. Copyright © 2015 China Pharmaceutical University. Published by Elsevier B.V. All rights reserved.

Evolution of continental-scale drainage in response to mantle dynamics and surface processes: An example from the Ethiopian Highlands

NASA Astrophysics Data System (ADS)

Sembroni, Andrea; Molin, Paola; Pazzaglia, Frank J.; Faccenna, Claudio; Abebe, Bekele

2016-05-01

Ethiopia offers an excellent opportunity to study the effects and linkage between mantle dynamics and surface processes on landscape evolution. The Ethiopian Highlands (NW Ethiopia), characterized by a huge basaltic plateau, is part of the African Superswell, a wide region of dynamically-supported anomalously high topography related to the rising of the Afar plume. The initiation and steadiness of dynamic support beneath Ethiopia has been explored in several studies. However the presence, role, and timing of dynamic support beneath Ethiopia and its relationship with continental flood basalts volcanism and surface processes are poorly defined. Here, we present a geomorphological analysis of the Ethiopian Highlands supplying new constraints on the evolution of river network. We investigated the general topographic features (filtered topography, swath profiles, local relief) and the river network (river longitudinal profiles) of the study area. We also apply a knickpoint celerity model in order to provide a chronological framework to the evolution of the river network. The results trace the long-term progressive capture of the Ethiopian Highlands drainage system and confirm the long-term dynamic support of the area, documenting its impact on the contrasting development of the Blue Nile and Tekeze basins.

Evolution of continental-scale drainage in response to mantle dynamics and surface processes: an example from the Ethiopian Highlands.

NASA Astrophysics Data System (ADS)

Sembroni, Andrea; Molin, Paola; Pazzaglia, Frank J.; Faccenna, Claudio; Abebe, Bekele

2016-04-01

Ethiopia offers an excellent opportunity to study the effects and linkage between mantle dynamics and surface processes on landscape evolution. The Ethiopian Highlands (NW Ethiopia), characterized by a huge basaltic plateau, is part of the African Superswell, a wide region of dynamically-supported anomalously high topography related to the rising of the Afar plume. The initiation and steadiness of dynamic support beneath Ethiopia has been explored in several studies. However the presence, role, and timing of dynamic support beneath Ethiopia and its relationship with continental flood basalts volcanism and surface processes are poorly defined. Here, we present a geomorphological analysis of the Ethiopian Highlands supplying new constrains on the evolution of river network. We investigated the general topographic features (filtered topography, swath profiles, local relief) and the river network (river longitudinal profiles) of the study area. We also apply a knickpoint celerity model in order to provide a chronological framework to the evolution of the river network. The results trace the long-term progressive capture of the Ethiopian Highlands drainage system and confirm the long-term dynamic support of the area, documenting its impact on the contrasting development of the Blue Nile and Tekeze basins.

Mass transport enhancement in redox flow batteries with corrugated fluidic networks

NASA Astrophysics Data System (ADS)

Lisboa, Kleber Marques; Marschewski, Julian; Ebejer, Neil; Ruch, Patrick; Cotta, Renato Machado; Michel, Bruno; Poulikakos, Dimos

2017-08-01

We propose a facile, novel concept of mass transfer enhancement in flow batteries based on electrolyte guidance in rationally designed corrugated channel systems. The proposed fluidic networks employ periodic throttling of the flow to optimally deflect the electrolytes into the porous electrode, targeting enhancement of the electrolyte-electrode interaction. Theoretical analysis is conducted with channels in the form of trapezoidal waves, confirming and detailing the mass transport enhancement mechanism. In dilute concentration experiments with an alkaline quinone redox chemistry, a scaling of the limiting current with Re0.74 is identified, which compares favourably against the Re0.33 scaling typical of diffusion-limited laminar processes. Experimental IR-corrected polarization curves are presented for high concentration conditions, and a significant performance improvement is observed with the narrowing of the nozzles. The adverse effects of periodic throttling on the pumping power are compared with the benefits in terms of power density, and an improvement of up to 102% in net power density is obtained in comparison with the flow-by case employing straight parallel channels. The proposed novel concept of corrugated fluidic networks comes with facile fabrication and contributes to the improvement of the transport characteristics and overall performance of redox flow battery systems.

Operational resilience: concepts, design and analysis.

PubMed

Ganin, Alexander A; Massaro, Emanuele; Gutfraind, Alexander; Steen, Nicolas; Keisler, Jeffrey M; Kott, Alexander; Mangoubi, Rami; Linkov, Igor

2016-01-19

Building resilience into today's complex infrastructures is critical to the daily functioning of society and its ability to withstand and recover from natural disasters, epidemics, and cyber-threats. This study proposes quantitative measures that capture and implement the definition of engineering resilience advanced by the National Academy of Sciences. The approach is applicable across physical, information, and social domains. It evaluates the critical functionality, defined as a performance function of time set by the stakeholders. Critical functionality is a source of valuable information, such as the integrated system resilience over a time interval, and its robustness. The paper demonstrates the formulation on two classes of models: 1) multi-level directed acyclic graphs, and 2) interdependent coupled networks. For both models synthetic case studies are used to explore trends. For the first class, the approach is also applied to the Linux operating system. Results indicate that desired resilience and robustness levels are achievable by trading off different design parameters, such as redundancy, node recovery time, and backup supply available. The nonlinear relationship between network parameters and resilience levels confirms the utility of the proposed approach, which is of benefit to analysts and designers of complex systems and networks.

Structure-based manual screening and automatic networking for systematically exploring sansanmycin analogues using high performance liquid chromatography tandem mass spectroscopy.

PubMed

Jiang, Zhi-Bo; Ren, Wei-Cong; Shi, Yuan-Yuan; Li, Xing-Xing; Lei, Xuan; Fan, Jia-Hui; Zhang, Cong; Gu, Ren-Jie; Wang, Li-Fei; Xie, Yun-Ying; Hong, Bin

2018-05-18

Sansanmycins (SS), one of several known uridyl peptide antibiotics (UPAs) possessing a unique chemical scaffold, showed a good inhibitory effect on the highly refractory pathogens Pseudomonas aeruginosa and Mycobacterium tuberculosis, especially on the multi-drug resistant M. tuberculosis. This study employed high performance liquid chromatography-mass spectrometry detector (HPLC-MSD) ion trap and LTQ orbitrap tandem mass spectrometry (MS/MS) to explore sansanmycin analogues manually and automatically by re-analysis of the Streptomyces sp. SS fermentation broth. The structure-based manual screening method, based on analysis of the fragmentation pathway of known UPAs and on comparisons of the MS/MS spectra with that of sansanmycin A (SS-A), resulted in identifying twenty sansanmycin analogues, including twelve new structures (1-12). Furthermore, to deeply explore sansanmycin analogues, we utilized a GNPS based molecular networking workflow to re-analyze the HPLC-MS/MS data automatically. As a result, eight more new sansanmycins (13-20) were discovered. Compound 1 was discovered to lose two amino acids of residue 1 (AA 1 ) and (2S, 3S)-N 3 -methyl-2,3-diamino butyric acid (DABA) from the N-terminus, and compounds 6, 11 and 12 were found to contain a 2',3'-dehydrated 4',5'-enamine-3'-deoxyuridyl moiety, which have not been reported before. Interestingly, three trace components with novel 5,6-dihydro-5'-aminouridyl group (16-18) were detected for the first time in the sansanmycin-producing strain. Their structures were primarily determined by detail analysis of the data from MS/MS. Compounds 8 and 10 were further confirmed by nuclear magnetic resonance (NMR) data, which proved the efficiency and accuracy of the method of HPLC-MS/MS for exploration of novel UPAs. Comparing to manual screening, the networking method can provide systematic visualization results. Manual screening and networking method may complement with each other to facilitate the mining of novel UPAs. Copyright © 2018 Elsevier B.V. All rights reserved.

Organellar proteome analyses of ricin toxin-treated HeLa cells.

PubMed

Liao, Peng; Li, Yunhu; Li, Hongyang; Liu, Wensen

2016-07-01

Apoptosis triggered by ricin toxin (RT) has previously been associated with certain cellular organellar compartments, but the diversity in the composition of the organellar proteins remains unclear. Here, we applied a shotgun proteomics strategy to examine the differential expression of proteins in the mitochondria, nuclei, and cytoplasm of HeLa cells treated and not treated with RT. Data were combined with a global bioinformatics analysis and experimental confirmations. A total of 3107 proteins were identified. Bioinformatics predictors (Proteome Analyst, WoLF PSORT, TargetP, MitoPred, Nucleo, MultiLoc, and k-nearest neighbor) and a Bayesian model that integrated these predictors were used to predict the locations of 1349 distinct organellar proteins. Our data indicate that the Bayesian model was more efficient than the individual implementation of these predictors. Additionally, a Biomolecular Interaction Network (BIN) analysis was used to identify 149 BIN subnetworks. Our experimental confirmations indicate that certain apoptosis-related proteins (e.g. cytochrome c, enolase, lamin B, Bax, and Drp1) were found to be translocated and had variable expression levels. These results provide new insights for the systematic understanding of RT-induced apoptosis responses. © The Author(s) 2014.

A Method of Predicting Queuing at Library Online PCs

ERIC Educational Resources Information Center

Beranek, Lea G.

2006-01-01

On-campus networked personal computer (PC) usage at La Trobe University Library was surveyed during September 2005. The survey's objectives were to confirm peak usage times, to measure some of the relevant parameters of online PC usage, and to determine the effect that 24 new networked PCs had on service quality. The survey found that clients…

Field trial of interworking between broadband applications and GMPLS/OXC network

NASA Astrophysics Data System (ADS)

Sameshima, Yasunori; Ohara, Takuya; Okano, Yukifusa

2006-09-01

This paper describes the interworking between 4K digital cinema and a GMPLS/OXC network in JGN II. Through three trials in JGN II, we confirmed that 4K real-time streams were successfully transmitted in GMPLS paths and that the GMPLS/OXC technology can be used for transmission in such a broadband application.

Meteorological Observations Available for the State of Utah

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wharton, S.

The National Weather Service’s Meteorological Assimilation Data Ingest System (MADIS) contains a large number of station networks of surface and upper air meteorological observations for the state of Utah. In addition to MADIS, observations from individual station networks may also be available. It has been confirmed that LLNL has access to the data sources listed below.

RNA Sequencing and Bioinformatics Analysis Implicate the Regulatory Role of a Long Noncoding RNA-mRNA Network in Hepatic Stellate Cell Activation.

PubMed

Guo, Can-Jie; Xiao, Xiao; Sheng, Li; Chen, Lili; Zhong, Wei; Li, Hai; Hua, Jing; Ma, Xiong

2017-01-01

To analyze the long noncoding (lncRNA)-mRNA expression network and potential roles in rat hepatic stellate cells (HSCs) during activation. LncRNA expression was analyzed in quiescent and culture-activated HSCs by RNA sequencing, and differentially expressed lncRNAs verified by quantitative reverse transcription polymerase chain reaction (qRT-PCR) were subjected to bioinformatics analysis. In vivo analyses of differential lncRNA-mRNA expression were performed on a rat model of liver fibrosis. We identified upregulation of 12 lncRNAs and 155 mRNAs and downregulation of 12 lncRNAs and 374 mRNAs in activated HSCs. Additionally, we identified the differential expression of upregulated lncRNAs (NONRATT012636.2, NONRATT016788.2, and NONRATT021402.2) and downregulated lncRNAs (NONRATT007863.2, NONRATT019720.2, and NONRATT024061.2) in activated HSCs relative to levels observed in quiescent HSCs, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses showed that changes in lncRNAs associated with HSC activation revealed 11 significantly enriched pathways according to their predicted targets. Moreover, based on the predicted co-expression network, the relative dynamic levels of NONRATT013819.2 and lysyl oxidase (Lox) were compared during HSC activation both in vitro and in vivo. Our results confirmed the upregulation of lncRNA NONRATT013819.2 and Lox mRNA associated with the extracellular matrix (ECM)-related signaling pathway in HSCs and fibrotic livers. Our results detailing a dysregulated lncRNA-mRNA network might provide new treatment strategies for hepatic fibrosis based on findings indicating potentially critical roles for NONRATT013819.2 and Lox in ECM remodeling during HSC activation. © 2017 The Author(s). Published by S. Karger AG, Basel.

EGFR Signal-Network Reconstruction Demonstrates Metabolic Crosstalk in EMT

PubMed Central

Choudhary, Kumari Sonal; Rohatgi, Neha; Briem, Eirikur; Gudjonsson, Thorarinn; Gudmundsson, Steinn; Rolfsson, Ottar

2016-01-01

Epithelial to mesenchymal transition (EMT) is an important event during development and cancer metastasis. There is limited understanding of the metabolic alterations that give rise to and take place during EMT. Dysregulation of signalling pathways that impact metabolism, including epidermal growth factor receptor (EGFR), are however a hallmark of EMT and metastasis. In this study, we report the investigation into EGFR signalling and metabolic crosstalk of EMT through constraint-based modelling and analysis of the breast epithelial EMT cell model D492 and its mesenchymal counterpart D492M. We built an EGFR signalling network for EMT based on stoichiometric coefficients and constrained the network with gene expression data to build epithelial (EGFR_E) and mesenchymal (EGFR_M) networks. Metabolic alterations arising from differential expression of EGFR genes was derived from a literature review of AKT regulated metabolic genes. Signaling flux differences between EGFR_E and EGFR_M models subsequently allowed metabolism in D492 and D492M cells to be assessed. Higher flux within AKT pathway in the D492 cells compared to D492M suggested higher glycolytic activity in D492 that we confirmed experimentally through measurements of glucose uptake and lactate secretion rates. The signaling genes from the AKT, RAS/MAPK and CaM pathways were predicted to revert D492M to D492 phenotype. Follow-up analysis of EGFR signaling metabolic crosstalk in three additional breast epithelial cell lines highlighted variability in in vitro cell models of EMT. This study shows that the metabolic phenotype may be predicted by in silico analyses of gene expression data of EGFR signaling genes, but this phenomenon is cell-specific and does not follow a simple trend. PMID:27253373

EGFR Signal-Network Reconstruction Demonstrates Metabolic Crosstalk in EMT.

PubMed

Choudhary, Kumari Sonal; Rohatgi, Neha; Halldorsson, Skarphedinn; Briem, Eirikur; Gudjonsson, Thorarinn; Gudmundsson, Steinn; Rolfsson, Ottar

2016-06-01

Epithelial to mesenchymal transition (EMT) is an important event during development and cancer metastasis. There is limited understanding of the metabolic alterations that give rise to and take place during EMT. Dysregulation of signalling pathways that impact metabolism, including epidermal growth factor receptor (EGFR), are however a hallmark of EMT and metastasis. In this study, we report the investigation into EGFR signalling and metabolic crosstalk of EMT through constraint-based modelling and analysis of the breast epithelial EMT cell model D492 and its mesenchymal counterpart D492M. We built an EGFR signalling network for EMT based on stoichiometric coefficients and constrained the network with gene expression data to build epithelial (EGFR_E) and mesenchymal (EGFR_M) networks. Metabolic alterations arising from differential expression of EGFR genes was derived from a literature review of AKT regulated metabolic genes. Signaling flux differences between EGFR_E and EGFR_M models subsequently allowed metabolism in D492 and D492M cells to be assessed. Higher flux within AKT pathway in the D492 cells compared to D492M suggested higher glycolytic activity in D492 that we confirmed experimentally through measurements of glucose uptake and lactate secretion rates. The signaling genes from the AKT, RAS/MAPK and CaM pathways were predicted to revert D492M to D492 phenotype. Follow-up analysis of EGFR signaling metabolic crosstalk in three additional breast epithelial cell lines highlighted variability in in vitro cell models of EMT. This study shows that the metabolic phenotype may be predicted by in silico analyses of gene expression data of EGFR signaling genes, but this phenomenon is cell-specific and does not follow a simple trend.

Reproducibility and Temporal Structure in Weekly Resting-State fMRI over a Period of 3.5 Years

PubMed Central

Choe, Ann S.; Jones, Craig K.; Joel, Suresh E.; Muschelli, John; Belegu, Visar; Caffo, Brian S.; Lindquist, Martin A.; van Zijl, Peter C. M.; Pekar, James J.

2015-01-01

Resting-state functional MRI (rs-fMRI) permits study of the brain’s functional networks without requiring participants to perform tasks. Robust changes in such resting state networks (RSNs) have been observed in neurologic disorders, and rs-fMRI outcome measures are candidate biomarkers for monitoring clinical trials, including trials of extended therapeutic interventions for rehabilitation of patients with chronic conditions. In this study, we aim to present a unique longitudinal dataset reporting on a healthy adult subject scanned weekly over 3.5 years and identify rs-fMRI outcome measures appropriate for clinical trials. Accordingly, we assessed the reproducibility, and characterized the temporal structure of, rs-fMRI outcome measures derived using independent component analysis (ICA). Data was compared to a 21-person dataset acquired on the same scanner in order to confirm that the values of the single-subject RSN measures were within the expected range as assessed from the multi-participant dataset. Fourteen RSNs were identified, and the inter-session reproducibility of outcome measures—network spatial map, temporal signal fluctuation magnitude, and between-network connectivity (BNC)–was high, with executive RSNs showing the highest reproducibility. Analysis of the weekly outcome measures also showed that many rs-fMRI outcome measures had a significant linear trend, annual periodicity, and persistence. Such temporal structure was most prominent in spatial map similarity, and least prominent in BNC. High reproducibility supports the candidacy of rs-fMRI outcome measures as biomarkers, but the presence of significant temporal structure needs to be taken into account when such outcome measures are considered as biomarkers for rehabilitation-style therapeutic interventions in chronic conditions. PMID:26517540

HOXB7 and Hsa-miR-222 as the Potential Therapeutic Candidates for Metastatic Colorectal Cancer.

PubMed

Iman, Maryam; Mostafavi, Seyede Samaneh; Arab, Seyed Shahriar; Azimzadeh, Sadegh; Poorebrahim, Mansour

2016-01-01

Recent studies have shown that the high mortality of patients with colorectal cancer (CRC) is related to its ability to spread the surrounding tissues, thus there is a need for designing and developing new drugs. Here, we proposed a combinational therapy strategy, an inhibitory peptide in combination with miRNA targeting, for modulating CRC metastasis. In this study, some of the recent patents were also reviewed. After data analysis with GEO2R and gene annotation using DAVID server, regulatory interactions of differentially expressed genes (DEGs) were obtained from STRING, GeneMANIA, KEGG and TRED databases. In parallel, the corresponding validated microRNAs (miRNAs) were obtained from mirDIP web server and a miRNA-DEG regulatory network was also reconstructed. Clustering and topological analyses of the regulatory networks were performed using Cytoscape plug-ins. We found the HOXB family as the most important functional complex in DEG-derived regulatory network. Accordingly, an anti-HOXB7 peptide was designed based on the binding interface of its coactivator, PBX1. Topological analysis of miRNA-DEG network indicated that hsa-miR-222 is one of the most important oncomirs involved in regulation of DEGs activities. Thus, this miRNA, along with HOXB7, was also considered as the potential target for inhibiting CRC metastasis. Molecular docking studies exhibited that the designed peptide can bind to desired binding pocket of HOXB7 in a highaffinity manner. Further confirmations were also observed in Molecular dynamics (MD) simulations carried out by GROMACS v5.0.2 simulation package. In conclusion, our findings suggest that simultaneous targeting of key regulatory genes and miRNAs may be a useful strategy for prevention of CRC metastasis.

Disordered eating in college sorority women: A social network analysis of a subset of members from a single sorority chapter.

PubMed

Becker, Kendra R; Stojek, Monika M; Clifton, Allan; Miller, Joshua D

2018-06-07

Disordered eating attitudes and behaviors are prevalent among college women, and peers appear to influence current and future eating pathology. Social network analysis (SNA) is an innovative quantitative method to examine relationships (i.e., ties) among people based on their various attributes. In this study, the social network of one sorority was modeled using exponential random graph model (ERGM) to explore if homophily, or the tendency for relationship ties to exist based on shared attributes, was present according to sorority members' disordered eating behaviors/attitudes and their body mass index (BMI). Participants included members of one sorority at a large Southeastern university. All members were included on a roster unless they elected to opt out during the consent process, and 41 (19%) of the members completed the study measures. Participants completed the Social Network Questionnaire developed for this study (degree of "liking" of every member on the roster), the Eating Disorder Examination-Questionnaire (EDE-Q), and a demographics questionnaire in exchange for 1 h of community service credit. The final sample consisted of mostly White women with an average age of 20. Homophily across liking ties was examined based on the EDE-Q Global scale, episodes of binge eating, and BMI. The greater the difference in EDE-Q scores, the more likely the participants were to like one another. The greater the difference in BMI, the less likely the participants were to like one another. Binge eating was unrelated to homophily. College sorority women appear to prefer other women with dissimilar levels of disordered eating attitudes, suggesting complex interactions between stigmatized or valued disordered eating attributes. Women with similar BMI were more likely to like one another, confirming past findings. Copyright © 2018. Published by Elsevier Ltd.

Time-dependence of graph theory metrics in functional connectivity analysis

PubMed Central

Chiang, Sharon; Cassese, Alberto; Guindani, Michele; Vannucci, Marina; Yeh, Hsiang J.; Haneef, Zulfi; Stern, John M.

2016-01-01

Brain graphs provide a useful way to computationally model the network structure of the connectome, and this has led to increasing interest in the use of graph theory to quantitate and investigate the topological characteristics of the healthy brain and brain disorders on the network level. The majority of graph theory investigations of functional connectivity have relied on the assumption of temporal stationarity. However, recent evidence increasingly suggests that functional connectivity fluctuates over the length of the scan. In this study, we investigate the stationarity of brain network topology using a Bayesian hidden Markov model (HMM) approach that estimates the dynamic structure of graph theoretical measures of whole-brain functional connectivity. In addition to extracting the stationary distribution and transition probabilities of commonly employed graph theory measures, we propose two estimators of temporal stationarity: the S-index and N-index. These indexes can be used to quantify different aspects of the temporal stationarity of graph theory measures. We apply the method and proposed estimators to resting-state functional MRI data from healthy controls and patients with temporal lobe epilepsy. Our analysis shows that several graph theory measures, including small-world index, global integration measures, and betweenness centrality, may exhibit greater stationarity over time and therefore be more robust. Additionally, we demonstrate that accounting for subject-level differences in the level of temporal stationarity of network topology may increase discriminatory power in discriminating between disease states. Our results confirm and extend findings from other studies regarding the dynamic nature of functional connectivity, and suggest that using statistical models which explicitly account for the dynamic nature of functional connectivity in graph theory analyses may improve the sensitivity of investigations and consistency across investigations. PMID:26518632

Time-dependence of graph theory metrics in functional connectivity analysis.

PubMed

Chiang, Sharon; Cassese, Alberto; Guindani, Michele; Vannucci, Marina; Yeh, Hsiang J; Haneef, Zulfi; Stern, John M

2016-01-15

Brain graphs provide a useful way to computationally model the network structure of the connectome, and this has led to increasing interest in the use of graph theory to quantitate and investigate the topological characteristics of the healthy brain and brain disorders on the network level. The majority of graph theory investigations of functional connectivity have relied on the assumption of temporal stationarity. However, recent evidence increasingly suggests that functional connectivity fluctuates over the length of the scan. In this study, we investigate the stationarity of brain network topology using a Bayesian hidden Markov model (HMM) approach that estimates the dynamic structure of graph theoretical measures of whole-brain functional connectivity. In addition to extracting the stationary distribution and transition probabilities of commonly employed graph theory measures, we propose two estimators of temporal stationarity: the S-index and N-index. These indexes can be used to quantify different aspects of the temporal stationarity of graph theory measures. We apply the method and proposed estimators to resting-state functional MRI data from healthy controls and patients with temporal lobe epilepsy. Our analysis shows that several graph theory measures, including small-world index, global integration measures, and betweenness centrality, may exhibit greater stationarity over time and therefore be more robust. Additionally, we demonstrate that accounting for subject-level differences in the level of temporal stationarity of network topology may increase discriminatory power in discriminating between disease states. Our results confirm and extend findings from other studies regarding the dynamic nature of functional connectivity, and suggest that using statistical models which explicitly account for the dynamic nature of functional connectivity in graph theory analyses may improve the sensitivity of investigations and consistency across investigations. Copyright © 2015 Elsevier Inc. All rights reserved.

Independent Component Analysis of Resting-State Functional Magnetic Resonance Imaging in Pedophiles.

PubMed

Cantor, J M; Lafaille, S J; Hannah, J; Kucyi, A; Soh, D W; Girard, T A; Mikulis, D J

2016-10-01

Neuroimaging and other studies have changed the common view that pedophilia is a result of childhood sexual abuse and instead is a neurologic phenomenon with prenatal origins. Previous research has identified differences in the structural connectivity of the brain in pedophilia. To identify analogous differences in functional connectivity. Functional magnetic resonance images were recorded from three groups of participants while they were at rest: pedophilic men with a history of sexual offenses against children (n = 37) and two control groups: non-pedophilic men who committed non-sexual offenses (n = 28) and non-pedophilic men with no criminal history (n = 39). Functional magnetic resonance imaging data were subjected to independent component analysis to identify known functional networks of the brain, and groups were compared to identify differences in connectivity with those networks (or "components"). The pedophilic group demonstrated wide-ranging increases in functional connectivity with the default mode network compared with controls and regional differences (increases and decreases) with the frontoparietal network. Of these brain regions (total = 23), 20 have been identified by meta-analytic studies to respond to sexually relevant stimuli. Conversely, of the brain areas known to be those that respond to sexual stimuli, nearly all emerged in the present data as significantly different in pedophiles. This study confirms the presence of significant differences in the functional connectivity of the brain in pedophilia consistent with previously reported differences in structural connectivity. The connectivity differences detected here and elsewhere are opposite in direction from those associated with anti-sociality, arguing against anti-sociality and for pedophilia as the source of the neuroanatomic differences detected. Copyright © 2016 International Society for Sexual Medicine. Published by Elsevier Inc. All rights reserved.

A concept mapping study on organic food consumers in Shanghai, China.

PubMed

Hasimu, Huliyeti; Marchesini, Sergio; Canavari, Maurizio

2017-01-01

Despite some similarities with developed countries, the growth of organic market in China seems to follow a different path. Thus, important questions are how Chinese urban consumers perceive organic food, and what are the main concepts associated to the organic attribute. We aimed at representing in graphic form the network of mental associations with the organic concept. We used an adapted version of the "Brand concept mapping" method to acquire, process, and draw individual concept networks perceived by 50 organic food consumers in Shanghai. We then analyzed the data using network and cluster analysis to create aggregated maps for two distinct groups of consumers. Similarly to their peers in developed countries, Chinese consumers perceive organic food as healthy, safe and expensive. However, organic is not necessarily synonymous with natural produce in China, also due to a translation of the term that conveys the idea of a "technology advanced" product. Organic overlaps with the green food label in terms of image and positioning in the market, since they are easily associated and often confused. The two groups we identified show clear differences in the way the organic concept is associated to other concepts and features. The study provides useful information for practitioners: marketers of organic products in China should invest in communication to emphasize the differences with Green Food products and they should consider the possibility of segmenting organic consumers; Chinese policy makers should consider implementing information campaigns aimed at achieving a better understanding of the features of these quality labels among consumers. For researchers, the study confirms that the BCM method is effective and its integration with network and cluster analysis improves the interpretation of individual and aggregated maps. Copyright © 2016 Elsevier Ltd. All rights reserved.

Cross disease analysis of co-functional microRNA pairs on a reconstructed network of disease-gene-microRNA tripartite.

PubMed

Peng, Hui; Lan, Chaowang; Zheng, Yi; Hutvagner, Gyorgy; Tao, Dacheng; Li, Jinyan

2017-03-24

MicroRNAs always function cooperatively in their regulation of gene expression. Dysfunctions of these co-functional microRNAs can play significant roles in disease development. We are interested in those multi-disease associated co-functional microRNAs that regulate their common dysfunctional target genes cooperatively in the development of multiple diseases. The research is potentially useful for human disease studies at the transcriptional level and for the study of multi-purpose microRNA therapeutics. We designed a computational method to detect multi-disease associated co-functional microRNA pairs and conducted cross disease analysis on a reconstructed disease-gene-microRNA (DGR) tripartite network. The construction of the DGR tripartite network is by the integration of newly predicted disease-microRNA associations with those relationships of diseases, microRNAs and genes maintained by existing databases. The prediction method uses a set of reliable negative samples of disease-microRNA association and a pre-computed kernel matrix instead of kernel functions. From this reconstructed DGR tripartite network, multi-disease associated co-functional microRNA pairs are detected together with their common dysfunctional target genes and ranked by a novel scoring method. We also conducted proof-of-concept case studies on cancer-related co-functional microRNA pairs as well as on non-cancer disease-related microRNA pairs. With the prioritization of the co-functional microRNAs that relate to a series of diseases, we found that the co-function phenomenon is not unusual. We also confirmed that the regulation of the microRNAs for the development of cancers is more complex and have more unique properties than those of non-cancer diseases.

Differential gene expression in granulosa cells from polycystic ovary syndrome patients with and without insulin resistance: identification of susceptibility gene sets through network analysis.

PubMed

Kaur, Surleen; Archer, Kellie J; Devi, M Gouri; Kriplani, Alka; Strauss, Jerome F; Singh, Rita

2012-10-01

Polycystic ovary syndrome (PCOS) is a heterogeneous, genetically complex, endocrine disorder of uncertain etiology in women. Our aim was to compare the gene expression profiles in stimulated granulosa cells of PCOS women with and without insulin resistance vs. matched controls. This study included 12 normal ovulatory women (controls), 12 women with PCOS without evidence for insulin resistance (PCOS non-IR), and 16 women with insulin resistance (PCOS-IR) undergoing in vitro fertilization. Granulosa cell gene expression profiling was accomplished using Affymetrix Human Genome-U133 arrays. Differentially expressed genes were classified according to gene ontology using ingenuity pathway analysis tools. Microarray results for selected genes were confirmed by real-time quantitative PCR. A total of 211 genes were differentially expressed in PCOS non-IR and PCOS-IR granulosa cells (fold change≥1.5; P≤0.001) vs. matched controls. Diabetes mellitus and inflammation genes were significantly increased in PCOS-IR patients. Real-time quantitative PCR confirmed higher expression of NCF2 (2.13-fold), TCF7L2 (1.92-fold), and SERPINA1 (5.35-fold). Increased expression of inflammation genes ITGAX (3.68-fold) and TAB2 (1.86-fold) was confirmed in PCOS non-IR. Different cardiometabolic disease genes were differentially expressed in the two groups. Decreased expression of CAV1 (-3.58-fold) in PCOS non-IR and SPARC (-1.88-fold) in PCOS-IR was confirmed. Differential expression of genes involved in TGF-β signaling (IGF2R, increased; and HAS2, decreased), and oxidative stress (TXNIP, increased) was confirmed in both groups. Microarray analysis demonstrated differential expression of genes linked to diabetes mellitus, inflammation, cardiovascular diseases, and infertility in the granulosa cells of PCOS women with and without insulin resistance. Because these dysregulated genes are also involved in oxidative stress, lipid metabolism, and insulin signaling, we hypothesize that these genes may be involved in follicular growth arrest and metabolic disorders associated with the different phenotypes of PCOS.

Group percolation in interdependent networks

NASA Astrophysics Data System (ADS)

Wang, Zexun; Zhou, Dong; Hu, Yanqing

2018-03-01

In many real network systems, nodes usually cooperate with each other and form groups to enhance their robustness to risks. This motivates us to study an alternative type of percolation, group percolation, in interdependent networks under attack. In this model, nodes belonging to the same group survive or fail together. We develop a theoretical framework for this group percolation and find that the formation of groups can improve the resilience of interdependent networks significantly. However, the percolation transition is always of first order, regardless of the distribution of group sizes. As an application, we map the interdependent networks with intersimilarity structures, which have attracted much attention recently, onto the group percolation and confirm the nonexistence of continuous phase transitions.

Electronic collaboration in dermatology resident training through social networking.

PubMed

Meeks, Natalie M; McGuire, April L; Carroll, Bryan T

2017-04-01

The use of online educational resources and professional social networking sites is increasing. The field of dermatology is currently under-utilizing online social networking as a means of professional collaboration and sharing of training materials. In this study, we sought to assess the current structure of and satisfaction with dermatology resident education and gauge interest for a professional social networking site for educational collaboration. Two surveys-one for residents and one for faculty-were electronically distributed via the American Society for Dermatologic Surgery and Association of Professors of Dermatology (APD) listserves. The surveys confirmed that there is interest among dermatology residents and faculty in a dermatology professional networking site with the goal to enhance educational collaboration.

Field demonstration of a continuous-variable quantum key distribution network.

PubMed

Huang, Duan; Huang, Peng; Li, Huasheng; Wang, Tao; Zhou, Yingming; Zeng, Guihua

2016-08-01

We report on what we believe is the first field implementation of a continuous-variable quantum key distribution (CV-QKD) network with point-to-point configuration. Four QKD nodes are deployed on standard communication infrastructures connected with commercial telecom optical fiber. Reliable key exchange is achieved in the wavelength-division-multiplexing CV-QKD network. The impact of a complex and volatile field environment on the excess noise is investigated, since excess noise controlling and reduction is arguably the major issue pertaining to distance and the secure key rate. We confirm the applicability and verify the maturity of the CV-QKD network in a metropolitan area, thus paving the way for a next-generation global secure communication network.

Subjective well-being associated with size of social network and social support of elderly.

PubMed

Wang, Xingmin

2016-06-01

The current study examined the impact of size of social network on subjective well-being of elderly, mainly focused on confirmation of the mediator role of perceived social support. The results revealed that both size of social network and perceived social support were significantly correlated with subjective well-being. Structural equation modeling indicated that perceived social support partially mediated size of social network to subjective well-being. The final model also revealed significant both paths from size of social network to subjective well-being through perceived social support. The findings extended prior researches and provided valuable evidence on how to promote mental health of the elderly. © The Author(s) 2014.

A complex of meteorite-forming bodies (the Innisfree - Ridgedale family).

NASA Astrophysics Data System (ADS)

Shestaka, I. S.

1994-12-01

For the first time a swarm of meteorite-forming bodies was identified. Yearly this swarm's orbit approaches the Earth's orbit in early February. This swarm contains the Innisfree and Ridgedale fireballs, 9 small meteoric swarms, several asteroids and 12 fireballs photographed by the cameras of the Prairie Network and Canadian Meteorite Observation and Discovery Project. The discovery of this complex, intensive bombardments of the Moon's surface recorded by means of seismographs left on the Moon, the analysis of the time distributions of meteorite falls on the Earth and other established facts confirm the existence of swarms of meteorite-forming bodies which are crossing the Earth's orbit.

Optimizing the Performance of Radionuclide Identification Software in the Hunt for Nuclear Security Threats

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fotion, Katherine A.

2016-08-18

The Radionuclide Analysis Kit (RNAK), my team’s most recent nuclide identification software, is entering the testing phase. A question arises: will removing rare nuclides from the software’s library improve its overall performance? An affirmative response indicates fundamental errors in the software’s framework, while a negative response confirms the effectiveness of the software’s key machine learning algorithms. After thorough testing, I found that the performance of RNAK cannot be improved with the library choice effect, thus verifying the effectiveness of RNAK’s algorithms—multiple linear regression, Bayesian network using the Viterbi algorithm, and branch and bound search.

Integrated RNA-Seq and sRNA-Seq Analysis Identifies Chilling and Freezing Responsive Key Molecular Players and Pathways in Tea Plant (Camellia sinensis)

PubMed Central

Zheng, Chao; Zhao, Lei; Wang, Yu; Shen, Jiazhi; Zhang, Yinfei; Jia, Sisi; Li, Yusheng; Ding, Zhaotang

2015-01-01

Tea [Camellia sinensis (L) O. Kuntze, Theaceae] is one of the most popular non-alcoholic beverages worldwide. Cold stress is one of the most severe abiotic stresses that limit tea plants’ growth, survival and geographical distribution. However, the genetic regulatory network and signaling pathways involved in cold stress responses in tea plants remain unearthed. Using RNA-Seq, DGE and sRNA-Seq technologies, we performed an integrative analysis of miRNA and mRNA expression profiling and their regulatory network of tea plants under chilling (4℃) and freezing (-5℃) stress. Differentially expressed (DE) miRNA and mRNA profiles were obtained based on fold change analysis, miRNAs and target mRNAs were found to show both coherent and incoherent relationships in the regulatory network. Furthermore, we compared several key pathways (e.g., ‘Photosynthesis’), GO terms (e.g., ‘response to karrikin’) and transcriptional factors (TFs, e.g., DREB1b/CBF1) which were identified as involved in the early chilling and/or freezing response of tea plants. Intriguingly, we found that karrikins, a new group of plant growth regulators, and β-primeverosidase (BPR), a key enzyme functionally relevant with the formation of tea aroma might play an important role in both early chilling and freezing response of tea plants. Quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) analysis further confirmed the results from RNA-Seq and sRNA-Seq analysis. This is the first study to simultaneously profile the expression patterns of both miRNAs and mRNAs on a genome-wide scale to elucidate the molecular mechanisms of early responses of tea plants to cold stress. In addition to gaining a deeper insight into the cold resistant characteristics of tea plants, we provide a good case study to analyse mRNA/miRNA expression and profiling of non-model plant species using next-generation sequencing technology. PMID:25901577

Integrated RNA-Seq and sRNA-Seq Analysis Identifies Chilling and Freezing Responsive Key Molecular Players and Pathways in Tea Plant (Camellia sinensis).

PubMed

Zheng, Chao; Zhao, Lei; Wang, Yu; Shen, Jiazhi; Zhang, Yinfei; Jia, Sisi; Li, Yusheng; Ding, Zhaotang

2015-01-01

Tea [Camellia sinensis (L) O. Kuntze, Theaceae] is one of the most popular non-alcoholic beverages worldwide. Cold stress is one of the most severe abiotic stresses that limit tea plants' growth, survival and geographical distribution. However, the genetic regulatory network and signaling pathways involved in cold stress responses in tea plants remain unearthed. Using RNA-Seq, DGE and sRNA-Seq technologies, we performed an integrative analysis of miRNA and mRNA expression profiling and their regulatory network of tea plants under chilling (4℃) and freezing (-5℃) stress. Differentially expressed (DE) miRNA and mRNA profiles were obtained based on fold change analysis, miRNAs and target mRNAs were found to show both coherent and incoherent relationships in the regulatory network. Furthermore, we compared several key pathways (e.g., 'Photosynthesis'), GO terms (e.g., 'response to karrikin') and transcriptional factors (TFs, e.g., DREB1b/CBF1) which were identified as involved in the early chilling and/or freezing response of tea plants. Intriguingly, we found that karrikins, a new group of plant growth regulators, and β-primeverosidase (BPR), a key enzyme functionally relevant with the formation of tea aroma might play an important role in both early chilling and freezing response of tea plants. Quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) analysis further confirmed the results from RNA-Seq and sRNA-Seq analysis. This is the first study to simultaneously profile the expression patterns of both miRNAs and mRNAs on a genome-wide scale to elucidate the molecular mechanisms of early responses of tea plants to cold stress. In addition to gaining a deeper insight into the cold resistant characteristics of tea plants, we provide a good case study to analyse mRNA/miRNA expression and profiling of non-model plant species using next-generation sequencing technology.

Feature selection for neural network based defect classification of ceramic components using high frequency ultrasound.

PubMed

Kesharaju, Manasa; Nagarajah, Romesh

2015-09-01

The motivation for this research stems from a need for providing a non-destructive testing method capable of detecting and locating any defects and microstructural variations within armour ceramic components before issuing them to the soldiers who rely on them for their survival. The development of an automated ultrasonic inspection based classification system would make possible the checking of each ceramic component and immediately alert the operator about the presence of defects. Generally, in many classification problems a choice of features or dimensionality reduction is significant and simultaneously very difficult, as a substantial computational effort is required to evaluate possible feature subsets. In this research, a combination of artificial neural networks and genetic algorithms are used to optimize the feature subset used in classification of various defects in reaction-sintered silicon carbide ceramic components. Initially wavelet based feature extraction is implemented from the region of interest. An Artificial Neural Network classifier is employed to evaluate the performance of these features. Genetic Algorithm based feature selection is performed. Principal Component Analysis is a popular technique used for feature selection and is compared with the genetic algorithm based technique in terms of classification accuracy and selection of optimal number of features. The experimental results confirm that features identified by Principal Component Analysis lead to improved performance in terms of classification percentage with 96% than Genetic algorithm with 94%. Copyright © 2015 Elsevier B.V. All rights reserved.

Determinism and Contingency Shape Metabolic Complementation in an Endosymbiotic Consortium

PubMed Central

Ponce-de-Leon, Miguel; Tamarit, Daniel; Calle-Espinosa, Jorge; Mori, Matteo; Latorre, Amparo; Montero, Francisco; Pereto, Juli

2017-01-01

Bacterial endosymbionts and their insect hosts establish an intimate metabolic relationship. Bacteria offer a variety of essential nutrients to their hosts, whereas insect cells provide the necessary sources of matter and energy to their tiny metabolic allies. These nutritional complementations sustain themselves on a diversity of metabolite exchanges between the cell host and the reduced yet highly specialized bacterial metabolism—which, for instance, overproduces a small set of essential amino acids and vitamins. A well-known case of metabolic complementation is provided by the cedar aphid Cinara cedri that harbors two co-primary endosymbionts, Buchnera aphidicola BCc and Ca. Serratia symbiotica SCc, and in which some metabolic pathways are partitioned between different partners. Here we present a genome-scale metabolic network (GEM) for the bacterial consortium from the cedar aphid iBSCc. The analysis of this GEM allows us the confirmation of cases of metabolic complementation previously described by genome analysis (i.e., tryptophan and biotin biosynthesis) and the redefinition of an event of metabolic pathway sharing between the two endosymbionts, namely the biosynthesis of tetrahydrofolate. In silico knock-out experiments with iBSCc showed that the consortium metabolism is a highly integrated yet fragile network. We also have explored the evolutionary pathways leading to the emergence of metabolic complementation between reduced metabolisms starting from individual, complete networks. Our results suggest that, during the establishment of metabolic complementation in endosymbionts, adaptive evolution is significant in the case of tryptophan biosynthesis, whereas vitamin production pathways seem to adopt suboptimal solutions. PMID:29213256

Determinism and Contingency Shape Metabolic Complementation in an Endosymbiotic Consortium.

PubMed

Ponce-de-Leon, Miguel; Tamarit, Daniel; Calle-Espinosa, Jorge; Mori, Matteo; Latorre, Amparo; Montero, Francisco; Pereto, Juli

2017-01-01

Bacterial endosymbionts and their insect hosts establish an intimate metabolic relationship. Bacteria offer a variety of essential nutrients to their hosts, whereas insect cells provide the necessary sources of matter and energy to their tiny metabolic allies. These nutritional complementations sustain themselves on a diversity of metabolite exchanges between the cell host and the reduced yet highly specialized bacterial metabolism-which, for instance, overproduces a small set of essential amino acids and vitamins. A well-known case of metabolic complementation is provided by the cedar aphid Cinara cedri that harbors two co-primary endosymbionts, Buchnera aphidicola BCc and Ca . Serratia symbiotica SCc, and in which some metabolic pathways are partitioned between different partners. Here we present a genome-scale metabolic network (GEM) for the bacterial consortium from the cedar aphid i BSCc. The analysis of this GEM allows us the confirmation of cases of metabolic complementation previously described by genome analysis (i.e., tryptophan and biotin biosynthesis) and the redefinition of an event of metabolic pathway sharing between the two endosymbionts, namely the biosynthesis of tetrahydrofolate. In silico knock-out experiments with i BSCc showed that the consortium metabolism is a highly integrated yet fragile network. We also have explored the evolutionary pathways leading to the emergence of metabolic complementation between reduced metabolisms starting from individual, complete networks. Our results suggest that, during the establishment of metabolic complementation in endosymbionts, adaptive evolution is significant in the case of tryptophan biosynthesis, whereas vitamin production pathways seem to adopt suboptimal solutions.

Dynamic Photorefractive Memory and its Application for Opto-Electronic Neural Networks.

NASA Astrophysics Data System (ADS)

Sasaki, Hironori

This dissertation describes the analysis of the photorefractive crystal dynamics and its application for opto-electronic neural network systems. The realization of the dynamic photorefractive memory is investigated in terms of the following aspects: fast memory update, uniform grating multiplexing schedules and the prevention of the partial erasure of existing gratings. The fast memory update is realized by the selective erasure process that superimposes a new grating on the original one with an appropriate phase shift. The dynamics of the selective erasure process is analyzed using the first-order photorefractive material equations and experimentally confirmed. The effects of beam coupling and fringe bending on the selective erasure dynamics are also analyzed by numerically solving a combination of coupled wave equations and the photorefractive material equation. Incremental recording technique is proposed as a uniform grating multiplexing schedule and compared with the conventional scheduled recording technique in terms of phase distribution in the presence of an external dc electric field, as well as the image gray scale dependence. The theoretical analysis and experimental results proved the superiority of the incremental recording technique over the scheduled recording. Novel recirculating information memory architecture is proposed and experimentally demonstrated to prevent partial degradation of the existing gratings by accessing the memory. Gratings are circulated through a memory feed back loop based on the incremental recording dynamics and demonstrate robust read/write/erase capabilities. The dynamic photorefractive memory is applied to opto-electronic neural network systems. Module architecture based on the page-oriented dynamic photorefractive memory is proposed. This module architecture can implement two complementary interconnection organizations, fan-in and fan-out. The module system scalability and the learning capabilities are theoretically investigated using the photorefractive dynamics described in previous chapters of the dissertation. The implementation of the feed-forward image compression network with 900 input and 9 output neurons with 6-bit interconnection accuracy is experimentally demonstrated. Learning of the Perceptron network that determines sex based on input face images of 900 pixels is also successfully demonstrated.

System identification of a tied arch bridge using reference-based wireless sensor networks

NASA Astrophysics Data System (ADS)

Hietbrink, Colby; Whelan, Matthew J.

2012-04-01

Vibration-based methods of structural health monitoring are generally founded on the principle that localized damage to a structure would exhibit changes within the global dynamic response. Upon this basis, accelerometers provide a unique health monitoring strategy in that a distributed network of sensors provides the technical feasibility to isolate the onset of damage without requiring that any sensor be located exactly on or in close proximity to the damage. While in theory this may be sufficient, practical experience has shown significant improvement in the application of damage diagnostic routines when mode shapes characterized by strongly localized behavior of specific elements are captured by the instrumentation array. In traditional applications, this presents a challenge since the cost and complexity of cable-based systems often effectively limits the number of instrumented locations thereby constraining the modal parameter extraction to only global modal responses. The advent of the low-cost RF chip transceiver with wireless networking capabilities has afforded a means by which a substantial number of output locations can be measured through referencebased testing using large-scale wireless sensor networks. In the current study, this approach was applied to the Prairie du Chien Bridge over the Mississippi River to extract operational mode shapes with high spatial reconstruction, including strongly localized modes. The tied arch bridge was instrumented at over 230 locations with single-axis accelerometers conditioned and acquired over a high-rate lossless wireless sensor network with simultaneous sampling capabilities. Acquisition of the dynamic response of the web plates of the arch rib was specifically targeted within the instrumentation array for diagnostic purposes. Reference-based operational modal analysis of the full structure through data-driven stochastic subspace identification is presented alongside finite element analysis results for confirmation of modal parameter plausibility. Particular emphasis is placed on the identification and reconstruction of modal response with large contribution from the arch rib web plates.

Aging effects on DNA methylation modules in human brain and blood tissue

PubMed Central

2012-01-01

Background Several recent studies reported aging effects on DNA methylation levels of individual CpG dinucleotides. But it is not yet known whether aging-related consensus modules, in the form of clusters of correlated CpG markers, can be found that are present in multiple human tissues. Such a module could facilitate the understanding of aging effects on multiple tissues. Results We therefore employed weighted correlation network analysis of 2,442 Illumina DNA methylation arrays from brain and blood tissues, which enabled the identification of an age-related co-methylation module. Module preservation analysis confirmed that this module can also be found in diverse independent data sets. Biological evaluation showed that module membership is associated with Polycomb group target occupancy counts, CpG island status and autosomal chromosome location. Functional enrichment analysis revealed that the aging-related consensus module comprises genes that are involved in nervous system development, neuron differentiation and neurogenesis, and that it contains promoter CpGs of genes known to be down-regulated in early Alzheimer's disease. A comparison with a standard, non-module based meta-analysis revealed that selecting CpGs based on module membership leads to significantly increased gene ontology enrichment, thus demonstrating that studying aging effects via consensus network analysis enhances the biological insights gained. Conclusions Overall, our analysis revealed a robustly defined age-related co-methylation module that is present in multiple human tissues, including blood and brain. We conclude that blood is a promising surrogate for brain tissue when studying the effects of age on DNA methylation profiles. PMID:23034122

Social networking addiction, attachment style, and validation of the Italian version of the Bergen Social Media Addiction Scale

PubMed Central

Monacis, Lucia; de Palo, Valeria; Griffiths, Mark D.; Sinatra, Maria

2017-01-01

Aim Research into social networking addiction has greatly increased over the last decade. However, the number of validated instruments assessing addiction to social networking sites (SNSs) remains few, and none have been validated in the Italian language. Consequently, this study tested the psychometric properties of the Italian version of the Bergen Social Media Addiction Scale (BSMAS), as well as providing empirical data concerning the relationship between attachment styles and SNS addiction. Methods A total of 769 participants were recruited to this study. Confirmatory factor analysis (CFA) and multigroup analyses were applied to assess construct validity of the Italian version of the BSMAS. Reliability analyses comprised the average variance extracted, the standard error of measurement, and the factor determinacy coefficient. Results Indices obtained from the CFA showed the Italian version of the BSMAS to have an excellent fit of the model to the data, thus confirming the single-factor structure of the instrument. Measurement invariance was established at configural, metric, and strict invariances across age groups, and at configural and metric levels across gender groups. Internal consistency was supported by several indicators. In addition, the theoretical associations between SNS addiction and attachment styles were generally supported. Conclusion This study provides evidence that the Italian version of the BSMAS is a psychometrically robust tool that can be used in future Italian research into social networking addiction. PMID:28494648

[Innovative care and self-care strategies for people with chronic diseases in Latin America].

PubMed

Sapag, Jaime C; Lange, Ilta; Campos, Solange; Piette, John D

2010-01-01

To identify innovative strategies for improved care and self-care of patients with chronic diseases (CD) in Latin America and to explore interest in creating a Latin American network of professionals in this field. A descriptive study based on a survey of key experts with recognized national or regional leadership in CD patient care. The 25-question questionnaire sought information on their experiences with care and self-care initiatives for CD patients, descriptions of successful initiatives, the perceived ability of countries to innovate in this area, their interest in participating in a network of Latin American professionals in this field, and more. Content analysis was performed to develop recommendations for the Region. Responses were obtained from 17 (37.8%) of the 45 experts approached; 82.4% confirmed their knowledge of of involvement with an innovative initiative related to the subject. Initial development does exist in each of the three innovative strategy types: peer care, informal caregivers, and telenursing, the latter being the least explored. There is real interest in forming a Latin American network that focuses on development of innovative self-care strategies for CD patients. Support for a joint network is promising. Priorities are building skills in this area and developing innovative proposals for improved CD patient care in the Region. Innovative measures should be complementary and adapted to the specific context of each scenario.

Single Step Laser Transfer and Laser Curing of Ag NanoWires: A Digital Process for the Fabrication of Flexible and Transparent Microelectrodes.

PubMed

Zacharatos, Filimon; Karvounis, Panagiotis; Theodorakos, Ioannis; Hatziapostolou, Antonios; Zergioti, Ioanna

2018-06-19

Ag nanowire (NW) networks have exquisite optical and electrical properties which make them ideal candidate materials for flexible transparent conductive electrodes. Despite the compatibility of Ag NW networks with laser processing, few demonstrations of laser fabricated Ag NW based components currently exist. In this work, we report on a novel single step laser transferring and laser curing process of micrometer sized pixels of Ag NW networks on flexible substrates. This process relies on the selective laser heating of the Ag NWs induced by the laser pulse energy and the subsequent localized melting of the polymeric substrate. We demonstrate that a single laser pulse can induce both transfer and curing of the Ag NW network. The feasibility of the process is confirmed experimentally and validated by Finite Element Analysis simulations, which indicate that selective heating is carried out within a submicron-sized heat affected zone. The resulting structures can be utilized as fully functional flexible transparent electrodes with figures of merit even higher than 100. Low sheet resistance (<50 Ohm/sq) and high visible light transparency (>90%) make the reported process highly desirable for a variety of applications, including selective heating or annealing of nanocomposite materials and laser processing of nanostructured materials on a large variety of optically transparent substrates, such as Polydimethylsiloxane (PDMS).

Development of flood probability charts for urban drainage network in coastal areas through a simplified joint assessment approach

NASA Astrophysics Data System (ADS)

Archetti, R.; Bolognesi, A.; Casadio, A.; Maglionico, M.

2011-04-01

The operating conditions of urban drainage networks during storm events certainly depend on the hydraulic conveying capacity of conduits but also on downstream boundary conditions. This is particularly true in costal areas where the level of the receiving water body is directly or indirectly affected by tidal or wave effects. In such cases, not just different rainfall conditions (varying intensity and duration), but also different sea-levels and their effects on the network operation should be considered. This paper aims to study the behaviour of a seaside town storm sewer network, estimating the threshold condition for flooding and proposing a simplified method to assess the urban flooding severity as a function of either climate variables. The case study is a portion of the drainage system of Rimini (Italy), implemented and numerically modelled by means of InfoWorks CS code. The hydraulic simulation of the sewerage system has therefore allowed to identify the percentage of nodes of the drainage system where flooding is expected to occur. Combining these percentages with both climate variables values has lead to the definition charts representing the combined degree of risk "sea-rainfall" for the drainage system under investigation. A final comparison between such charts and the results obtained from a one-year sea-rainfall time series has confirmed the reliability of the analysis.

Interplay of Noisy Gene Expression and Dynamics Explains Patterns of Bacterial Operon Organization

NASA Astrophysics Data System (ADS)

Igoshin, Oleg

2011-03-01

Bacterial chromosomes are organized into operons -- sets of genes co-transcribed into polycistronic messenger RNA. Hypotheses explaining the emergence and maintenance of operons include proportional co-regulation, horizontal transfer of intact ``selfish'' operons, emergence via gene duplication, and co-production of physically interacting proteins to speed their association. We hypothesized an alternative: operons can reduce or increase intrinsic gene expression noise in a manner dependent on the post-translational interactions, thereby resulting in selection for or against operons in depending on the network architecture. We devised five classes of two-gene network modules and show that the effects of operons on intrinsic noise depend on class membership. Two classes exhibit decreased noise with co-transcription, two others reveal increased noise, and the remaining one does not show a significant difference. To test our modeling predictions we employed bioinformatic analysis to determine the relationship gene expression noise and operon organization. The results confirm the overrepresentation of noise-minimizing operon architectures and provide evidence against other hypotheses. Our results thereby suggest a central role for gene expression noise in selecting for or maintaining operons in bacterial chromosomes. This demonstrates how post-translational network dynamics may provide selective pressure for organizing bacterial chromosomes, and has practical consequences for designing synthetic gene networks. This work is supported by National Institutes of Health grant 1R01GM096189-01.

Synchronous wearable wireless body sensor network composed of autonomous textile nodes.

PubMed

Vanveerdeghem, Peter; Van Torre, Patrick; Stevens, Christiaan; Knockaert, Jos; Rogier, Hendrik

2014-10-09

A novel, fully-autonomous, wearable, wireless sensor network is presented, where each flexible textile node performs cooperative synchronous acquisition and distributed event detection. Computationally efficient situational-awareness algorithms are implemented on the low-power microcontroller present on each flexible node. The detected events are wirelessly transmitted to a base station, directly, as well as forwarded by other on-body nodes. For each node, a dual-polarized textile patch antenna serves as a platform for the flexible electronic circuitry. Therefore, the system is particularly suitable for comfortable and unobtrusive integration into garments. In the meantime, polarization diversity can be exploited to improve the reliability and energy-efficiency of the wireless transmission. Extensive experiments in realistic conditions have demonstrated that this new autonomous, body-centric, textile-antenna, wireless sensor network is able to correctly detect different operating conditions of a firefighter during an intervention. By relying on four network nodes integrated into the protective garment, this functionality is implemented locally, on the body, and in real time. In addition, the received sensor data are reliably transferred to a central access point at the command post, for more detailed and more comprehensive real-time visualization. This information provides coordinators and commanders with situational awareness of the entire rescue operation. A statistical analysis of measured on-body node-to-node, as well as off-body person-to-person channels is included, confirming the reliability of the communication system.

Social networking addiction, attachment style, and validation of the Italian version of the Bergen Social Media Addiction Scale.

PubMed

Monacis, Lucia; de Palo, Valeria; Griffiths, Mark D; Sinatra, Maria

2017-06-01

Aim Research into social networking addiction has greatly increased over the last decade. However, the number of validated instruments assessing addiction to social networking sites (SNSs) remains few, and none have been validated in the Italian language. Consequently, this study tested the psychometric properties of the Italian version of the Bergen Social Media Addiction Scale (BSMAS), as well as providing empirical data concerning the relationship between attachment styles and SNS addiction. Methods A total of 769 participants were recruited to this study. Confirmatory factor analysis (CFA) and multigroup analyses were applied to assess construct validity of the Italian version of the BSMAS. Reliability analyses comprised the average variance extracted, the standard error of measurement, and the factor determinacy coefficient. Results Indices obtained from the CFA showed the Italian version of the BSMAS to have an excellent fit of the model to the data, thus confirming the single-factor structure of the instrument. Measurement invariance was established at configural, metric, and strict invariances across age groups, and at configural and metric levels across gender groups. Internal consistency was supported by several indicators. In addition, the theoretical associations between SNS addiction and attachment styles were generally supported. Conclusion This study provides evidence that the Italian version of the BSMAS is a psychometrically robust tool that can be used in future Italian research into social networking addiction.

Synchronous Wearable Wireless Body Sensor Network Composed of Autonomous Textile Nodes

PubMed Central

Vanveerdeghem, Peter; Van Torre, Patrick; Stevens, Christiaan; Knockaert, Jos; Rogier, Hendrik

2014-01-01

A novel, fully-autonomous, wearable, wireless sensor network is presented, where each flexible textile node performs cooperative synchronous acquisition and distributed event detection. Computationally efficient situational-awareness algorithms are implemented on the low-power microcontroller present on each flexible node. The detected events are wirelessly transmitted to a base station, directly, as well as forwarded by other on-body nodes. For each node, a dual-polarized textile patch antenna serves as a platform for the flexible electronic circuitry. Therefore, the system is particularly suitable for comfortable and unobtrusive integration into garments. In the meantime, polarization diversity can be exploited to improve the reliability and energy-efficiency of the wireless transmission. Extensive experiments in realistic conditions have demonstrated that this new autonomous, body-centric, textile-antenna, wireless sensor network is able to correctly detect different operating conditions of a firefighter during an intervention. By relying on four network nodes integrated into the protective garment, this functionality is implemented locally, on the body, and in real time. In addition, the received sensor data are reliably transferred to a central access point at the command post, for more detailed and more comprehensive real-time visualization. This information provides coordinators and commanders with situational awareness of the entire rescue operation. A statistical analysis of measured on-body node-to-node, as well as off-body person-to-person channels is included, confirming the reliability of the communication system. PMID:25302808

Selection of Multiarmed Spiral Waves in a Regular Network of Neurons

PubMed Central

Hu, Bolin; Ma, Jun; Tang, Jun

2013-01-01

Formation and selection of multiarmed spiral wave due to spontaneous symmetry breaking are investigated in a regular network of Hodgkin-Huxley neuron by changing the excitability and imposing spatial forcing currents on the neurons in the network. The arm number of the multiarmed spiral wave is dependent on the distribution of spatial forcing currents and excitability diversity in the network, and the selection criterion for supporting multiarmed spiral waves is discussed. A broken spiral segment is measured by a short polygonal line connected by three adjacent points (controlled nodes), and a double-spiral wave can be developed from the spiral segment. Multiarmed spiral wave is formed when a group of double-spiral waves rotate in the same direction in the network. In the numerical studies, a group of controlled nodes are selected and spatial forcing currents are imposed on these nodes, and our results show that l-arm stable spiral wave (l = 2, 3, 4,...8) can be induced to occupy the network completely. It is also confirmed that low excitability is critical to induce multiarmed spiral waves while high excitability is important to propagate the multiarmed spiral wave outside so that distinct multiarmed spiral wave can occupy the network completely. Our results confirm that symmetry breaking of target wave in the media accounts for emergence of multiarmed spiral wave, which can be developed from a group of spiral waves with single arm under appropriate condition, thus the potential formation mechanism of multiarmed spiral wave in the media is explained. PMID:23935966

Uncovering the hidden risk architecture of the schizophrenias: confirmation in three independent genome-wide association studies.

PubMed

Arnedo, Javier; Svrakic, Dragan M; Del Val, Coral; Romero-Zaliz, Rocío; Hernández-Cuervo, Helena; Fanous, Ayman H; Pato, Michele T; Pato, Carlos N; de Erausquin, Gabriel A; Cloninger, C Robert; Zwir, Igor

2015-02-01

The authors sought to demonstrate that schizophrenia is a heterogeneous group of heritable disorders caused by different genotypic networks that cause distinct clinical syndromes. In a large genome-wide association study of cases with schizophrenia and controls, the authors first identified sets of interacting single-nucleotide polymorphisms (SNPs) that cluster within particular individuals (SNP sets) regardless of clinical status. Second, they examined the risk of schizophrenia for each SNP set and tested replicability in two independent samples. Third, they identified genotypic networks composed of SNP sets sharing SNPs or subjects. Fourth, they identified sets of distinct clinical features that cluster in particular cases (phenotypic sets or clinical syndromes) without regard for their genetic background. Fifth, they tested whether SNP sets were associated with distinct phenotypic sets in a replicable manner across the three studies. The authors identified 42 SNP sets associated with a 70% or greater risk of schizophrenia, and confirmed 34 (81%) or more with similar high risk of schizophrenia in two independent samples. Seventeen networks of SNP sets did not share any SNP or subject. These disjoint genotypic networks were associated with distinct gene products and clinical syndromes (i.e., the schizophrenias) varying in symptoms and severity. Associations between genotypic networks and clinical syndromes were complex, showing multifinality and equifinality. The interactive networks explained the risk of schizophrenia more than the average effects of all SNPs (24%). Schizophrenia is a group of heritable disorders caused by a moderate number of separate genotypic networks associated with several distinct clinical syndromes.

Design and Evaluation of a Proxy-Based Monitoring System for OpenFlow Networks.

PubMed

Taniguchi, Yoshiaki; Tsutsumi, Hiroaki; Iguchi, Nobukazu; Watanabe, Kenzi

2016-01-01

Software-Defined Networking (SDN) has attracted attention along with the popularization of cloud environment and server virtualization. In SDN, the control plane and the data plane are decoupled so that the logical topology and routing control can be configured dynamically depending on network conditions. To obtain network conditions precisely, a network monitoring mechanism is necessary. In this paper, we focus on OpenFlow which is a core technology to realize SDN. We propose, design, implement, and evaluate a network monitoring system for OpenFlow networks. Our proposed system acts as a proxy between an OpenFlow controller and OpenFlow switches. Through experimental evaluations, we confirm that our proposed system can capture packets and monitor traffic information depending on administrator's configuration. In addition, we show that our proposed system does not influence significant performance degradation to overall network performance.

Circle of Care: Extending Beyond Primary Caregivers to Examine Collaborative Caretaking in Adolescent Development

PubMed Central

Margolis, Kathryn L.; Fosco, Gregory M.; Stormshak, Elizabeth A.

2013-01-01

In the contemporary family, which is increasingly shaped by multicultural influences, parents rarely are the sole caretakers of their children. To improve understanding of family dynamics, researchers must redefine caregiving networks to include multiple caregivers, such as extended family members. This study explored the influences of caregiving networks on youth depression by examining who youths perceived as caretakers, how many caretakers were in their networks, the youths’ connectedness with adults in their network, and harmony of relationships among adults within the network. Data from an ethnically diverse, urban sample of 180 middle school youths revealed participation of multiple caregivers for all groups, but ethnic differences existed in network composition. These differences in network composition are discussed within a socio-cultural context, considering how positive relationships with specific caregivers may buffer future depression. Longitudinal analyses confirmed the importance of positive relationships with caregiving networks for youth of color when predicting future depression. PMID:27453615

Design and Evaluation of a Proxy-Based Monitoring System for OpenFlow Networks

PubMed Central

Taniguchi, Yoshiaki; Tsutsumi, Hiroaki; Iguchi, Nobukazu; Watanabe, Kenzi

2016-01-01

Software-Defined Networking (SDN) has attracted attention along with the popularization of cloud environment and server virtualization. In SDN, the control plane and the data plane are decoupled so that the logical topology and routing control can be configured dynamically depending on network conditions. To obtain network conditions precisely, a network monitoring mechanism is necessary. In this paper, we focus on OpenFlow which is a core technology to realize SDN. We propose, design, implement, and evaluate a network monitoring system for OpenFlow networks. Our proposed system acts as a proxy between an OpenFlow controller and OpenFlow switches. Through experimental evaluations, we confirm that our proposed system can capture packets and monitor traffic information depending on administrator's configuration. In addition, we show that our proposed system does not influence significant performance degradation to overall network performance. PMID:27006977

An adaptive Hinfinity controller design for bank-to-turn missiles using ridge Gaussian neural networks.

PubMed

Lin, Chuan-Kai; Wang, Sheng-De

2004-11-01

A new autopilot design for bank-to-turn (BTT) missiles is presented. In the design of autopilot, a ridge Gaussian neural network with local learning capability and fewer tuning parameters than Gaussian neural networks is proposed to model the controlled nonlinear systems. We prove that the proposed ridge Gaussian neural network, which can be a universal approximator, equals the expansions of rotated and scaled Gaussian functions. Although ridge Gaussian neural networks can approximate the nonlinear and complex systems accurately, the small approximation errors may affect the tracking performance significantly. Therefore, by employing the Hinfinity control theory, it is easy to attenuate the effects of the approximation errors of the ridge Gaussian neural networks to a prescribed level. Computer simulation results confirm the effectiveness of the proposed ridge Gaussian neural networks-based autopilot with Hinfinity stabilization.

Adaptive neural network motion control of manipulators with experimental evaluations.

PubMed

Puga-Guzmán, S; Moreno-Valenzuela, J; Santibáñez, V

2014-01-01

A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the neural network weights are shown to be uniformly bounded. The proposed scheme has been experimentally validated in real time. These experimental evaluations were carried in two different mechanical systems: a horizontal two degrees-of-freedom robot and a vertical one degree-of-freedom arm which is affected by the gravitational force. In each one of the two experimental set-ups, the proposed scheme was implemented without and with adaptive neural network compensation. Experimental results confirmed the tracking accuracy of the proposed adaptive neural network-based controller.

Adaptive Neural Network Motion Control of Manipulators with Experimental Evaluations

PubMed Central

Puga-Guzmán, S.; Moreno-Valenzuela, J.; Santibáñez, V.

2014-01-01

A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the neural network weights are shown to be uniformly bounded. The proposed scheme has been experimentally validated in real time. These experimental evaluations were carried in two different mechanical systems: a horizontal two degrees-of-freedom robot and a vertical one degree-of-freedom arm which is affected by the gravitational force. In each one of the two experimental set-ups, the proposed scheme was implemented without and with adaptive neural network compensation. Experimental results confirmed the tracking accuracy of the proposed adaptive neural network-based controller. PMID:24574910

Blur identification by multilayer neural network based on multivalued neurons.

PubMed

Aizenberg, Igor; Paliy, Dmitriy V; Zurada, Jacek M; Astola, Jaakko T

2008-05-01

A multilayer neural network based on multivalued neurons (MLMVN) is a neural network with a traditional feedforward architecture. At the same time, this network has a number of specific different features. Its backpropagation learning algorithm is derivative-free. The functionality of MLMVN is superior to that of the traditional feedforward neural networks and of a variety kernel-based networks. Its higher flexibility and faster adaptation to the target mapping enables to model complex problems using simpler networks. In this paper, the MLMVN is used to identify both type and parameters of the point spread function, whose precise identification is of crucial importance for the image deblurring. The simulation results show the high efficiency of the proposed approach. It is confirmed that the MLMVN is a powerful tool for solving classification problems, especially multiclass ones.

Detection of network attacks based on adaptive resonance theory

NASA Astrophysics Data System (ADS)

Bukhanov, D. G.; Polyakov, V. M.

2018-05-01

The paper considers an approach to intrusion detection systems using a neural network of adaptive resonant theory. It suggests the structure of an intrusion detection system consisting of two types of program modules. The first module manages connections of user applications by preventing the undesirable ones. The second analyzes the incoming network traffic parameters to check potential network attacks. After attack detection, it notifies the required stations using a secure transmission channel. The paper describes the experiment on the detection and recognition of network attacks using the test selection. It also compares the obtained results with similar experiments carried out by other authors. It gives findings and conclusions on the sufficiency of the proposed approach. The obtained information confirms the sufficiency of applying the neural networks of adaptive resonant theory to analyze network traffic within the intrusion detection system.

Identification of eight candidate target genes of the recurrent 3p12-p14 loss in cervical cancer by integrative genomic profiling.

PubMed

Lando, Malin; Wilting, Saskia M; Snipstad, Kristin; Clancy, Trevor; Bierkens, Mariska; Aarnes, Eva-Katrine; Holden, Marit; Stokke, Trond; Sundfør, Kolbein; Holm, Ruth; Kristensen, Gunnar B; Steenbergen, Renske D M; Lyng, Heidi

2013-05-01

The pathogenetic role, including its target genes, of the recurrent 3p12-p14 loss in cervical cancer has remained unclear. To determine the onset of the event during carcinogenesis, we used microarray techniques and found that the loss was the most frequent 3p event, occurring in 61% of 92 invasive carcinomas, in only 2% of 43 high-grade intraepithelial lesions (CIN2/3), and in 33% of 6 CIN3 lesions adjacent to invasive carcinomas, suggesting a role in acquisition of invasiveness or early during the invasive phase. We performed an integrative DNA copy number and expression analysis of 77 invasive carcinomas, where all genes within the recurrent region were included. We selected eight genes, THOC7, PSMD6, SLC25A26, TMF1, RYBP, SHQ1, EBLN2, and GBE1, which were highly down-regulated in cases with loss, as confirmed at the protein level for RYBP and TMF1 by immunohistochemistry. The eight genes were subjected to network analysis based on the expression profiles, revealing interaction partners of proteins encoded by the genes that were coordinately regulated in tumours with loss. Several partners were shared among the eight genes, indicating crosstalk in their signalling. Gene ontology analysis showed enrichment of biological processes such as apoptosis, proliferation, and stress response in the network and suggested a relationship between down-regulation of the eight genes and activation of tumourigenic pathways. Survival analysis showed prognostic impact of the eight-gene signature that was confirmed in a validation cohort of 74 patients and was independent of clinical parameters. These results support the role of the eight candidate genes as targets of the 3p12-p14 loss in cervical cancer and suggest that the strong selection advantage of the loss during carcinogenesis might be caused by a synergetic effect of several tumourigenic processes controlled by these targets. Copyright © 2013 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

PyPathway: Python Package for Biological Network Analysis and Visualization.

PubMed

Xu, Yang; Luo, Xiao-Chun

2018-05-01

Life science studies represent one of the biggest generators of large data sets, mainly because of rapid sequencing technological advances. Biological networks including interactive networks and human curated pathways are essential to understand these high-throughput data sets. Biological network analysis offers a method to explore systematically not only the molecular complexity of a particular disease but also the molecular relationships among apparently distinct phenotypes. Currently, several packages for Python community have been developed, such as BioPython and Goatools. However, tools to perform comprehensive network analysis and visualization are still needed. Here, we have developed PyPathway, an extensible free and open source Python package for functional enrichment analysis, network modeling, and network visualization. The network process module supports various interaction network and pathway databases such as Reactome, WikiPathway, STRING, and BioGRID. The network analysis module implements overrepresentation analysis, gene set enrichment analysis, network-based enrichment, and de novo network modeling. Finally, the visualization and data publishing modules enable users to share their analysis by using an easy web application. For package availability, see the first Reference.

A green approach to prepare silver nanoparticles loaded gum acacia/poly(acrylate) hydrogels.

PubMed

Bajpai, S K; Kumari, Mamta

2015-09-01

In this work, gum acacia (GA)/poly(sodium acrylate) semi-interpenetrating polymer networks (Semi-IPN) have been fabricated via free radical initiated aqueous polymerization of monomer sodium acrylate (SA) in the presence of dissolved Gum acacia (GA), using N,N'-methylenebisacrylamide (MB) as cross-linker and potassium persulphate (KPS) as initiator. The semi-IPNs, synthesized, were characterized by various techniques such as X-ray diffraction (XRD), thermo gravimetric analysis (TGA) and Fourier transform infrared (FTIR) spectroscopy. The dynamic water uptake behavior of semi-IPNs was investigated and the data were interpreted by various kinetic models. The equilibrium swelling data were used to evaluate various network parameters. The semi-IPNs were used as template for the in situ preparation of silver nanoparticles using extract of Syzygium aromaticum (clove). The formation of silver nanoparticles was confirmed by surface plasmon resonance (SPR), XRD and transmission electron microscopy (TEM). Finally, the antibacterial activity of GA/poly(SA)/silver nanocomposites was tested against E. coli. Copyright © 2015 Elsevier B.V. All rights reserved.

Experimental validation of a predicted feedback loop in the multi-oscillator clock of Arabidopsis thaliana

PubMed Central

Locke, James C W; Kozma-Bognár, László; Gould, Peter D; Fehér, Balázs; Kevei, Éva; Nagy, Ferenc; Turner, Matthew S; Hall, Anthony; Millar, Andrew J

2006-01-01

Our computational model of the circadian clock comprised the feedback loop between LATE ELONGATED HYPOCOTYL (LHY), CIRCADIAN CLOCK ASSOCIATED 1 (CCA1) and TIMING OF CAB EXPRESSION 1 (TOC1), and a predicted, interlocking feedback loop involving TOC1 and a hypothetical component Y. Experiments based on model predictions suggested GIGANTEA (GI) as a candidate for Y. We now extend the model to include a recently demonstrated feedback loop between the TOC1 homologues PSEUDO-RESPONSE REGULATOR 7 (PRR7), PRR9 and LHY and CCA1. This three-loop network explains the rhythmic phenotype of toc1 mutant alleles. Model predictions fit closely to new data on the gi;lhy;cca1 mutant, which confirm that GI is a major contributor to Y function. Analysis of the three-loop network suggests that the plant clock consists of morning and evening oscillators, coupled intracellularly, which may be analogous to coupled, morning and evening clock cells in Drosophila and the mouse. PMID:17102804

Hybrid ANN optimized artificial fish swarm algorithm based classifier for classification of suspicious lesions in breast DCE-MRI

NASA Astrophysics Data System (ADS)

Janaki Sathya, D.; Geetha, K.

2017-12-01

Automatic mass or lesion classification systems are developed to aid in distinguishing between malignant and benign lesions present in the breast DCE-MR images, the systems need to improve both the sensitivity and specificity of DCE-MR image interpretation in order to be successful for clinical use. A new classifier (a set of features together with a classification method) based on artificial neural networks trained using artificial fish swarm optimization (AFSO) algorithm is proposed in this paper. The basic idea behind the proposed classifier is to use AFSO algorithm for searching the best combination of synaptic weights for the neural network. An optimal set of features based on the statistical textural features is presented. The investigational outcomes of the proposed suspicious lesion classifier algorithm therefore confirm that the resulting classifier performs better than other such classifiers reported in the literature. Therefore this classifier demonstrates that the improvement in both the sensitivity and specificity are possible through automated image analysis.

Quantum Monte Carlo study of the transverse-field quantum Ising model on infinite-dimensional structures

NASA Astrophysics Data System (ADS)

Baek, Seung Ki; Um, Jaegon; Yi, Su Do; Kim, Beom Jun

2011-11-01

In a number of classical statistical-physical models, there exists a characteristic dimensionality called the upper critical dimension above which one observes the mean-field critical behavior. Instead of constructing high-dimensional lattices, however, one can also consider infinite-dimensional structures, and the question is whether this mean-field character extends to quantum-mechanical cases as well. We therefore investigate the transverse-field quantum Ising model on the globally coupled network and on the Watts-Strogatz small-world network by means of quantum Monte Carlo simulations and the finite-size scaling analysis. We confirm that both of the structures exhibit critical behavior consistent with the mean-field description. In particular, we show that the existing cumulant method has difficulty in estimating the correct dynamic critical exponent and suggest that an order parameter based on the quantum-mechanical expectation value can be a practically useful numerical observable to determine critical behavior when there is no well-defined dimensionality.

Calibration of an electronic nose for poultry farm

NASA Astrophysics Data System (ADS)

Abdullah, A. H.; Shukor, S. A.; Kamis, M. S.; Shakaff, A. Y. M.; Zakaria, A.; Rahim, N. A.; Mamduh, S. M.; Kamarudin, K.; Saad, F. S. A.; Masnan, M. J.; Mustafa, H.

2017-03-01

Malodour from the poultry farms could cause air pollution and therefore potentially dangerous to humans' and animals' health. This issue also poses sustainability risk to the poultry industries due to objections from local community. The aim of this paper is to develop and calibrate a cost effective and efficient electronic nose for poultry farm air monitoring. The instrument main components include sensor chamber, array of specific sensors, microcontroller, signal conditioning circuits and wireless sensor networks. The instrument was calibrated to allow classification of different concentrations of main volatile compounds in the poultry farm malodour. The outcome of the process will also confirm the device's reliability prior to being used for poultry farm malodour assessment. The Multivariate Analysis (HCA and KNN) and Artificial Neural Network (ANN) pattern recognition technique was used to process the acquired data. The results show that the instrument is able to calibrate the samples using ANN classification model with high accuracy. The finding verifies the instrument's performance to be used as an effective poultry farm malodour monitoring.

Report on the survey for electrostatic discharges on Mars using NASA's Deep Space Network (DSN)

NASA Astrophysics Data System (ADS)

Arabshahi, S.; Majid, W.; Geldzahler, B.; Kocz, J.; Schulter, T.; White, L.

2017-12-01

Mars atmosphere has strong dust activity. It is suggested that the larger regional storms are capable of producing electric fields large enough to initiate electrostatic discharges. The storms have charging process similar to terrestrial dust devils and have hot cores and complicated vortex winds similar to terrestrial thunderstorms. However, due to uncertainties in our understanding of the electrical environment of the storms and absence of related in-situ measurements, the existence (or non-existence) of such electrostatic discharges on the planet is yet to be confirmed. Knowing about the electrical activity on Mars is essential for future human explorations of the planet. We have recently launched a long-term monitoring campaign at NASA's Madrid Deep Space Communication Complex (MDSCC) to search for powerful discharges on Mars. The search occurs during routine tracking of Mars orbiting spacecraft by Deep Space Network (DSN) radio telescope. In this presentation, we will report on the result of processing and analysis of the data from the first six months of our campaign.

Social network analysis: Presenting an underused method for nursing research.

PubMed

Parnell, James Michael; Robinson, Jennifer C

2018-06-01

This paper introduces social network analysis as a versatile method with many applications in nursing research. Social networks have been studied for years in many social science fields. The methods continue to advance but remain unknown to most nursing scholars. Discussion paper. English language and interpreted literature was searched from Ovid Healthstar, CINAHL, PubMed Central, Scopus and hard copy texts from 1965 - 2017. Social network analysis first emerged in nursing literature in 1995 and appears minimally through present day. To convey the versatility and applicability of social network analysis in nursing, hypothetical scenarios are presented. The scenarios are illustrative of three approaches to social network analysis and include key elements of social network research design. The methods of social network analysis are underused in nursing research, primarily because they are unknown to most scholars. However, there is methodological flexibility and epistemological versatility capable of supporting quantitative and qualitative research. The analytic techniques of social network analysis can add new insight into many areas of nursing inquiry, especially those influenced by cultural norms. Furthermore, visualization techniques associated with social network analysis can be used to generate new hypotheses. Social network analysis can potentially uncover findings not accessible through methods commonly used in nursing research. Social networks can be analysed based on individual-level attributes, whole networks and subgroups within networks. Computations derived from social network analysis may stand alone to answer a research question or incorporated as variables into robust statistical models. © 2018 John Wiley & Sons Ltd.

Factor analysis of auto-associative neural networks with application in speaker verification.

PubMed

Garimella, Sri; Hermansky, Hynek

2013-04-01

Auto-associative neural network (AANN) is a fully connected feed-forward neural network, trained to reconstruct its input at its output through a hidden compression layer, which has fewer numbers of nodes than the dimensionality of input. AANNs are used to model speakers in speaker verification, where a speaker-specific AANN model is obtained by adapting (or retraining) the universal background model (UBM) AANN, an AANN trained on multiple held out speakers, using corresponding speaker data. When the amount of speaker data is limited, this adaptation procedure may lead to overfitting as all the parameters of UBM-AANN are adapted. In this paper, we introduce and develop the factor analysis theory of AANNs to alleviate this problem. We hypothesize that only the weight matrix connecting the last nonlinear hidden layer and the output layer is speaker-specific, and further restrict it to a common low-dimensional subspace during adaptation. The subspace is learned using large amounts of development data, and is held fixed during adaptation. Thus, only the coordinates in a subspace, also known as i-vector, need to be estimated using speaker-specific data. The update equations are derived for learning both the common low-dimensional subspace and the i-vectors corresponding to speakers in the subspace. The resultant i-vector representation is used as a feature for the probabilistic linear discriminant analysis model. The proposed system shows promising results on the NIST-08 speaker recognition evaluation (SRE), and yields a 23% relative improvement in equal error rate over the previously proposed weighted least squares-based subspace AANNs system. The experiments on NIST-10 SRE confirm that these improvements are consistent and generalize across datasets.

Causality within the Epileptic Network: An EEG-fMRI Study Validated by Intracranial EEG.

PubMed

Vaudano, Anna Elisabetta; Avanzini, Pietro; Tassi, Laura; Ruggieri, Andrea; Cantalupo, Gaetano; Benuzzi, Francesca; Nichelli, Paolo; Lemieux, Louis; Meletti, Stefano

2013-01-01

Accurate localization of the Seizure Onset Zone (SOZ) is crucial in patients with drug-resistance focal epilepsy. EEG with fMRI recording (EEG-fMRI) has been proposed as a complementary non-invasive tool, which can give useful additional information in the pre-surgical work-up. However, fMRI maps related to interictal epileptiform activities (IED) often show multiple regions of signal change, or "networks," rather than highly focal ones. Effective connectivity approaches like Dynamic Causal Modeling (DCM) applied to fMRI data potentially offers a framework to address which brain regions drives the generation of seizures and IED within an epileptic network. Here, we present a first attempt to validate DCM on EEG-fMRI data in one patient affected by frontal lobe epilepsy. Pre-surgical EEG-fMRI demonstrated two distinct clusters of blood oxygenation level dependent (BOLD) signal increases linked to IED, one located in the left frontal pole and the other in the ipsilateral dorso-lateral frontal cortex. DCM of the IED-related BOLD signal favored a model corresponding to the left dorso-lateral frontal cortex as driver of changes in the fronto-polar region. The validity of DCM was supported by: (a) the results of two different non-invasive analysis obtained on the same dataset: EEG source imaging (ESI), and "psycho-physiological interaction" analysis; (b) the failure of a first surgical intervention limited to the fronto-polar region; (c) the results of the intracranial EEG monitoring performed after the first surgical intervention confirming a SOZ located over the dorso-lateral frontal cortex. These results add evidence that EEG-fMRI together with advanced methods of BOLD signal analysis is a promising tool that can give relevant information within the epilepsy surgery diagnostic work-up.

Alterations of proteins in MDCK cells during acute potassium deficiency.

PubMed

Peerapen, Paleerath; Ausakunpipat, Nardtaya; Chanchaem, Prangwalai; Thongboonkerd, Visith

2016-06-01

Chronic K(+) deficiency can cause hypokalemic nephropathy associated with metabolic alkalosis, polyuria, tubular dilatation, and tubulointerstitial injury. However, effects of acute K(+) deficiency on the kidney remained unclear. This study aimed to explore such effects by evaluating changes in levels of proteins in renal tubular cells during acute K(+) deficiency. MDCK cells were cultivated in normal K(+) (NK) (K(+)=5.3 mM), low K(+) (LK) (K(+)=2.5 mM), or K(+) depleted (KD) (K(+)=0 mM) medium for 24 h and then harvested. Cellular proteins were resolved by two-dimensional gel electrophoresis (2-DE) and visualized by SYPRO Ruby staining (5 gels per group). Spot matching and quantitative intensity analysis revealed a total 48 protein spots that had significantly differential levels among the three groups. Among these, 46 and 30 protein spots had differential levels in KD group compared to NK and LK groups, respectively. Comparison between LK and NK groups revealed only 10 protein spots that were differentially expressed. All of these differentially expressed proteins were successfully identified by Q-TOF MS and/or MS/MS analyses. The altered levels of heat shock protein 90 (HSP90), ezrin, lamin A/C, tubulin, chaperonin-containing TCP1 (CCT1), and calpain 1 were confirmed by Western blot analysis. Global protein network analysis showed three main functional networks, including 1) cell growth and proliferation, 2) cell morphology, cellular assembly and organization, and 3) protein folding in which the altered proteins were involved. Further investigations on these networks may lead to better understanding of pathogenic mechanisms of low K(+)-induced renal injury. Copyright © 2016 Elsevier B.V. All rights reserved.

The real-time fMRI neurofeedback based stratification of Default Network Regulation Neuroimaging data repository.

PubMed

McDonald, Amalia R; Muraskin, Jordan; Dam, Nicholas T Van; Froehlich, Caroline; Puccio, Benjamin; Pellman, John; Bauer, Clemens C C; Akeyson, Alexis; Breland, Melissa M; Calhoun, Vince D; Carter, Steven; Chang, Tiffany P; Gessner, Chelsea; Gianonne, Alyssa; Giavasis, Steven; Glass, Jamie; Homann, Steven; King, Margaret; Kramer, Melissa; Landis, Drew; Lieval, Alexis; Lisinski, Jonathan; Mackay-Brandt, Anna; Miller, Brittny; Panek, Laura; Reed, Hayley; Santiago, Christine; Schoell, Eszter; Sinnig, Richard; Sital, Melissa; Taverna, Elise; Tobe, Russell; Trautman, Kristin; Varghese, Betty; Walden, Lauren; Wang, Runtang; Waters, Abigail B; Wood, Dylan C; Castellanos, F Xavier; Leventhal, Bennett; Colcombe, Stanley J; LaConte, Stephen; Milham, Michael P; Craddock, R Cameron

2017-02-01

This data descriptor describes a repository of openly shared data from an experiment to assess inter-individual differences in default mode network (DMN) activity. This repository includes cross-sectional functional magnetic resonance imaging (fMRI) data from the Multi Source Interference Task, to assess DMN deactivation, the Moral Dilemma Task, to assess DMN activation, a resting state fMRI scan, and a DMN neurofeedback paradigm, to assess DMN modulation, along with accompanying behavioral and cognitive measures. We report technical validation from n=125 participants of the final targeted sample of 180 participants. Each session includes acquisition of one whole-brain anatomical scan and whole-brain echo-planar imaging (EPI) scans, acquired during the aforementioned tasks and resting state. The data includes several self-report measures related to perseverative thinking, emotion regulation, and imaginative processes, along with a behavioral measure of rapid visual information processing. Technical validation of the data confirms that the tasks deactivate and activate the DMN as expected. Group level analysis of the neurofeedback data indicates that the participants are able to modulate their DMN with considerable inter-subject variability. Preliminary analysis of behavioral responses and specifically self-reported sleep indicate that as many as 73 participants may need to be excluded from an analysis depending on the hypothesis being tested. The present data are linked to the enhanced Nathan Kline Institute, Rockland Sample and builds on the comprehensive neuroimaging and deep phenotyping available therein. As limited information is presently available about individual differences in the capacity to directly modulate the default mode network, these data provide a unique opportunity to examine DMN modulation ability in relation to numerous phenotypic characteristics. Copyright © 2016 Elsevier Inc. All rights reserved.

Strain Prioritization and Genome Mining for Enediyne Natural Products

PubMed Central

Yan, Xiaohui; Ge, Huiming; Huang, Tingting; Hindra; Yang, Dong; Teng, Qihui; Crnovčić, Ivana; Li, Xiuling; Rudolf, Jeffrey D.; Lohman, Jeremy R.; Gansemans, Yannick; Zhu, Xiangcheng; Huang, Yong; Zhao, Li-Xing; Jiang, Yi; Van Nieuwerburgh, Filip; Rader, Christoph

2016-01-01

ABSTRACT The enediyne family of natural products has had a profound impact on modern chemistry, biology, and medicine, and yet only 11 enediynes have been structurally characterized to date. Here we report a genome survey of 3,400 actinomycetes, identifying 81 strains that harbor genes encoding the enediyne polyketide synthase cassettes that could be grouped into 28 distinct clades based on phylogenetic analysis. Genome sequencing of 31 representative strains confirmed that each clade harbors a distinct enediyne biosynthetic gene cluster. A genome neighborhood network allows prediction of new structural features and biosynthetic insights that could be exploited for enediyne discovery. We confirmed one clade as new C-1027 producers, with a significantly higher C-1027 titer than the original producer, and discovered a new family of enediyne natural products, the tiancimycins (TNMs), that exhibit potent cytotoxicity against a broad spectrum of cancer cell lines. Our results demonstrate the feasibility of rapid discovery of new enediynes from a large strain collection. PMID:27999165

Structure Shapes Dynamics and Directionality in Diverse Brain Networks: Mathematical Principles and Empirical Confirmation in Three Species

NASA Astrophysics Data System (ADS)

Moon, Joon-Young; Kim, Junhyeok; Ko, Tae-Wook; Kim, Minkyung; Iturria-Medina, Yasser; Choi, Jee-Hyun; Lee, Joseph; Mashour, George A.; Lee, Uncheol

2017-04-01

Identifying how spatially distributed information becomes integrated in the brain is essential to understanding higher cognitive functions. Previous computational and empirical studies suggest a significant influence of brain network structure on brain network function. However, there have been few analytical approaches to explain the role of network structure in shaping regional activities and directionality patterns. In this study, analytical methods are applied to a coupled oscillator model implemented in inhomogeneous networks. We first derive a mathematical principle that explains the emergence of directionality from the underlying brain network structure. We then apply the analytical methods to the anatomical brain networks of human, macaque, and mouse, successfully predicting simulation and empirical electroencephalographic data. The results demonstrate that the global directionality patterns in resting state brain networks can be predicted solely by their unique network structures. This study forms a foundation for a more comprehensive understanding of how neural information is directed and integrated in complex brain networks.

Machine Learning Topological Invariants with Neural Networks

NASA Astrophysics Data System (ADS)

Zhang, Pengfei; Shen, Huitao; Zhai, Hui

2018-02-01

In this Letter we supervisedly train neural networks to distinguish different topological phases in the context of topological band insulators. After training with Hamiltonians of one-dimensional insulators with chiral symmetry, the neural network can predict their topological winding numbers with nearly 100% accuracy, even for Hamiltonians with larger winding numbers that are not included in the training data. These results show a remarkable success that the neural network can capture the global and nonlinear topological features of quantum phases from local inputs. By opening up the neural network, we confirm that the network does learn the discrete version of the winding number formula. We also make a couple of remarks regarding the role of the symmetry and the opposite effect of regularization techniques when applying machine learning to physical systems.

Bluetooth Roaming for Sensor Network System in Clinical Environment.

PubMed

Kuroda, Tomohiro; Noma, Haruo; Takase, Kazuhiko; Sasaki, Shigeto; Takemura, Tadamasa

2015-01-01

A sensor network is key infrastructure for advancing a hospital information system (HIS). The authors proposed a method to provide roaming functionality for Bluetooth to realize a Bluetooth-based sensor network, which is suitable to connect clinical devices. The proposed method makes the average response time of a Bluetooth connection less than one second by making the master device repeat the inquiry process endlessly and modifies parameters of the inquiry process. The authors applied the developed sensor network for daily clinical activities in an university hospital, and confirmed the stabilitya and effectiveness of the sensor network. As Bluetooth becomes a quite common wireless interface for medical devices, the proposed protocol that realizes Bluetooth-based sensor network enables HIS to equip various clinical devices and, consequently, lets information and communication technologies advance clinical services.

Age-Related Patterns in Social Networks among European Americans and African Americans: Implications for Socioemotional Selectivity across the Life Span.

ERIC Educational Resources Information Center

Fung, Helene H.; Carstensen, Laura L.; Lang, Frieder, R.

2001-01-01

Tests socioemotional selectivity theory among African Americans and European Americans. Older people reported as many close partners but fewer peripheral partners as their younger counterparts, thus confirming the theory. A greater percentage of close social partners in social networks related to lower levels of happiness among the young age group…

Scale-Free and Multifractal Time Dynamics of fMRI Signals during Rest and Task

PubMed Central

Ciuciu, P.; Varoquaux, G.; Abry, P.; Sadaghiani, S.; Kleinschmidt, A.

2012-01-01

Scaling temporal dynamics in functional MRI (fMRI) signals have been evidenced for a decade as intrinsic characteristics of ongoing brain activity (Zarahn et al., 1997). Recently, scaling properties were shown to fluctuate across brain networks and to be modulated between rest and task (He, 2011): notably, Hurst exponent, quantifying long memory, decreases under task in activating and deactivating brain regions. In most cases, such results were obtained: First, from univariate (voxelwise or regionwise) analysis, hence focusing on specific cognitive systems such as Resting-State Networks (RSNs) and raising the issue of the specificity of this scale-free dynamics modulation in RSNs. Second, using analysis tools designed to measure a single scaling exponent related to the second order statistics of the data, thus relying on models that either implicitly or explicitly assume Gaussianity and (asymptotic) self-similarity, while fMRI signals may significantly depart from those either of those two assumptions (Ciuciu et al., 2008; Wink et al., 2008). To address these issues, the present contribution elaborates on the analysis of the scaling properties of fMRI temporal dynamics by proposing two significant variations. First, scaling properties are technically investigated using the recently introduced Wavelet Leader-based Multifractal formalism (WLMF; Wendt et al., 2007). This measures a collection of scaling exponents, thus enables a richer and more versatile description of scale invariance (beyond correlation and Gaussianity), referred to as multifractality. Also, it benefits from improved estimation performance compared to tools previously used in the literature. Second, scaling properties are investigated in both RSN and non-RSN structures (e.g., artifacts), at a broader spatial scale than the voxel one, using a multivariate approach, namely the Multi-Subject Dictionary Learning (MSDL) algorithm (Varoquaux et al., 2011) that produces a set of spatial components that appear more sparse than their Independent Component Analysis (ICA) counterpart. These tools are combined and applied to a fMRI dataset comprising 12 subjects with resting-state and activation runs (Sadaghiani et al., 2009). Results stemming from those analysis confirm the already reported task-related decrease of long memory in functional networks, but also show that it occurs in artifacts, thus making this feature not specific to functional networks. Further, results indicate that most fMRI signals appear multifractal at rest except in non-cortical regions. Task-related modulation of multifractality appears only significant in functional networks and thus can be considered as the key property disentangling functional networks from artifacts. These finding are discussed in the light of the recent literature reporting scaling dynamics of EEG microstate sequences at rest and addressing non-stationarity issues in temporally independent fMRI modes. PMID:22715328

Identification of Potential Anticancer Activities of Novel Ganoderma lucidum Extracts Using Gene Expression and Pathway Network Analysis

PubMed Central

Kao, Chi H.J.; Bishop, Karen S.; Xu, Yuanye; Han, Dug Yeo; Murray, Pamela M.; Marlow, Gareth J.; Ferguson, Lynnette R.

2016-01-01

Ganoderma lucidum (lingzhi) has been used for the general promotion of health in Asia for many centuries. The common method of consumption is to boil lingzhi in water and then drink the liquid. In this study, we examined the potential anticancer activities of G. lucidum submerged in two commonly consumed forms of alcohol in East Asia: malt whiskey and rice wine. The anticancer effect of G. lucidum, using whiskey and rice wine-based extraction methods, has not been previously reported. The growth inhibition of G. lucidum whiskey and rice wine extracts on the prostate cancer cell lines, PC3 and DU145, was determined. Using Affymetrix gene expression assays, several biologically active pathways associated with the anticancer activities of G. lucidum extracts were identified. Using gene expression analysis (real-time polymerase chain reaction [RT-PCR]) and protein analysis (Western blotting), we confirmed the expression of key genes and their associated proteins that were initially identified with Affymetrix gene expression analysis. PMID:27006591

Semi-supervised vibration-based classification and condition monitoring of compressors

NASA Astrophysics Data System (ADS)

Potočnik, Primož; Govekar, Edvard

2017-09-01

Semi-supervised vibration-based classification and condition monitoring of the reciprocating compressors installed in refrigeration appliances is proposed in this paper. The method addresses the problem of industrial condition monitoring where prior class definitions are often not available or difficult to obtain from local experts. The proposed method combines feature extraction, principal component analysis, and statistical analysis for the extraction of initial class representatives, and compares the capability of various classification methods, including discriminant analysis (DA), neural networks (NN), support vector machines (SVM), and extreme learning machines (ELM). The use of the method is demonstrated on a case study which was based on industrially acquired vibration measurements of reciprocating compressors during the production of refrigeration appliances. The paper presents a comparative qualitative analysis of the applied classifiers, confirming the good performance of several nonlinear classifiers. If the model parameters are properly selected, then very good classification performance can be obtained from NN trained by Bayesian regularization, SVM and ELM classifiers. The method can be effectively applied for the industrial condition monitoring of compressors.

Deffuant model of opinion formation in one-dimensional multiplex networks

NASA Astrophysics Data System (ADS)

Shang, Yilun

2015-10-01

Complex systems in the real world often operate through multiple kinds of links connecting their constituents. In this paper we propose an opinion formation model under bounded confidence over multiplex networks, consisting of edges at different topological and temporal scales. We determine rigorously the critical confidence threshold by exploiting probability theory and network science when the nodes are arranged on the integers, {{Z}}, evolving in continuous time. It is found that the existence of ‘multiplexity’ impedes the convergence, and that working with the aggregated or summarized simplex network is inaccurate since it misses vital information. Analytical calculations are confirmed by extensive numerical simulations.

Deep convolutional neural networks for estimating porous material parameters with ultrasound tomography

NASA Astrophysics Data System (ADS)

Lähivaara, Timo; Kärkkäinen, Leo; Huttunen, Janne M. J.; Hesthaven, Jan S.

2018-02-01

We study the feasibility of data based machine learning applied to ultrasound tomography to estimate water-saturated porous material parameters. In this work, the data to train the neural networks is simulated by solving wave propagation in coupled poroviscoelastic-viscoelastic-acoustic media. As the forward model, we consider a high-order discontinuous Galerkin method while deep convolutional neural networks are used to solve the parameter estimation problem. In the numerical experiment, we estimate the material porosity and tortuosity while the remaining parameters which are of less interest are successfully marginalized in the neural networks-based inversion. Computational examples confirms the feasibility and accuracy of this approach.

Influence of network topology on the swelling of polyelectrolyte nanogels.

PubMed

Rizzi, L G; Levin, Y

2016-03-21

It is well-known that the swelling behavior of ionic nanogels depends on their cross-link density; however, it is unclear how different topologies should affect the response of the polyelectrolyte network. Here we perform Monte Carlo simulations to obtain the equilibrium properties of ionic nanogels as a function of salt concentration Cs and the fraction f of ionizable groups in a polyelectrolyte network formed by cross-links of functionality z. Our results indicate that the network with cross-links of low connectivity result in nanogel particles with higher swelling ratios. We also confirm a de-swelling effect of salt on nanogel particles.

Abnormal inter- and intra-hemispheric integration in male paranoid schizophrenia: a graph-theoretical analysis.

PubMed

Chen, Jianhuai; Yao, Zhijian; Qin, Jiaolong; Yan, Rui; Hua, Lingling; Lu, Qing

2015-06-25

The human brain is a complex network of regions that are structurally interconnected by white matter (WM) tracts. Schizophrenia (SZ) can be conceptualized as a disconnection syndrome characterized by widespread disconnections in WM pathways. To assess whether or not anatomical disconnections are associated with disruption of the topological properties of inter- and intra-hemispheric networks in SZ. We acquired the diffusion tensor imaging data from 24 male patients with paranoid SZ during an acute phase of their illness and from 24 healthy age-matched male controls. The brain FA-weighted (fractional anisotropy-weighted) structural networks were constructed and the inter- and intra-hemispheric integration was assessed by estimating the average characteristic path lengths (CPLs) between and within the left and right hemisphere networks. The mean CPLs for all 18 inter-and intra-hemispheric CPLs assessed were longer in the SZ patient group than in the control group, but only some of these differences were significantly different: the CPLs for the overall inter-hemispheric and the left and right intra-hemispheric networks; the CPLs for the interhemisphere subnetworks of the frontal lobes, temporal lobes, and subcortical structures; and the CPL for the intra- frontal subnetwork in the right hemisphere. Among the 24 patients, the CPL of the inter-frontal subnetwork was positively associated with negative symptom severity, but this was the only significant result among 72 assessed correlations, so it may be a statistical artifact. Our findings suggest that the integrity of intra- and inter-hemispheric WM tracts is disrupted in males with paranoid SZ, supporting the brain network disconnection model (i.e., the (')connectivity hypothesis(')) of schizophrenia. Larger studies with less narrowly defined samples of individuals with schizophrenia are needed to confirm these results.

Abnormal Resting-State Functional Connectivity in Patients with Chronic Fatigue Syndrome: Results of Seed and Data-Driven Analyses.

PubMed

Gay, Charles W; Robinson, Michael E; Lai, Song; O'Shea, Andrew; Craggs, Jason G; Price, Donald D; Staud, Roland

2016-02-01

Although altered resting-state functional connectivity (FC) is a characteristic of many chronic pain conditions, it has not yet been evaluated in patients with chronic fatigue. Our objective was to investigate the association between fatigue and altered resting-state FC in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Thirty-six female subjects, 19 ME/CFS and 17 healthy controls, completed a fatigue inventory before undergoing functional magnetic resonance imaging. Two methods, (1) data driven and (2) model based, were used to estimate and compare the intraregional FC between both groups during the resting state (RS). The first approach using independent component analysis was applied to investigate five RS networks: the default mode network, salience network (SN), left frontoparietal networks (LFPN) and right frontoparietal networks, and the sensory motor network (SMN). The second approach used a priori selected seed regions demonstrating abnormal regional cerebral blood flow (rCBF) in ME/CFS patients at rest. In ME/CFS patients, Method-1 identified decreased intrinsic connectivity among regions within the LFPN. Furthermore, the FC of the left anterior midcingulate with the SMN and the connectivity of the left posterior cingulate cortex with the SN were significantly decreased. For Method-2, five distinct clusters within the right parahippocampus and occipital lobes, demonstrating significant rCBF reductions in ME/CFS patients, were used as seeds. The parahippocampal seed and three occipital lobe seeds showed altered FC with other brain regions. The degree of abnormal connectivity correlated with the level of self-reported fatigue. Our results confirm altered RS FC in patients with ME/CFS, which was significantly correlated with the severity of their chronic fatigue.

Abnormal Resting-State Functional Connectivity in Patients with Chronic Fatigue Syndrome: Results of Seed and Data-Driven Analyses

PubMed Central

Gay, Charles W.; Robinson, Michael E.; Lai, Song; O'Shea, Andrew; Craggs, Jason G.; Price, Donald D.

2016-01-01

Abstract Although altered resting-state functional connectivity (FC) is a characteristic of many chronic pain conditions, it has not yet been evaluated in patients with chronic fatigue. Our objective was to investigate the association between fatigue and altered resting-state FC in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Thirty-six female subjects, 19 ME/CFS and 17 healthy controls, completed a fatigue inventory before undergoing functional magnetic resonance imaging. Two methods, (1) data driven and (2) model based, were used to estimate and compare the intraregional FC between both groups during the resting state (RS). The first approach using independent component analysis was applied to investigate five RS networks: the default mode network, salience network (SN), left frontoparietal networks (LFPN) and right frontoparietal networks, and the sensory motor network (SMN). The second approach used a priori selected seed regions demonstrating abnormal regional cerebral blood flow (rCBF) in ME/CFS patients at rest. In ME/CFS patients, Method-1 identified decreased intrinsic connectivity among regions within the LFPN. Furthermore, the FC of the left anterior midcingulate with the SMN and the connectivity of the left posterior cingulate cortex with the SN were significantly decreased. For Method-2, five distinct clusters within the right parahippocampus and occipital lobes, demonstrating significant rCBF reductions in ME/CFS patients, were used as seeds. The parahippocampal seed and three occipital lobe seeds showed altered FC with other brain regions. The degree of abnormal connectivity correlated with the level of self-reported fatigue. Our results confirm altered RS FC in patients with ME/CFS, which was significantly correlated with the severity of their chronic fatigue. PMID:26449441

Analysis of a Plant Transcriptional Regulatory Network Using Transient Expression Systems.

PubMed

Díaz-Triviño, Sara; Long, Yuchen; Scheres, Ben; Blilou, Ikram

2017-01-01

In plant biology, transient expression systems have become valuable approaches used routinely to rapidly study protein expression, subcellular localization, protein-protein interactions, and transcriptional activity prior to in vivo studies. When studying transcriptional regulation, luciferase reporter assays offer a sensitive readout for assaying promoter behavior in response to different regulators or environmental contexts and to confirm and assess the functional relevance of predicted binding sites in target promoters. This chapter aims to provide detailed methods for using luciferase reporter system as a rapid, efficient, and versatile assay to analyze transcriptional regulation of target genes by transcriptional regulators. We describe a series of optimized transient expression systems consisting of Arabidopsis thaliana protoplasts, infiltrated Nicotiana benthamiana leaves, and human HeLa cells to study the transcriptional regulations of two well-characterized transcriptional regulators SCARECROW (SCR) and SHORT-ROOT (SHR) on one of their targets, CYCLIN D6 (CYCD6).Here, we illustrate similarities and differences in outcomes when using different systems. The plant-based systems revealed that the SCR-SHR complex enhances CYCD6 transcription, while analysis in HeLa cells showed that the complex is not sufficient to strongly induce CYCD6 transcription, suggesting that additional, plant-specific regulators are required for full activation. These results highlight the importance of the system and suggest that including heterologous systems, such as HeLa cells, can provide a more comprehensive analysis of a complex gene regulatory network.

FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine.

PubMed

Adouni, Amel; Chariag, Dhia; Diallo, Demba; Ben Hamed, Mouna; Sbita, Lassaâd

2016-09-01

As per modern electrical grid rules, Wind Turbine needs to operate continually even in presence severe grid faults as Low Voltage Ride Through (LVRT). Hence, a new LVRT Fault Detection and Identification (FDI) procedure has been developed to take the appropriate decision in order to develop the convenient control strategy. To obtain much better decision and enhanced FDI during grid fault, the proposed procedure is based on voltage indicators analysis using a new Artificial Neural Network architecture (ANN). In fact, two features are extracted (the amplitude and the angle phase). It is divided into two steps. The first is fault indicators generation and the second is indicators analysis for fault diagnosis. The first step is composed of six ANNs which are dedicated to describe the three phases of the grid (three amplitudes and three angle phases). Regarding to the second step, it is composed of a single ANN which analysis the indicators and generates a decision signal that describes the function mode (healthy or faulty). On other hand, the decision signal identifies the fault type. It allows distinguishing between the four faulty types. The diagnosis procedure is tested in simulation and experimental prototype. The obtained results confirm and approve its efficiency, rapidity, robustness and immunity to the noise and unknown inputs. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Assessment of an air pollution monitoring network to generate urban air pollution maps using Shannon information index, fuzzy overlay, and Dempster-Shafer theory, A case study: Tehran, Iran

NASA Astrophysics Data System (ADS)

Pahlavani, Parham; Sheikhian, Hossein; Bigdeli, Behnaz

2017-10-01

Air pollution assessment is an imperative part of megacities planning and control. Hence, a new comprehensive approach for air pollution monitoring and assessment was introduced in this research. It comprises of three main sections: optimizing the existing air pollutant monitoring network, locating new stations to complete the coverage of the existing network, and finally, generating an air pollution map. In the first section, Shannon information index was used to find less informative stations to be candidate for removal. Then, a methodology was proposed to determine the areas which are not sufficiently covered by the current network. These areas are candidates for establishing new monitoring stations. The current air pollution monitoring network of Tehran was used as a case study, where the air pollution issue has been worsened due to the huge population, considerable commuters' absorption and topographic barriers. In this regard, O3, NO, NO2, NOx, CO, PM10, and PM2.5 were considered as the main pollutants of Tehran. Optimization step concluded that all the 16 active monitoring stations should be preserved. Analysis showed that about 35% of the Tehran's area is not properly covered by monitoring stations and about 30% of the area needs additional stations. The winter period in Tehran always faces the most severe air pollution in the year. Hence, to produce the air pollution map of Tehran, three-month of winter measurements of the mentioned pollutants, repeated for five years in the same period, were selected and extended to the entire area using the kriging method. Experts specified the contribution of each pollutant in overall air pollution. Experts' rankings aggregated by a fuzzy-overlay process. Resulted maps characterized the study area with crucial air pollution situation. According to the maps, more than 45% of the city area faced high pollution in the study period, while only less than 10% of the area showed low pollution. This situation confirms the need for effective plans to mitigate the severity of the problem. In addition, an effort made to investigate the rationality of the acquired air pollution map respect to the urban, cultural, and environmental characteristics of Tehran, which also confirmed the results.

An optimal design of wind turbine and ship structure based on neuro-response surface method

NASA Astrophysics Data System (ADS)

Lee, Jae-Chul; Shin, Sung-Chul; Kim, Soo-Young

2015-07-01

The geometry of engineering systems affects their performances. For this reason, the shape of engineering systems needs to be optimized in the initial design stage. However, engineering system design problems consist of multi-objective optimization and the performance analysis using commercial code or numerical analysis is generally time-consuming. To solve these problems, many engineers perform the optimization using the approximation model (response surface). The Response Surface Method (RSM) is generally used to predict the system performance in engineering research field, but RSM presents some prediction errors for highly nonlinear systems. The major objective of this research is to establish an optimal design method for multi-objective problems and confirm its applicability. The proposed process is composed of three parts: definition of geometry, generation of response surface, and optimization process. To reduce the time for performance analysis and minimize the prediction errors, the approximation model is generated using the Backpropagation Artificial Neural Network (BPANN) which is considered as Neuro-Response Surface Method (NRSM). The optimization is done for the generated response surface by non-dominated sorting genetic algorithm-II (NSGA-II). Through case studies of marine system and ship structure (substructure of floating offshore wind turbine considering hydrodynamics performances and bulk carrier bottom stiffened panels considering structure performance), we have confirmed the applicability of the proposed method for multi-objective side constraint optimization problems.

Global and national laboratory networks support high quality surveillance for measles and rubella.

PubMed

Xu, Wenbo; Zhang, Yan; Wang, Huiling; Zhu, Zhen; Mao, Naiying; Mulders, Mick N; Rota, Paul A

2017-05-01

Laboratory networks are an essential component of disease surveillance systems because they provide accurate and timely confirmation of infection. WHO coordinates global laboratory surveillance of vaccine preventable diseases, including measles and rubella. The more than 700 laboratories within the WHO Global Measles and Rubella Laboratory Network (GMRLN) supports surveillance for measles, rubella and congenial rubella syndrome in 191 counties. This paper describes the overall structure and function of the GMRLN and highlights the largest of the national laboratory networks, the China Measles and Rubella Laboratory Network. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene 2017. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Cluster synchronization induced by one-node clusters in networks with asymmetric negative couplings

NASA Astrophysics Data System (ADS)

Zhang, Jianbao; Ma, Zhongjun; Zhang, Gang

2013-12-01

This paper deals with the problem of cluster synchronization in networks with asymmetric negative couplings. By decomposing the coupling matrix into three matrices, and employing Lyapunov function method, sufficient conditions are derived for cluster synchronization. The conditions show that the couplings of multi-node clusters from one-node clusters have beneficial effects on cluster synchronization. Based on the effects of the one-node clusters, an effective and universal control scheme is put forward for the first time. The obtained results may help us better understand the relation between cluster synchronization and cluster structures of the networks. The validity of the control scheme is confirmed through two numerical simulations, in a network with no cluster structure and in a scale-free network.

A Novel Quantitative Approach to Concept Analysis: The Internomological Network

PubMed Central

Cook, Paul F.; Larsen, Kai R.; Sakraida, Teresa J.; Pedro, Leli

2012-01-01

Background When a construct such as patients’ transition to self-management of chronic illness is studied by researchers across multiple disciplines, the meaning of key terms can become confused. This results from inherent problems in language where a term can have multiple meanings (polysemy) and different words can mean the same thing (synonymy). Objectives To test a novel quantitative method for clarifying the meaning of constructs by examining the similarity of published contexts in which they are used. Method Published terms related to the concept transition to self-management of chronic illness were analyzed using the internomological network (INN), a type of latent semantic analysis to calculate the mathematical relationships between constructs based on the contexts in which researchers use each term. This novel approach was tested by comparing results to those from concept analysis, a best-practice qualitative approach to clarifying meanings of terms. By comparing results of the two methods, the best synonyms of transition to self-management, as well as key antecedent, attribute, and consequence terms, were identified. Results Results from INN analysis were consistent with those from concept analysis. The potential synonyms self-management, transition, and adaptation had the greatest utility. Adaptation was the clearest overall synonym, but had lower cross-disciplinary use. The terms coping and readiness had more circumscribed meanings. The INN analysis confirmed key features of transition to self-management, and suggested related concepts not found by the previous review. Discussion The INN analysis is a promising novel methodology that allows researchers to quantify the semantic relationships between constructs. The method works across disciplinary boundaries, and may help to integrate the diverse literature on self-management of chronic illness. PMID:22592387

Contemporary Network Proteomics and Its Requirements

PubMed Central

Goh, Wilson Wen Bin; Wong, Limsoon; Sng, Judy Chia Ghee

2013-01-01

The integration of networks with genomics (network genomics) is a familiar field. Conventional network analysis takes advantage of the larger coverage and relative stability of gene expression measurements. Network proteomics on the other hand has to develop further on two critical factors: (1) expanded data coverage and consistency, and (2) suitable reference network libraries, and data mining from them. Concerning (1) we discuss several contemporary themes that can improve data quality, which in turn will boost the outcome of downstream network analysis. For (2), we focus on network analysis developments, specifically, the need for context-specific networks and essential considerations for localized network analysis. PMID:24833333

Potential molecular mechanisms of overgrazing-induced dwarfism in sheepgrass (Leymus chinensis) analyzed using proteomic data.

PubMed

Ren, Weibo; Xie, Jihong; Hou, Xiangyang; Li, Xiliang; Guo, Huiqin; Hu, Ningning; Kong, Lingqi; Zhang, Jize; Chang, Chun; Wu, Zinian

2018-05-08

This study was designed to reveal potential molecular mechanisms of long-term overgrazing-induced dwarfism in sheepgrass (Leymus chinensis). An electrospray ionisation mass spectrometry system was used to generate proteomic data of dwarf sheepgrass from a long-term overgrazed rangeland and normal sheepgrass from a long-term enclosed rangeland. Differentially expressed proteins (DEPs) between dwarf and normal sheepgrass were identified, after which their potential functions and interactions with each other were predicted. The expression of key DEPs was confirmed by high-performance liquid chromatography mass spectrometry (HPLC-MS) using a multiple reaction monitoring method. Compared with normal sheepgrass, a total of 51 upregulated and 53 downregulated proteins were identified in dwarf sheepgrass. The amino acids biosynthesis pathway was differentially enriched between the two conditions presenting DEPs, such as SAT5_ARATH and DAPA_MAIZE. The protein-protein interaction (PPI) network revealed a possible interaction between RPOB2_LEPTE, A0A023H9M8_9STRA, ATPB_DIOEL, RBL_AMOTI and DNAK_GRATL. Four modules were also extracted from the PPI network. The HPLC-MS analysis confirmed the upregulation and downregulation of ATPB_DIOEL and DNAK_GRATL, respectively in dwarf samples compared with in the controls. The upregulated ATPB_DIOEL and downregulated DNAK_GRATL as well as proteins that interact with them, such as RPOB2_LEPTE, A0A023H9M8_9STRA and RBL_AMOTI, may be associated with the long-term overgrazing-induced dwarfism in sheepgrass.