Cross-cultural comparison of neurobehavioral performance in Asian workers.

Science.gov (United States)

Chung, Jong-Hak; Sakong, Joon; Kang, Pock-Soo; Kim, Chang-Yoon; Lee, Kyeong-Soo; Jeon, Man-Joong; Sung, Nak-Jung; Ahn, Sang-Ho; Won, Kyu-Chang

2003-08-01

Widely-used neurobehavioral tests have been developed and standardized on Western populations, but studies on subject factors for Asian populations have been very limited. For the effective application and interpretation of neurobehavioral tests in Asian populations, an evaluation of the effects of subject factors, including cultural background, is necessary. A cross-cultural study was conducted to evaluate the effects of cultural background and the interaction between cultural background and education on neurobehavioral tests in Asian populations. The Korean version of the Swedish Performance Evaluation System (Simple Reaction Time, Symbol Digit, and Finger Tapping Speed) and a pegboard test were administered to 537 workers who were not exposed to chemicals at work from Fareast (Korea and Chinese), Central (Uzbekistan and Tajikistan), and South Asia (Sri Lanka and Indonesia). The Fareast Asian group exhibited better performance in adjusted test scores than other Asian groups, achieving significance for Symbol Digit and Finger Tapping Speed in both genders. The magnitude of the effect of cultural background on Symbol Digit was comparable to the effect of about 10 years of education. Cultural background did not modify the relation between years of education and Symbol Digit in either males or females. This study may provide the first evidence that cultural background has a large impact on neurobehavioral test performance, even within Asian populations, and suggests that cultural background is a critical confounding factor that must be controlled in epidemiologic studies which include Asian populations in the sample.

Large scale network-centric distributed systems

CERN Document Server

Sarbazi-Azad, Hamid

2014-01-01

A highly accessible reference offering a broad range of topics and insights on large scale network-centric distributed systems Evolving from the fields of high-performance computing and networking, large scale network-centric distributed systems continues to grow as one of the most important topics in computing and communication and many interdisciplinary areas. Dealing with both wired and wireless networks, this book focuses on the design and performance issues of such systems. Large Scale Network-Centric Distributed Systems provides in-depth coverage ranging from ground-level hardware issu

Neurobehavioral morbidity associated with disordered breathing during sleep in children: a comprehensive review.

Science.gov (United States)

Beebe, Dean W

2006-09-01

To comprehensively review research on the association between childhood sleep-disordered breathing (SDB) and neurobehavioral functioning. Qualitative and quantitative literature review. N/A. N/A. N/A. The findings of 61 studies of the relationship between childhood SDB and neurobehavioral functioning were critically evaluated and synthesized. There is strong evidence that childhood SDB is associated with deficits in behavior and emotion regulation, scholastic performance, sustained attention, selective attention, and alertness. There is also evidence that SDB has minimal association with a child's typical mood, expressive language skills, visual perception, and working memory. Findings have been insufficient to draw conclusions about intelligence, memory, and some aspects of executive functioning. Mechanisms by which SDB might result in neurobehavioral morbidity are being explored, but clinical symptoms such as chronic snoring remain the best predictors of morbidity. Short-term SDB treatment outcome studies are encouraging, but the long-term outcomes are not known. Failing to treat SDB appears to leave children at risk for long-term neurobehavioral deficits. Childhood SDB is associated with neurobehavioral morbidity. Applying commonly used guidelines for causal inference, even in the absence of a much-needed randomized clinical trial, there is strong evidence of association, consistent findings, and specificity of effect. There is suggestive evidence that this association fits the expected temporal pattern and that SDB is a biologically plausible cause of neurobehavioral deficits. Clinicians should be alert to the coexistence of SDB symptoms and concerns about a child's academic progress, attention, arousal, or behavior or emotion regulation.

Development and psychometric properties of the Patient-Head Injury Participation Scale (P-HIPS) and the Patient-Head Injury Neurobehavioral Assessment Scale (P-HINAS): patient and family determined outcomes scales.

Science.gov (United States)

Deb, Shoumitro; Bryant, Eleanor; Morris, Paul G; Prior, Lindsay; Lewis, Glyn; Haque, Sayeed

2007-06-01

To develop a measure to assess post-acute outcome following from traumatic brain injury (TBI) with particular emphasis on the emotional and the behavioral outcome. The second objective was to assess the test-retest reliability, internal consistency, and factor structure of the newly developed patient version of the Head Injury Participation Scale (P-HIPS) and Patient-Head Injury Neurobehavioral Scale (P-HINAS). Thirty-two TBI individuals and 27 carers took part in in-depth qualitative interviews exploring the consequences of the TBI. Interview transcripts were analyzed and key themes and concepts were used to construct the 49-item P-HIPS. A postal survey was then conducted on a cohort of 113 TBI patients to 'field test' the P-HIPS and the P-HINAS. All individual 49 items of the P-HIPS and their total score showed good test-retest reliability (0.93) and internal consistency (0.95). The P-HIPS showed a very good correlations with the Mayo Portland Adaptability Inventory-3 (MPAI-3) (0.87) and a moderate negative correlation with the Glasgow Outcome Scale-Extended (GOSE) (-0.51). Factor analysis extracted the following domains: 'Emotion/Behavior,' 'Independence/Community Living,' 'Cognition' and 'Physical'. The 'Emotion/Behavior' factor constituted the P-HINAS, which showed good internal consistency (0.93), test-retest reliability (0.91) and concurrent validity with MPAI subscale (0.82). Both the P-HIPS and the P-HINAS show strong psychometric properties. The qualitative methodology employed in the construction stage of the questionnaires provided good evidence of face and content validity.

Placental FKBP5 genetic and epigenetic variation is associated with infant neurobehavioral outcomes in the RICHS cohort.

Directory of Open Access Journals (Sweden)

Alison G Paquette

Full Text Available Adverse maternal environments can lead to increased fetal exposure to maternal cortisol, which can cause infant neurobehavioral deficits. The placenta regulates fetal cortisol exposure and response, and placental DNA methylation can influence this function. FK506 binding protein (FKBP5 is a negative regulator of cortisol response, FKBP5 methylation has been linked to brain morphology and mental disorder risk, and genetic variation of FKBP5 was associated with post-traumatic stress disorder in adults. We hypothesized that placental FKBP5 methylation and genetic variation contribute to gene expression control, and are associated with infant neurodevelopmental outcomes assessed using the Neonatal Intensive Care Unit (NICU Network Neurobehavioral Scales (NNNS. In 509 infants enrolled in the Rhode Island Child Health Study, placental FKBP5 methylation was measured at intron 7 using quantitative bisulfite pyrosequencing. Placental FKBP5 mRNA was measured in a subset of 61 infants by quantitative PCR, and the SNP rs1360780 was genotyped using a quantitative allelic discrimination assay. Relationships between methylation, expression and NNNS scores were examined using linear models adjusted for confounding variables, then logistic models were created to determine the influence of methylation on membership in high risk groups of infants. FKBP5 methylation was negatively associated with expression (P = 0.08, r = -0.22; infants with the TT genotype had higher expression than individuals with CC and CT genotypes (P = 0.06, and those with CC genotype displayed a negative relationship between methylation and expression (P = 0.06, r = -0.43. Infants in the highest quartile of FKBP5 methylation had increased risk of NNNS high arousal compared to infants in the lowest quartile (OR 2.22, CI 1.07-4.61. TT genotype infants had increased odds of high NNNS stress abstinence (OR 1.98, CI 0.92-4.26. Placental FKBP5 methylation reduces expression in

Neurobehavioral effects of arsenic exposure among secondary school children in the Kandal Province, Cambodia

International Nuclear Information System (INIS)

Vibol, Sao; Hashim, Jamal Hisham; Sarmani, Sukiman

2015-01-01

The research was carried out at 3 study sites with varying groundwater arsenic (As) levels in the Kandal Province of Cambodia. Kampong Kong Commune was chosen as a highly contaminated site (300–500 μg/L), Svay Romiet Commune was chosen as a moderately contaminated site (50–300 μg/L) and Anlong Romiet Commune was chosen as a control site. Neurobehavioral tests on the 3 exposure groups were conducted using a modified WHO neurobehavioral core test battery. Seven neurobehavioral tests including digit symbol, digit span, Santa Ana manual dexterity, Benton visual retention, pursuit aiming, trail making and simple reaction time were applied. Children's hair samples were also collected to investigate the influence of hair As levels on the neurobehavioral test scores. The results from the inductively coupled plasma-mass spectrometry (ICP-MS) analyses of hair samples showed that hair As levels at the 3 study sites were significantly different (p<0.001), whereby hair samples from the highly contaminated site (n=157) had a median hair As level of 0.93 μg/g, while the moderately contaminated site (n=151) had a median hair As level of 0.22 μg/g, and the control site (n=214) had a median hair As level of 0.08 μg/g. There were significant differences among the 3 study sites for all the neurobehavioral tests scores, except for digit span (backward) test. Multiple linear regression clearly shows a positive significant influence of hair As levels on all the neurobehavioral test scores, except for digit span (backward) test, after controlling for hair lead (Pb), manganese (Mn) and cadmium (Cd). Children with high hair As levels experienced 1.57–4.67 times greater risk of having lower neurobehavioral test scores compared to those with low hair As levels, after adjusting for hair Pb, Mn and Cd levels and BMI status. In conclusion, arsenic-exposed school children from the Kandal Province of Cambodia with a median hair As level of 0.93 µg/g among those from the highly

Neurobehavioral effects of arsenic exposure among secondary school children in the Kandal Province, Cambodia

Energy Technology Data Exchange (ETDEWEB)

Vibol, Sao [United Nations University – International Institute for Global Health, Kuala Lumpur (Malaysia); Faculty of Agricultural Technology and Management, Royal University of Agriculture, Phnom Penh (Cambodia); Hashim, Jamal Hisham, E-mail: jamalhas@hotmail.com [United Nations University – International Institute for Global Health, Kuala Lumpur (Malaysia); Department of Community Health, National University of Malaysia, Kuala Lumpur (Malaysia); Sarmani, Sukiman [Faculty of Science and Technology, National University of Malaysia, Bangi (Malaysia)

2015-02-15

The research was carried out at 3 study sites with varying groundwater arsenic (As) levels in the Kandal Province of Cambodia. Kampong Kong Commune was chosen as a highly contaminated site (300–500 μg/L), Svay Romiet Commune was chosen as a moderately contaminated site (50–300 μg/L) and Anlong Romiet Commune was chosen as a control site. Neurobehavioral tests on the 3 exposure groups were conducted using a modified WHO neurobehavioral core test battery. Seven neurobehavioral tests including digit symbol, digit span, Santa Ana manual dexterity, Benton visual retention, pursuit aiming, trail making and simple reaction time were applied. Children's hair samples were also collected to investigate the influence of hair As levels on the neurobehavioral test scores. The results from the inductively coupled plasma-mass spectrometry (ICP-MS) analyses of hair samples showed that hair As levels at the 3 study sites were significantly different (p<0.001), whereby hair samples from the highly contaminated site (n=157) had a median hair As level of 0.93 μg/g, while the moderately contaminated site (n=151) had a median hair As level of 0.22 μg/g, and the control site (n=214) had a median hair As level of 0.08 μg/g. There were significant differences among the 3 study sites for all the neurobehavioral tests scores, except for digit span (backward) test. Multiple linear regression clearly shows a positive significant influence of hair As levels on all the neurobehavioral test scores, except for digit span (backward) test, after controlling for hair lead (Pb), manganese (Mn) and cadmium (Cd). Children with high hair As levels experienced 1.57–4.67 times greater risk of having lower neurobehavioral test scores compared to those with low hair As levels, after adjusting for hair Pb, Mn and Cd levels and BMI status. In conclusion, arsenic-exposed school children from the Kandal Province of Cambodia with a median hair As level of 0.93 µg/g among those from the highly

Long-lasting neurobehavioral effects of prenatal exposure to xylene in rats

DEFF Research Database (Denmark)

Hass, Ulla; Lund, S. P.; Simonsen, L.

1997-01-01

The persistence of neurobehavioral effects in female rats (Mol:WIST) exposed to 500 ppm technical xylene (dimethylbenzene, GAS-no 1330-20-7) for 6 hours per day on days 7-20 of prenatal development was studied. The dose level was selected so as not to induce maternal toxicity or decreased viabili...... are planned to investigate whether neurobehavioral effects resulting from prenatal xylene exposure can interact with neurophysiological aging processes. (C) 1997 Inter Press, Inc....

[Assessment for effect of low level lead-exposure on neurobehavior in workers of printing house].

Science.gov (United States)

Niu, Q; Dai, F; Chen, Y

1998-11-30

WHO Neurobehavioral Core Test Battery (NCTB) was conducted among 28 lead-exposed workers (mean age 24.84, SD2.85) in printing house and 46 controls (mean age 22.78, SD1.45), in order to assess whether low level lead exposure may be related to neurobehavioral dysfunction. The items of test were: 1. Profile of mood state(POMS), (2) Simple reaction time, (3) Digit span, (4) Santa Anna manual dexterity, (5) Digit simbol, (6) Benton visual retention; and Prusuit aiming test. In all the NCTB test values, there was no significant difference between two groups. Multiple stepwise regression analysis shows that exposure duration is related to neurobehavior scores. Mild lead exposure may affect neurobehavior in some degree but not significant.

Neurobehavioral impairments caused by developmental imidacloprid exposure in zebrafish.

Science.gov (United States)

Crosby, Emily B; Bailey, Jordan M; Oliveri, Anthony N; Levin, Edward D

2015-01-01

Neonicotinoid insecticides are becoming more widely applied as organophosphate (OP) insecticides are decreasing in use. Because of their relative specificity to insect nicotinic receptors, they are thought to have reduced risk of neurotoxicity in vertebrates. However, there is scant published literature concerning the neurobehavioral effects of developmental exposure of vertebrates to neonicotinoids. Using zebrafish, we investigated the neurobehavioral effects of developmental exposure to imidacloprid, a prototypic neonicotinoid pesticide. Nicotine was also administered for comparison. Zebrafish were exposed via immersion in aqueous solutions containing 45 μM or 60 μM of imidacloprid or nicotine (or vehicle control) from 4h to 5d post fertilization. The functional effects of developmental exposure to both imidacloprid and nicotine were assessed in larvae using an activity assay and during adolescence and adulthood using a battery of neurobehavioral assays, including assessment of sensorimotor response and habituation in a tactile startle test, novel tank swimming, and shoaling behavior. In larvae, developmental imidacloprid exposure at both doses significantly decreased swimming activity. The 5D strains of zebrafish were more sensitive to both nicotine and imidacloprid than the AB* strain. In adolescent and adult fish, developmental exposure to imidacloprid significantly decreased novel tank exploration and increased sensorimotor response to startle stimuli. While nicotine did not affect novel tank swimming, it increased sensorimotor response to startle stimuli at the low dose. No effects of either compound were found on shoaling behavior or habituation to a startling stimulus. Early developmental exposure to imidacloprid has both early-life and persisting effects on neurobehavioral function in zebrafish. Its developmental neurotoxicity should be further investigated. Copyright © 2015 Elsevier Inc. All rights reserved.

The prisoner's dilemma in structured scale-free networks

International Nuclear Information System (INIS)

Li Xing; Wu Yonghui; Zhang Zhongzhi; Zhou Shuigeng; Rong Zhihai

2009-01-01

The conventional wisdom is that scale-free networks are prone to cooperation spreading. In this paper we investigate the cooperative behavior on the structured scale-free network. In contrast to the conventional wisdom that scale-free networks are prone to cooperation spreading, the evolution of cooperation is inhibited on the structured scale-free network when the prisoner's dilemma (PD) game is modeled. First, we demonstrate that neither the scale-free property nor the high clustering coefficient is responsible for the inhibition of cooperation spreading on the structured scale-free network. Then we provide one heuristic method to argue that the lack of age correlations and its associated 'large-world' behavior in the structured scale-free network inhibit the spread of cooperation. These findings may help enlighten further studies on the evolutionary dynamics of the PD game in scale-free networks

Sleep disturbances and neurobehavioral functioning in children with and without juvenile idiopathic arthritis.

Science.gov (United States)

Ward, Teresa M; Ringold, Sarah; Metz, Jonika; Archbold, Kristen; Lentz, Martha; Wallace, Carol A; Landis, Carol A

2011-07-01

To compare sleep disturbances and neurobehavioral function in children with juvenile idiopathic arthritis (JIA) to age- and sex-matched control children. Children (n = 116) ages 6-11 years with (n = 70) and without (n = 46) JIA and their parents participated. Parents completed questionnaires on sleep habits, sleep behavior, and school competence of their children; children completed computerized neurobehavioral performance tests. Compared to control children, children with JIA had a statistically significant (P sleep disturbance score and higher scores on 6 of 8 subscales (all P Sleep Habits Questionnaire (CSHQ). There were no group differences on neurobehavioral performance test scores. However, regardless of group, children with an overall CSHQ score above an established cutoff for clinically significant sleep disturbances had slower mean simple reaction time (t = -2.2, P sleep disturbance score predicted reaction time (P sleep disturbances, but performed as well as control children on a series of standardized computer tests of neurobehavioral performance. Children with more disturbed sleep had slower reaction times. Copyright © 2011 by the American College of Rheumatology.

Scaling architecture-on-demand based optical networks

NARCIS (Netherlands)

Meyer, Hugo; Sancho, Jose Carlos; Mrdakovic, Milica; Peng, Shuping; Simeonidou, Dimitra; Miao, Wang; Calabretta, Nicola

2016-01-01

This paper analyzes methodologies that allow scaling properly Architecture-On-Demand (AoD) based optical networks. As Data Centers and HPC systems are growing in size and complexity, optical networks seem to be the way to scale the bandwidth of current network infrastructures. To scale the number of

Growth Limits in Large Scale Networks

DEFF Research Database (Denmark)

Knudsen, Thomas Phillip

limitations. The rising complexity of network management with the convergence of communications platforms is shown as problematic for both automatic management feasibility and for manpower resource management. In the fourth step the scope is extended to include the present society with the DDN project as its......The Subject of large scale networks is approached from the perspective of the network planner. An analysis of the long term planning problems is presented with the main focus on the changing requirements for large scale networks and the potential problems in meeting these requirements. The problems...... the fundamental technological resources in network technologies are analysed for scalability. Here several technological limits to continued growth are presented. The third step involves a survey of major problems in managing large scale networks given the growth of user requirements and the technological...

Neurobehavioral toxicity of cadmium sulfate to the planarian Dugesia dorotocephala

Energy Technology Data Exchange (ETDEWEB)

Grebe, E.; Schaeffer, D.J. (Univ. of Illinois, Urbana (United States))

1991-05-01

The authors are developing bioassays which use planarians (free-living platyhelminthes) for the rapid determination of various types of toxicity, including acute mortality, tumorigenicity, and short-term neurobehavioral responses. Their motivation for using these animals is due to their importance as components of the aquatic ecology of unpolluted streams their sensitivity to low concentrations of environmental toxicants and the presence of a sensitive neurological system with a true brain which allows for complex social behavior. A previous paper described the results of a neurobehavioral bioassay using phenol in a crossover study. This paper reports a similar crossover study using cadmium sulfate.

WDM networking on a European Scale

DEFF Research Database (Denmark)

Parnis, Noel; Limal, Emmanuel; Hjelme, Dag R.

1998-01-01

Four different topological approaches to designing a pan-European optical network are discussed. For such an ultra-high capacity large-scale network, it is necessary to overcome physical path length limitations and to limit Optical Cross-Connect (OXC) complexity.......Four different topological approaches to designing a pan-European optical network are discussed. For such an ultra-high capacity large-scale network, it is necessary to overcome physical path length limitations and to limit Optical Cross-Connect (OXC) complexity....

The Genome-Scale Integrated Networks in Microorganisms

Directory of Open Access Journals (Sweden)

Tong Hao

2018-02-01

Full Text Available The genome-scale cellular network has become a necessary tool in the systematic analysis of microbes. In a cell, there are several layers (i.e., types of the molecular networks, for example, genome-scale metabolic network (GMN, transcriptional regulatory network (TRN, and signal transduction network (STN. It has been realized that the limitation and inaccuracy of the prediction exist just using only a single-layer network. Therefore, the integrated network constructed based on the networks of the three types attracts more interests. The function of a biological process in living cells is usually performed by the interaction of biological components. Therefore, it is necessary to integrate and analyze all the related components at the systems level for the comprehensively and correctly realizing the physiological function in living organisms. In this review, we discussed three representative genome-scale cellular networks: GMN, TRN, and STN, representing different levels (i.e., metabolism, gene regulation, and cellular signaling of a cell’s activities. Furthermore, we discussed the integration of the networks of the three types. With more understanding on the complexity of microbial cells, the development of integrated network has become an inevitable trend in analyzing genome-scale cellular networks of microorganisms.

Scaling in public transport networks

Directory of Open Access Journals (Sweden)

C. von Ferber

2005-01-01

Full Text Available We analyse the statistical properties of public transport networks. These networks are defined by a set of public transport routes (bus lines and the stations serviced by these. For larger networks these appear to possess a scale-free structure, as it is demonstrated e.g. by the Zipf law distribution of the number of routes servicing a given station or for the distribution of the number of stations which can be visited from a chosen one without changing the means of transport. Moreover, a rather particular feature of the public transport network is that many routes service common subsets of stations. We discuss the possibility of new scaling laws that govern intrinsic properties of such subsets.

Psychometrics of the neonatal oral motor assessment scale.

Science.gov (United States)

Zarem, Cori; Kidokoro, Hiroyuki; Neil, Jeffrey; Wallendorf, Michael; Inder, Terrie; Pineda, Roberta

2013-12-01

To establish the psychometrics of the Neonatal Oral Motor Assessment Scale (NOMAS). In this prospective cohort study of 75 preterm infants (39 females, 36 males) born at or before 30 weeks gestation (mean gestational age 26.56 wks, SD 1.90, range 23-30 wks; mean birthweight 967.33 g, SD 288.54, range 480-2240), oral feeding was videotaped before discharge from the neonatal intensive care unit (NICU). The NOMAS was used to classify feeding as normal, disorganized, or dysfunctional. Neurobehavior was assessed at term equivalent, and infants underwent magnetic resonance imaging. Children returned for developmental testing at 2 years corrected age. Associations between NOMAS scores and (1) neurobehavior; (2) cerebral injury and metrics; and (3) developmental outcome were investigated using χ(2) -analyses, t-tests, and linear regression. For reliability, six certified NOMAS evaluators rated five randomly selected NOMAS recordings and re-scored them 2 weeks later in a second randomized order. Reliability was calculated with Cohen's kappa statistics. Dysfunctional NOMAS scores were associated with lower Dubowitz scores [t=-2.14; mean difference -2.32 (95% confidence interval [CI] -0.157 to -4.49); p=0.036], higher stress on the NICU Network Neurobehavioral Scale (t=2.61; mean difference 0.073 [95% CI 0.017-0.129]; p=0.0110), and decreased transcerebellar diameter (t=-2.22; mean difference -2.04 [CI=-3.89 to -0.203]; p=0.03). No significant associations were found between NOMAS scores and 2-year outcome. Some concurrent validity was established with associations between NOMAS scores and measures of infant behavior and cerebral structure. The NOMAS did not show predictive validity in this study of preterm infants at high risk of developmental delay. Reliability was variable and suboptimal. © 2013 Mac Keith Press.

Scaling of load in communications networks.

Science.gov (United States)

Narayan, Onuttom; Saniee, Iraj

2010-09-01

We show that the load at each node in a preferential attachment network scales as a power of the degree of the node. For a network whose degree distribution is p(k)∼k{-γ} , we show that the load is l(k)∼k{η} with η=γ-1 , implying that the probability distribution for the load is p(l)∼1/l{2} independent of γ . The results are obtained through scaling arguments supported by finite size scaling studies. They contradict earlier claims, but are in agreement with the exact solution for the special case of tree graphs. Results are also presented for real communications networks at the IP layer, using the latest available data. Our analysis of the data shows relatively poor power-law degree distributions as compared to the scaling of the load versus degree. This emphasizes the importance of the load in network analysis.

Geometry of river networks. I. Scaling, fluctuations, and deviations

International Nuclear Information System (INIS)

Dodds, Peter Sheridan; Rothman, Daniel H.

2001-01-01

This paper is the first in a series of three papers investigating the detailed geometry of river networks. Branching networks are a universal structure employed in the distribution and collection of material. Large-scale river networks mark an important class of two-dimensional branching networks, being not only of intrinsic interest but also a pervasive natural phenomenon. In the description of river network structure, scaling laws are uniformly observed. Reported values of scaling exponents vary, suggesting that no unique set of scaling exponents exists. To improve this current understanding of scaling in river networks and to provide a fuller description of branching network structure, here we report a theoretical and empirical study of fluctuations about and deviations from scaling. We examine data for continent-scale river networks such as the Mississippi and the Amazon and draw inspiration from a simple model of directed, random networks. We center our investigations on the scaling of the length of a subbasin's dominant stream with its area, a characterization of basin shape known as Hack's law. We generalize this relationship to a joint probability density, and provide observations and explanations of deviations from scaling. We show that fluctuations about scaling are substantial, and grow with system size. We find strong deviations from scaling at small scales which can be explained by the existence of a linear network structure. At intermediate scales, we find slow drifts in exponent values, indicating that scaling is only approximately obeyed and that universality remains indeterminate. At large scales, we observe a breakdown in scaling due to decreasing sample space and correlations with overall basin shape. The extent of approximate scaling is significantly restricted by these deviations, and will not be improved by increases in network resolution

Neurobehavioral evaluation for a community with chronic exposure to hydrogen sulfide gas

International Nuclear Information System (INIS)

Inserra, S.G.; Phifer, B.L.; Anger, W.K.; Lewin, Michael; Hilsdon, Roberta; White, M.C.

2004-01-01

In May 2000, the Agency for Toxic Substances and Disease Registry of the US government conducted a health investigation in response to community concerns regarding ambient and indoor hydrogen sulfide (H 2 S), odor, and health symptoms in Dakota City, Nebraska. The objective was to determine whether adult residents in an area with repeated exposure to H 2 S showed poorer performance on neurobehavioral tests than unexposed residents. Study participants were required to meet age (≥16 years of age) and length of residency (2 years) eligibility requirements. A battery of computer-assisted standardized neurobehavioral tests was administered in English or Spanish. A questionnaire was used to collect information about participants, demographic and health status. Three hundred forty-five people agreed to participate. After the exclusion of 10 persons, analyses were conducted on 335 participants; 171 residents in the target area and 164 residents in the comparison area. The two groups were comparable in demographic characteristics and various health conditions. Overall, neurobehavioral test results for the target and comparison groups were similar. Residence in the H 2 S-exposed area was associated with marginally poorer performance on a test of memory, namely, match to sample score, and a test of grip strength. However, these differences were not significant. Deficits in overall neurobehavioral performance were not associated with exposure to H 2 S in this study

Transgenerational Inheritance of Paternal Neurobehavioral Phenotypes: Stress, Addiction, Ageing and Metabolism.

Science.gov (United States)

Yuan, Ti-Fei; Li, Ang; Sun, Xin; Ouyang, Huan; Campos, Carlos; Rocha, Nuno B F; Arias-Carrión, Oscar; Machado, Sergio; Hou, Gonglin; So, Kwok Fai

2016-11-01

Epigenetic modulation is found to get involved in multiple neurobehavioral processes. It is believed that different types of environmental stimuli could alter the epigenome of the whole brain or related neural circuits, subsequently contributing to the long-lasting neural plasticity of certain behavioral phenotypes. While the maternal influence on the health of offsprings has been long recognized, recent findings highlight an alternative way for neurobehavioral phenotypes to be passed on to the next generation, i.e., through the male germ line. In this review, we focus specifically on the transgenerational modulation induced by environmental stress, drugs of abuse, and other physical or mental changes (e.g., ageing, metabolism, fear) in fathers, and recapitulate the underlying mechanisms potentially mediating the alterations in epigenome or gene expression of offsprings. Together, these findings suggest that the inheritance of phenotypic traits through male germ-line epigenome may represent the unique manner of adaptation during evolution. Hence, more attention should be paid to the paternal health, given its equivalently important role in affecting neurobehaviors of descendants.

Weighted Scaling in Non-growth Random Networks

International Nuclear Information System (INIS)

Chen Guang; Yang Xuhua; Xu Xinli

2012-01-01

We propose a weighted model to explain the self-organizing formation of scale-free phenomenon in non-growth random networks. In this model, we use multiple-edges to represent the connections between vertices and define the weight of a multiple-edge as the total weights of all single-edges within it and the strength of a vertex as the sum of weights for those multiple-edges attached to it. The network evolves according to a vertex strength preferential selection mechanism. During the evolution process, the network always holds its total number of vertices and its total number of single-edges constantly. We show analytically and numerically that a network will form steady scale-free distributions with our model. The results show that a weighted non-growth random network can evolve into scale-free state. It is interesting that the network also obtains the character of an exponential edge weight distribution. Namely, coexistence of scale-free distribution and exponential distribution emerges.

Cholinergic Modulation of Restraint Stress Induced Neurobehavioral ...

African Journals Online (AJOL)

The involvement of the cholinergic system in restraint stress induced neurobehavioral alterations was investigated in rodents using the hole board, elevated plus maze, the open field and the light and dark box tests. Restraint stress (3h) reduced significantly (p<0.05) the number of entries and time spent in the open arm, ...

Fractal scale-free networks resistant to disease spread

International Nuclear Information System (INIS)

Zhang, Zhongzhi; Zhou, Shuigeng; Zou, Tao; Chen, Guisheng

2008-01-01

The conventional wisdom is that scale-free networks are prone to epidemic propagation; in the paper we demonstrate that, on the contrary, disease spreading is inhibited in fractal scale-free networks. We first propose a novel network model and show that it simultaneously has the following rich topological properties: scale-free degree distribution, tunable clustering coefficient, 'large-world' behavior, and fractal scaling. Existing network models do not display these characteristics. Then, we investigate the susceptible–infected–removed (SIR) model of the propagation of diseases in our fractal scale-free networks by mapping it to the bond percolation process. We establish the existence of non-zero tunable epidemic thresholds by making use of the renormalization group technique, which implies that power law degree distribution does not suffice to characterize the epidemic dynamics on top of scale-free networks. We argue that the epidemic dynamics are determined by the topological properties, especially the fractality and its accompanying 'large-world' behavior

Neurobehavioral and Psychosocial Issues in Klinefelter Syndrome

Science.gov (United States)

Geschwind, Daniel H.; Dykens, Elisabeth

2004-01-01

Klinefelter Syndrome (KS) is a relatively common (1/500 to 1/1,000) genetic syndrome caused by an extra X chromosome in males, leading to an XXY karyotype. In most cases, the physical and neurobehavioral characteristics of KS are relatively mild, and KS is not usually associated with moderate or severe mental retardation. However, KS is often…

Model studies for evaluating the neurobehavioral effects of complex hydrocarbon solvents. II. Neurobehavioral effects of white spirit in rat and human

NARCIS (Netherlands)

Lammers, J.H.C.M.; Emmen, H.H.; Muijser, H.; Hoogendijk, E.M.G.; McKee, R.H.; Owen, D.E.; Kulig, B.M.

2007-01-01

To evaluate the neurobehavioral effects of hydrocarbon solvents and to establish a working model for extrapolating animal test data to humans, studies were conducted which involved inhalation exposure of rats and humans to white spirit (WS). The specific objectives of these studies were to evaluate

Organization and scaling in water supply networks

Science.gov (United States)

Cheng, Likwan; Karney, Bryan W.

2017-12-01

Public water supply is one of the society's most vital resources and most costly infrastructures. Traditional concepts of these networks capture their engineering identity as isolated, deterministic hydraulic units, but overlook their physics identity as related entities in a probabilistic, geographic ensemble, characterized by size organization and property scaling. Although discoveries of allometric scaling in natural supply networks (organisms and rivers) raised the prospect for similar findings in anthropogenic supplies, so far such a finding has not been reported in public water or related civic resource supplies. Examining an empirical ensemble of large number and wide size range, we show that water supply networks possess self-organized size abundance and theory-explained allometric scaling in spatial, infrastructural, and resource- and emission-flow properties. These discoveries establish scaling physics for water supply networks and may lead to novel applications in resource- and jurisdiction-scale water governance.

Neurobehavioral Performance Impairment in Insomnia: Relationships with Self-Reported Sleep and Daytime Functioning

Science.gov (United States)

Shekleton, Julia A.; Flynn-Evans, Erin E.; Miller, Belinda; Epstein, Lawrence J.; Kirsch, Douglas; Brogna, Lauren A.; Burke, Liza M.; Bremer, Erin; Murray, Jade M.; Gehrman, Philip; Lockley, Steven W.; Rajaratnam, Shantha M. W.

2014-01-01

Study Objectives: Despite the high prevalence of insomnia, daytime consequences of the disorder are poorly characterized. This study aimed to identify neurobehavioral impairments associated with insomnia, and to investigate relationships between these impairments and subjective ratings of sleep and daytime dysfunction. Design: Cross-sectional, multicenter study. Setting: Three sleep laboratories in the USA and Australia. Patients: Seventy-six individuals who met the Research Diagnostic Criteria (RDC) for Primary Insomnia, Psychophysiological Insomnia, Paradoxical Insomnia, and/or Idiopathic Childhood Insomnia (44F, 35.8 ± 12.0 years [mean ± SD]) and 20 healthy controls (14F, 34.8 ± 12.1 years). Interventions: N/A. Measurements and Results: Participants completed a 7-day sleep-wake diary, questionnaires assessing daytime dysfunction, and a neurobehavioral test battery every 60-180 minutes during an afternoon/evening sleep laboratory visit. Included were tasks assessing sustained and switching attention, working memory, subjective sleepiness, and effort. Switching attention and working memory were significantly worse in insomnia patients than controls, while no differences were found for simple or complex sustained attention tasks. Poorer sustained attention in the control, but not the insomnia group, was significantly associated with increased subjective sleepiness. In insomnia patients, poorer sustained attention performance was associated with reduced health-related quality of life and increased insomnia severity. Conclusions: We found that insomnia patients exhibit deficits in higher level neurobehavioral functioning, but not in basic attention. The findings indicate that neurobehavioral deficits in insomnia are due to neurobiological alterations, rather than sleepiness resulting from chronic sleep deficiency. Citation: Shekleton JA; Flynn-Evans EE; Miller B; Epstein LJ; Kirsch D; Brogna LA; Burke LM; Cremer E; Murray JM; Gehrman P; Lockley SW; Rajaratnam SMW

Optimal defense resource allocation in scale-free networks

Science.gov (United States)

Zhang, Xuejun; Xu, Guoqiang; Xia, Yongxiang

2018-02-01

The robustness research of networked systems has drawn widespread attention in the past decade, and one of the central topics is to protect the network from external attacks through allocating appropriate defense resource to different nodes. In this paper, we apply a specific particle swarm optimization (PSO) algorithm to optimize the defense resource allocation in scale-free networks. Results reveal that PSO based resource allocation shows a higher robustness than other resource allocation strategies such as uniform, degree-proportional, and betweenness-proportional allocation strategies. Furthermore, we find that assigning less resource to middle-degree nodes under small-scale attack while more resource to low-degree nodes under large-scale attack is conductive to improving the network robustness. Our work provides an insight into the optimal defense resource allocation pattern in scale-free networks and is helpful for designing a more robust network.

PKI security in large-scale healthcare networks.

Science.gov (United States)

Mantas, Georgios; Lymberopoulos, Dimitrios; Komninos, Nikos

2012-06-01

During the past few years a lot of PKI (Public Key Infrastructures) infrastructures have been proposed for healthcare networks in order to ensure secure communication services and exchange of data among healthcare professionals. However, there is a plethora of challenges in these healthcare PKI infrastructures. Especially, there are a lot of challenges for PKI infrastructures deployed over large-scale healthcare networks. In this paper, we propose a PKI infrastructure to ensure security in a large-scale Internet-based healthcare network connecting a wide spectrum of healthcare units geographically distributed within a wide region. Furthermore, the proposed PKI infrastructure facilitates the trust issues that arise in a large-scale healthcare network including multi-domain PKI infrastructures.

How does sex matter? Behavior, stress and animal models of neurobehavioral disorders.

Science.gov (United States)

Palanza, Paola; Parmigiani, Stefano

2017-05-01

Many aspects of brain functioning exhibit important sex differences that affect behavior, mental health and mental disorders. However, most translational neuroscience research related to animal models of neurobehavioral disorders are carried out in male animals only. Based on published data from our laboratory on the House mouse, we discuss the following issues: (1) sex differences in social behavior of wild-derived mice; (2) artificial selection of laboratory strains and its consequences on social and reproductive competition; (3) sex-dependent effects of common experimental procedures; (4) differential effects of developmental events: the case of endocrine disruption; (5) implications for female models of stress and neurobehavioral disorders. Altogether, this review of data outline the marked differences of male and female responses to different social challenges and evinces the current lack of a relevant female mouse model of social stress. Whilst animal modelling is an important approach towards understanding mechanisms of neurobehavioral disorders, it is evident that data obtained in males may be irrelevant for inferring psychopathology and efficacy of pharmacological treatments for females. Copyright © 2017 Elsevier Ltd. All rights reserved.

Effects of degree correlation on scale-free gradient networks

International Nuclear Information System (INIS)

Pan Guijun; Yan Xiaoqing; Ma Weichuan; Luo Yihui; Huang Zhongbing

2010-01-01

We have studied the effects of degree correlation on congestion pressure in scale-free gradient networks. It is observed that the jamming coefficient J is insensitive to the degree correlation coefficient r for assortative and strongly disassortative scale-free networks, and J markedly decreases with an increase in r for weakly disassortative scale-free networks. We have also investigated the effects of degree correlation on the topology structure of scale-free gradient networks, and discussed the relation between the topology structure properties and transport efficiency of gradient networks.

Scaling a network with positive gains to a lossy or gainy network

NARCIS (Netherlands)

Koene, J.

1979-01-01

Necessary and sufficient conditions are presented under which it is possible to scale a network with positive gains to a lossy or a gainy network. A procedure to perform such a scaling operation is given.

Parenting behavior is associated with the early neurobehavioral development of very preterm children.

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Treyvaud, Karli; Anderson, Vicki A; Howard, Kelly; Bear, Merilyn; Hunt, Rod W; Doyle, Lex W; Inder, Terrie E; Woodward, Lianne; Anderson, Peter J

2009-02-01

There is an increasing focus on social and environmental factors that promote and support the early development of highly vulnerable children such as those born very preterm. The aim of this study was to assess the relationship between parenting behavior, parent-child synchrony, and neurobehavioral development in very preterm children at 24 months of age. Participants were 152 very preterm children (Cognitive and motor development was assessed by using the Bayley Scales of Infant Development II, and the Infant Toddler Social and Emotional Assessment was used to assess socioemotional development (social-emotional competence and internalizing and externalizing behavior). fter controlling for social risk, most parenting domains were associated with cognitive development, with parent-child synchrony emerging as the most predictive. Greater parent-child synchrony was also associated with greater social-emotional competence, as was parenting that was positive, warm, and sensitive. Parents who displayed higher levels of negative affect were more likely to rate their children as withdrawn, anxious, and inhibited, but, unexpectedly, higher negative affect was also associated with more optimal psychomotor development. Parenting was not associated with externalizing behaviors at this age. Specific parenting behaviors, particularly parent-child synchrony, were associated with neurobehavioral development. These findings have implications for the development of targeted parent-based interventions to promote positive outcomes across different developmental domains during the first 2 years of life for very preterm children.

Prevalence of Neurobehavioral, Social, and Emotional Dysfunction in Patients Treated for Childhood Craniopharyngioma: A Systematic Literature Review

Science.gov (United States)

Zada, Gabriel; Kintz, Natalie; Pulido, Mario; Amezcua, Lilyana

2013-01-01

Background Craniopharyngiomas (CP) are locally invasive and frequently recurring neoplasms often resulting in neurological and endocrinological dysfunction in children. In addition, social-behavioral impairment is commonly reported following treatment for childhood CP, yet remains to be fully understood. The authors aimed to further characterize the prevalence of neurobehavioral, social, and emotional dysfunction in survivors of childhood craniopharyngiomas. Materials and Methods A systematic literature review was conducted in PubMed to identify studies formally assessing neurobehavioral, social, and emotional outcomes in patients treated for CP prior to 18 years of age. Studies published between the years 1990-2012 that reported the primary outcome (prevalence of neurobehavioral, social, emotional/affective dysfunction, and/or impaired quality of life (QoL)) in ≥10 patients were included. Results Of the 471 studies screened, 11 met inclusion criteria. Overall neurobehavioral dysfunction was reported in 51 of 90 patients (57%) with available data. Social impairment (i.e. withdrawal, internalizing behavior) was reported in 91 of 222 cases (41%). School dysfunction was reported in 48 of 136 patients (35%). Emotional/affective dysfunction was reported in 58 of 146 patients (40%), primarily consisting of depressive symptoms. Health related quality of life was affected in 49 of 95 patients (52%). Common descriptors of behavior in affected children included irritability, impulsivity, aggressiveness, and emotional outbursts. Conclusions Neurobehavioral, social, and emotional impairment is highly prevalent in survivors of childhood craniopharyngioma, and often affects quality of life. Thorough neurobehavioral/emotional screening and appropriate counseling is recommended in this population. Additional research is warranted to identify risk factors and treatment strategies for these disorders. PMID:24223703

Prevalence of neurobehavioral, social, and emotional dysfunction in patients treated for childhood craniopharyngioma: a systematic literature review.

Directory of Open Access Journals (Sweden)

Gabriel Zada

Full Text Available Craniopharyngiomas (CP are locally invasive and frequently recurring neoplasms often resulting in neurological and endocrinological dysfunction in children. In addition, social-behavioral impairment is commonly reported following treatment for childhood CP, yet remains to be fully understood. The authors aimed to further characterize the prevalence of neurobehavioral, social, and emotional dysfunction in survivors of childhood craniopharyngiomas.A systematic literature review was conducted in PubMed to identify studies formally assessing neurobehavioral, social, and emotional outcomes in patients treated for CP prior to 18 years of age. Studies published between the years 1990-2012 that reported the primary outcome (prevalence of neurobehavioral, social, emotional/affective dysfunction, and/or impaired quality of life (QoL in ≥ 10 patients were included.Of the 471 studies screened, 11 met inclusion criteria. Overall neurobehavioral dysfunction was reported in 51 of 90 patients (57% with available data. Social impairment (i.e. withdrawal, internalizing behavior was reported in 91 of 222 cases (41%. School dysfunction was reported in 48 of 136 patients (35%. Emotional/affective dysfunction was reported in 58 of 146 patients (40%, primarily consisting of depressive symptoms. Health related quality of life was affected in 49 of 95 patients (52%. Common descriptors of behavior in affected children included irritability, impulsivity, aggressiveness, and emotional outbursts.Neurobehavioral, social, and emotional impairment is highly prevalent in survivors of childhood craniopharyngioma, and often affects quality of life. Thorough neurobehavioral/emotional screening and appropriate counseling is recommended in this population. Additional research is warranted to identify risk factors and treatment strategies for these disorders.

Self-similarity and scaling theory of complex networks

Science.gov (United States)

Song, Chaoming

Scale-free networks have been studied extensively due to their relevance to many real systems as diverse as the World Wide Web (WWW), the Internet, biological and social networks. We present a novel approach to the analysis of scale-free networks, revealing that their structure is self-similar. This result is achieved by the application of a renormalization procedure which coarse-grains the system into boxes containing nodes within a given "size". Concurrently, we identify a power-law relation between the number of boxes needed to cover the network and the size of the box defining a self-similar exponent, which classifies fractal and non-fractal networks. By using the concept of renormalization as a mechanism for the growth of fractal and non-fractal modular networks, we show that the key principle that gives rise to the fractal architecture of networks is a strong effective "repulsion" between the most connected nodes (hubs) on all length scales, rendering them very dispersed. We show that a robust network comprised of functional modules, such as a cellular network, necessitates a fractal topology, suggestive of a evolutionary drive for their existence. These fundamental properties help to understand the emergence of the scale-free property in complex networks.

Multilevel method for modeling large-scale networks.

Energy Technology Data Exchange (ETDEWEB)

Safro, I. M. (Mathematics and Computer Science)

2012-02-24

Understanding the behavior of real complex networks is of great theoretical and practical significance. It includes developing accurate artificial models whose topological properties are similar to the real networks, generating the artificial networks at different scales under special conditions, investigating a network dynamics, reconstructing missing data, predicting network response, detecting anomalies and other tasks. Network generation, reconstruction, and prediction of its future topology are central issues of this field. In this project, we address the questions related to the understanding of the network modeling, investigating its structure and properties, and generating artificial networks. Most of the modern network generation methods are based either on various random graph models (reinforced by a set of properties such as power law distribution of node degrees, graph diameter, and number of triangles) or on the principle of replicating an existing model with elements of randomization such as R-MAT generator and Kronecker product modeling. Hierarchical models operate at different levels of network hierarchy but with the same finest elements of the network. However, in many cases the methods that include randomization and replication elements on the finest relationships between network nodes and modeling that addresses the problem of preserving a set of simplified properties do not fit accurately enough the real networks. Among the unsatisfactory features are numerically inadequate results, non-stability of algorithms on real (artificial) data, that have been tested on artificial (real) data, and incorrect behavior at different scales. One reason is that randomization and replication of existing structures can create conflicts between fine and coarse scales of the real network geometry. Moreover, the randomization and satisfying of some attribute at the same time can abolish those topological attributes that have been undefined or hidden from

Effects of perinatal exposure to environmentally persistent organic pollutants and heavy metals on neurobehavioral development in Japanese children: IV. Thyroid hormones and neonatal neurobehavioral status

Energy Technology Data Exchange (ETDEWEB)

Suzuki, K.; Nakai, K.; Oka, T.; Kurokawa, N.; Satoh, H. [Dept. of Environmental Health Sciences, Tohoku Univ. Graduate School of Medicine, Sendai (Japan); Hosokawa, T. [Dept. of Human Development, Tohoku Univ., Sendai (Japan); Okamura, K. [Dept. of Obstetrics, Tohoku Univ. Graduate School of Medicine, Sendai (Japan); Sakai, T. [Miyagi Childrens Hospital, Sendai (Japan)

2004-09-15

From several epidemiological studies, it has been reported that there are some associations between perinatal exposures to PCBs, dioxins and heavy metals, and neurobehavioral defects such as postnatal growth delay and poorer cognitive function. We have started a prospective cohort study to examine the effects of perinatal exposures to environmentally persistent organic pollutants on neurobehavioral development in Japanese children. Thyroid hormones (THs) are essential for normal brain development. A lack of THs in pregnancy can result in congenital hypothyroidism, which causes moderate to severe intellectual defects. It has been reported that perinatal exposure to PCBs adversely affects on children's intellectual functions. The chemical structures of some PCBs resembles thyroxine (T4), and therefore, it is suspected that the action mechanism of PCBs is disruption of TH function. Some PCBs and their metabolites are thought to bind with transthyretine (TTR), which is necessary for the transfer of T4 into the brain, and this may cause a shortage of T4 in the developing brain. To examine the effects of perinatal exposure to PCBs on children's development, it is essential to evaluate the functions of THs at a fundamental level. In this report, we examined the correlations of THs in maternal peripheral blood and cord blood, and the association between THs and neonatal neurobehavioral status.

Evaluating and treating neurobehavioral symptoms in professional American football players

Science.gov (United States)

Possin, Katherine L.; Hess, Christopher P.; Huang, Eric J.; Grinberg, Lea T.; Nolan, Amber L.; Cohn-Sheehy, Brendan I.; Ghosh, Pia M.; Lanata, Serggio; Merrilees, Jennifer; Kramer, Joel H.; Berger, Mitchel S.; Miller, Bruce L.; Yaffe, Kristine; Rabinovici, Gil D.

2015-01-01

Summary In the aftermath of multiple high-profile cases of chronic traumatic encephalopathy (CTE) in professional American football players, physicians in clinical practice are likely to face an increasing number of retired football players seeking evaluation for chronic neurobehavioral symptoms. Guidelines for the evaluation and treatment of these patients are sparse. Clinical criteria for a diagnosis of CTE are under development. The contribution of CTE vs other neuropathologies to neurobehavioral symptoms in these players remains unclear. Here we describe the experience of our academic memory clinic in evaluating and treating a series of 14 self-referred symptomatic players. Our aim is to raise awareness in the neurology community regarding the different clinical phenotypes, idiosyncratic but potentially treatable symptoms, and the spectrum of underlying neuropathologies in these players. PMID:26336629

Neurobehavioral performance impairment in insomnia: relationships with self-reported sleep and daytime functioning.

Science.gov (United States)

Shekleton, Julia A; Flynn-Evans, Erin E; Miller, Belinda; Epstein, Lawrence J; Kirsch, Douglas; Brogna, Lauren A; Burke, Liza M; Bremer, Erin; Murray, Jade M; Gehrman, Philip; Lockley, Steven W; Rajaratnam, Shantha M W

2014-01-01

Despite the high prevalence of insomnia, daytime consequences of the disorder are poorly characterized. This study aimed to identify neurobehavioral impairments associated with insomnia, and to investigate relationships between these impairments and subjective ratings of sleep and daytime dysfunction. Cross-sectional, multicenter study. Three sleep laboratories in the USA and Australia. Seventy-six individuals who met the Research Diagnostic Criteria (RDC) for Primary Insomnia, Psychophysiological Insomnia, Paradoxical Insomnia, and/or Idiopathic Childhood Insomnia (44F, 35.8 ± 12.0 years [mean ± SD]) and 20 healthy controls (14F, 34.8 ± 12.1 years). N/A. Participants completed a 7-day sleep-wake diary, questionnaires assessing daytime dysfunction, and a neurobehavioral test battery every 60-180 minutes during an afternoon/evening sleep laboratory visit. Included were tasks assessing sustained and switching attention, working memory, subjective sleepiness, and effort. Switching attention and working memory were significantly worse in insomnia patients than controls, while no differences were found for simple or complex sustained attention tasks. Poorer sustained attention in the control, but not the insomnia group, was significantly associated with increased subjective sleepiness. In insomnia patients, poorer sustained attention performance was associated with reduced health-related quality of life and increased insomnia severity. We found that insomnia patients exhibit deficits in higher level neurobehavioral functioning, but not in basic attention. The findings indicate that neurobehavioral deficits in insomnia are due to neurobiological alterations, rather than sleepiness resulting from chronic sleep deficiency.

Scaling properties in time-varying networks with memory

Science.gov (United States)

Kim, Hyewon; Ha, Meesoon; Jeong, Hawoong

2015-12-01

The formation of network structure is mainly influenced by an individual node's activity and its memory, where activity can usually be interpreted as the individual inherent property and memory can be represented by the interaction strength between nodes. In our study, we define the activity through the appearance pattern in the time-aggregated network representation, and quantify the memory through the contact pattern of empirical temporal networks. To address the role of activity and memory in epidemics on time-varying networks, we propose temporal-pattern coarsening of activity-driven growing networks with memory. In particular, we focus on the relation between time-scale coarsening and spreading dynamics in the context of dynamic scaling and finite-size scaling. Finally, we discuss the universality issue of spreading dynamics on time-varying networks for various memory-causality tests.

Model studies for evaluating the acute neurobehavioral effects of complex hydrocarbon solvents. I. Validation of methods with ethanol

NARCIS (Netherlands)

McKee, R.H.; Lammers, J.H.C.M.; Hoogendijk, E.M.G.; Emmen, H.H.; Muijser, H.; Barsotti, D.A.; Owen, D.E.; Kulig, B.M.

2006-01-01

As a preliminary step to evaluating the acute neurobehavioral effects of hydrocarbon solvents and to establish a working model for extrapolating animal test data to humans, joint neurobehavioral/toxicokinetic studies were conducted which involved administering ethanol to rats and volunteers. The

Indoor mold exposure associated with neurobehavioral and pulmonary impairment: a preliminary report.

Science.gov (United States)

Kilburn, Kaye H

2003-07-01

Recently, patients who have been exposed indoors to mixed molds, spores, and mycotoxins have reported asthma, airway irritation and bleeding, dizziness, and impaired memory and concentration, all of which suggest the presence of pulmonary and neurobehavioral problems. The author evaluated whether such patients had measurable pulmonary and neurobehavioral impairments by comparing consecutive cases in a series vs. a referent group. Sixty-five consecutive outpatients exposed to mold in their respective homes in Arizona, California, and Texas were compared with 202 community subjects who had no known mold or chemical exposures. Balance, choice reaction time, color discrimination, blink reflex, visual fields, grip, hearing, problem-solving, verbal recall, perceptual motor speed, and memory were measured. Medical histories, mood states, and symptom frequencies were recorded with checklists, and spirometry was used to measure various pulmonary volumes and flows. Neurobehavioral comparisons were made after individual measurements were adjusted for age, educational attainment, and sex. Significant differences between groups were assessed by analysis of variance; a p value of less than 0.05 was used for all statistical tests. The mold-exposed group exhibited decreased function for balance, reaction time, blink-reflex latency, color discrimination, visual fields, and grip, compared with referents. The exposed group's scores were reduced for the following tests: digit-symbol substitution, peg placement, trail making, verbal recall, and picture completion. Twenty-one of 26 functions tested were abnormal. Airway obstructions were found, and vital capacities were reduced. Mood state scores and symptom frequencies were elevated. The author concluded that indoor mold exposures were associated with neurobehavioral and pulmonary impairments that likely resulted from the presence of mycotoxins, such as trichothecenes.

Neurobehavioral assessment of rats exposed to pristine polystyrene nanoplastics upon oral exposure.

Science.gov (United States)

Rafiee, Mohammad; Dargahi, Leila; Eslami, Akbar; Beirami, Elmira; Jahangiri-Rad, Mahsa; Sabour, Siamak; Amereh, Fatemeh

2018-02-01

The increasing use of plastics has raised concerns about pollution of freshwater by these polymeric materials. Knowledge about their potential effects on environmental and public health is limited. Recent publications have suggested that the degradation of plastics will result in the release of nano-sized plastic particles to the environment. Therefore, it is of utmost importance to gain knowledge about whether and how nanoplastics affect living organisms. The present study aimed to analyse potential neurobehavioral effects of polystyrene nanoparticles (PS-NPs) after long-term exposure on rat. Potential effects of PS-NPs were investigated using four test dosages (1, 3, 6, and 10 mg PS-NPs/kg of body weight/day) administrated orally with adult Wistar male rats for five weeks. Neurobehavioral tests were chosen to assess a variety of behavioral domains. Particle diameters in test suspensions were determined through dynamic light scattering and showed an average hydrodynamic diameter of approximately 38.92 nm. No statistically significant behavioral effects were observed in all tests performed (p > 0.05). In the elevated plus maze, PS-NPs-exposed rats showed greater number of entries into open arms compared to controls. Also, PS-NPs had no significant influence on body weight of animals. Taking into account the subtle and transient nature of neurobehavioral consequences, however, these results underline the possibility of even pristine plastic nanoparticles to induce behavioral alteration in the rest of the food web, including for marine biota and humans. Indeed even though studied neurobehavioral effects in our study was not statistically significant, the observed subtle effects may be clinically considerable. Copyright © 2017 Elsevier Ltd. All rights reserved.

Effect of Zishenpingchan Granule on Neurobehavioral Manifestations and the Activity and Gene Expression of Striatal Dopamine D1 and D2 Receptors of Rats with Levodopa-Induced Dyskinesias

Directory of Open Access Journals (Sweden)

Qing Ye

2014-01-01

Full Text Available This study was performed to observe the effects of Zishenpingchan granule on neurobehavioral manifestations and the activity and gene expression of striatal dopamine D1 and D2 receptors of rats with levodopa-induced dyskinesias (LID. We established normal control group, LID model group, and TCM intervention group. Each group received treatment for 4 weeks. Artificial neural network (ANN was applied to excavate the main factor influencing variation in neurobehavioral manifestations of rats with LID. The results showed that overactivation in direct pathway mediated by dopamine D1 receptor and overinhibition in indirect pathway mediated by dopamine D2 receptor may be the main mechanism of LID. TCM increased the efficacy time of LD to ameliorate LID symptoms effectively mainly by upregulating dopamine D2 receptor gene expression.

Weighted Scale-Free Network Properties of Ecological Network

International Nuclear Information System (INIS)

Lee, Jae Woo; Maeng, Seong Eun

2013-01-01

We investigate the scale-free network properties of the bipartite ecological network, in particular, the plant-pollinator network. In plant-pollinator network, the pollinators visit the plant to get the nectars. In contrast to the other complex network, the plant-pollinator network has not only the trophic relationships among the interacting partners but also the complexities of the coevolutionary effects. The interactions between the plant and pollinators are beneficial relations. The plant-pollinator network is a bipartite and weighted network. The networks have two types of the nodes: plant and pollinator. We consider the visiting frequency of a pollinator to a plant as the weighting value of the link. We defined the strength of a node as the sum of the weighting value of the links. We reported the cumulative distribution function (CDF) of the degree and the strength of the plant-pollinator network. The CDF of the plants followed stretched exponential functions for both degree and strength, but the CDF of the pollinators showed the power law for both degree and strength. The average strength of the links showed the nonlinear dependence on the degree of the networks.

The effect of one night's sleep deprivation on adolescent neurobehavioral performance.

Science.gov (United States)

Louca, Mia; Short, Michelle A

2014-11-01

To investigate the effects of one night's sleep deprivation on neurobehavioral functioning in adolescents. Participants completed a neurobehavioral test battery measuring sustained attention, reaction speed, cognitive processing speed, sleepiness, and fatigue every 2 h during wakefulness. Baseline performance (defined as those test bouts between 09:00 and 19:00 on days 2 and 3, following two 10-h sleep opportunities) were compared to performance at the same clock time the day following total sleep deprivation. The sleep laboratory at the Centre for Sleep Research. Twelve healthy adolescents (6 male), aged 14-18 years (mean = 16.17, standard deviation = 0.83). Sustained attention, reaction speed, cognitive processing speed, and subjective sleepiness were all significantly worse following one night without sleep than following 10-h sleep opportunities (all main effects of day, P Sleep deprivation led to increased variability on objective performance measures. There were between-subjects differences in response to sleep loss that were task-specific, suggesting that adolescents may not only vary in terms of the degree to which they are affected by sleep loss but also the domains in which they are affected. These findings suggest that one night of total sleep deprivation has significant deleterious effects upon neurobehavioral performance and subjective sleepiness. These factors impair daytime functioning in adolescents, leaving them at greater risk of poor academic and social functioning and accidents and injuries.

Radiation-induced neurobehavioral dysfunctions

International Nuclear Information System (INIS)

Manda, Kailash

2013-01-01

There is a lacuna between sparsely reported immediate effects and the well documented delayed effects on cognitive functions seen after ionizing radiation exposure. We reported the radiation-dose dependent incongruity in the early cognitive changes and its correlation with the structural aberration as reported by imaging study. The delayed effect of radiation was investigated to understand the role of hippocampal neurogenesis in the functional recovery of cognition. C57BL/6 mice were exposed to different doses of γ-radiation and 24 hrs after exposure, the stress and anxiety levels were examined in the Open Field Exploratory Paradigms (OFT). 48hrs after irradiation, the hippocampal dependent recognition memory was observed by the Novel Object Recognition Test (NORT) and the cognitive function related to memory processing and recall was tested using the Elevated Plus Maze (EPM). Visualization of damage to the brain was done by diffusion tensor imaging at 48 hours post-irradiation. Results indicate a complex dose independent effect on the cognitive functions immediately after exposure to gamma rays. Radiation exposure caused short term memory dysfunctions at lower doses which were seen to be abrogated at higher doses, but the long term memory processing was disrupted at higher doses. The Hippocampus emerged as one of the sensitive regions to be affected by whole body exposure to gamma rays, which led to profound immediate alterations in cognitive functions. Furthermore, the results indicate a cognitive recovery process, which might be dependent on the extent of damage to the hippocampal region. While evaluating the delayed effect of radiation on the hippocampal neurogenesis, we observed that higher doses groups showed comparatively more adaptive regenerative neurogenic potential which they could not sustain at later stages. Our studies reported an important hitherto uncovered phenomenon of neurobehavioral dysfunctions in relation to radiation dose. Nevertheless, a

The networks scale and coupling parameter in synchronization of neural networks with diluted synapses

International Nuclear Information System (INIS)

Li Yanlong; Ma Jun; Chen Yuhong; Xu Wenke; Wang Yinghai

2008-01-01

In this paper the influence of the networks scale on the coupling parameter in the synchronization of neural networks with diluted synapses is investigated. Using numerical simulations, an exponential decay form is observed in the extreme case of global coupling among networks and full connection in each network; the larger linked degree becomes, the larger critical coupling intensity becomes; and the oscillation phenomena in the relationship of critical coupling intensity and the number of neural networks layers in the case of small-scale networks are found

Influence of Dopamine-Related Genes on Neurobehavioral Recovery after Traumatic Brain Injury during Early Childhood.

Science.gov (United States)

Treble-Barna, Amery; Wade, Shari L; Martin, Lisa J; Pilipenko, Valentina; Yeates, Keith Owen; Taylor, H Gerry; Kurowski, Brad G

2017-06-01

The present study examined the association of dopamine-related genes with short- and long-term neurobehavioral recovery, as well as neurobehavioral recovery trajectories over time, in children who had sustained early childhood traumatic brain injuries (TBI) relative to children who had sustained orthopedic injuries (OI). Participants were recruited from a prospective, longitudinal study evaluating outcomes of children who sustained a TBI (n = 68) or OI (n = 72) between the ages of 3 and 7 years. Parents completed ratings of child executive function and behavior at the immediate post-acute period (0-3 months after injury); 6, 12, and 18 months after injury; and an average of 3.5 and 7 years after injury. Thirty-two single nucleotide polymorphisms (SNPs) in dopamine-related genes (dopamine receptor D2 [DRD2], solute carrier family 6 member 3 [SLC6A3], solute carrier family 18 member A2 [SLC18A2], catechol-o-methyltransferase [COMT], and ankyrin repeat and kinase domain containing 1 [ANKK1]) were examined in association with short- and long-term executive function and behavioral adjustment, as well as their trajectories over time. After controlling for premorbid child functioning, genetic variation within the SLC6A3 (rs464049 and rs460000) gene was differentially associated with neurobehavioral recovery trajectories over time following TBI relative to OI, with rs464049 surviving multiple testing corrections. In addition, genetic variation within the ANKK1 (rs1800497 and rs2734849) and SLC6A3 (rs464049, rs460000, and rs1042098) genes was differentially associated with short- and long-term neurobehavioral recovery following TBI, with rs460000 and rs464049 surviving multiple testing corrections. The findings provide preliminary evidence that genetic variation in genes involved in DRD2 expression and density (ANKK1) and dopamine transport (SLC6A3) plays a role in neurobehavioral recovery following pediatric TBI.

Neurobehavioral effects of ambient air pollution on cognitive performance in US adults.

Science.gov (United States)

Chen, Jiu-Chiuan; Schwartz, Joel

2009-03-01

In vivo animal experiments demonstrate neurotoxicity of exposures to particulate matter (PM) and ozone, but only one small epidemiological study had linked ambient air pollution with central nervous system (CNS) functions in children. To examine the neurobehavioral effects associated with long-term exposure to ambient PM and ozone in adults. We conducted a secondary analysis of the Neurobehavioral Evaluation System-2 (NES2) data (including a simple reaction time test [SRTT] measuring motor response speed to a visual stimulus; a symbol-digit substitution test [SDST] for coding ability; and a serial-digit learning test [SDLT] for attention and short-term memory) from 1764 adult participants (aged 37.5+/-10.9 years) of the Third National Health and Nutrition Examination Survey in 1988-1991. Based on ambient PM(10) (PM with aerodynamic diameter SDLT, but not in SRTT. Each 10-ppb increase in annual ozone was associated with increased SDST and SDLT scores by 0.16 (95%CI: 0.01, 0.23) and 0.56 (95%CI: 0.07, 1.05), equivalent to 3.5 and 5.3 years of aging-related decline in cognitive performance. Our study provides the first epidemiological data supporting the adverse neurobehavioral effects of ambient air pollutants in adults.

The neurobehavioral teratology of retinoids: a 50-year history.

Science.gov (United States)

Adams, Jane

2010-10-01

This review of the central nervous system (CNS) and behavioral teratology of the retinoids over the last 50 years is a commemorative retrospective organized by decade to show the prominent research focus within each period and the most salient findings. In the 1960s, research focused on the gross CNS malformations associated with exposure and the delineation of dose-response and stage-specific responses in rodent models. Relevant scientific events before and during the 1960s are also discussed to provide the zeitgeist in which the field of neurobehavioral teratology emerged in the 1970s. During this period, studies demonstrated that adverse effects on postnatal behavior could be produced in animals exposed to doses of vitamin A lower than those that were teratogenic or impacted growth. Work during the 1980s showed an overrepresentation of behavioral studies focused on the reliability of screening methods, while the marked effects of human exposure were illustrated in children born to women treated with isotretinoin during pregnancy. The human catastrophe invigorated research during the 1990s, a period when technological advances allowed more elegant examinations of the developing CNS, of biochemical, cellular, and molecular developmental events and regulatory actions, and of the effects of direct genetic manipulations. Likewise, research in the 1990s reflected a reinvigoration of research in neurobehavioral teratology evinced in studies that used animal models to try to better understand human vulnerability. These foci continued in the 2000-2010 period while examinations of the role of retinoids in brain development and lifelong functioning became increasingly sophisticated and broader in scope. This review of the work on retinoids also provides a lens on the more general ontogeny of the field of neurobehavioral teratology. Birth Defects Research (Part A), 2010. © 2010 Wiley-Liss, Inc.

Development and psychometric properties of the Carer - Head Injury Neurobehavioral Assessment Scale (C-HINAS) and the Carer - Head Injury Participation Scale (C-HIPS): patient and family determined outcome scales.

Science.gov (United States)

Deb, Shoumitro; Bryant, Eleanor; Morris, Paul G; Prior, Lindsay; Lewis, Glyn; Haque, Sayeed

2007-06-01

Develop and assess the psychometric properties of the Carer - Head Injury Participation Scale (C-HIPS) and its biggest factor the Carer - Head Injury Neurobehavioral Assessment Scale (C-HINAS). Furthermore, the aim was to examine the inter-informant reliability by comparing the self reports of individuals with traumatic brain injury (TBI) with the carer reports on the C-HIPS and the C-HINAS. Thirty-two TBI individuals and 27 carers took part in in-depth qualitative interviews exploring the consequences of the TBI. Interview transcripts were analysed and key themes and concepts were used to construct a 49-item and 58-item patient (Patient - Head Injury Participation Scale [P-HIPS]) and carer outcome measure (C-HIPS) respectively, of which 49 were parallel items and nine additional items were used to assess carer burden. Postal versions of the P-HIPS, C-HIPS, Mayo Portland Adaptability Inventory-3 (MPAI-3), and the Glasgow Outcome Scale-Extended (GOSE) were completed by a cohort of 113 TBI individuals and 80 carers. Data from a sub-group of 66 patient/carer pairs were used to compare inter-informant reliability between the P-HIPS and the C-HIPS, and the P-HINAS and the C-HINAS respectively. All individual 49 items of the C-HIPS and their total score showed good test-retest reliability (0.95) and internal consistency (0.95). Comparisons with the MPAI-3 and GOSE found a good correlation with the MPAI-3 (0.7) and a moderate negative correlation with the GOSE (-0.6). Factor analysis of these items extracted a 4-factor structure which represented the domains 'Emotion/Behavior' (C-HINAS), 'Independence/Community Living', 'Cognition', and 'Physical'. The C-HINAS showed good internal consistency (0.92), test-retest reliability (0.93), and concurrent validity with one MPAI subscale (0.7). Assessment of inter-informant reliability revealed good correspondence between the reports of the patients and the carers for both the C-HIPS (0.83) and the C-HINAS (0.82). Both the C

Scaling laws for nonintercommuting cosmic string networks

International Nuclear Information System (INIS)

Martins, C.J.A.P.

2004-01-01

We study the evolution of noninteracting and entangled cosmic string networks in the context of the velocity-dependent one-scale model. Such networks may be formed in several contexts, including brane inflation. We show that the frozen network solution L∝a, although generic, is only a transient one, and that the asymptotic solution is still L∝t as in the case of ordinary (intercommuting) strings, although in the present context the universe will usually be string dominated. Thus the behavior of two strings when they cross does not seem to affect their scaling laws, but only their densities relative to the background

Neurobehavioral Effects of Sodium Tungstate Exposure on Rats and Their Progeny

National Research Council Canada - National Science Library

Mclnturf, S. M; Bekkedal, M. Y; Olabisi, A; Arfsten, D; Wilfong, E; Casavant, R; Jederberg, W; Gunasekar, P. G; Chapman, G

2007-01-01

... consequences of exposure. The purpose of this study was to use a battery of tests as an initial screen for potential neurobehavioral effects that may be associated with 70 days of daily tungsten exposure via drinking water...

Neonatal neurobehavioral organization after exposure to maternal epidural analgesia in labor.

Science.gov (United States)

Bell, Aleeca F; White-Traut, Rosemary; Medoff-Cooper, Barbara

2010-01-01

To explore relationships between maternal epidural analgesia and two measures of neurobehavioral organization in infants at the initial feeding 1 hour after birth. Prospective comparative design. Inner-city community hospital, Chicago, Illinois. Convenience sample of 52 low-risk, mainly Black and Latino, mother/infant dyads. Mothers self-selected to labor with epidural or no labor pain medication. Neonatal neurobehavioral organization was measured in term infants at the initial feeding 1 hour after birth. A nutritive sucking apparatus generated data on total number of sucks and sucking pressure. Video recordings of infants (before and after the initial feeding) were coded for behavioral states, with analysis on frequency of alertness. Total number of sucks and sucking pressure were not related to epidural exposure, although an epidural drug dosage effect on total number of sucks was evident when gender was a factor. Unmedicated girls demonstrated more sucks than girls in the high-dosage epidural group (p=.027). Overall, girls exhibited stronger sucking pressure than boys (p=.042). Frequency of alertness was not related to epidural exposure, although longer labor was related to greater alertness (p=.003), and Latino infants were more alert than Black infants (p=.002). Results suggest attenuated neonatal nutritive sucking organization in girls after exposure to high maternal epidural dosages. In comparison to boys, girls may have enhanced neurobehavioral organization at birth. Race/ethnicity and alertness may have spurious associations in which hidden factors drive the relationship.

Unifying Inference of Meso-Scale Structures in Networks.

Science.gov (United States)

Tunç, Birkan; Verma, Ragini

2015-01-01

Networks are among the most prevalent formal representations in scientific studies, employed to depict interactions between objects such as molecules, neuronal clusters, or social groups. Studies performed at meso-scale that involve grouping of objects based on their distinctive interaction patterns form one of the main lines of investigation in network science. In a social network, for instance, meso-scale structures can correspond to isolated social groupings or groups of individuals that serve as a communication core. Currently, the research on different meso-scale structures such as community and core-periphery structures has been conducted via independent approaches, which precludes the possibility of an algorithmic design that can handle multiple meso-scale structures and deciding which structure explains the observed data better. In this study, we propose a unified formulation for the algorithmic detection and analysis of different meso-scale structures. This facilitates the investigation of hybrid structures that capture the interplay between multiple meso-scale structures and statistical comparison of competing structures, all of which have been hitherto unavailable. We demonstrate the applicability of the methodology in analyzing the human brain network, by determining the dominant organizational structure (communities) of the brain, as well as its auxiliary characteristics (core-periphery).

Unifying Inference of Meso-Scale Structures in Networks.

Directory of Open Access Journals (Sweden)

Birkan Tunç

Full Text Available Networks are among the most prevalent formal representations in scientific studies, employed to depict interactions between objects such as molecules, neuronal clusters, or social groups. Studies performed at meso-scale that involve grouping of objects based on their distinctive interaction patterns form one of the main lines of investigation in network science. In a social network, for instance, meso-scale structures can correspond to isolated social groupings or groups of individuals that serve as a communication core. Currently, the research on different meso-scale structures such as community and core-periphery structures has been conducted via independent approaches, which precludes the possibility of an algorithmic design that can handle multiple meso-scale structures and deciding which structure explains the observed data better. In this study, we propose a unified formulation for the algorithmic detection and analysis of different meso-scale structures. This facilitates the investigation of hybrid structures that capture the interplay between multiple meso-scale structures and statistical comparison of competing structures, all of which have been hitherto unavailable. We demonstrate the applicability of the methodology in analyzing the human brain network, by determining the dominant organizational structure (communities of the brain, as well as its auxiliary characteristics (core-periphery.

Scaling Laws in Chennai Bus Network

OpenAIRE

Chatterjee, Atanu; Ramadurai, Gitakrishnan

2015-01-01

In this paper, we study the structural properties of the complex bus network of Chennai. We formulate this extensive network structure by identifying each bus stop as a node, and a bus which stops at any two adjacent bus stops as an edge connecting the nodes. Rigorous statistical analysis of this data shows that the Chennai bus network displays small-world properties and a scale-free degree distribution with the power-law exponent, $\\gamma > 3$.

Group Centric Networking: Large Scale Over the Air Testing of Group Centric Networking

Science.gov (United States)

2016-11-01

Large Scale Over-the-Air Testing of Group Centric Networking Logan Mercer, Greg Kuperman, Andrew Hunter, Brian Proulx MIT Lincoln Laboratory...performance of Group Centric Networking (GCN), a networking protocol developed for robust and scalable communications in lossy networks where users are...devices, and the ad-hoc nature of the network . Group Centric Networking (GCN) is a proposed networking protocol that addresses challenges specific to

Neurobehavioral effects during experimental exposure to 1-octanol and isopropanol.

Science.gov (United States)

van Thriel, Christoph; Kiesswetter, Erns; Blaszkewicz, Meinolf; Golka, Klaus; Seeber, Andreas

2003-04-01

The study examined acute neurobehavioral effects provoked by controlled exposure to 1-octanol and isopropanol among male volunteers. In a 29-m3 exposure laboratory, 24 male students (mean age 25.8 years) were exposed to 1-octanol and isopropanol. Each substance was used in two concentrations (0.1 and 6.4 ppm for 1-octanol; 34.9 and 189.9 ppm for isopropanol:). In a crossover design, each subject was exposed for 4 hours to the conditions. Twelve subjects reported enhanced chemical sensitivity; the other 12 were age-matched controls. At the onset and end of the exposures neurobehavioral tests were administered and symptoms were rated. At the end of the high and low isopropanol exposures the tiredness ratings were elevated, but no dose-dependence could be confirmed. For both substances and concentrations, the annoyance ratings increased during the exposure, but only for isopropanol did the increase show a dose-response relation. The subjects reported olfactory symptoms during the exposure to the high isopropanol and both 1-octanol concentrations. Isopropanol provoked no sensory irritation, whereas high 1-octanol exposure slightly enhanced it. Only among the subjects with enhanced chemical sensitivity were both 1-octanol concentrations associated with a stronger increase in annoyance, and lower detection rates were observed in a divided attention task. Previous studies reporting no neurobehavioral effects for isopropanol (up to 400 ppm) were confirmed. The results obtained for 1-octanol lacked dose-dependency, and their evaluation, is difficult. The annoying odor of 1-octanol may mask sensory irritation and prevent subjects with enhanced chemical sensitivity from concentrating on performance in a demanding task.

Neurobehavioral Outcomes 11 Years After Neonatal Caffeine Therapy for Apnea of Prematurity.

Science.gov (United States)

Mürner-Lavanchy, Ines M; Doyle, Lex W; Schmidt, Barbara; Roberts, Robin S; Asztalos, Elizabeth V; Costantini, Lorrie; Davis, Peter G; Dewey, Deborah; D'Ilario, Judy; Grunau, Ruth E; Moddemann, Diane; Nelson, Harvey; Ohlsson, Arne; Solimano, Alfonso; Tin, Win; Anderson, Peter J

2018-05-01

Caffeine is effective in the treatment of apnea of prematurity. Although caffeine therapy has a benefit on gross motor skills in school-aged children, effects on neurobehavioral outcomes are not fully understood. We aimed to investigate effects of neonatal caffeine therapy in very low birth weight (500-1250 g) infants on neurobehavioral outcomes in 11-year-old participants of the Caffeine for Apnea of Prematurity trial. Thirteen academic hospitals in Canada, Australia, Great Britain, and Sweden participated in this part of the 11-year follow-up of the double-blind, randomized, placebo-controlled trial. Measures of general intelligence, attention, executive function, visuomotor integration and perception, and behavior were obtained in up to 870 children. The effects of caffeine therapy were assessed by using regression models. Neurobehavioral outcomes were generally similar for both the caffeine and placebo group. The caffeine group performed better than the placebo group in fine motor coordination (mean difference [MD] = 2.9; 95% confidence interval [CI]: 0.7 to 5.1; P = .01), visuomotor integration (MD = 1.8; 95% CI: 0.0 to 3.7; P prematurity improved visuomotor, visuoperceptual, and visuospatial abilities at age 11 years. General intelligence, attention, and behavior were not adversely affected by caffeine, which highlights the long-term safety of caffeine therapy for apnea of prematurity in very low birth weight neonates. Copyright © 2018 by the American Academy of Pediatrics.

Emergence, evolution and scaling of online social networks.

Science.gov (United States)

Wang, Le-Zhi; Huang, Zi-Gang; Rong, Zhi-Hai; Wang, Xiao-Fan; Lai, Ying-Cheng

2014-01-01

Online social networks have become increasingly ubiquitous and understanding their structural, dynamical, and scaling properties not only is of fundamental interest but also has a broad range of applications. Such networks can be extremely dynamic, generated almost instantaneously by, for example, breaking-news items. We investigate a common class of online social networks, the user-user retweeting networks, by analyzing the empirical data collected from Sina Weibo (a massive twitter-like microblogging social network in China) with respect to the topic of the 2011 Japan earthquake. We uncover a number of algebraic scaling relations governing the growth and structure of the network and develop a probabilistic model that captures the basic dynamical features of the system. The model is capable of reproducing all the empirical results. Our analysis not only reveals the basic mechanisms underlying the dynamics of the retweeting networks, but also provides general insights into the control of information spreading on such networks.

Emergence, evolution and scaling of online social networks.

Directory of Open Access Journals (Sweden)

Le-Zhi Wang

Full Text Available Online social networks have become increasingly ubiquitous and understanding their structural, dynamical, and scaling properties not only is of fundamental interest but also has a broad range of applications. Such networks can be extremely dynamic, generated almost instantaneously by, for example, breaking-news items. We investigate a common class of online social networks, the user-user retweeting networks, by analyzing the empirical data collected from Sina Weibo (a massive twitter-like microblogging social network in China with respect to the topic of the 2011 Japan earthquake. We uncover a number of algebraic scaling relations governing the growth and structure of the network and develop a probabilistic model that captures the basic dynamical features of the system. The model is capable of reproducing all the empirical results. Our analysis not only reveals the basic mechanisms underlying the dynamics of the retweeting networks, but also provides general insights into the control of information spreading on such networks.

Scaling properties of domain wall networks

International Nuclear Information System (INIS)

Leite, A. M. M.; Martins, C. J. A. P.

2011-01-01

We revisit the cosmological evolution of domain wall networks, taking advantage of recent improvements in computing power. We carry out high-resolution field theory simulations in two, three and four spatial dimensions to study the effects of dimensionality and damping on the evolution of the network. Our results are consistent with the expected scale-invariant evolution of the network, which suggests that previous hints of deviations from this behavior may have been due to the limited dynamical range of those simulations. We also use the results of very large (1024 3 ) simulations in three cosmological epochs to provide a calibration for the velocity-dependent one-scale model for domain walls: we numerically determine the two free model parameters to have the values c w =0.5±0.2 and k w =1.1±0.3.

Convergence speed of consensus problems over undirected scale-free networks

International Nuclear Information System (INIS)

Sun Wei; Dou Li-Hua

2010-01-01

Scale-free networks and consensus behaviour among multiple agents have both attracted much attention. To investigate the consensus speed over scale-free networks is the major topic of the present work. A novel method is developed to construct scale-free networks due to their remarkable power-law degree distributions, while preserving the diversity of network topologies. The time cost or iterations for networks to reach a certain level of consensus is discussed, considering the influence from power-law parameters. They are both demonstrated to be reversed power-law functions of the algebraic connectivity, which is viewed as a measurement on convergence speed of the consensus behaviour. The attempts of tuning power-law parameters may speed up the consensus procedure, but it could also make the network less robust over time delay at the same time. Large scale of simulations are supportive to the conclusions. (general)

Large-scale networks in engineering and life sciences

CERN Document Server

Findeisen, Rolf; Flockerzi, Dietrich; Reichl, Udo; Sundmacher, Kai

2014-01-01

This edited volume provides insights into and tools for the modeling, analysis, optimization, and control of large-scale networks in the life sciences and in engineering. Large-scale systems are often the result of networked interactions between a large number of subsystems, and their analysis and control are becoming increasingly important. The chapters of this book present the basic concepts and theoretical foundations of network theory and discuss its applications in different scientific areas such as biochemical reactions, chemical production processes, systems biology, electrical circuits, and mobile agents. The aim is to identify common concepts, to understand the underlying mathematical ideas, and to inspire discussions across the borders of the various disciplines.  The book originates from the interdisciplinary summer school “Large Scale Networks in Engineering and Life Sciences” hosted by the International Max Planck Research School Magdeburg, September 26-30, 2011, and will therefore be of int...

Neurobehavioral and Cardiovascular Effects of Potassium Cyanide Administered Orally to Mice.

Science.gov (United States)

Hawk, Michael A; Ritchie, Glenn D; Henderson, Kim A; Knostman, Katherine A B; Roche, Brian M; Ma, Zhenxu J; Matthews, Claire M; Sabourin, Carol L; Wakayama, Edward J; Sabourin, Patrick J

2016-09-01

The Food and Drug Administration Animal Rule requires evaluation of cardiovascular and central nervous system (CNS) effects of new therapeutics. To characterize an adult and juvenile mouse model, neurobehavioral and cardiovascular effects and pathology of a single sublethal but toxic, 8 mg/kg, oral dose of potassium cyanide (KCN) for up to 41 days postdosing were investigated. This study describes the short- and long-term sensory, motor, cognitive, and behavioral changes associated with oral dosing of a sublethal but toxic dose of KCN utilizing functional observation battery and Tier II CNS testing in adult and juvenile mice of both sexes. Selected tissues (histopathology) were evaluated for changes associated with KCN exposure with special attention to brain regions. Telemetry (adult mice only) was used to evaluate cardiovascular and temperature changes. Neurobehavioral capacity, sensorimotor responsivity or spontaneous locomotor activity, and rectal temperature were significantly reduced in adult and juvenile mice at 30 minutes post-8 mg/kg KCN dose. Immediate effects of cyanide included bradycardia, adverse electrocardiogram arrhythmic events, hypotension, and hypothermia with recovery by approximately 1 hour for blood pressure and heart rate effects and by 2 hours for body temperature. Lesions consistent with hypoxia, such as mild acute tubular necrosis in the kidneys corticomedullary junction, were the only histopathological findings and occurred at a very low incidence. The mouse KCN intoxication model indicates rapid and completely reversible effects in adult and juvenile mice following a single oral 8 mg/kg dose. Neurobehavioral and cardiovascular measurements can be used in this animal model as a trigger for treatment. © The Author(s) 2016.

Emergence of cooperation in non-scale-free networks

International Nuclear Information System (INIS)

Zhang, Yichao; Aziz-Alaoui, M A; Bertelle, Cyrille; Zhou, Shi; Wang, Wenting

2014-01-01

Evolutionary game theory is one of the key paradigms behind many scientific disciplines from science to engineering. Previous studies proposed a strategy updating mechanism, which successfully demonstrated that the scale-free network can provide a framework for the emergence of cooperation. Instead, individuals in random graphs and small-world networks do not favor cooperation under this updating rule. However, a recent empirical result shows the heterogeneous networks do not promote cooperation when humans play a prisoner’s dilemma. In this paper, we propose a strategy updating rule with payoff memory. We observe that the random graphs and small-world networks can provide even better frameworks for cooperation than the scale-free networks in this scenario. Our observations suggest that the degree heterogeneity may be neither a sufficient condition nor a necessary condition for the widespread cooperation in complex networks. Also, the topological structures are not sufficed to determine the level of cooperation in complex networks. (paper)

The Multi-Scale Network Landscape of Collaboration.

Science.gov (United States)

Bae, Arram; Park, Doheum; Ahn, Yong-Yeol; Park, Juyong

2016-01-01

Propelled by the increasing availability of large-scale high-quality data, advanced data modeling and analysis techniques are enabling many novel and significant scientific understanding of a wide range of complex social, natural, and technological systems. These developments also provide opportunities for studying cultural systems and phenomena--which can be said to refer to all products of human creativity and way of life. An important characteristic of a cultural product is that it does not exist in isolation from others, but forms an intricate web of connections on many levels. In the creation and dissemination of cultural products and artworks in particular, collaboration and communication of ideas play an essential role, which can be captured in the heterogeneous network of the creators and practitioners of art. In this paper we propose novel methods to analyze and uncover meaningful patterns from such a network using the network of western classical musicians constructed from a large-scale comprehensive Compact Disc recordings data. We characterize the complex patterns in the network landscape of collaboration between musicians across multiple scales ranging from the macroscopic to the mesoscopic and microscopic that represent the diversity of cultural styles and the individuality of the artists.

Aggregated Representation of Distribution Networks for Large-Scale Transmission Network Simulations

DEFF Research Database (Denmark)

Göksu, Ömer; Altin, Müfit; Sørensen, Poul Ejnar

2014-01-01

As a common practice of large-scale transmission network analysis the distribution networks have been represented as aggregated loads. However, with increasing share of distributed generation, especially wind and solar power, in the distribution networks, it became necessary to include...... the distributed generation within those analysis. In this paper a practical methodology to obtain aggregated behaviour of the distributed generation is proposed. The methodology, which is based on the use of the IEC standard wind turbine models, is applied on a benchmark distribution network via simulations....

Spatiotemporal Scaling Effect on Rainfall Network Design Using Entropy

Directory of Open Access Journals (Sweden)

Chiang Wei

2014-08-01

Full Text Available Because of high variation in mountainous areas, rainfall data at different spatiotemporal scales may yield potential uncertainty for network design. However, few studies focus on the scaling effect on both the spatial and the temporal scale. By calculating the maximum joint entropy of hourly typhoon events, monthly, six dry and wet months and annual rainfall between 1992 and 2012 for 1-, 3-, and 5-km grids, the relocated candidate rain gauges in the National Taiwan University Experimental Forest of Central Taiwan are prioritized. The results show: (1 the network exhibits different locations for first prioritized candidate rain gauges for different spatiotemporal scales; (2 the effect of spatial scales is insignificant compared to temporal scales; and (3 a smaller number and a lower percentage of required stations (PRS reach stable joint entropy for a long duration at finer spatial scale. Prioritized candidate rain gauges provide key reference points for adjusting the network to capture more accurate information and minimize redundancy.

Some scale-free networks could be robust under selective node attacks

Science.gov (United States)

Zheng, Bojin; Huang, Dan; Li, Deyi; Chen, Guisheng; Lan, Wenfei

2011-04-01

It is a mainstream idea that scale-free network would be fragile under the selective attacks. Internet is a typical scale-free network in the real world, but it never collapses under the selective attacks of computer viruses and hackers. This phenomenon is different from the deduction of the idea above because this idea assumes the same cost to delete an arbitrary node. Hence this paper discusses the behaviors of the scale-free network under the selective node attack with different cost. Through the experiments on five complex networks, we show that the scale-free network is possibly robust under the selective node attacks; furthermore, the more compact the network is, and the larger the average degree is, then the more robust the network is; with the same average degrees, the more compact the network is, the more robust the network is. This result would enrich the theory of the invulnerability of the network, and can be used to build robust social, technological and biological networks, and also has the potential to find the target of drugs.

Neurobehavioral effects among inhabitants around mobile phone base stations.

Science.gov (United States)

Abdel-Rassoul, G; El-Fateh, O Abou; Salem, M Abou; Michael, A; Farahat, F; El-Batanouny, M; Salem, E

2007-03-01

There is a general concern on the possible hazardous health effects of exposure to radiofrequency electromagnetic radiations (RFR) emitted from mobile phone base station antennas on the human nervous system. To identify the possible neurobehavioral deficits among inhabitants living nearby mobile phone base stations. A cross-sectional study was conducted on (85) inhabitants living nearby the first mobile phone station antenna in Menoufiya governorate, Egypt, 37 are living in a building under the station antenna while 48 opposite the station. A control group (80) participants were matched with the exposed for age, sex, occupation and educational level. All participants completed a structured questionnaire containing: personal, educational and medical histories; general and neurological examinations; neurobehavioral test battery (NBTB) [involving tests for visuomotor speed, problem solving, attention and memory]; in addition to Eysenck personality questionnaire (EPQ). The prevalence of neuropsychiatric complaints as headache (23.5%), memory changes (28.2%), dizziness (18.8%), tremors (9.4%), depressive symptoms (21.7%), and sleep disturbance (23.5%) were significantly higher among exposed inhabitants than controls: (10%), (5%), (5%), (0%), (8.8%) and (10%), respectively (Pstation exhibited a lower performance in the problem solving test (block design) than those under the station. All inhabitants exhibited a better performance in the two tests of visuomotor speed (Digit symbol and Trailmaking B) and one test of attention (Trailmaking A) than controls. The last available measures of RFR emitted from the first mobile phone base station antennas in Menoufiya governorate were less than the allowable standard level. Inhabitants living nearby mobile phone base stations are at risk for developing neuropsychiatric problems and some changes in the performance of neurobehavioral functions either by facilitation or inhibition. So, revision of standard guidelines for public

Neurobehavioral outcomes of school-age children born preterm: a preliminary study in the Arabic community

Directory of Open Access Journals (Sweden)

Mohammed M.J. Alqahtani

2016-07-01

Full Text Available Introduction: Preterm survivors from the neonatal intensive care unit (NICU are considered as high risk group for some neurobehavioral impairments such as cognitive disabilities, developmental delays, social/emotional limitations, attention-deficit/hyperactivity disorder (ADHD, and academic difficulties. Objective: The current study aimed to investigate the neurobehavioral outcome of premature infants in Saudi Arabia at the school age.Methods: At the school age, preterm children (range 23-29 weeks or ≤ 1.52 kg born from April, 2006 through September, 2008, and who were admitted following birth to a NICU, were evaluated with several neurobehavioral tools. Results: This study includes 53 preterm children, who were followed up at the chronological age that ranged from 6.4-8.0 years. The results of the neurobehavioral assessments showed in general normal social adaptive levels and cognitive abilities, with mean total score of about 91.0 and 90.0, respectively. The prevalence of ADHD among preterm children was high, with result of 34.0% for the inattentive type and 11.3% for the hyperactive/impulsive type. None of the preterm children repeats a grade, but 22.6% utilize a form of special educational supports. Some of the preterm children showed poor school performance in reading skills, writing skills and mathematics skills, with percentages of 26.4%, 28.3% and 15.1%, respectively.Conclusions: The present results emphasize that preterm children are a group of high-risk children who need regular follow-up to track the developmental conditions and to provide the early developmental intervention for optimal outcome.

The relationships between pesticide metabolites and neurobehavioral test performance in the third National Health and Nutrition Examination Survey.

Science.gov (United States)

Krieg, Edward F

2013-01-01

Regression analysis was used to estimate and test for relationships between urinary pesticide metabolites and neurobehavioral test performance in adults, 20 to 59 years old, participating in the third National Health and Nutrition Examination Survey. The 12 pesticide metabolites included 2 naphthols, 8 phenols, a phenoxyacetic acid, and a pyridinol. The 3 neurobehavioral tests included in the survey were simple reaction time, symbol-digit substitution, and serial digit learning. As the 2,4-dichlorophenol, 2,5-dichlorophenol, and the pentachlorophenol concentrations increased, performance on the serial digit learning test improved. As the 2,5-dichlorophenol concentration increased, performance on the symbol-digit substitution test improved. At low concentrations, the parent compounds of these metabolites may act at acetylcholine and γ-aminobutyric acid synapses in the central nervous system to improve neurobehavioral test performance.

Salience Network and Depressive Severities in Parkinson's Disease with Mild Cognitive Impairment: A Structural Covariance Network Analysis.

Science.gov (United States)

Chang, Ya-Ting; Lu, Cheng-Hsien; Wu, Ming-Kung; Hsu, Shih-Wei; Huang, Chi-Wei; Chang, Wen-Neng; Lien, Chia-Yi; Lee, Jun-Jun; Chang, Chiung-Chih

2017-01-01

Purpose: In Parkinson's disease with mild cognitive impairment (PD-MCI), we investigated the clinical significance of salience network (SN) in depression and cognitive performance. Methods: Seventy seven PD-MCI patients that fulfilled multi-domain and non-amnestic subtype were included. Gray matter structural covariance networks were constructed by 3D T1-magnetic resonance imaging and seed based analysis. The patients were divided into two groups by psychiatric interviews and screening of Geriatric Depression Scale (GDS): PD-MCI with depression (PD-MCI-D) or without depression (PD-MCI-ND). The seed or peak cluster volume, or the significant differences in the regression slopes in each seed-peak cluster correlation, were used to evaluate the significance with the neurobehavioral scores. Results: This study is the first to demonstrate that the PD-MCI-ND group presented a larger number of voxels of structural covariance in SN than the PD-MCI-D group. The right fronto-insular seed volumes and the peak cluster of left lingual gyrus showed significant inverse correlation with the Geriatric Depression Scale (GDS; r = -0.231, P = 0.046). Conclusions: This study is the first to validate the clinical significance of the SN in PD-MCI-D. The right insular seed value and the SN correlated with the severity of depression in PD-MCI.

Childhood Fears, Neurobehavioral Functioning and Behavior Problems in School-Age Children

Science.gov (United States)

Kushnir, Jonathan; Sadeh, Avi

2010-01-01

The objective is to examine underlying associations between childhood fears, behavior problems and neurobehavioral functioning (NBF) in school-age children. Healthy, regular school children (N = 135), from second, fourth and sixth grade classes were assessed. Data regarding children's fears and behavioral problems were obtained with the Revised…

A Network Contention Model for the Extreme-scale Simulator

Energy Technology Data Exchange (ETDEWEB)

Engelmann, Christian [ORNL; Naughton III, Thomas J [ORNL

2015-01-01

The Extreme-scale Simulator (xSim) is a performance investigation toolkit for high-performance computing (HPC) hardware/software co-design. It permits running a HPC application with millions of concurrent execution threads, while observing its performance in a simulated extreme-scale system. This paper details a newly developed network modeling feature for xSim, eliminating the shortcomings of the existing network modeling capabilities. The approach takes a different path for implementing network contention and bandwidth capacity modeling using a less synchronous and accurate enough model design. With the new network modeling feature, xSim is able to simulate on-chip and on-node networks with reasonable accuracy and overheads.

THE BUILDUP OF A SCALE-FREE PHOTOSPHERIC MAGNETIC NETWORK

Energy Technology Data Exchange (ETDEWEB)

Thibault, K.; Charbonneau, P. [Departement de Physique, Universite de Montreal, 2900 Edouard-Montpetit, Montreal, Quebec H3C 3J7 (Canada); Crouch, A. D., E-mail: kim@astro.umontreal.ca-a, E-mail: paulchar@astro.umontreal.ca-b, E-mail: ash@cora.nwra.com-c [CORA/NWRA, 3380 Mitchell Lane, Boulder, CO 80301 (United States)

2012-10-01

We use a global Monte Carlo simulation of the formation of the solar photospheric magnetic network to investigate the origin of the scale invariance characterizing magnetic flux concentrations visible on high-resolution magnetograms. The simulations include spatially and temporally homogeneous injection of small-scale magnetic elements over the whole photosphere, as well as localized episodic injection associated with the emergence and decay of active regions. Network elements form in response to cumulative pairwise aggregation or cancellation of magnetic elements, undergoing a random walk on the sphere and advected on large spatial scales by differential rotation and a poleward meridional flow. The resulting size distribution of simulated network elements is in very good agreement with observational inferences. We find that the fractal index and size distribution of network elements are determined primarily by these post-emergence surface mechanisms, and carry little or no memory of the scales at which magnetic flux is injected in the simulation. Implications for models of dynamo action in the Sun are briefly discussed.

THE BUILDUP OF A SCALE-FREE PHOTOSPHERIC MAGNETIC NETWORK

International Nuclear Information System (INIS)

Thibault, K.; Charbonneau, P.; Crouch, A. D.

2012-01-01

We use a global Monte Carlo simulation of the formation of the solar photospheric magnetic network to investigate the origin of the scale invariance characterizing magnetic flux concentrations visible on high-resolution magnetograms. The simulations include spatially and temporally homogeneous injection of small-scale magnetic elements over the whole photosphere, as well as localized episodic injection associated with the emergence and decay of active regions. Network elements form in response to cumulative pairwise aggregation or cancellation of magnetic elements, undergoing a random walk on the sphere and advected on large spatial scales by differential rotation and a poleward meridional flow. The resulting size distribution of simulated network elements is in very good agreement with observational inferences. We find that the fractal index and size distribution of network elements are determined primarily by these post-emergence surface mechanisms, and carry little or no memory of the scales at which magnetic flux is injected in the simulation. Implications for models of dynamo action in the Sun are briefly discussed.

The Buildup of a Scale-free Photospheric Magnetic Network

Science.gov (United States)

Thibault, K.; Charbonneau, P.; Crouch, A. D.

2012-10-01

We use a global Monte Carlo simulation of the formation of the solar photospheric magnetic network to investigate the origin of the scale invariance characterizing magnetic flux concentrations visible on high-resolution magnetograms. The simulations include spatially and temporally homogeneous injection of small-scale magnetic elements over the whole photosphere, as well as localized episodic injection associated with the emergence and decay of active regions. Network elements form in response to cumulative pairwise aggregation or cancellation of magnetic elements, undergoing a random walk on the sphere and advected on large spatial scales by differential rotation and a poleward meridional flow. The resulting size distribution of simulated network elements is in very good agreement with observational inferences. We find that the fractal index and size distribution of network elements are determined primarily by these post-emergence surface mechanisms, and carry little or no memory of the scales at which magnetic flux is injected in the simulation. Implications for models of dynamo action in the Sun are briefly discussed.

Epigenetic Mechanisms in Developmental Alcohol-Induced Neurobehavioral Deficits

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Balapal S. Basavarajappa

2016-04-01

Full Text Available Alcohol consumption during pregnancy and its damaging consequences on the developing infant brain are significant public health, social, and economic issues. The major distinctive features of prenatal alcohol exposure in humans are cognitive and behavioral dysfunction due to damage to the central nervous system (CNS, which results in a continuum of disarray that is collectively called fetal alcohol spectrum disorder (FASD. Many rodent models have been developed to understand the mechanisms of and to reproduce the human FASD phenotypes. These animal FASD studies have provided several molecular pathways that are likely responsible for the neurobehavioral abnormalities that are associated with prenatal alcohol exposure of the developing CNS. Recently, many laboratories have identified several immediate, as well as long-lasting, epigenetic modifications of DNA methylation, DNA-associated histone proteins and microRNA (miRNA biogenesis by using a variety of epigenetic approaches in rodent FASD models. Because DNA methylation patterns, DNA-associated histone protein modifications and miRNA-regulated gene expression are crucial for synaptic plasticity and learning and memory, they can therefore offer an answer to many of the neurobehavioral abnormalities that are found in FASD. In this review, we briefly discuss the current literature of DNA methylation, DNA-associated histone proteins modification and miRNA and review recent developments concerning epigenetic changes in FASD.

Selective vulnerability related to aging in large-scale resting brain networks.

Science.gov (United States)

Zhang, Hong-Ying; Chen, Wen-Xin; Jiao, Yun; Xu, Yao; Zhang, Xiang-Rong; Wu, Jing-Tao

2014-01-01

Normal aging is associated with cognitive decline. Evidence indicates that large-scale brain networks are affected by aging; however, it has not been established whether aging has equivalent effects on specific large-scale networks. In the present study, 40 healthy subjects including 22 older (aged 60-80 years) and 18 younger (aged 22-33 years) adults underwent resting-state functional MRI scanning. Four canonical resting-state networks, including the default mode network (DMN), executive control network (ECN), dorsal attention network (DAN) and salience network, were extracted, and the functional connectivities in these canonical networks were compared between the younger and older groups. We found distinct, disruptive alterations present in the large-scale aging-related resting brain networks: the ECN was affected the most, followed by the DAN. However, the DMN and salience networks showed limited functional connectivity disruption. The visual network served as a control and was similarly preserved in both groups. Our findings suggest that the aged brain is characterized by selective vulnerability in large-scale brain networks. These results could help improve our understanding of the mechanism of degeneration in the aging brain. Additional work is warranted to determine whether selective alterations in the intrinsic networks are related to impairments in behavioral performance.

Scaling and percolation in the small-world network model

Energy Technology Data Exchange (ETDEWEB)

Newman, M. E. J. [Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, New Mexico 87501 (United States); Watts, D. J. [Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, New Mexico 87501 (United States)

1999-12-01

In this paper we study the small-world network model of Watts and Strogatz, which mimics some aspects of the structure of networks of social interactions. We argue that there is one nontrivial length-scale in the model, analogous to the correlation length in other systems, which is well-defined in the limit of infinite system size and which diverges continuously as the randomness in the network tends to zero, giving a normal critical point in this limit. This length-scale governs the crossover from large- to small-world behavior in the model, as well as the number of vertices in a neighborhood of given radius on the network. We derive the value of the single critical exponent controlling behavior in the critical region and the finite size scaling form for the average vertex-vertex distance on the network, and, using series expansion and Pade approximants, find an approximate analytic form for the scaling function. We calculate the effective dimension of small-world graphs and show that this dimension varies as a function of the length-scale on which it is measured, in a manner reminiscent of multifractals. We also study the problem of site percolation on small-world networks as a simple model of disease propagation, and derive an approximate expression for the percolation probability at which a giant component of connected vertices first forms (in epidemiological terms, the point at which an epidemic occurs). The typical cluster radius satisfies the expected finite size scaling form with a cluster size exponent close to that for a random graph. All our analytic results are confirmed by extensive numerical simulations of the model. (c) 1999 The American Physical Society.

Scaling and percolation in the small-world network model

International Nuclear Information System (INIS)

Newman, M. E. J.; Watts, D. J.

1999-01-01

In this paper we study the small-world network model of Watts and Strogatz, which mimics some aspects of the structure of networks of social interactions. We argue that there is one nontrivial length-scale in the model, analogous to the correlation length in other systems, which is well-defined in the limit of infinite system size and which diverges continuously as the randomness in the network tends to zero, giving a normal critical point in this limit. This length-scale governs the crossover from large- to small-world behavior in the model, as well as the number of vertices in a neighborhood of given radius on the network. We derive the value of the single critical exponent controlling behavior in the critical region and the finite size scaling form for the average vertex-vertex distance on the network, and, using series expansion and Pade approximants, find an approximate analytic form for the scaling function. We calculate the effective dimension of small-world graphs and show that this dimension varies as a function of the length-scale on which it is measured, in a manner reminiscent of multifractals. We also study the problem of site percolation on small-world networks as a simple model of disease propagation, and derive an approximate expression for the percolation probability at which a giant component of connected vertices first forms (in epidemiological terms, the point at which an epidemic occurs). The typical cluster radius satisfies the expected finite size scaling form with a cluster size exponent close to that for a random graph. All our analytic results are confirmed by extensive numerical simulations of the model. (c) 1999 The American Physical Society

The Multi-Scale Network Landscape of Collaboration.

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Arram Bae

Full Text Available Propelled by the increasing availability of large-scale high-quality data, advanced data modeling and analysis techniques are enabling many novel and significant scientific understanding of a wide range of complex social, natural, and technological systems. These developments also provide opportunities for studying cultural systems and phenomena--which can be said to refer to all products of human creativity and way of life. An important characteristic of a cultural product is that it does not exist in isolation from others, but forms an intricate web of connections on many levels. In the creation and dissemination of cultural products and artworks in particular, collaboration and communication of ideas play an essential role, which can be captured in the heterogeneous network of the creators and practitioners of art. In this paper we propose novel methods to analyze and uncover meaningful patterns from such a network using the network of western classical musicians constructed from a large-scale comprehensive Compact Disc recordings data. We characterize the complex patterns in the network landscape of collaboration between musicians across multiple scales ranging from the macroscopic to the mesoscopic and microscopic that represent the diversity of cultural styles and the individuality of the artists.

Predicting the neurobehavioral side effects of dexamethasone in pediatric acute lymphoblastic leukemia

NARCIS (Netherlands)

Warris, Lidewij T.; van den Akker, Erica L. T.; Aarsen, Femke K.; Bierings, Marc B.; van den Bos, Cor; Tissing, Wim J. E.; Sassen, Sebastiaan D. T.; Veening, Margreet A.; Zwaan, Christian M.; Pieters, Rob; van den Heuvel-Eibrink, Marry M.

2016-01-01

Although dexamethasone is an effective treatment for acute lymphoblastic leukemia (ALL), it can induce a variety of serious neurobehavioral side effects. We hypothesized that these side effects are influenced by glucocorticoid sensitivity at the tissue level. We therefore prospectively studied

Triadic closure dynamics drives scaling laws in social multiplex networks

International Nuclear Information System (INIS)

Klimek, Peter; Thurner, Stefan

2013-01-01

Social networks exhibit scaling laws for several structural characteristics, such as degree distribution, scaling of the attachment kernel and clustering coefficients as a function of node degree. A detailed understanding if and how these scaling laws are inter-related is missing so far, let alone whether they can be understood through a common, dynamical principle. We propose a simple model for stationary network formation and show that the three mentioned scaling relations follow as natural consequences of triadic closure. The validity of the model is tested on multiplex data from a well-studied massive multiplayer online game. We find that the three scaling exponents observed in the multiplex data for the friendship, communication and trading networks can simultaneously be explained by the model. These results suggest that triadic closure could be identified as one of the fundamental dynamical principles in social multiplex network formation. (paper)

A Topology Visualization Early Warning Distribution Algorithm for Large-Scale Network Security Incidents

Directory of Open Access Journals (Sweden)

Hui He

2013-01-01

Full Text Available It is of great significance to research the early warning system for large-scale network security incidents. It can improve the network system’s emergency response capabilities, alleviate the cyber attacks’ damage, and strengthen the system’s counterattack ability. A comprehensive early warning system is presented in this paper, which combines active measurement and anomaly detection. The key visualization algorithm and technology of the system are mainly discussed. The large-scale network system’s plane visualization is realized based on the divide and conquer thought. First, the topology of the large-scale network is divided into some small-scale networks by the MLkP/CR algorithm. Second, the sub graph plane visualization algorithm is applied to each small-scale network. Finally, the small-scale networks’ topologies are combined into a topology based on the automatic distribution algorithm of force analysis. As the algorithm transforms the large-scale network topology plane visualization problem into a series of small-scale network topology plane visualization and distribution problems, it has higher parallelism and is able to handle the display of ultra-large-scale network topology.

Comparative Analysis of Different Protocols to Manage Large Scale Networks

OpenAIRE

Anil Rao Pimplapure; Dr Jayant Dubey; Prashant Sen

2013-01-01

In recent year the numbers, complexity and size is increased in Large Scale Network. The best example of Large Scale Network is Internet, and recently once are Data-centers in Cloud Environment. In this process, involvement of several management tasks such as traffic monitoring, security and performance optimization is big task for Network Administrator. This research reports study the different protocols i.e. conventional protocols like Simple Network Management Protocol and newly Gossip bas...

Electroacupuncture improves neurobehavioral function and brain injury in rat model of intracerebral hemorrhage.

Science.gov (United States)

Zhu, Yan; Deng, Li; Tang, Huajun; Gao, Xiaoqing; Wang, Youhua; Guo, Kan; Kong, Jiming; Yang, Chaoxian

2017-05-01

Acupuncture has been widely used as a treatment for stroke in China for a long time. Recently, studies have demonstrated that electroacupuncture (EA) can accelerate intracerebral hemorrhage (ICH)-induced angiogenesis in rats. In the present study, we investigated the effect of EA on neurobehavioral function and brain injury in ICH rats. ICH was induced by stereotactic injection of collagenase type I and heparin into the right caudate putamen. Adult ICH rats were randomly divided into the following three groups: model control group (MC), EA at non-acupoint points group (non-acupoint EA) and EA at Baihui and Dazhui acupoints group (EA). The neurobehavioral deficits of ICH rats were assessed by modified neurological severity score (mNSS) and gait analysis. The hemorrhage volume and glucose metabolism of hemorrhagic foci were detected by PET/CT. The expression levels of MBP, NSE and S100-B proteins in serum were tested by ELISA. The histopathological features were examined by haematoxylin-eosin (H&E) staining. Apoptosis-associated proteins in the perihematomal region were observed by immunohistochemistry. EA treatment significantly promoted the recovery of neurobehavioral function in ICH rats. Hemorrhage volume reduced in EA group at day 14 when compared with MC and non-acupoint EA groups. ELISA showed that the levels of MBP, NSE and S100-B in serum were all down-regulated by EA treatment. The brain tissue of ICH rat in the EA group was more intact and compact than that in the MC and non-acupoint groups. In the perihematomal regions, the expression of Bcl-2 protein increased and expressions of Caspase-3 and Bax proteins decreased in the EA group vs MC and non-acupoint EA groups. Our data suggest that EA treatment can improve neurobehavioral function and brain injury, which were likely connected with the absorption of hematoma and regulation of apoptosis-related proteins. Copyright © 2017 Elsevier Inc. All rights reserved.

A general model for metabolic scaling in self-similar asymmetric networks.

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Alexander Byers Brummer

2017-03-01

Full Text Available How a particular attribute of an organism changes or scales with its body size is known as an allometry. Biological allometries, such as metabolic scaling, have been hypothesized to result from selection to maximize how vascular networks fill space yet minimize internal transport distances and resistances. The West, Brown, Enquist (WBE model argues that these two principles (space-filling and energy minimization are (i general principles underlying the evolution of the diversity of biological networks across plants and animals and (ii can be used to predict how the resulting geometry of biological networks then governs their allometric scaling. Perhaps the most central biological allometry is how metabolic rate scales with body size. A core assumption of the WBE model is that networks are symmetric with respect to their geometric properties. That is, any two given branches within the same generation in the network are assumed to have identical lengths and radii. However, biological networks are rarely if ever symmetric. An open question is: Does incorporating asymmetric branching change or influence the predictions of the WBE model? We derive a general network model that relaxes the symmetric assumption and define two classes of asymmetrically bifurcating networks. We show that asymmetric branching can be incorporated into the WBE model. This asymmetric version of the WBE model results in several theoretical predictions for the structure, physiology, and metabolism of organisms, specifically in the case for the cardiovascular system. We show how network asymmetry can now be incorporated in the many allometric scaling relationships via total network volume. Most importantly, we show that the 3/4 metabolic scaling exponent from Kleiber's Law can still be attained within many asymmetric networks.

Mercury-induced epigenetic transgenerational inheritance of abnormal neurobehavior is correlated with sperm epimutations in zebrafish.

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Michael J Carvan

Full Text Available Methylmercury (MeHg is a ubiquitous environmental neurotoxicant, with human exposures predominantly resulting from fish consumption. Developmental exposure of zebrafish to MeHg is known to alter their neurobehavior. The current study investigated the direct exposure and transgenerational effects of MeHg, at tissue doses similar to those detected in exposed human populations, on sperm epimutations (i.e., differential DNA methylation regions [DMRs] and neurobehavior (i.e., visual startle and spontaneous locomotion in zebrafish, an established human health model. F0 generation embryos were exposed to MeHg (0, 1, 3, 10, 30, and 100 nM for 24 hours ex vivo. F0 generation control and MeHg-exposed lineages were reared to adults and bred to yield the F1 generation, which was subsequently bred to the F2 generation. Direct exposure (F0 generation and transgenerational actions (F2 generation were then evaluated. Hyperactivity and visual deficit were observed in the unexposed descendants (F2 generation of the MeHg-exposed lineage compared to control. An increase in F2 generation sperm epimutations was observed relative to the F0 generation. Investigation of the DMRs in the F2 generation MeHg-exposed lineage sperm revealed associated genes in the neuroactive ligand-receptor interaction and actin-cytoskeleton pathways being effected, which correlate to the observed neurobehavioral phenotypes. Developmental MeHg-induced epigenetic transgenerational inheritance of abnormal neurobehavior is correlated with sperm epimutations in F2 generation adult zebrafish. Therefore, mercury can promote the epigenetic transgenerational inheritance of disease in zebrafish, which significantly impacts its environmental health considerations in all species including humans.

Piperine Augments the Protective Effect of Curcumin Against Lipopolysaccharide-Induced Neurobehavioral and Neurochemical Deficits in Mice.

Science.gov (United States)

Jangra, Ashok; Kwatra, Mohit; Singh, Tavleen; Pant, Rajat; Kushwah, Pawan; Sharma, Yogita; Saroha, Babita; Datusalia, Ashok Kumar; Bezbaruah, Babul Kumar

2016-06-01

The aim of the present study was to investigate the protective effects of curcumin alone and in combination with piperine against lipopolysaccharide (LPS)-induced neurobehavioral and neurochemical deficits in the mice hippocampus. Mice were treated with curcumin (100, 200, and 400 mg/kg, p.o.) and piperine (20 mg/kg, p.o.) for 7 days followed by LPS (0.83 mg/kg, i.p.) administration. Animals exhibited anxiety and depressive-like phenotype after 3 and 24 h of LPS exposure, respectively. LPS administration increased the oxido-nitrosative stress as evident by elevated levels of malondialdehyde, nitrite, and depletion of glutathione level in the hippocampus. Furthermore, we found raised level of pro-inflammatory cytokines (IL-1β and TNF-α) in the hippocampus of LPS-treated mice. Pretreatment with curcumin alleviated LPS-induced neurobehavioral and neurochemical deficits. Furthermore, co-administration of curcumin with piperine significantly potentiated the neuroprotective effect of curcumin. These results demonstrate that piperine enhanced the neuroprotective effect of curcumin against LPS-induced neurobehavioral and neurochemical deficits.

Serum neuron-specific enolase, biogenic amino-acids and neurobehavioral function in lead-exposed workers from lead-acid battery manufacturing process.

Science.gov (United States)

Ravibabu, K; Barman, T; Rajmohan, H R

2015-01-01

The interaction between serum neuron-specific enolase (NSE), biogenic amino-acids and neurobehavioral function with blood lead levels in workers exposed to lead form lead-acid battery manufacturing process was not studied. To evaluate serum NSE and biogenic amino-acids (dopamine and serotonin) levels, and neurobehavioral performance among workers exposed to lead from lead-acid storage battery plant, and its relation with blood lead levels (BLLs). In a cross-sectional study, we performed biochemical and neurobehavioral function tests on 146 workers exposed to lead from lead-acid battery manufacturing process. BLLs were assessed by an atomic absorption spectrophotometer. Serum NSE, dopamine and serotonin were measured by ELISA. Neurobehavioral functions were assessed by CDC-recommended tests---simple reaction time (SRT), symbol digit substitution test (SDST), and serial digit learning test (SDLT). There was a significant correlation (r 0.199, pSDLT and SRT had also a significant positive correlation (r 0.238, p<0.01). NSE had a negative correlation (r -0.194, p<0.05) with serotonin level. Multiple linear regression analysis revealed that both SRT and SDST had positive significant associations with BLL. SRT also had a positive significant association with age. Serum NSE cannot be used as a marker for BLL. The only domain of neurobehavioral function tests that is affected by increased BLL in workers of lead-acid battery manufacturing process is that of the "attention and perception" (SDST).

Fractal properties and small-scale structure of cosmic string networks

International Nuclear Information System (INIS)

Martins, C.J.A.P.; Shellard, E.P.S.

2006-01-01

We present results from a detailed numerical study of the small-scale and loop production properties of cosmic string networks, based on the largest and highest resolution string simulations to date. We investigate the nontrivial fractal properties of cosmic strings, in particular, the fractal dimension and renormalized string mass per unit length, and we also study velocity correlations. We demonstrate important differences between string networks in flat (Minkowski) spacetime and the two very similar expanding cases. For high resolution matter era network simulations, we provide strong evidence that small-scale structure has converged to 'scaling' on all dynamical length scales, without the need for other radiative damping mechanisms. We also discuss preliminary evidence that the dominant loop production size is also approaching scaling

Organizational topology of brain and its relationship to ADHD in adolescents with d-transposition of the great arteries.

Science.gov (United States)

Schmithorst, Vincent J; Panigrahy, Ashok; Gaynor, J William; Watson, Christopher G; Lee, Vince; Bellinger, David C; Rivkin, Michael J; Newburger, Jane W

2016-08-01

Little is currently known about the impact of congenital heart disease (CHD) on the organization of large-scale brain networks in relation to neurobehavioral outcome. We investigated whether CHD might impact ADHD symptoms via changes in brain structural network topology in a cohort of adolescents with d-transposition of the great arteries (d-TGA) repaired with the arterial switch operation in early infancy and referent subjects. We also explored whether these effects might be modified by apolipoprotein E (APOE) genotype, as the APOE ε2 allele has been associated with worse neurodevelopmental outcomes after repair of d-TGA in infancy. We applied graph analysis techniques to diffusion tensor imaging (DTI) data obtained from 47 d-TGA adolescents and 29 healthy referents to construct measures of structural topology at the global and regional levels. We developed statistical mediation models revealing the respective contributions of d-TGA, APOE genotype, and structural network topology on ADHD outcome as measured by the Connors ADHD/DSM-IV Scales (CADS). Changes in overall network connectivity, integration, and segregation mediated worse ADHD outcomes in d-TGA patients compared to healthy referents; these changes were predominantly in the left and right intrahemispheric regional subnetworks. Exploratory analysis revealed that network topology also mediated detrimental effects of the APOE ε4 allele but improved neurobehavioral outcomes for the APOE ε2 allele. Our results suggest that disruption of organization of large-scale networks may contribute to neurobehavioral dysfunction in adolescents with CHD and that this effect may interact with APOE genotype.

No effects of power line frequency extremely low frequency electromagnetic field exposure on selected neurobehavior tests of workers inspecting transformers and distribution line stations versus controls.

Science.gov (United States)

Li, Li; Xiong, De-fu; Liu, Jia-wen; Li, Zi-xin; Zeng, Guang-cheng; Li, Hua-liang

2014-03-01

We aimed to evaluate the interference of 50 Hz extremely low frequency electromagnetic field (ELF-EMF) occupational exposure on the neurobehavior tests of workers performing tour-inspection close to transformers and distribution power lines. Occupational short-term "spot" measurements were carried out. 310 inspection workers and 300 logistics staff were selected as exposure and control. The neurobehavior tests were performed through computer-based neurobehavior evaluation system, including mental arithmetic, curve coincide, simple visual reaction time, visual retention, auditory digit span and pursuit aiming. In 500 kV areas electric field intensity at 71.98% of total measured 590 spots were above 5 kV/m (national occupational standard), while in 220 kV areas electric field intensity at 15.69% of total 701 spots were above 5 kV/m. Magnetic field flux density at all the spots was below 1,000 μT (ICNIRP occupational standard). The neurobehavior score changes showed no statistical significance. Results of neurobehavior tests among different age, seniority groups showed no significant changes. Neurobehavior changes caused by daily repeated ELF-EMF exposure were not observed in the current study.

Experimental performance evaluation of software defined networking (SDN) based data communication networks for large scale flexi-grid optical networks.

Science.gov (United States)

Zhao, Yongli; He, Ruiying; Chen, Haoran; Zhang, Jie; Ji, Yuefeng; Zheng, Haomian; Lin, Yi; Wang, Xinbo

2014-04-21

Software defined networking (SDN) has become the focus in the current information and communication technology area because of its flexibility and programmability. It has been introduced into various network scenarios, such as datacenter networks, carrier networks, and wireless networks. Optical transport network is also regarded as an important application scenario for SDN, which is adopted as the enabling technology of data communication networks (DCN) instead of general multi-protocol label switching (GMPLS). However, the practical performance of SDN based DCN for large scale optical networks, which is very important for the technology selection in the future optical network deployment, has not been evaluated up to now. In this paper we have built a large scale flexi-grid optical network testbed with 1000 virtual optical transport nodes to evaluate the performance of SDN based DCN, including network scalability, DCN bandwidth limitation, and restoration time. A series of network performance parameters including blocking probability, bandwidth utilization, average lightpath provisioning time, and failure restoration time have been demonstrated under various network environments, such as with different traffic loads and different DCN bandwidths. The demonstration in this work can be taken as a proof for the future network deployment.

Neurobehavioral effects among subjects exposed to high static and gradient magnetic fields from a 1.5 Tesla magnetic resonance imaging system--a case-crossover pilot study.

Science.gov (United States)

de Vocht, Frank; van-Wendel-de-Joode, Berna; Engels, Hans; Kromhout, Hans

2003-10-01

The interactive use of magnetic resonance imaging (MRI) techniques is increasing in operating theaters. A study was performed on 17 male company volunteers to assess the neurobehavioral effects of exposure to magnetic fields from a 1.5 Tesla MRI system. The subjects' neurobehavioral performances on a neurobehavioral test battery were compared in four 1-hr sessions with and without exposure to magnetic fields, and with and without additional movements. Adverse effects were found for hand coordination (-4%, P Tesla MRI system may lead to neurobehavioral effects. Further research is recommended, especially in members of operating teams using interactive MRI systems. Copyright 2003 Wiley-Liss, Inc.

Mindfulness Training among Individuals with Parkinson’s Disease: Neurobehavioral Effects

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Barbara Pickut

2015-01-01

Full Text Available Objective. To investigate possible neurobehavioral changes secondary to a mindfulness based intervention (MBI training for individuals living with Parkinson’s disease (PD. Background. In the context of complementary medicine, MBIs are increasingly being used for stress reduction and in patient populations coping with chronic illness. The use of alternative and complementary medicine may be higher in patients with chronic conditions such as PD. However, behavioral effects of mindfulness training in PD have not yet been reported in the literature and this points to an unmet need and warrants further examination. Methods. A total of 27 out of 30 PD patients completed a randomized controlled longitudinal trial. Questionnaires and the UPDRS I–IV were obtained at baseline and 8-week follow-up. Results. Significant changes after the MBI were found including a 5.5 point decrease on the UPDRS motor score, an increase of 0.79 points on Parkinson’s disease questionnaire (PDQ-39 pain item, and a 3.15 point increase in the Five Facet Mindfulness Questionnaire observe facet. Conclusions. To the best of our knowledge, this is the first quantitative analysis of neurobehavioral effects of MBI in PD.

Utilizing Maximal Independent Sets as Dominating Sets in Scale-Free Networks

Science.gov (United States)

Derzsy, N.; Molnar, F., Jr.; Szymanski, B. K.; Korniss, G.

Dominating sets provide key solution to various critical problems in networked systems, such as detecting, monitoring, or controlling the behavior of nodes. Motivated by graph theory literature [Erdos, Israel J. Math. 4, 233 (1966)], we studied maximal independent sets (MIS) as dominating sets in scale-free networks. We investigated the scaling behavior of the size of MIS in artificial scale-free networks with respect to multiple topological properties (size, average degree, power-law exponent, assortativity), evaluated its resilience to network damage resulting from random failure or targeted attack [Molnar et al., Sci. Rep. 5, 8321 (2015)], and compared its efficiency to previously proposed dominating set selection strategies. We showed that, despite its small set size, MIS provides very high resilience against network damage. Using extensive numerical analysis on both synthetic and real-world (social, biological, technological) network samples, we demonstrate that our method effectively satisfies four essential requirements of dominating sets for their practical applicability on large-scale real-world systems: 1.) small set size, 2.) minimal network information required for their construction scheme, 3.) fast and easy computational implementation, and 4.) resiliency to network damage. Supported by DARPA, DTRA, and NSF.

Influence of the time scale on the construction of financial networks.

Science.gov (United States)

Emmert-Streib, Frank; Dehmer, Matthias

2010-09-30

In this paper we investigate the definition and formation of financial networks. Specifically, we study the influence of the time scale on their construction. For our analysis we use correlation-based networks obtained from the daily closing prices of stock market data. More precisely, we use the stocks that currently comprise the Dow Jones Industrial Average (DJIA) and estimate financial networks where nodes correspond to stocks and edges correspond to none vanishing correlation coefficients. That means only if a correlation coefficient is statistically significant different from zero, we include an edge in the network. This construction procedure results in unweighted, undirected networks. By separating the time series of stock prices in non-overlapping intervals, we obtain one network per interval. The length of these intervals corresponds to the time scale of the data, whose influence on the construction of the networks will be studied in this paper. Numerical analysis of four different measures in dependence on the time scale for the construction of networks allows us to gain insights about the intrinsic time scale of the stock market with respect to a meaningful graph-theoretical analysis.

Bursting synchronization in scale-free networks

International Nuclear Information System (INIS)

Batista, C.A.S.; Batista, A.M.; Pontes, J.C.A. de; Lopes, S.R.; Viana, R.L.

2009-01-01

Neuronal networks in some areas of the brain cortex present the scale-free property, i.e., the neuron connectivity is distributed according to a power-law, such that neurons are more likely to couple with other already well-connected ones. Neuron activity presents two timescales, a fast one related to action-potential spiking, and a slow timescale in which bursting takes place. Some pathological conditions are related with the synchronization of the bursting activity in a weak sense, meaning the adjustment of the bursting phase due to coupling. Hence it has been proposed that an externally applied time-periodic signal be applied in order to control undesirable synchronized bursting rhythms. We investigated this kind of intervention using a two-dimensional map to describe neurons with spiking-bursting activity in a scale-free network.

Super-transient scaling in time-delay autonomous Boolean network motifs

Energy Technology Data Exchange (ETDEWEB)

D' Huys, Otti, E-mail: otti.dhuys@phy.duke.edu; Haynes, Nicholas D. [Department of Physics, Duke University, Durham, North Carolina 27708 (United States); Lohmann, Johannes [Department of Physics, Duke University, Durham, North Carolina 27708 (United States); Institut für Theoretische Physik, Technische Universität Berlin, Hardenbergstraße 36, 10623 Berlin (Germany); Gauthier, Daniel J. [Department of Physics, Duke University, Durham, North Carolina 27708 (United States); Department of Physics, The Ohio State University, Columbus, Ohio 43210 (United States)

2016-09-15

Autonomous Boolean networks are commonly used to model the dynamics of gene regulatory networks and allow for the prediction of stable dynamical attractors. However, most models do not account for time delays along the network links and noise, which are crucial features of real biological systems. Concentrating on two paradigmatic motifs, the toggle switch and the repressilator, we develop an experimental testbed that explicitly includes both inter-node time delays and noise using digital logic elements on field-programmable gate arrays. We observe transients that last millions to billions of characteristic time scales and scale exponentially with the amount of time delays between nodes, a phenomenon known as super-transient scaling. We develop a hybrid model that includes time delays along network links and allows for stochastic variation in the delays. Using this model, we explain the observed super-transient scaling of both motifs and recreate the experimentally measured transient distributions.

Racial and ethnic disparities in functional, psychosocial, and neurobehavioral outcomes after brain injury.

Science.gov (United States)

Arango-Lasprilla, Juan Carlos; Kreutzer, Jeffrey S

2010-01-01

Because of the growing minority population in the past 3 decades in the United States and the increasing numbers of individuals who sustain a traumatic brain injury (TBI), researchers and clinicians have started to pay more attention to the role of race and ethnicity in outcomes after TBI, with the goal of better serving this population. The aim of this article is to review the literature on the influence of race/ethnicity on functional, psychosocial, and neurobehavioral outcomes after TBI. Specifically, the following 8 areas of outcomes will be examined: (1) treatment outcomes, (2) neuropsychological outcomes, (3) employment/productivity, (4) functional outcomes, (5) community integration, (6) marital status, (7) quality of life/life satisfaction, and (8) emotional/neurobehavioral outcomes. To conclude this review, suggestions for improvements in professional competency, research, systems of care, and training are proposed.

Serum Neuron-Specific Enolase, Biogenic Amino-Acids and Neurobehavioral Function in Lead-Exposed Workers from Lead-Acid Battery Manufacturing Process

Directory of Open Access Journals (Sweden)

K Ravibabu

2015-01-01

Full Text Available Background: The interaction between serum neuron-specific enolase (NSE, biogenic amino-acids and neurobehavioral function with blood lead levels in workers exposed to lead form lead-acid battery manufacturing process was not studied. Objective: To evaluate serum NSE and biogenic amino-acids (dopamine and serotonin levels, and neurobehavioral performance among workers exposed to lead from lead-acid storage battery plant, and its relation with blood lead levels (BLLs. Methods: In a cross-sectional study, we performed biochemical and neurobehavioral function tests on 146 workers exposed to lead from lead-acid battery manufacturing process. BLLs were assessed by an atomic absorption spectrophotometer. Serum NSE, dopamine and serotonin were measured by ELISA. Neurobehavioral functions were assessed by CDC-recommended tests—simple reaction time (SRT, symbol digit substitution test (SDST, and serial digit learning test (SDLT. Results: There was a significant correlation (r 0.199, p<0.05 between SDST and BLL. SDLT and SRT had also a significant positive correlation (r 0.238, p<0.01. NSE had a negative correlation (r –0.194, p<0.05 with serotonin level. Multiple linear regression analysis revealed that both SRT and SDST had positive significant associations with BLL. SRT also had a positive significant association with age. Conclusion: Serum NSE cannot be used as a marker for BLL. The only domain of neurobehavioral function tests that is affected by increased BLL in workers of lead-acid battery manufacturing process is that of the “attention and perception” (SDST.

Innovation diffusion equations on correlated scale-free networks

Energy Technology Data Exchange (ETDEWEB)

Bertotti, M.L., E-mail: marialetizia.bertotti@unibz.it [Free University of Bozen–Bolzano, Faculty of Science and Technology, Bolzano (Italy); Brunner, J., E-mail: johannes.brunner@tis.bz.it [TIS Innovation Park, Bolzano (Italy); Modanese, G., E-mail: giovanni.modanese@unibz.it [Free University of Bozen–Bolzano, Faculty of Science and Technology, Bolzano (Italy)

2016-07-29

Highlights: • The Bass diffusion model can be formulated on scale-free networks. • In the trickle-down version, the hubs adopt earlier and act as monitors. • We improve the equations in order to describe trickle-up diffusion. • Innovation is generated at the network periphery, and hubs can act as stiflers. • We compare diffusion times, in dependence on the scale-free exponent. - Abstract: We introduce a heterogeneous network structure into the Bass diffusion model, in order to study the diffusion times of innovation or information in networks with a scale-free structure, typical of regions where diffusion is sensitive to geographic and logistic influences (like for instance Alpine regions). We consider both the diffusion peak times of the total population and of the link classes. In the familiar trickle-down processes the adoption curve of the hubs is found to anticipate the total adoption in a predictable way. In a major departure from the standard model, we model a trickle-up process by introducing heterogeneous publicity coefficients (which can also be negative for the hubs, thus turning them into stiflers) and a stochastic term which represents the erratic generation of innovation at the periphery of the network. The results confirm the robustness of the Bass model and expand considerably its range of applicability.

Innovation diffusion equations on correlated scale-free networks

International Nuclear Information System (INIS)

Bertotti, M.L.; Brunner, J.; Modanese, G.

2016-01-01

Highlights: • The Bass diffusion model can be formulated on scale-free networks. • In the trickle-down version, the hubs adopt earlier and act as monitors. • We improve the equations in order to describe trickle-up diffusion. • Innovation is generated at the network periphery, and hubs can act as stiflers. • We compare diffusion times, in dependence on the scale-free exponent. - Abstract: We introduce a heterogeneous network structure into the Bass diffusion model, in order to study the diffusion times of innovation or information in networks with a scale-free structure, typical of regions where diffusion is sensitive to geographic and logistic influences (like for instance Alpine regions). We consider both the diffusion peak times of the total population and of the link classes. In the familiar trickle-down processes the adoption curve of the hubs is found to anticipate the total adoption in a predictable way. In a major departure from the standard model, we model a trickle-up process by introducing heterogeneous publicity coefficients (which can also be negative for the hubs, thus turning them into stiflers) and a stochastic term which represents the erratic generation of innovation at the periphery of the network. The results confirm the robustness of the Bass model and expand considerably its range of applicability.

Development and psychometric properties of the Carer – Head Injury Neurobehavioral Assessment Scale (C-HINAS) and the Carer – Head Injury Participation Scale (C-HIPS): patient and family determined outcome scales

Science.gov (United States)

Deb, Shoumitro; Bryant, Eleanor; Morris, Paul G; Prior, Lindsay; Lewis, Glyn; Haque, Sayeed

2007-01-01

Objective Develop and assess the psychometric properties of the Carer – Head Injury Participation Scale (C-HIPS) and its biggest factor the Carer – Head Injury Neurobehavioral Assessment Scale (C-HINAS). Furthermore, the aim was to examine the inter-informant reliability by comparing the self reports of individuals with traumatic brain injury (TBI) with the carer reports on the C-HIPS and the C-HINAS. Method Thirty-two TBI individuals and 27 carers took part in in-depth qualitative interviews exploring the consequences of the TBI. Interview transcripts were analysed and key themes and concepts were used to construct a 49-item and 58-item patient (Patient – Head Injury Participation Scale [P-HIPS]) and carer outcome measure (C-HIPS) respectively, of which 49 were parallel items and nine additional items were used to assess carer burden. Postal versions of the P-HIPS, C-HIPS, Mayo Portland Adaptability Inventory-3 (MPAI-3), and the Glasgow Outcome Scale-Extended (GOSE) were completed by a cohort of 113 TBI individuals and 80 carers. Data from a sub-group of 66 patient/carer pairs were used to compare inter-informant reliability between the P-HIPS and the C-HIPS, and the P-HINAS and the C-HINAS respectively. Results All individual 49 items of the C-HIPS and their total score showed good test-retest reliability (0.95) and internal consistency (0.95). Comparisons with the MPAI-3 and GOSE found a good correlation with the MPAI-3 (0.7) and a moderate negative correlation with the GOSE (−0.6). Factor analysis of these items extracted a 4-factor structure which represented the domains ‘Emotion/Behavior’ (C-HINAS), ‘Independence/Community Living’, ‘Cognition’, and ‘Physical’. The C-HINAS showed good internal consistency (0.92), test-retest reliability (0.93), and concurrent validity with one MPAI subscale (0.7). Assessment of inter-informant reliability revealed good correspondence between the reports of the patients and the carers for both the C

Consensus of Multi-Agent Systems with Prestissimo Scale-Free Networks

International Nuclear Information System (INIS)

Yang Hongyong; Lu Lan; Cao Kecai; Zhang Siying

2010-01-01

In this paper, the relations of the network topology and the moving consensus of multi-agent systems are studied. A consensus-prestissimo scale-free network model with the static preferential-consensus attachment is presented on the rewired link of the regular network. The effects of the static preferential-consensus BA network on the algebraic connectivity of the topology graph are compared with the regular network. The robustness gain to delay is analyzed for variable network topology with the same scale. The time to reach the consensus is studied for the dynamic network with and without communication delays. By applying the computer simulations, it is validated that the speed of the convergence of multi-agent systems can be greatly improved in the preferential-consensus BA network model with different configuration. (interdisciplinary physics and related areas of science and technology)

Broad-scale small-world network topology induces optimal synchronization of flexible oscillators

International Nuclear Information System (INIS)

Markovič, Rene; Gosak, Marko; Marhl, Marko

2014-01-01

The discovery of small-world and scale-free properties of many man-made and natural complex networks has attracted increasing attention. Of particular interest is how the structural properties of a network facilitate and constrain its dynamical behavior. In this paper we study the synchronization of weakly coupled limit-cycle oscillators in dependence on the network topology as well as the dynamical features of individual oscillators. We show that flexible oscillators, characterized by near zero values of divergence, express maximal correlation in broad-scale small-world networks, whereas the non-flexible (rigid) oscillators are best correlated in more heterogeneous scale-free networks. We found that the synchronization behavior is governed by the interplay between the networks global efficiency and the mutual frequency adaptation. The latter differs for flexible and rigid oscillators. The results are discussed in terms of evolutionary advantages of broad-scale small-world networks in biological systems

Tests of peak flow scaling in simulated self-similar river networks

Science.gov (United States)

Menabde, M.; Veitzer, S.; Gupta, V.; Sivapalan, M.

2001-01-01

The effect of linear flow routing incorporating attenuation and network topology on peak flow scaling exponent is investigated for an instantaneously applied uniform runoff on simulated deterministic and random self-similar channel networks. The flow routing is modelled by a linear mass conservation equation for a discrete set of channel links connected in parallel and series, and having the same topology as the channel network. A quasi-analytical solution for the unit hydrograph is obtained in terms of recursion relations. The analysis of this solution shows that the peak flow has an asymptotically scaling dependence on the drainage area for deterministic Mandelbrot-Vicsek (MV) and Peano networks, as well as for a subclass of random self-similar channel networks. However, the scaling exponent is shown to be different from that predicted by the scaling properties of the maxima of the width functions. ?? 2001 Elsevier Science Ltd. All rights reserved.

Power Laws, Scale-Free Networks and Genome Biology

CERN Document Server

Koonin, Eugene V; Karev, Georgy P

2006-01-01

Power Laws, Scale-free Networks and Genome Biology deals with crucial aspects of the theoretical foundations of systems biology, namely power law distributions and scale-free networks which have emerged as the hallmarks of biological organization in the post-genomic era. The chapters in the book not only describe the interesting mathematical properties of biological networks but moves beyond phenomenology, toward models of evolution capable of explaining the emergence of these features. The collection of chapters, contributed by both physicists and biologists, strives to address the problems in this field in a rigorous but not excessively mathematical manner and to represent different viewpoints, which is crucial in this emerging discipline. Each chapter includes, in addition to technical descriptions of properties of biological networks and evolutionary models, a more general and accessible introduction to the respective problems. Most chapters emphasize the potential of theoretical systems biology for disco...

Parameters affecting the resilience of scale-free networks to random failures.

Energy Technology Data Exchange (ETDEWEB)

Link, Hamilton E.; LaViolette, Randall A.; Lane, Terran (University of New Mexico, Albuquerque, NM); Saia, Jared (University of New Mexico, Albuquerque, NM)

2005-09-01

It is commonly believed that scale-free networks are robust to massive numbers of random node deletions. For example, Cohen et al. in (1) study scale-free networks including some which approximate the measured degree distribution of the Internet. Their results suggest that if each node in this network failed independently with probability 0.99, most of the remaining nodes would still be connected in a giant component. In this paper, we show that a large and important subclass of scale-free networks are not robust to massive numbers of random node deletions. In particular, we study scale-free networks which have minimum node degree of 1 and a power-law degree distribution beginning with nodes of degree 1 (power-law networks). We show that, in a power-law network approximating the Internet's reported distribution, when the probability of deletion of each node is 0.5 only about 25% of the surviving nodes in the network remain connected in a giant component, and the giant component does not persist beyond a critical failure rate of 0.9. The new result is partially due to improved analytical accommodation of the large number of degree-0 nodes that result after node deletions. Our results apply to power-law networks with a wide range of power-law exponents, including Internet-like networks. We give both analytical and empirical evidence that such networks are not generally robust to massive random node deletions.

Developmental and neurobehavioral effects of perinatal exposure to polychlorinated biphenyls in mice.

Science.gov (United States)

Sugawara, Norio; Nakai, Kunihiko; Nakamura, Tomoyuki; Ohba, Takashi; Suzuki, Keita; Kameo, Satomi; Satoh, Chieko; Satoh, Hiroshi

2006-05-01

Because behavioral deficits associated with gestational exposure to polychlorinated biphenyls (PCBs) have been a concern, we studied the developmental and neurobehavioral effects of perinatal exposure to Aroclor 1254 (A1254), a commercial mixture of PCBs, in mice. The PCB mixture (A1254; 0, 6, 18, and 54 mg/kg body weight) was administered to pregnant mice (C57BL/6Cr) every 3 days by gavage from gestational day (GD) 6 to postnatal day (PND) 20. Compared with the control, treatment with A1254 did not alter the maternal body weight during the gestation and lactation periods. The body weight of the offspring did not differ among treatments. To assess the effects on offspring following such exposure, physical and neurobehavioral development (i.e., pinna detachment, hair growth, eye opening, incisor eruption, grasp reflex, righting reflex, walking, negative geotaxis, and cliff avoidance) was observed before weaning. At PND 7, poor adult-like responses in negative geotaxis were observed in all exposed groups. When the offspring were at 8-week old, the PCB-treated (18 mg/kg body weight) mice showed a decreased walking speed in the open-field test, and a prolonged time to reach the platform in the water maze test. Spontaneous locomotion activity was not affected by PCB exposure at 9 weeks . These results showed that perinatal exposure to PCBs produces several behavioral alterations in mice. Although dose-dependent changes were not observed, the neurobehavioral effects such as a decreased walking speed in the open-field test and a prolonged time to reach the platform in the water maze test remained in adulthood after the seeming recovery from the transient delay in development before weaning.

Maternal smoking, drinking or cannabis use during pregnancy and neurobehavioral and cognitive functioning in human offspring.

Science.gov (United States)

Huizink, Anja C; Mulder, Eduard J H

2006-01-01

Teratological investigations have demonstrated that agents that are relatively harmless to the mother may have significant negative consequences to the fetus. Among these agents, prenatal alcohol, nicotine or cannabis exposure have been related to adverse offspring outcomes. Although there is a relatively extensive body of literature that has focused upon birth and behavioral outcomes in newborns and infants after prenatal exposure to maternal smoking, drinking and, to a lesser extent, cannabis use, information on neurobehavioral and cognitive teratogenic findings beyond these early ages is still quite limited. Furthermore, most studies have focused on prenatal exposure to heavy levels of smoking, drinking or cannabis use. Few recent studies have paid attention to low or moderate levels of exposure to these substances. This review endeavors to provide an overview of such studies, and includes animal findings and potential mechanisms that may explain the mostly subtle effects found on neurobehavioral and cognitive outcomes. It is concluded that prenatal exposure to either maternal smoking, alcohol or cannabis use is related to some common neurobehavioral and cognitive outcomes, including symptoms of ADHD (inattention, impulsivity), increased externalizing behavior, decreased general cognitive functioning, and deficits in learning and memory tasks.

Scaling properties of cosmic (super)string networks

International Nuclear Information System (INIS)

Martins, C J A P

2014-01-01

I use a combination of state-of-the-art numerical simulations and analytic modelling to discuss the scaling properties of cosmic defect networks, including superstrings. Particular attention is given to the role of extra degrees of freedom in the evolution of these networks. Compared to the 'plain vanilla' case of Goto-Nambu strings, three such extensions play important but distinct roles in the network dynamics: the presence of charges/currents on the string worldsheet, the existence of junctions, and the possibility of a hierarchy of string tensions. I also comment on insights gained from studying simpler defect networks, including Goto-Nambu strings themselves, domain walls and semilocal strings

Single Image Super-Resolution Based on Multi-Scale Competitive Convolutional Neural Network.

Science.gov (United States)

Du, Xiaofeng; Qu, Xiaobo; He, Yifan; Guo, Di

2018-03-06

Deep convolutional neural networks (CNNs) are successful in single-image super-resolution. Traditional CNNs are limited to exploit multi-scale contextual information for image reconstruction due to the fixed convolutional kernel in their building modules. To restore various scales of image details, we enhance the multi-scale inference capability of CNNs by introducing competition among multi-scale convolutional filters, and build up a shallow network under limited computational resources. The proposed network has the following two advantages: (1) the multi-scale convolutional kernel provides the multi-context for image super-resolution, and (2) the maximum competitive strategy adaptively chooses the optimal scale of information for image reconstruction. Our experimental results on image super-resolution show that the performance of the proposed network outperforms the state-of-the-art methods.

Autonomous smart sensor network for full-scale structural health monitoring

Science.gov (United States)

Rice, Jennifer A.; Mechitov, Kirill A.; Spencer, B. F., Jr.; Agha, Gul A.

2010-04-01

The demands of aging infrastructure require effective methods for structural monitoring and maintenance. Wireless smart sensor networks offer the ability to enhance structural health monitoring (SHM) practices through the utilization of onboard computation to achieve distributed data management. Such an approach is scalable to the large number of sensor nodes required for high-fidelity modal analysis and damage detection. While smart sensor technology is not new, the number of full-scale SHM applications has been limited. This slow progress is due, in part, to the complex network management issues that arise when moving from a laboratory setting to a full-scale monitoring implementation. This paper presents flexible network management software that enables continuous and autonomous operation of wireless smart sensor networks for full-scale SHM applications. The software components combine sleep/wake cycling for enhanced power management with threshold detection for triggering network wide tasks, such as synchronized sensing or decentralized modal analysis, during periods of critical structural response.

Fetal Neurobehavioral Development and the Role of Maternal Nutrient Intake and Psychological Health

Science.gov (United States)

Spann, Marisa; Smerling, Jennifer; Gustafsson, Hanna C.; Foss, Sophie; Monk, Catherine

2014-01-01

Measuring and understanding fetal neurodevelopment provides insight regarding the developing brain. Maternal nutrient intake and psychological stress during pregnancy each impact fetal neurodevelopment and influence childhood outcomes and are thus important factors to consider when studying fetal neurobehavioral development. The authors provide an…

Scaling of counter-current imbibition recovery curves using artificial neural networks

Science.gov (United States)

Jafari, Iman; Masihi, Mohsen; Nasiri Zarandi, Masoud

2018-06-01

Scaling imbibition curves are of great importance in the characterization and simulation of oil production from naturally fractured reservoirs. Different parameters such as matrix porosity and permeability, oil and water viscosities, matrix dimensions, and oil/water interfacial tensions have an effective on the imbibition process. Studies on the scaling imbibition curves along with the consideration of different assumptions have resulted in various scaling equations. In this work, using an artificial neural network (ANN) method, a novel technique is presented for scaling imbibition recovery curves, which can be used for scaling the experimental and field-scale imbibition cases. The imbibition recovery curves for training and testing the neural network were gathered through the simulation of different scenarios using a commercial reservoir simulator. In this ANN-based method, six parameters were assumed to have an effect on the imbibition process and were considered as the inputs for training the network. Using the ‘Bayesian regularization’ training algorithm, the network was trained and tested. Training and testing phases showed superior results in comparison with the other scaling methods. It is concluded that using the new technique is useful for scaling imbibition recovery curves, especially for complex cases, for which the common scaling methods are not designed.

Scale-Free Networks and Commercial Air Carrier Transportation in the United States

Science.gov (United States)

Conway, Sheila R.

2004-01-01

Network science, or the art of describing system structure, may be useful for the analysis and control of large, complex systems. For example, networks exhibiting scale-free structure have been found to be particularly well suited to deal with environmental uncertainty and large demand growth. The National Airspace System may be, at least in part, a scalable network. In fact, the hub-and-spoke structure of the commercial segment of the NAS is an often-cited example of an existing scale-free network After reviewing the nature and attributes of scale-free networks, this assertion is put to the test: is commercial air carrier transportation in the United States well explained by this model? If so, are the positive attributes of these networks, e.g. those of efficiency, flexibility and robustness, fully realized, or could we effect substantial improvement? This paper first outlines attributes of various network types, then looks more closely at the common carrier air transportation network from perspectives of the traveler, the airlines, and Air Traffic Control (ATC). Network models are applied within each paradigm, including discussion of implied strengths and weaknesses of each model. Finally, known limitations of scalable networks are discussed. With an eye towards NAS operations, utilizing the strengths and avoiding the weaknesses of scale-free networks are addressed.

Federated queries of clinical data repositories: Scaling to a national network.

Science.gov (United States)

Weber, Griffin M

2015-06-01

Federated networks of clinical research data repositories are rapidly growing in size from a handful of sites to true national networks with more than 100 hospitals. This study creates a conceptual framework for predicting how various properties of these systems will scale as they continue to expand. Starting with actual data from Harvard's four-site Shared Health Research Information Network (SHRINE), the framework is used to imagine a future 4000 site network, representing the majority of hospitals in the United States. From this it becomes clear that several common assumptions of small networks fail to scale to a national level, such as all sites being online at all times or containing data from the same date range. On the other hand, a large network enables researchers to select subsets of sites that are most appropriate for particular research questions. Developers of federated clinical data networks should be aware of how the properties of these networks change at different scales and design their software accordingly. Copyright © 2015 Elsevier Inc. All rights reserved.

SIZE SCALING RELATIONSHIPS IN FRACTURE NETWORKS

International Nuclear Information System (INIS)

Wilson, Thomas H.

2000-01-01

The research conducted under DOE grant DE-FG26-98FT40385 provides a detailed assessment of size scaling issues in natural fracture and active fault networks that extend over scales from several tens of kilometers to less than a tenth of a meter. This study incorporates analysis of data obtained from several sources, including: natural fracture patterns photographed in the Appalachian field area, natural fracture patterns presented by other workers in the published literature, patterns of active faulting in Japan mapping at a scale of 1:100,000, and lineament patterns interpreted from satellite-based radar imagery obtained over the Appalachian field area. The complexity of these patterns is always found to vary with scale. In general,but not always, patterns become less complex with scale. This tendency may reverse as can be inferred from the complexity of high-resolution radar images (8 meter pixel size) which are characterized by patterns that are less complex than those observed over smaller areas on the ground surface. Model studies reveal that changes in the complexity of a fracture pattern can be associated with dominant spacings between the fractures comprising the pattern or roughly to the rock areas bounded by fractures of a certain scale. While the results do not offer a magic number (the fractal dimension) to characterize fracture networks at all scales, the modeling and analysis provide results that can be interpreted directly in terms of the physical properties of the natural fracture or active fault complex. These breaks roughly define the size of fracture bounded regions at different scales. The larger more extensive sets of fractures will intersect and enclose regions of a certain size, whereas smaller less extensive sets will do the same--i.e. subdivide the rock into even smaller regions. The interpretation varies depending on the number of sets that are present, but the scale breaks in the logN/logr plots serve as a guide to interpreting the

Effects of perinatal asphyxia on the neurobehavioral and retinal development of newborn rats.

Science.gov (United States)

Kiss, Peter; Szogyi, Donat; Reglodi, Dora; Horvath, Gabor; Farkas, Jozsef; Lubics, Andrea; Tamas, Andrea; Atlasz, Tamas; Szabadfi, Krisztina; Babai, Norbert; Gabriel, Robert; Koppan, Miklos

2009-02-19

Perinatal asphyxia during delivery produces long-term deficits and represents a major problem in both neonatal and pediatric care. Several morphological, biochemical and behavioral changes have been described in rats exposed to perinatal asphyxia. The aim of the present study was to evaluate how perinatal asphyxia affects the complex early neurobehavioral development and retinal structure of newborn rats. Asphyxia was induced in ready-to-deliver mothers by removing the pups by cesarian section after 15 min of asphyxia. Somatic and neurobehavioral development was tested daily during the first 3 weeks, and motor coordination tests were performed on postnatal weeks 3-5. After completion of the testing procedure, retinas were removed for histological analysis. We found that in spite of the fast catch-up-growth of asphyctic pups, nearly all examined reflexes were delayed by 1-4 days: negative geotaxis, sensory reflexes, righting reflexes, development of fore- and hindlimb grasp and placing, gait and auditory startle reflexes. Time to perform negative geotaxis, surface righting and gait reflexes was significantly longer during the first few weeks in asphyctic pups. Among the motor coordination tests, a markedly weaker performance was observed in the grid walking and footfault test and in the walk initiation test. Retinal structure showed severe degeneration in the layer of the photoreceptor and bipolar cell bodies. In summary, our present study provided a detailed description of reflex and motor development following perinatal asphyxia, showing that asphyxia led to a marked delay in neurobehavioral development and a severe retinal degeneration.

Salience Network and Depressive Severities in Parkinson’s Disease with Mild Cognitive Impairment: A Structural Covariance Network Analysis

Directory of Open Access Journals (Sweden)

Ya-Ting Chang

2018-01-01

Full Text Available Purpose: In Parkinson’s disease with mild cognitive impairment (PD-MCI, we investigated the clinical significance of salience network (SN in depression and cognitive performance.Methods: Seventy seven PD-MCI patients that fulfilled multi-domain and non-amnestic subtype were included. Gray matter structural covariance networks were constructed by 3D T1-magnetic resonance imaging and seed based analysis. The patients were divided into two groups by psychiatric interviews and screening of Geriatric Depression Scale (GDS: PD-MCI with depression (PD-MCI-D or without depression (PD-MCI-ND. The seed or peak cluster volume, or the significant differences in the regression slopes in each seed-peak cluster correlation, were used to evaluate the significance with the neurobehavioral scores.Results: This study is the first to demonstrate that the PD-MCI-ND group presented a larger number of voxels of structural covariance in SN than the PD-MCI-D group. The right fronto-insular seed volumes and the peak cluster of left lingual gyrus showed significant inverse correlation with the Geriatric Depression Scale (GDS; r = -0.231, P = 0.046.Conclusions: This study is the first to validate the clinical significance of the SN in PD-MCI-D. The right insular seed value and the SN correlated with the severity of depression in PD-MCI.

Salience Network and Depressive Severities in Parkinson’s Disease with Mild Cognitive Impairment: A Structural Covariance Network Analysis

Science.gov (United States)

Chang, Ya-Ting; Lu, Cheng-Hsien; Wu, Ming-Kung; Hsu, Shih-Wei; Huang, Chi-Wei; Chang, Wen-Neng; Lien, Chia-Yi; Lee, Jun-Jun; Chang, Chiung-Chih

2018-01-01

Purpose: In Parkinson’s disease with mild cognitive impairment (PD-MCI), we investigated the clinical significance of salience network (SN) in depression and cognitive performance. Methods: Seventy seven PD-MCI patients that fulfilled multi-domain and non-amnestic subtype were included. Gray matter structural covariance networks were constructed by 3D T1-magnetic resonance imaging and seed based analysis. The patients were divided into two groups by psychiatric interviews and screening of Geriatric Depression Scale (GDS): PD-MCI with depression (PD-MCI-D) or without depression (PD-MCI-ND). The seed or peak cluster volume, or the significant differences in the regression slopes in each seed-peak cluster correlation, were used to evaluate the significance with the neurobehavioral scores. Results: This study is the first to demonstrate that the PD-MCI-ND group presented a larger number of voxels of structural covariance in SN than the PD-MCI-D group. The right fronto-insular seed volumes and the peak cluster of left lingual gyrus showed significant inverse correlation with the Geriatric Depression Scale (GDS; r = -0.231, P = 0.046). Conclusions: This study is the first to validate the clinical significance of the SN in PD-MCI-D. The right insular seed value and the SN correlated with the severity of depression in PD-MCI. PMID:29375361

Serum Neuron-Specific Enolase, Biogenic Amino-Acids and Neurobehavioral Function in Lead-Exposed Workers from Lead-Acid Battery Manufacturing Process

OpenAIRE

K Ravibabu; T Barman; HR Rajmohan

2015-01-01

Background: The interaction between serum neuron-specific enolase (NSE), biogenic amino-acids and neurobehavioral function with blood lead levels in workers exposed to lead form lead-acid battery manufacturing process was not studied. Objective: To evaluate serum NSE and biogenic amino-acids (dopamine and serotonin) levels, and neurobehavioral performance among workers exposed to lead from lead-acid storage battery plant, and its relation with blood lead levels (BLLs). Methods: In a c...

Deep multi-scale convolutional neural network for hyperspectral image classification

Science.gov (United States)

Zhang, Feng-zhe; Yang, Xia

2018-04-01

In this paper, we proposed a multi-scale convolutional neural network for hyperspectral image classification task. Firstly, compared with conventional convolution, we utilize multi-scale convolutions, which possess larger respective fields, to extract spectral features of hyperspectral image. We design a deep neural network with a multi-scale convolution layer which contains 3 different convolution kernel sizes. Secondly, to avoid overfitting of deep neural network, dropout is utilized, which randomly sleeps neurons, contributing to improve the classification accuracy a bit. In addition, new skills like ReLU in deep learning is utilized in this paper. We conduct experiments on University of Pavia and Salinas datasets, and obtained better classification accuracy compared with other methods.

Enumeration of smallest intervention strategies in genome-scale metabolic networks.

Directory of Open Access Journals (Sweden)

Axel von Kamp

2014-01-01

Full Text Available One ultimate goal of metabolic network modeling is the rational redesign of biochemical networks to optimize the production of certain compounds by cellular systems. Although several constraint-based optimization techniques have been developed for this purpose, methods for systematic enumeration of intervention strategies in genome-scale metabolic networks are still lacking. In principle, Minimal Cut Sets (MCSs; inclusion-minimal combinations of reaction or gene deletions that lead to the fulfilment of a given intervention goal provide an exhaustive enumeration approach. However, their disadvantage is the combinatorial explosion in larger networks and the requirement to compute first the elementary modes (EMs which itself is impractical in genome-scale networks. We present MCSEnumerator, a new method for effective enumeration of the smallest MCSs (with fewest interventions in genome-scale metabolic network models. For this we combine two approaches, namely (i the mapping of MCSs to EMs in a dual network, and (ii a modified algorithm by which shortest EMs can be effectively determined in large networks. In this way, we can identify the smallest MCSs by calculating the shortest EMs in the dual network. Realistic application examples demonstrate that our algorithm is able to list thousands of the most efficient intervention strategies in genome-scale networks for various intervention problems. For instance, for the first time we could enumerate all synthetic lethals in E.coli with combinations of up to 5 reactions. We also applied the new algorithm exemplarily to compute strain designs for growth-coupled synthesis of different products (ethanol, fumarate, serine by E.coli. We found numerous new engineering strategies partially requiring less knockouts and guaranteeing higher product yields (even without the assumption of optimal growth than reported previously. The strength of the presented approach is that smallest intervention strategies can be

Large-Scale Recurrent Neural Network Based Modelling of Gene Regulatory Network Using Cuckoo Search-Flower Pollination Algorithm.

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Mandal, Sudip; Khan, Abhinandan; Saha, Goutam; Pal, Rajat K

2016-01-01

The accurate prediction of genetic networks using computational tools is one of the greatest challenges in the postgenomic era. Recurrent Neural Network is one of the most popular but simple approaches to model the network dynamics from time-series microarray data. To date, it has been successfully applied to computationally derive small-scale artificial and real-world genetic networks with high accuracy. However, they underperformed for large-scale genetic networks. Here, a new methodology has been proposed where a hybrid Cuckoo Search-Flower Pollination Algorithm has been implemented with Recurrent Neural Network. Cuckoo Search is used to search the best combination of regulators. Moreover, Flower Pollination Algorithm is applied to optimize the model parameters of the Recurrent Neural Network formalism. Initially, the proposed method is tested on a benchmark large-scale artificial network for both noiseless and noisy data. The results obtained show that the proposed methodology is capable of increasing the inference of correct regulations and decreasing false regulations to a high degree. Secondly, the proposed methodology has been validated against the real-world dataset of the DNA SOS repair network of Escherichia coli. However, the proposed method sacrifices computational time complexity in both cases due to the hybrid optimization process.

Impact of Tactile Stimulation on Neurobehavioral Development of Premature Infants in Assiut City

Science.gov (United States)

Sayed, Atyat Mohammed Hassan; Youssef, Magda Mohamed E.; Hassanein, Farouk El-Sayed; Mobarak, Amal Ahmed

2015-01-01

Objective: To assess impact of tactile stimulation on neurobehavioral development of premature infants in Assiut City. Design: Quasi-experimental research design. Setting: The study was conducted in the Neonatal Intensive Care Unit at Assiut University Children Hospital, Assiut General Hospital, Health Insurance Hospital (ElMabarah Hospital) and…

Sparse cliques trump scale-free networks in coordination and competition

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Gianetto, David A.; Heydari, Babak

2016-02-01

Cooperative behavior, a natural, pervasive and yet puzzling phenomenon, can be significantly enhanced by networks. Many studies have shown how global network characteristics affect cooperation; however, it is difficult to understand how this occurs based on global factors alone, low-level network building blocks, or motifs are necessary. In this work, we systematically alter the structure of scale-free and clique networks and show, through a stochastic evolutionary game theory model, that cooperation on cliques increases linearly with community motif count. We further show that, for reactive stochastic strategies, network modularity improves cooperation in the anti-coordination Snowdrift game and the Prisoner’s Dilemma game but not in the Stag Hunt coordination game. We also confirm the negative effect of the scale-free graph on cooperation when effective payoffs are used. On the flip side, clique graphs are highly cooperative across social environments. Adding cycles to the acyclic scale-free graph increases cooperation when multiple games are considered; however, cycles have the opposite effect on how forgiving agents are when playing the Prisoner’s Dilemma game.

Generating clustered scale-free networks using Poisson based localization of edges

Science.gov (United States)

Türker, İlker

2018-05-01

We introduce a variety of network models using a Poisson-based edge localization strategy, which result in clustered scale-free topologies. We first verify the success of our localization strategy by realizing a variant of the well-known Watts-Strogatz model with an inverse approach, implying a small-world regime of rewiring from a random network through a regular one. We then apply the rewiring strategy to a pure Barabasi-Albert model and successfully achieve a small-world regime, with a limited capacity of scale-free property. To imitate the high clustering property of scale-free networks with higher accuracy, we adapted the Poisson-based wiring strategy to a growing network with the ingredients of both preferential attachment and local connectivity. To achieve the collocation of these properties, we used a routine of flattening the edges array, sorting it, and applying a mixing procedure to assemble both global connections with preferential attachment and local clusters. As a result, we achieved clustered scale-free networks with a computational fashion, diverging from the recent studies by following a simple but efficient approach.

Neurobehavioral Effects of Space Radiation on Psychomotor Vigilance Tests

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Hienz, Robert; Davis, Catherine; Weed, Michael; Guida, Peter; Gooden, Virginia; Brady, Joseph; Roma, Peter

Neurobehavioral Effects of Space Radiation on Psychomotor Vigilance Tests INTRODUCTION Risk assessment of the biological consequences of living in the space radiation environment represents one of the highest priority areas of NASA radiation research. Of critical importance is the need for a risk assessment of damage to the central nervous system (CNS) leading to functional cognitive/behavioral changes during long-term space missions, and the development of effective shielding or biological countermeasures to such risks. The present research focuses on the use of an animal model that employs neurobehavioral tests identical or homologous to those currently in use in human models of risk assessment by U.S. agencies such as the Depart-ment of Defense and Federal Aviation and Federal Railroad Administrations for monitoring performance and estimating accident risks associated with such variables as fatigue and/or alcohol or drug abuse. As a first approximation for establishing human risk assessments due to exposure to space radiation, the present work provides animal performance data obtained with the rPVT (rat Psychomotor Vigilance Test), an animal analog of the human PVT that is currently employed for human risk assessments via quantification of sustained attention (e.g., 'vigilance' or 'readiness to perform' tasks). Ground-based studies indicate that radiation can induce neurobehavioral changes in rodents, including impaired performance on motor tasks and deficits in spatial learning and memory. The present study is testing the hypothesis that radiation exposure impairs motor function, performance accuracy, vigilance, motivation, and memory in adult male rats. METHODS The psychomotor vigilance test (PVT) was originally developed as a human cognitive neurobe-havioral assay for tracking the temporally dynamic changes in sustained attention, and has also been used to track changes in circadian rhythm. In humans the test requires responding to a small, bright

[Scale effect of Nanjing urban green infrastructure network pattern and connectivity analysis.

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Yu, Ya Ping; Yin, Hai Wei; Kong, Fan Hua; Wang, Jing Jing; Xu, Wen Bin

2016-07-01

Based on ArcGIS, Erdas, GuidosToolbox, Conefor and other software platforms, using morphological spatial pattern analysis (MSPA) and landscape connectivity analysis methods, this paper quantitatively analysed the scale effect, edge effect and distance effect of the Nanjing urban green infrastructure network pattern in 2013 by setting different pixel sizes (P) and edge widths in MSPA analysis, and setting different dispersal distance thresholds in landscape connectivity analysis. The results showed that the type of landscape acquired based on the MSPA had a clear scale effect and edge effect, and scale effects only slightly affected landscape types, whereas edge effects were more obvious. Different dispersal distances had a great impact on the landscape connectivity, 2 km or 2.5 km dispersal distance was a critical threshold for Nanjing. When selecting the pixel size 30 m of the input data and the edge wide 30 m used in the morphological model, we could get more detailed landscape information of Nanjing UGI network. Based on MSPA and landscape connectivity, analysis of the scale effect, edge effect, and distance effect on the landscape types of the urban green infrastructure (UGI) network was helpful for selecting the appropriate size, edge width, and dispersal distance when developing these networks, and for better understanding the spatial pattern of UGI networks and the effects of scale and distance on the ecology of a UGI network. This would facilitate a more scientifically valid set of design parameters for UGI network spatiotemporal pattern analysis. The results of this study provided an important reference for Nanjing UGI networks and a basis for the analysis of the spatial and temporal patterns of medium-scale UGI landscape networks in other regions.

Pesticide exposure and stunting as independent predictors of neurobehavioral deficits in Ecuadorian school children.

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Grandjean, Philippe; Harari, Raul; Barr, Dana B; Debes, Frodi

2006-03-01

To examine possible effects on blood pressure, neurological function, and neurobehavioral tests in school-aged children with and without prenatal pesticide exposure in an area where stunting is common. In a community of Northern Ecuador with intensive floriculture and a high female employment rate, we invited 79 children attending the 2 lowest grades of a public school for clinical examinations. In addition to a thorough physical examination, we administered simple reaction time, Santa Ana dexterity test, Stanford-Binet copying, and Wechsler Intelligence Scale for Children-Revised Digit Spans forward. Maternal interview included detailed assessment of occupational history to determine pesticide exposure during pregnancy. Recent and current pesticide exposure was assessed by erythrocyte acetylcholine esterase activity and urinary excretion of organophosphate metabolites. All eligible children participated in the study, but 7 children were excluded from data analysis due to other disease or age >9 years. A total of 31 of the remaining 72 children were classified as stunted based on their height for age. Maternal occupational history revealed that 37 children had been exposed to pesticides during development. After confounder adjustment, prenatal pesticide exposure was associated with a higher systolic blood pressure than in the controls. On neurological examination, 14 exposed children and 9 controls showed > or =1 abnormalities. Of 5 neurobehavioral tests, the Stanford-Binet copying test showed a lower drawing score for copying designs in exposed children than in controls. Stunting was associated with a lower score on this test only, and both risk factors remained statistically significant in a multiple regression analysis with adjustment for demographic and social confounders. Increased excretion of dimethyl and diethyl metabolites of organophosphates was associated with increased reaction time and no other outcomes. Prenatal pesticide exposure may cause lasting

Metabolite coupling in genome-scale metabolic networks

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Palsson Bernhard Ø

2006-03-01

Full Text Available Abstract Background Biochemically detailed stoichiometric matrices have now been reconstructed for various bacteria, yeast, and for the human cardiac mitochondrion based on genomic and proteomic data. These networks have been manually curated based on legacy data and elementally and charge balanced. Comparative analysis of these well curated networks is now possible. Pairs of metabolites often appear together in several network reactions, linking them topologically. This co-occurrence of pairs of metabolites in metabolic reactions is termed herein "metabolite coupling." These metabolite pairs can be directly computed from the stoichiometric matrix, S. Metabolite coupling is derived from the matrix ŜŜT, whose off-diagonal elements indicate the number of reactions in which any two metabolites participate together, where Ŝ is the binary form of S. Results Metabolite coupling in the studied networks was found to be dominated by a relatively small group of highly interacting pairs of metabolites. As would be expected, metabolites with high individual metabolite connectivity also tended to be those with the highest metabolite coupling, as the most connected metabolites couple more often. For metabolite pairs that are not highly coupled, we show that the number of reactions a pair of metabolites shares across a metabolic network closely approximates a line on a log-log scale. We also show that the preferential coupling of two metabolites with each other is spread across the spectrum of metabolites and is not unique to the most connected metabolites. We provide a measure for determining which metabolite pairs couple more often than would be expected based on their individual connectivity in the network and show that these metabolites often derive their principal biological functions from existing in pairs. Thus, analysis of metabolite coupling provides information beyond that which is found from studying the individual connectivity of individual

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction.

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Ma, Xiaolei; Dai, Zhuang; He, Zhengbing; Ma, Jihui; Wang, Yong; Wang, Yunpeng

2017-04-10

This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.

MR brain volumetric measurements are predictive of neurobehavioral impairment in the HIV-1 transgenic rat.

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Casas, Rafael; Muthusamy, Siva; Wakim, Paul G; Sinharay, Sanhita; Lentz, Margaret R; Reid, William C; Hammoud, Dima A

2018-01-01

HIV infection is known to be associated with brain volume loss, even in optimally treated patients. In this study, we assessed whether dynamic brain volume changes over time are predictive of neurobehavorial performance in the HIV-1 transgenic (Tg) rat, a model of treated HIV-positive patients. Cross-sectional brain MRI imaging was first performed comparing Tg and wild type (WT) rats at 3 and 19 months of age. Longitudinal MRI and neurobehavioral testing of another group of Tg and WT rats was then performed from 5 to 23 weeks of age. Whole brain and subregional image segmentation was used to assess the rate of brain growth over time. We used repeated-measures mixed models to assess differences in brain volumes and to establish how predictive the volume differences are of specific neurobehavioral deficits. Cross-sectional imaging showed smaller whole brain volumes in Tg compared to WT rats at 3 and at 19 months of age. Longitudinally, Tg brain volumes were smaller than age-matched WT rats at all time points, starting as early as 5 weeks of age. The Tg striatal growth rate delay between 5 and 9 weeks of age was greater than that of the whole brain. Striatal volume in combination with genotype was the most predictive of rota-rod scores and in combination with genotype and age was the most predictive of total exploratory activity scores in the Tg rats. The disproportionately delayed striatal growth compared to whole brain between 5 and 9 weeks of age and the role of striatal volume in predicting neurobehavioral deficits suggest an important role of the dopaminergic system in HIV associated neuropathology. This might explain problems with motor coordination and executive decisions in this animal model. Smaller brain and subregional volumes and neurobehavioral deficits were seen as early as 5 weeks of age, suggesting an early brain insult in the Tg rat. Neuroprotective therapy testing in this model should thus target this early stage of development, before brain

Design of steady-state positron emission tomography protocols for neurobehavioral studies: CO15O and 19Ne

International Nuclear Information System (INIS)

Kearfott, K.J.; Rottenberg, D.A.; Volpe, B.T.

1983-01-01

Although the [ 18 F]-2-fluoro-2-deoxyglucose positron emission tomographic technique for measuring regional glucose metabolic rate has been successfully employed for neurobehavioral studies, the long (greater than 30 min) equilibration time required may complicate the interpretation of experimental results. Positron emission tomography neurobehavioral protocols employing the continuous inhalation of CO 15 O and 19 Ne were developed for measuring regional cerebral blood flow during multiple control and stimulation periods. Timing, lung absorbed dose, statistical accuracy, and resolution were considered. Studies with 19 Ne require shorter equilibration and stimulation times than do CO 15 O studies but entail higher absorbed doses and yield poorer imaging statistics

PKI security in large-scale healthcare networks

OpenAIRE

Mantas, G.; Lymberopoulos, D.; Komninos, N.

2012-01-01

During the past few years a lot of PKI (Public Key Infrastructures) infrastructures have been proposed for healthcare networks in order to ensure secure communication services and exchange of data among healthcare professionals. However, there is a plethora of challenges in these healthcare PKI infrastructures. Especially, there are a lot of challenges for PKI infrastructures deployed over large-scale healthcare networks. In this paper, we propose a PKI infrastructure to ensure security in a ...

New Visions for Large Scale Networks: Research and Applications

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Networking and Information Technology Research and Development, Executive Office of the President — This paper documents the findings of the March 12-14, 2001 Workshop on New Visions for Large-Scale Networks: Research and Applications. The workshops objectives were...

Node-node correlations and transport properties in scale-free networks

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Obregon, Bibiana; Guzman, Lev

2011-03-01

We study some transport properties of complex networks. We focus our attention on transport properties of scale-free and small-world networks and compare two types of transport: Electric and max-flow cases. In particular, we construct scale-free networks, with a given degree sequence, to estimate the distribution of conductances for different values of assortative/dissortative mixing. For the electric case we find that the distributions of conductances are affect ed by the assortative mixing of the network whereas for the max-flow case, the distributions almost do not show changes when node-node correlations are altered. Finally, we compare local and global transport in terms of the average conductance for the small-world (Watts-Strogatz) model

Epidemic spreading in weighted scale-free networks with community structure

International Nuclear Information System (INIS)

Chu, Xiangwei; Guan, Jihong; Zhang, Zhongzhi; Zhou, Shuigeng

2009-01-01

Many empirical studies reveal that the weights and community structure are ubiquitous in various natural and artificial networks. In this paper, based on the SI disease model, we investigate the epidemic spreading in weighted scale-free networks with community structure. Two exponents, α and β, are introduced to weight the internal edges and external edges, respectively; and a tunable probability parameter q is also introduced to adjust the strength of community structure. We find the external weighting exponent β plays a much more important role in slackening the epidemic spreading and reducing the danger brought by the epidemic than the internal weighting exponent α. Moreover, a novel result we find is that the strong community structure is no longer helpful for slackening the danger brought by the epidemic in the weighted cases. In addition, we show the hierarchical dynamics of the epidemic spreading in the weighted scale-free networks with communities which is also displayed in the famous BA scale-free networks

Effect of pretreatment female lactating rats with albendazole on preventing developmental and neurobehavioral toxicity of enrofloxacin in suckling pups

Directory of Open Access Journals (Sweden)

M. K. Shindala

2012-01-01

Full Text Available The aim of the present study was to evaluated the effect of treated female lactating rats with enrofloxacin alone and itsinteraction with albendazole on the occurrence of developmental and neurobehavioral toxicity in suckling pups by usingpercentage of survival of pups to weaning as well as neurobehavioral test (surface righting reflex. The exposure of sucklingpups to enrofloxacin alone through the milk caused sever toxic effects manifested by significant decrease in percentage ofsurvival in pups to weaning to (0% as result from death all pups from dams were treated with enrofloxacin at high dose (480mg/kg, i.m. during the first 5 days of lactation. Whereas, treated lactating female rats with albendazole at (300 mg/kg, orally,1 hour before enrofloxacin (480 mg/kg, i.m. during the first 5 days of lactation protected suckling pups from developmentaltoxic effects of enrofloxacin which mainly appeared as a significant increase in percentage of survival of pups to 100% asresult from survival all suckling pups to weaning, accompanied by preventing the neurobehavioral toxicity of enrofloxacin insuckling pups manifested by highly significant decreased response time to surface righting reflex to (2.64 ± 0.57 minuets inthe postnatal day 3 in compared with pups from dams that treated with enrofloxacin alone which reached to (15.82 ± 0.27minuets. In conclusion, our results suggest that pretreatment of female lactating rats with albendazole protecte suckling pupsfrom developme-ntal and neurobehavioral toxicity of enrofloxacin.

Antimony-Induced Neurobehavioral and Biochemical Perturbations in Mice.

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Tanu, Tanzina; Anjum, Adiba; Jahan, Momotaj; Nikkon, Farjana; Hoque, Mominul; Roy, Apurba Kumar; Haque, Azizul; Himeno, Seiichiro; Hossain, Khaled; Saud, Zahangir Alam

2018-03-08

Groundwater used for drinking has been contaminated with naturally occurring inorganic arsenic and other metals, and metal-contaminated drinking water is the biggest threat to public health in Bangladesh. Toxic metals present in the drinking water have a strong relationship with chronic diseases in humans. Antimony (Sb), a naturally occurring metal, has been reported to be present in the drinking water along with other heavy metals in Bangladesh. Although Sb is present in the environment, very little attention has been given to the toxic effects of Sb. The present study was designed to investigate the in vivo effects of Sb on neurobehavioral changes like anxiety, learning and memory impairment, and blood indices related to organ dysfunction. Mice exposed to antimony potassium-tartrate hydrate (Sb) (10 mg/kg body weight) significantly (p < 0.05) decreased the time spent in open arms while increased the time spent in closed arms compared to the control mice in elevated plus maze. The mean latency time of control group to find the platform decreased (p < 0.05) significantly during 7 days learning as compared to Sb-treated group in Morris water maze test, and Sb-exposed group spent significantly (p < 0.05) less time in the desired quadrant as compared to the control group in probe trial. Sb treatment also significantly altered blood indices related to liver and kidney dysfunction. Additionally, Sb-induced biochemical alterations were associated with significant perturbations in histological architecture of liver and kidney of Sb-exposed mice. These data suggest that Sb has a toxic effect on neurobehavioral and biochemical changes in mice.

Network synchronization: optimal and pessimal scale-free topologies

International Nuclear Information System (INIS)

Donetti, Luca; Hurtado, Pablo I; Munoz, Miguel A

2008-01-01

By employing a recently introduced optimization algorithm we construct optimally synchronizable (unweighted) networks for any given scale-free degree distribution. We explore how the optimization process affects degree-degree correlations and observe a generic tendency toward disassortativity. Still, we show that there is not a one-to-one correspondence between synchronizability and disassortativity. On the other hand, we study the nature of optimally un-synchronizable networks, that is, networks whose topology minimizes the range of stability of the synchronous state. The resulting 'pessimal networks' turn out to have a highly assortative string-like structure. We also derive a rigorous lower bound for the Laplacian eigenvalue ratio controlling synchronizability, which helps understanding the impact of degree correlations on network synchronizability

Effect of trap position on the efficiency of trapping in treelike scale-free networks

International Nuclear Information System (INIS)

Zhang Zhongzhi; Lin Yuan; Ma Youjun

2011-01-01

The conventional wisdom is that the role and impact of nodes on dynamical processes in scale-free networks are not homogenous, because of the presence of highly connected nodes at the tail of their power-law degree distribution. In this paper, we explore the influence of different nodes as traps on the trapping efficiency of the trapping problem taking place on scale-free networks. To this end, we study in detail the trapping problem in two families of deterministically growing scale-free networks with treelike structure: one family is non-fractal, the other is fractal. In the first part of this work, we attack a special case of random walks on the two network families with a perfect trap located at a hub, i.e. node with the highest degree. The second study addresses the case with trap distributed uniformly over all nodes in the networks. For these two cases, we compute analytically the mean trapping time (MTT), a quantitative indicator characterizing the trapping efficiency of the trapping process. We show that in the non-fractal scale-free networks the MTT for both cases follows different scalings with the network order (number of network nodes), implying that trap's position has a significant effect on the trapping efficiency. In contrast, it is presented that for both cases in the fractal scale-free networks, the two leading scalings exhibit the same dependence on the network order, suggesting that the location of trap has no essential impact on the trapping efficiency. We also show that for both cases of the trapping problem, the trapping efficiency is more efficient in the non-fractal scale-free networks than in their fractal counterparts.

Evaluating the transport in small-world and scale-free networks

International Nuclear Information System (INIS)

Juárez-López, R.; Obregón-Quintana, B.; Hernández-Pérez, R.; Reyes-Ramírez, I.; Guzmán-Vargas, L.

2014-01-01

We present a study of some properties of transport in small-world and scale-free networks. Particularly, we compare two types of transport: subject to friction (electrical case) and in the absence of friction (maximum flow). We found that in clustered networks based on the Watts–Strogatz (WS) model, for both transport types the small-world configurations exhibit the best trade-off between local and global levels. For non-clustered WS networks the local transport is independent of the rewiring parameter, while the transport improves globally. Moreover, we analyzed both transport types in scale-free networks considering tendencies in the assortative or disassortative mixing of nodes. We construct the distribution of the conductance G and flow F to evaluate the effects of the assortative (disassortative) mixing, finding that for scale-free networks, as we introduce different levels of the degree–degree correlations, the power-law decay in the conductances is altered, while for the flow, the power-law tail remains unchanged. In addition, we analyze the effect on the conductance and the flow of the minimum degree and the shortest path between the source and destination nodes, finding notable differences between these two types of transport

Network synchronization: optimal and pessimal scale-free topologies

Energy Technology Data Exchange (ETDEWEB)

Donetti, Luca [Departamento de Electronica y Tecnologia de Computadores and Instituto de Fisica Teorica y Computacional Carlos I, Facultad de Ciencias, Universidad de Granada, 18071 Granada (Spain); Hurtado, Pablo I; Munoz, Miguel A [Departamento de Electromagnetismo y Fisica de la Materia and Instituto Carlos I de Fisica Teorica y Computacional Facultad de Ciencias, Universidad de Granada, 18071 Granada (Spain)], E-mail: mamunoz@onsager.ugr.es

2008-06-06

By employing a recently introduced optimization algorithm we construct optimally synchronizable (unweighted) networks for any given scale-free degree distribution. We explore how the optimization process affects degree-degree correlations and observe a generic tendency toward disassortativity. Still, we show that there is not a one-to-one correspondence between synchronizability and disassortativity. On the other hand, we study the nature of optimally un-synchronizable networks, that is, networks whose topology minimizes the range of stability of the synchronous state. The resulting 'pessimal networks' turn out to have a highly assortative string-like structure. We also derive a rigorous lower bound for the Laplacian eigenvalue ratio controlling synchronizability, which helps understanding the impact of degree correlations on network synchronizability.

Base Station Placement Algorithm for Large-Scale LTE Heterogeneous Networks.

Science.gov (United States)

Lee, Seungseob; Lee, SuKyoung; Kim, Kyungsoo; Kim, Yoon Hyuk

2015-01-01

Data traffic demands in cellular networks today are increasing at an exponential rate, giving rise to the development of heterogeneous networks (HetNets), in which small cells complement traditional macro cells by extending coverage to indoor areas. However, the deployment of small cells as parts of HetNets creates a key challenge for operators' careful network planning. In particular, massive and unplanned deployment of base stations can cause high interference, resulting in highly degrading network performance. Although different mathematical modeling and optimization methods have been used to approach various problems related to this issue, most traditional network planning models are ill-equipped to deal with HetNet-specific characteristics due to their focus on classical cellular network designs. Furthermore, increased wireless data demands have driven mobile operators to roll out large-scale networks of small long term evolution (LTE) cells. Therefore, in this paper, we aim to derive an optimum network planning algorithm for large-scale LTE HetNets. Recently, attempts have been made to apply evolutionary algorithms (EAs) to the field of radio network planning, since they are characterized as global optimization methods. Yet, EA performance often deteriorates rapidly with the growth of search space dimensionality. To overcome this limitation when designing optimum network deployments for large-scale LTE HetNets, we attempt to decompose the problem and tackle its subcomponents individually. Particularly noting that some HetNet cells have strong correlations due to inter-cell interference, we propose a correlation grouping approach in which cells are grouped together according to their mutual interference. Both the simulation and analytical results indicate that the proposed solution outperforms the random-grouping based EA as well as an EA that detects interacting variables by monitoring the changes in the objective function algorithm in terms of system

Cognitive requirements of competing neuro-behavioral decision systems: some implications of temporal horizon for managerial behavior in organizations.

Science.gov (United States)

Foxall, Gordon R

2014-01-01

Interpretation of managerial activity in terms of neuroscience is typically concerned with extreme behaviors such as corporate fraud or reckless investment (Peterson, 2007; Wargo et al., 2010a). This paper is concerned to map out the neurophysiological and cognitive mechanisms at work across the spectrum of managerial behaviors encountered in more day-to-day contexts. It proposes that the competing neuro-behavioral decisions systems (CNBDS) hypothesis (Bickel et al., 2012b) captures well the range of managerial behaviors that can be characterized as hyper- or hypo-activity in either the limbically-based impulsive system or the frontal-cortically based executive system with the corresponding level of activity encountered in the alternative brain region. This pattern of neurophysiological responding also features in the Somatic Marker Hypothesis (Damasio, 1994) and in Reinforcement Sensitivity Theory (RST; Gray and McNaughton, 2000; McNaughton and Corr, 2004), which usefully extend the thesis, for example in the direction of personality. In discussing these theories, the paper has three purposes: to clarify the role of cognitive explanation in neuro-behavioral decision theory, to propose picoeconomics (Ainslie, 1992) as the cognitive component of competing neuro-behavioral decision systems theory and to suggest solutions to the problems of imbalanced neurophysiological activity in managerial behavior. The first is accomplished through discussion of the role of picoeconomics in neuro-behavioral decision theory; the second, by consideration of adaptive-innovative cognitive styles (Kirton, 2003) in the construction of managerial teams, a theme that can now be investigated by a dedicated research program that incorporates psychometric analysis of personality types and cognitive styles involved in managerial decision-making and the underlying neurophysiological bases of such decision-making.

Cognitive requirements of competing neuro-behavioral decision systems: Some implications of temporal horizon for managerial behavior in organizations

Directory of Open Access Journals (Sweden)

Gordon Robert Foxall

2014-04-01

Full Text Available Interpretation of managerial activity in terms of neuroscience is typically concerned with extreme behaviors such as corporate fraud or reckless investment (Wargo, Baglini & Nelson, 2010a; Peterson, 2007. This paper is concerned to map out the neurophysiological and cognitive mechanisms at work across the spectrum of managerial behaviors encountered in more day-to-day contexts. It proposes that the competing neuro-behavioral decisions systems (CNBDS hypothesis (Bickel, Mueller & Jarmolowicz, 2012 captures well the range of managerial behaviors that can be characterized as hyper- or hypo-activity in either the limbically-based impulsive system or the frontal-cortically based executive system with the corresponding level of activity encountered in the alternative brain region. This pattern of neurophysiological responding also features in the Somatic Marker Hypothesis (Damasio, 1994 and in Reinforcement Sensitivity Theory (Gray & McNaughton, 2000; McNaughton & Corr, 2004, which usefully extend the thesis, for example in the direction of personality. In discussing these theories, the paper has three purposes: to clarify the role of cognitive explanation in neuro-behavioral decision theory, to propose picoeconomics (Ainslie, 1992 as the cognitive component of competing neuro-behavioral decision systems theory and to suggest solutions to the problems of imbalanced neurophysiological activity in managerial behaviour. The first is accomplished through discussion of the role of picoeconomics in neuro-behavioral decision theory; the second, by consideration of adaptive-innovative cognitive styles (Kirton, 2003 in the construction of managerial teams, a theme that can now be investigated by a dedicated research program that incorporates psychometric analysis of personality types and cognitive styles involved in managerial decision-making and the underlying neurophysiological bases of such decision-making.

Integration and segregation of large-scale brain networks during short-term task automatization.

Science.gov (United States)

Mohr, Holger; Wolfensteller, Uta; Betzel, Richard F; Mišić, Bratislav; Sporns, Olaf; Richiardi, Jonas; Ruge, Hannes

2016-11-03

The human brain is organized into large-scale functional networks that can flexibly reconfigure their connectivity patterns, supporting both rapid adaptive control and long-term learning processes. However, it has remained unclear how short-term network dynamics support the rapid transformation of instructions into fluent behaviour. Comparing fMRI data of a learning sample (N=70) with a control sample (N=67), we find that increasingly efficient task processing during short-term practice is associated with a reorganization of large-scale network interactions. Practice-related efficiency gains are facilitated by enhanced coupling between the cingulo-opercular network and the dorsal attention network. Simultaneously, short-term task automatization is accompanied by decreasing activation of the fronto-parietal network, indicating a release of high-level cognitive control, and a segregation of the default mode network from task-related networks. These findings suggest that short-term task automatization is enabled by the brain's ability to rapidly reconfigure its large-scale network organization involving complementary integration and segregation processes.

Open Problems in Network-aware Data Management in Exa-scale Computing and Terabit Networking Era

Energy Technology Data Exchange (ETDEWEB)

Balman, Mehmet; Byna, Surendra

2011-12-06

Accessing and managing large amounts of data is a great challenge in collaborative computing environments where resources and users are geographically distributed. Recent advances in network technology led to next-generation high-performance networks, allowing high-bandwidth connectivity. Efficient use of the network infrastructure is necessary in order to address the increasing data and compute requirements of large-scale applications. We discuss several open problems, evaluate emerging trends, and articulate our perspectives in network-aware data management.

Dynamic Circadian Modulation in a Biomathematical Model for the Effects of Sleep and Sleep Loss on Waking Neurobehavioral Performance

Science.gov (United States)

McCauley, Peter; Kalachev, Leonid V.; Mollicone, Daniel J.; Banks, Siobhan; Dinges, David F.; Van Dongen, Hans P. A.

2013-01-01

Recent experimental observations and theoretical advances have indicated that the homeostatic equilibrium for sleep/wake regulation—and thereby sensitivity to neurobehavioral impairment from sleep loss—is modulated by prior sleep/wake history. This phenomenon was predicted by a biomathematical model developed to explain changes in neurobehavioral performance across days in laboratory studies of total sleep deprivation and sustained sleep restriction. The present paper focuses on the dynamics of neurobehavioral performance within days in this biomathematical model of fatigue. Without increasing the number of model parameters, the model was updated by incorporating time-dependence in the amplitude of the circadian modulation of performance. The updated model was calibrated using a large dataset from three laboratory experiments on psychomotor vigilance test (PVT) performance, under conditions of sleep loss and circadian misalignment; and validated using another large dataset from three different laboratory experiments. The time-dependence of circadian amplitude resulted in improved goodness-of-fit in night shift schedules, nap sleep scenarios, and recovery from prior sleep loss. The updated model predicts that the homeostatic equilibrium for sleep/wake regulation—and thus sensitivity to sleep loss—depends not only on the duration but also on the circadian timing of prior sleep. This novel theoretical insight has important implications for predicting operator alertness during work schedules involving circadian misalignment such as night shift work. Citation: McCauley P; Kalachev LV; Mollicone DJ; Banks S; Dinges DF; Van Dongen HPA. Dynamic circadian modulation in a biomathematical model for the effects of sleep and sleep loss on waking neurobehavioral performance. SLEEP 2013;36(12):1987-1997. PMID:24293775

Enriched environment palliates nicotine-induced addiction and associated neurobehavioral deficits in rats.

Science.gov (United States)

Nawaz, Amber; Batool, Zehra; Ahmed, Saara; Tabassum, Saiqa; Khaliq, Saima; Mehdi, Bushra Jabeen; Sajid, Irfan; Ahmad, Shoaib; Saleem, Sadia; Naqvi, Fizza; Naqvi, Faizan; Haider, Saida

2017-11-01

This study was designed to investigate the role of enriched environment in preventing and/or reducing the neurobehavioral deficits produced after nicotine administration in albino Wistar rats. Equal numbers of rat in two groups were either placed in social environment (control group) or social along with physically enriched environment for four weeks before the administration of nicotine. Exposure to different environmental conditions was followed by the intraperitoneal injection of nicotine at the dose of 0.6 mg/kg for seven consecutive days during which addictive behavior was monitored using conditioned placed preference paradigm. Behavioral responses to locomotor activity, anxiety and retention of short term memory were investigated in control and nicotine injected groups exposed to different environments. Results of this study showed that the rats pre-exposed to physical along with social enrichment exhibited a decrease in drug seeking behavior, hyper locomotion, anxiogenic effects along with improvement of working memory as compared to control and nicotine injected groups that were kept in social environment alone. This behavioral study suggests that the exposure to physical enrichment along with socialization in young age can later reduce the chances of compulsive dependence on nicotine and related neurobehavioral deficits.

Small-World and Scale-Free Network Models for IoT Systems

Directory of Open Access Journals (Sweden)

Insoo Sohn

2017-01-01

Full Text Available It is expected that Internet of Things (IoT revolution will enable new solutions and business for consumers and entrepreneurs by connecting billions of physical world devices with varying capabilities. However, for successful realization of IoT, challenges such as heterogeneous connectivity, ubiquitous coverage, reduced network and device complexity, enhanced power savings, and enhanced resource management have to be solved. All these challenges are heavily impacted by the IoT network topology supported by massive number of connected devices. Small-world networks and scale-free networks are important complex network models with massive number of nodes and have been actively used to study the network topology of brain networks, social networks, and wireless networks. These models, also, have been applied to IoT networks to enhance synchronization, error tolerance, and more. However, due to interdisciplinary nature of the network science, with heavy emphasis on graph theory, it is not easy to study the various tools provided by complex network models. Therefore, in this paper, we attempt to introduce basic concepts of graph theory, including small-world networks and scale-free networks, and provide system models that can be easily implemented to be used as a powerful tool in solving various research problems related to IoT.

Designing of network planning system for small-scale manufacturing

Science.gov (United States)

Kapulin, D. V.; Russkikh, P. A.; Vinnichenko, M. V.

2018-05-01

The paper presents features of network planning in small-scale discrete production. The procedure of explosion of the production order, considering multilevel representation, is developed. The software architecture is offered. Approbation of the network planning system is carried out. This system allows carrying out dynamic updating of the production plan.

The function of communities in protein interaction networks at multiple scales

Directory of Open Access Journals (Sweden)

Jones Nick S

2010-07-01

Full Text Available Abstract Background If biology is modular then clusters, or communities, of proteins derived using only protein interaction network structure should define protein modules with similar biological roles. We investigate the link between biological modules and network communities in yeast and its relationship to the scale at which we probe the network. Results Our results demonstrate that the functional homogeneity of communities depends on the scale selected, and that almost all proteins lie in a functionally homogeneous community at some scale. We judge functional homogeneity using a novel test and three independent characterizations of protein function, and find a high degree of overlap between these measures. We show that a high mean clustering coefficient of a community can be used to identify those that are functionally homogeneous. By tracing the community membership of a protein through multiple scales we demonstrate how our approach could be useful to biologists focusing on a particular protein. Conclusions We show that there is no one scale of interest in the community structure of the yeast protein interaction network, but we can identify the range of resolution parameters that yield the most functionally coherent communities, and predict which communities are most likely to be functionally homogeneous.

Identifying Controlling Nodes in Neuronal Networks in Different Scales

Science.gov (United States)

Tang, Yang; Gao, Huijun; Zou, Wei; Kurths, Jürgen

2012-01-01

Recent studies have detected hubs in neuronal networks using degree, betweenness centrality, motif and synchronization and revealed the importance of hubs in their structural and functional roles. In addition, the analysis of complex networks in different scales are widely used in physics community. This can provide detailed insights into the intrinsic properties of networks. In this study, we focus on the identification of controlling regions in cortical networks of cats’ brain in microscopic, mesoscopic and macroscopic scales, based on single-objective evolutionary computation methods. The problem is investigated by considering two measures of controllability separately. The impact of the number of driver nodes on controllability is revealed and the properties of controlling nodes are shown in a statistical way. Our results show that the statistical properties of the controlling nodes display a concave or convex shape with an increase of the allowed number of controlling nodes, revealing a transition in choosing driver nodes from the areas with a large degree to the areas with a low degree. Interestingly, the community Auditory in cats’ brain, which has sparse connections with other communities, plays an important role in controlling the neuronal networks. PMID:22848475

Exposure to Enriched Environment Decreases Neurobehavioral Deficits Induced by Neonatal Glutamate Toxicity

Directory of Open Access Journals (Sweden)

Peter Kiss

2013-09-01

Full Text Available Environmental enrichment is a popular strategy to enhance motor and cognitive performance and to counteract the effects of various harmful stimuli. The protective effects of enriched environment have been shown in traumatic, ischemic and toxic nervous system lesions. Monosodium glutamate (MSG is a commonly used taste enhancer causing excitotoxic effects when given in newborn animals. We have previously demonstrated that MSG leads to a delay in neurobehavioral development, as shown by the delayed appearance of neurological reflexes and maturation of motor coordination. In the present study we aimed at investigating whether environmental enrichment is able to decrease the neurobehavioral delay caused by neonatal MSG treatment. Newborn pups were treated with MSG subcutaneously on postnatal days 1, 5 and 9. For environmental enrichment, we placed rats in larger cages, supplemented with different toys that were altered daily. Normal control and enriched control rats received saline treatment only. Physical parameters such as weight, day of eye opening, incisor eruption and ear unfolding were recorded. Animals were observed for appearance of reflexes such as negative geotaxis, righting reflexes, fore- and hindlimb grasp, fore- and hindlimb placing, sensory reflexes and gait. In cases of negative geotaxis, surface righting and gait, the time to perform the reflex was also recorded daily. For examining motor coordination, we performed grid walking, footfault, rope suspension, rota-rod, inclined board and walk initiation tests. We found that enriched environment alone did not lead to marked alterations in the course of development. On the other hand, MSG treatment caused a slight delay in reflex development and a pronounced delay in weight gain and motor coordination maturation. This delay in most signs and tests could be reversed by enriched environment: MSG-treated pups kept under enriched conditions showed no weight retardation, no reflex delay in

Intermittent exploration on a scale-free network

International Nuclear Information System (INIS)

Ramezanpour, A

2007-02-01

We study an intermittent random walk on a random network of scale-free degree distribution. The walk is a combination of simple random walks of duration t w and random long-range jumps. While the time the walker needs to cover all the nodes increases with t w , the corresponding time for the edges displays a non monotonic behavior with a minimum for some nontrivial value of t w . This is a heterogeneity-induced effect that is not observed in homogeneous small-world networks. The optimal t w increases with the degree of assortativity in the network. Depending on the nature of degree correlations and the elapsed time the walker finds an over/underestimate of the degree distribution exponent. (author)

Quantifying the connectivity of scale-free and biological networks

Energy Technology Data Exchange (ETDEWEB)

Shiner, J.S. E-mail: shiner@alumni.duke.edu; Davison, Matt E-mail: mdavison@uwo.ca

2004-07-01

Scale-free and biological networks follow a power law distribution p{sub k}{proportional_to}k{sup -{alpha}} for the probability that a node is connected to k other nodes; the corresponding ranges for {alpha} (biological: 1<{alpha}<2; scale-free: 2<{alpha}{<=}3) yield a diverging variance for the connectivity k and lack of predictability for the average connectivity. Predictability can be achieved with the Renyi, Tsallis and Landsberg-Vedral extended entropies and corresponding 'disorders' for correctly chosen values of the entropy index q. Escort distributions p{sub k}{proportional_to}k{sup -{alpha}}{sup q} with q>3/{alpha} also yield a nondiverging variance and predictability. It is argued that the Tsallis entropies may be the appropriate quantities for the study of scale-free and biological networks.

Living in a network of scaling cities and finite resources.

Science.gov (United States)

Qubbaj, Murad R; Shutters, Shade T; Muneepeerakul, Rachata

2015-02-01

Many urban phenomena exhibit remarkable regularity in the form of nonlinear scaling behaviors, but their implications on a system of networked cities has never been investigated. Such knowledge is crucial for our ability to harness the complexity of urban processes to further sustainability science. In this paper, we develop a dynamical modeling framework that embeds population-resource dynamics-a generalized Lotka-Volterra system with modifications to incorporate the urban scaling behaviors-in complex networks in which cities may be linked to the resources of other cities and people may migrate in pursuit of higher welfare. We find that isolated cities (i.e., no migration) are susceptible to collapse if they do not have access to adequate resources. Links to other cities may help cities that would otherwise collapse due to insufficient resources. The effects of inter-city links, however, can vary due to the interplay between the nonlinear scaling behaviors and network structure. The long-term population level of a city is, in many settings, largely a function of the city's access to resources over which the city has little or no competition. Nonetheless, careful investigation of dynamics is required to gain mechanistic understanding of a particular city-resource network because cities and resources may collapse and the scaling behaviors may influence the effects of inter-city links, thereby distorting what topological metrics really measure.

Different behaviors of epidemic spreading in scale-free networks with identical degree sequence

Energy Technology Data Exchange (ETDEWEB)

Chu Xiangwei; Guan Jihong [School of Electronics and Information, Tongji University, 4800 Cao' an Road, Shanghai 201804 (China); Zhang Zhongzhi; Zhou Shuigeng [School of Computer Science, Fudan University, Shanghai 200433 (China); Li Mo, E-mail: zhangzz@fudan.edu.c, E-mail: jhguan@tongj.edu.c, E-mail: sgzhou@fudan.edu.c [Software School, Fudan University, Shanghai 200433 (China)

2010-02-12

Recently, the study of dynamical behaviors of the susceptible-infected (SI) disease model in complex networks, especially in Barabasi-Albert (BA) scale-free networks, has attracted much attention. Although some interesting phenomena have been observed, the formative reasons for those particular dynamical behaviors are still not well understood, despite the speculation that topological properties (for example the degree distribution) have a strong impact on epidemic spreading. In this paper, we study the evolution behaviors of epidemic spreading on a class of scale-free networks sharing identical degree sequence, and observe significantly different evolution behaviors in the whole family of networks. We show that the power-law degree distribution does not suffice to characterize the dynamical behaviors of disease diffusion on scale-free networks.

Different behaviors of epidemic spreading in scale-free networks with identical degree sequence

International Nuclear Information System (INIS)

Chu Xiangwei; Guan Jihong; Zhang Zhongzhi; Zhou Shuigeng; Li Mo

2010-01-01

Recently, the study of dynamical behaviors of the susceptible-infected (SI) disease model in complex networks, especially in Barabasi-Albert (BA) scale-free networks, has attracted much attention. Although some interesting phenomena have been observed, the formative reasons for those particular dynamical behaviors are still not well understood, despite the speculation that topological properties (for example the degree distribution) have a strong impact on epidemic spreading. In this paper, we study the evolution behaviors of epidemic spreading on a class of scale-free networks sharing identical degree sequence, and observe significantly different evolution behaviors in the whole family of networks. We show that the power-law degree distribution does not suffice to characterize the dynamical behaviors of disease diffusion on scale-free networks.

Multiple dynamical time-scales in networks with hierarchically

Indian Academy of Sciences (India)

Modular networks; hierarchical organization; synchronization. ... we show that such a topological structure gives rise to characteristic time-scale separation ... This suggests a possible functional role of such mesoscopic organization principle in ...

Sandpile on scale-free networks with assortative mixing

International Nuclear Information System (INIS)

Yin Yanping; Zhang Duanming; Pan Guijun; He Minhua; Tan Jin

2007-01-01

We numerically investigate the Bak-Tang-Wiesenfeld sandpile model on scale-free networks with assortative mixing, where the threshold height of each node is equal to its degree. It is observed that a large fraction of multiple topplings are included in avalanches on assortative networks, which is absent on uncorrelated networks. We introduce a parameter F-bar(a) to characterize the fraction of multiple topplings in avalanches of area a. The fraction of multiple topplings increases dramatically with the degree of assortativity and has a peak for small a whose height also increase with the assortativity of the networks. Unlike the case on uncorrelated networks, the distributions of avalanche size, area and duration do not follow pure power law, but deviate more obviously from pure power law with the growing degree of assortativity. The results show that the assortative mixing has a strong influence on the behavior of avalanche dynamics on complex networks

Cerebral oxygenation in patients undergoing shoulder surgery in beach chair position: comparing general to regional anesthesia and the impact on neurobehavioral outcome.

Science.gov (United States)

Aguirre, J; Borgeat, A; Trachsel, T; Cobo Del Prado, I; De Andrés, J; Bühler, P

2014-02-01

Ischemic brain damage has been reported in healthy patients after beach chair position for surgery due to cerebral hypoperfusion. Near-infrared spectroscopy has been described as a non-invasive, continuous method to monitor cerebral oxygen saturation. However, its impact on neurobehavioral outcome comparing different anesthesia regimens has been poorly described. In this prospective, assessor-blinded study, 90 patients undergoing shoulder surgery in beach chair position following general (G-group, n=45) or regional anesthesia (R-group; n=45) were enrolled to assess the prevalence of cerebral desaturation events comparing anesthesia regimens and their impact on neurobehavioral and neurological outcome. Anesthesiologists were blinded to regional cerebral oxygen saturation values. Baseline data assessed the day before surgery included neurological and neurobehavioral tests, which were repeated the day after surgery. The baseline data for regional cerebral oxygen saturation/bispectral index and invasive blood pressure both at heart and auditory meatus levels were taken prior to anesthesia, 5 min after induction of anesthesia, 5 min after beach chair positioning, after skin incision and thereafter all 20 min until discharge. Patients in the R-group showed significantly less cerebral desaturation events (psurgery (pshoulder surgery in beach chair position influencing neurobehavioral test results at 24h. Copyright © 2013 Sociedad Española de Anestesiología, Reanimación y Terapéutica del Dolor. Published by Elsevier España. All rights reserved.

Scale-free models for the structure of business firm networks.

Science.gov (United States)

Kitsak, Maksim; Riccaboni, Massimo; Havlin, Shlomo; Pammolli, Fabio; Stanley, H Eugene

2010-03-01

We study firm collaborations in the life sciences and the information and communication technology sectors. We propose an approach to characterize industrial leadership using k -shell decomposition, with top-ranking firms in terms of market value in higher k -shell layers. We find that the life sciences industry network consists of three distinct components: a "nucleus," which is a small well-connected subgraph, "tendrils," which are small subgraphs consisting of small degree nodes connected exclusively to the nucleus, and a "bulk body," which consists of the majority of nodes. Industrial leaders, i.e., the largest companies in terms of market value, are in the highest k -shells of both networks. The nucleus of the life sciences sector is very stable: once a firm enters the nucleus, it is likely to stay there for a long time. At the same time we do not observe the above three components in the information and communication technology sector. We also conduct a systematic study of these three components in random scale-free networks. Our results suggest that the sizes of the nucleus and the tendrils in scale-free networks decrease as the exponent of the power-law degree distribution lambda increases, and disappear for lambda>or=3 . We compare the k -shell structure of random scale-free model networks with two real-world business firm networks in the life sciences and in the information and communication technology sectors. We argue that the observed behavior of the k -shell structure in the two industries is consistent with the coexistence of both preferential and random agreements in the evolution of industrial networks.

Environmental enrichment decreases asphyxia-induced neurobehavioral developmental delay in neonatal rats.

Science.gov (United States)

Kiss, Peter; Vadasz, Gyongyver; Kiss-Illes, Blanka; Horvath, Gabor; Tamas, Andrea; Reglodi, Dora; Koppan, Miklos

2013-11-13

Perinatal asphyxia during delivery produces long-term disability and represents a major problem in neonatal and pediatric care. Numerous neuroprotective approaches have been described to decrease the effects of perinatal asphyxia. Enriched environment is a popular strategy to counteract nervous system injuries. The aim of the present study was to investigate whether enriched environment is able to decrease the asphyxia-induced neurobehavioral developmental delay in neonatal rats. Asphyxia was induced in ready-to-deliver mothers by removing the pups by caesarian section after 15 min of asphyxia. Somatic and neurobehavioral development was tested daily and motor coordination weekly. Our results show that rats undergoing perinatal asphyxia had a marked developmental delay and worse performance in motor coordination tests. However, pups kept in enriched environment showed a decrease in the developmental delay observed in control asphyctic pups. Rats growing up in enriched environment did not show decrease in weight gain after the first week and the delay in reflex appearance was not as marked as in control rats. In addition, the development of motor coordination was not as strikingly delayed as in the control group. Short-term neurofunctional outcome are known to correlate with long-term deficits. Our results thus show that enriched environment could be a powerful strategy to decrease the deleterious developmental effects of perinatal asphyxia.

Emergence of scale-free close-knit friendship structure in online social networks.

Directory of Open Access Journals (Sweden)

Ai-Xiang Cui

Full Text Available Although the structural properties of online social networks have attracted much attention, the properties of the close-knit friendship structures remain an important question. Here, we mainly focus on how these mesoscale structures are affected by the local and global structural properties. Analyzing the data of four large-scale online social networks reveals several common structural properties. It is found that not only the local structures given by the indegree, outdegree, and reciprocal degree distributions follow a similar scaling behavior, the mesoscale structures represented by the distributions of close-knit friendship structures also exhibit a similar scaling law. The degree correlation is very weak over a wide range of the degrees. We propose a simple directed network model that captures the observed properties. The model incorporates two mechanisms: reciprocation and preferential attachment. Through rate equation analysis of our model, the local-scale and mesoscale structural properties are derived. In the local-scale, the same scaling behavior of indegree and outdegree distributions stems from indegree and outdegree of nodes both growing as the same function of the introduction time, and the reciprocal degree distribution also shows the same power-law due to the linear relationship between the reciprocal degree and in/outdegree of nodes. In the mesoscale, the distributions of four closed triples representing close-knit friendship structures are found to exhibit identical power-laws, a behavior attributed to the negligible degree correlations. Intriguingly, all the power-law exponents of the distributions in the local-scale and mesoscale depend only on one global parameter, the mean in/outdegree, while both the mean in/outdegree and the reciprocity together determine the ratio of the reciprocal degree of a node to its in/outdegree. Structural properties of numerical simulated networks are analyzed and compared with each of the four

Emergence of scale-free close-knit friendship structure in online social networks.

Science.gov (United States)

Cui, Ai-Xiang; Zhang, Zi-Ke; Tang, Ming; Hui, Pak Ming; Fu, Yan

2012-01-01

Although the structural properties of online social networks have attracted much attention, the properties of the close-knit friendship structures remain an important question. Here, we mainly focus on how these mesoscale structures are affected by the local and global structural properties. Analyzing the data of four large-scale online social networks reveals several common structural properties. It is found that not only the local structures given by the indegree, outdegree, and reciprocal degree distributions follow a similar scaling behavior, the mesoscale structures represented by the distributions of close-knit friendship structures also exhibit a similar scaling law. The degree correlation is very weak over a wide range of the degrees. We propose a simple directed network model that captures the observed properties. The model incorporates two mechanisms: reciprocation and preferential attachment. Through rate equation analysis of our model, the local-scale and mesoscale structural properties are derived. In the local-scale, the same scaling behavior of indegree and outdegree distributions stems from indegree and outdegree of nodes both growing as the same function of the introduction time, and the reciprocal degree distribution also shows the same power-law due to the linear relationship between the reciprocal degree and in/outdegree of nodes. In the mesoscale, the distributions of four closed triples representing close-knit friendship structures are found to exhibit identical power-laws, a behavior attributed to the negligible degree correlations. Intriguingly, all the power-law exponents of the distributions in the local-scale and mesoscale depend only on one global parameter, the mean in/outdegree, while both the mean in/outdegree and the reciprocity together determine the ratio of the reciprocal degree of a node to its in/outdegree. Structural properties of numerical simulated networks are analyzed and compared with each of the four real networks. This

REAL-TIME VIDEO SCALING BASED ON CONVOLUTION NEURAL NETWORK ARCHITECTURE

Directory of Open Access Journals (Sweden)

S Safinaz

2017-08-01

Full Text Available In recent years, video super resolution techniques becomes mandatory requirements to get high resolution videos. Many super resolution techniques researched but still video super resolution or scaling is a vital challenge. In this paper, we have presented a real-time video scaling based on convolution neural network architecture to eliminate the blurriness in the images and video frames and to provide better reconstruction quality while scaling of large datasets from lower resolution frames to high resolution frames. We compare our outcomes with multiple exiting algorithms. Our extensive results of proposed technique RemCNN (Reconstruction error minimization Convolution Neural Network shows that our model outperforms the existing technologies such as bicubic, bilinear, MCResNet and provide better reconstructed motioning images and video frames. The experimental results shows that our average PSNR result is 47.80474 considering upscale-2, 41.70209 for upscale-3 and 36.24503 for upscale-4 for Myanmar dataset which is very high in contrast to other existing techniques. This results proves our proposed model real-time video scaling based on convolution neural network architecture’s high efficiency and better performance.

Localization Algorithm Based on a Spring Model (LASM for Large Scale Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Shuai Li

2008-03-01

Full Text Available A navigation method for a lunar rover based on large scale wireless sensornetworks is proposed. To obtain high navigation accuracy and large exploration area, highnode localization accuracy and large network scale are required. However, thecomputational and communication complexity and time consumption are greatly increasedwith the increase of the network scales. A localization algorithm based on a spring model(LASM method is proposed to reduce the computational complexity, while maintainingthe localization accuracy in large scale sensor networks. The algorithm simulates thedynamics of physical spring system to estimate the positions of nodes. The sensor nodesare set as particles with masses and connected with neighbor nodes by virtual springs. Thevirtual springs will force the particles move to the original positions, the node positionscorrespondingly, from the randomly set positions. Therefore, a blind node position can bedetermined from the LASM algorithm by calculating the related forces with the neighbornodes. The computational and communication complexity are O(1 for each node, since thenumber of the neighbor nodes does not increase proportionally with the network scale size.Three patches are proposed to avoid local optimization, kick out bad nodes and deal withnode variation. Simulation results show that the computational and communicationcomplexity are almost constant despite of the increase of the network scale size. The time consumption has also been proven to remain almost constant since the calculation steps arealmost unrelated with the network scale size.

Improved Efficient Routing Strategy on Scale-Free Networks

Science.gov (United States)

Jiang, Zhong-Yuan; Liang, Man-Gui

Since the betweenness of nodes in complex networks can theoretically represent the traffic load of nodes under the currently used routing strategy, we propose an improved efficient (IE) routing strategy to enhance to the network traffic capacity based on the betweenness centrality. Any node with the highest betweenness is susceptible to traffic congestion. An efficient way to improve the network traffic capacity is to redistribute the heavy traffic load from these central nodes to non-central nodes, so in this paper, we firstly give a path cost function by considering the sum of node betweenness with a tunable parameter β along the actual path. Then, by minimizing the path cost, our IE routing strategy achieved obvious improvement on the network transport efficiency. Simulations on scale-free Barabási-Albert (BA) networks confirmed the effectiveness of our strategy, when compared with the efficient routing (ER) and the shortest path (SP) routing.

Limitations and tradeoffs in synchronization of large-scale networks with uncertain links

Science.gov (United States)

Diwadkar, Amit; Vaidya, Umesh

2016-01-01

The synchronization of nonlinear systems connected over large-scale networks has gained popularity in a variety of applications, such as power grids, sensor networks, and biology. Stochastic uncertainty in the interconnections is a ubiquitous phenomenon observed in these physical and biological networks. We provide a size-independent network sufficient condition for the synchronization of scalar nonlinear systems with stochastic linear interactions over large-scale networks. This sufficient condition, expressed in terms of nonlinear dynamics, the Laplacian eigenvalues of the nominal interconnections, and the variance and location of the stochastic uncertainty, allows us to define a synchronization margin. We provide an analytical characterization of important trade-offs between the internal nonlinear dynamics, network topology, and uncertainty in synchronization. For nearest neighbour networks, the existence of an optimal number of neighbours with a maximum synchronization margin is demonstrated. An analytical formula for the optimal gain that produces the maximum synchronization margin allows us to compare the synchronization properties of various complex network topologies. PMID:27067994

Structural Quality of Service in Large-Scale Networks

DEFF Research Database (Denmark)

Pedersen, Jens Myrup

, telephony and data. To meet the requirements of the different applications, and to handle the increased vulnerability to failures, the ability to design robust networks providing good Quality of Service is crucial. However, most planning of large-scale networks today is ad-hoc based, leading to highly...... complex networks lacking predictability and global structural properties. The thesis applies the concept of Structural Quality of Service to formulate desirable global properties, and it shows how regular graph structures can be used to obtain such properties.......Digitalization has created the base for co-existence and convergence in communications, leading to an increasing use of multi service networks. This is for example seen in the Fiber To The Home implementations, where a single fiber is used for virtually all means of communication, including TV...

Early Malnutrition and Child Neurobehavioral Development: Insights from the Study of Children of Diabetic Mothers.

Science.gov (United States)

Rizzo, Thomas A.; And Others

1997-01-01

Studied whether disturbances in mothers' metabolism (N=139) during pregnancy may exert long-range effects on neurobehavioral development of singleton progeny. Examined detailed pregnancy and perinatal records of mothers who experienced diabetes in pregnancy and intelligence tests of their offspring, administered at ages 7 to 11 years. All…

Spatial connections in regional climate model rainfall outputs at different temporal scales: Application of network theory

Science.gov (United States)

Naufan, Ihsan; Sivakumar, Bellie; Woldemeskel, Fitsum M.; Raghavan, Srivatsan V.; Vu, Minh Tue; Liong, Shie-Yui

2018-01-01

Understanding the spatial and temporal variability of rainfall has always been a great challenge, and the impacts of climate change further complicate this issue. The present study employs the concepts of complex networks to study the spatial connections in rainfall, with emphasis on climate change and rainfall scaling. Rainfall outputs (during 1961-1990) from a regional climate model (i.e. Weather Research and Forecasting (WRF) model that downscaled the European Centre for Medium-range Weather Forecasts, ECMWF ERA-40 reanalyses) over Southeast Asia are studied, and data corresponding to eight different temporal scales (6-hr, 12-hr, daily, 2-day, 4-day, weekly, biweekly, and monthly) are analyzed. Two network-based methods are applied to examine the connections in rainfall: clustering coefficient (a measure of the network's local density) and degree distribution (a measure of the network's spread). The influence of rainfall correlation threshold (T) on spatial connections is also investigated by considering seven different threshold levels (ranging from 0.5 to 0.8). The results indicate that: (1) rainfall networks corresponding to much coarser temporal scales exhibit properties similar to that of small-world networks, regardless of the threshold; (2) rainfall networks corresponding to much finer temporal scales may be classified as either small-world networks or scale-free networks, depending upon the threshold; and (3) rainfall spatial connections exhibit a transition phase at intermediate temporal scales, especially at high thresholds. These results suggest that the most appropriate model for studying spatial connections may often be different at different temporal scales, and that a combination of small-world and scale-free network models might be more appropriate for rainfall upscaling/downscaling across all scales, in the strict sense of scale-invariance. The results also suggest that spatial connections in the studied rainfall networks in Southeast Asia are

Early life trauma and attachment: Immediate and enduring effects on neurobehavioral and stress axis development

Directory of Open Access Journals (Sweden)

Millie eRincón-Cortés

2014-03-01

Full Text Available Over half a century of converging clinical and animal research indicates that early life experiences induce enduring neuroplasticity of the HPA-axis and the developing brain. This experience-induced neuroplasticity is due to alterations in the frequency and intensity of stimulation of pups’ sensory systems (i.e. olfactory, somatosensory, gustatory embedded in mother-infant interactions. This stimulation provides hidden regulators of pups’ behavioral, physiological and neural responses that have both immediate and enduring consequences, including those involving the stress response. While variation in stimulation can produce individual differences and adaptive behaviors, pathological early life experiences can induce maladaptive behaviors, initiate a pathway to pathology and increase risk for later life psychopathologies, such as mood and affective disorders, suggesting that infant attachment relationships program later life neurobehavioral function. Recent evidence suggests that the effects of maternal presence or absence during this sensory stimulation provide a major modulatory role in neural and endocrine system responses, which have minimal impact on pups’ immediate neurobehavior but a robust impact on neurobehavioral development. This concept is reviewed here using two complementary rodent models of infant trauma within attachment: infant paired odor-shock conditioning (mimicking maternal odor attachment learning and rearing with an abusive mother, that converge in producing a similar behavioral phenotype in later life including depressive-like behavior as well as disrupted HPA-axis and amygdala function. The importance of maternal social presence on pups’ immediate and enduring brain and behavior suggests unique processing of sensory stimuli in early life that could provide insight into the development of novel strategies for prevention and therapeutic interventions for trauma experienced with the abusive caregiver.

Integration of expression data in genome-scale metabolic network reconstructions

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Anna S. Blazier

2012-08-01

Full Text Available With the advent of high-throughput technologies, the field of systems biology has amassed an abundance of omics data, quantifying thousands of cellular components across a variety of scales, ranging from mRNA transcript levels to metabolite quantities. Methods are needed to not only integrate this omics data but to also use this data to heighten the predictive capabilities of computational models. Several recent studies have successfully demonstrated how flux balance analysis (FBA, a constraint-based modeling approach, can be used to integrate transcriptomic data into genome-scale metabolic network reconstructions to generate predictive computational models. In this review, we summarize such FBA-based methods for integrating expression data into genome-scale metabolic network reconstructions, highlighting their advantages as well as their limitations.

The role of apitoxin in alleviating propionic acid-induced neurobehavioral impairments in rat pups: The expression pattern of Reelin gene.

Science.gov (United States)

Daghestani, Maha H; Selim, Manar E; Abd-Elhakim, Yasmina M; Said, Enas N; El-Hameed, Noura E Abd; Khalil, Samah R; El-Tawil, Osama S

2017-09-01

The efficacy of apitoxin (bee venom; BV) in ameliorating propionic acid (PPA) -induced neurobehavioral impacts was studied. Sixty rat pups were enrolled in a split litter design to six groups: a control group, a PPA-treated group, a BV-treated group, a BV/PPA protective group, a PPA/BV therapeutic group, and a BV/PPA/BV protective and therapeutic group. Exploratory, social, locomotor, and repetitive/stereotype-like activities were assessed and prosocial, empathy, and acquired behavior were evaluated. Levels of neurotransmitter including serotonin, dopamine, and gamma-aminobutyric acid (GABA) were determined and a quantitative analysis of Reelin gene expression was performed. PPA treatment induced several behavioral alterations, as reduced exploratory activity and social behaviors, increased repetitive/stereotypic behaviors, and hyperactivity. In addition, a marked decline of neurotransmitters and down-regulation of Reelin mRNA expression were observed. BV exhibited high efficiency in ameliorating the PPA-induced neurobehavioral alterations, particularly when applied both before and after PPA administration. Overall, the results implied that BV has merit as a candidate therapeutic treatment to alleviate PPA-induced neurobehavioral disorders. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Neurobehavioral Effects of Levetiracetam in Patients with Traumatic Brain Injury

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Jared F Benge

2013-12-01

Full Text Available Moderate to severe traumatic brain injury (TBI is one of the leading causes of acquired epilepsy. Prophylaxis for seizures is the standard of care for individuals with moderate to severe injuries at risk for developing seizures, though relatively limited comparative data is available to guide clinicians in their choice of agents. There have however been experimental studies which demonstrate potential neuroprotective qualities of levetiracetam after TBI, and in turn there is hope that eventually such agents may improve neurobehavioral outcomes post-TBI. This mini-review summarizes the available studies and suggests areas for future studies.

Unified Tractable Model for Large-Scale Networks Using Stochastic Geometry: Analysis and Design

KAUST Repository

Afify, Laila H.

2016-12-01

The ever-growing demands for wireless technologies necessitate the evolution of next generation wireless networks that fulfill the diverse wireless users requirements. However, upscaling existing wireless networks implies upscaling an intrinsic component in the wireless domain; the aggregate network interference. Being the main performance limiting factor, it becomes crucial to develop a rigorous analytical framework to accurately characterize the out-of-cell interference, to reap the benefits of emerging networks. Due to the different network setups and key performance indicators, it is essential to conduct a comprehensive study that unifies the various network configurations together with the different tangible performance metrics. In that regard, the focus of this thesis is to present a unified mathematical paradigm, based on Stochastic Geometry, for large-scale networks with different antenna/network configurations. By exploiting such a unified study, we propose an efficient automated network design strategy to satisfy the desired network objectives. First, this thesis studies the exact aggregate network interference characterization, by accounting for each of the interferers signals in the large-scale network. Second, we show that the information about the interferers symbols can be approximated via the Gaussian signaling approach. The developed mathematical model presents twofold analysis unification for uplink and downlink cellular networks literature. It aligns the tangible decoding error probability analysis with the abstract outage probability and ergodic rate analysis. Furthermore, it unifies the analysis for different antenna configurations, i.e., various multiple-input multiple-output (MIMO) systems. Accordingly, we propose a novel reliable network design strategy that is capable of appropriately adjusting the network parameters to meet desired design criteria. In addition, we discuss the diversity-multiplexing tradeoffs imposed by differently favored

Complex modular structure of large-scale brain networks

Science.gov (United States)

Valencia, M.; Pastor, M. A.; Fernández-Seara, M. A.; Artieda, J.; Martinerie, J.; Chavez, M.

2009-06-01

Modular structure is ubiquitous among real-world networks from related proteins to social groups. Here we analyze the modular organization of brain networks at a large scale (voxel level) extracted from functional magnetic resonance imaging signals. By using a random-walk-based method, we unveil the modularity of brain webs and show modules with a spatial distribution that matches anatomical structures with functional significance. The functional role of each node in the network is studied by analyzing its patterns of inter- and intramodular connections. Results suggest that the modular architecture constitutes the structural basis for the coexistence of functional integration of distant and specialized brain areas during normal brain activities at rest.

Cooperative Dynamics in Lattice-Embedded Scale-Free Networks

International Nuclear Information System (INIS)

Shang Lihui; Zhang Mingji; Yang Yanqing

2009-01-01

We investigate cooperative behaviors of lattice-embedded scale-free networking agents in the prisoner's dilemma game model by employing two initial strategy distribution mechanisms, which are specific distribution to the most connected sites (hubs) and random distribution. Our study indicates that the game dynamics crucially depends on the underlying spatial network structure with different strategy distribution mechanism. The cooperators' specific distribution contributes to an enhanced level of cooperation in the system compared with random one, and cooperation is robust to cooperators' specific distribution but fragile to defectors' specific distribution. Especially, unlike the specific case, increasing heterogeneity of network does not always favor the emergence of cooperation under random mechanism. Furthermore, we study the geographical effects and find that the graphically constrained network structure tends to improve the evolution of cooperation in random case and in specific one for a large temptation to defect.

Meeting the memory challenges of brain-scale network simulation

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Susanne eKunkel

2012-01-01

Full Text Available The development of high-performance simulation software is crucial for studying the brain connectome. Using connectome data to generate neurocomputational models requires software capable of coping with models on a variety of scales: from the microscale, investigating plasticity and dynamics of circuits in local networks, to the macroscale, investigating the interactions between distinct brain regions. Prior to any serious dynamical investigation, the first task of network simulations is to check the consistency of data integrated in the connectome and constrain ranges for yet unknown parameters. Thanks to distributed computing techniques, it is possible today to routinely simulate local cortical networks of around 10^5 neurons with up to 10^9 synapses on clusters and multi-processor shared-memory machines. However, brain-scale networks are one or two orders of magnitude larger than such local networks, in terms of numbers of neurons and synapses as well as in terms of computational load. Such networks have been studied in individual studies, but the underlying simulation technologies have neither been described in sufficient detail to be reproducible nor made publicly available. Here, we discover that as the network model sizes approach the regime of meso- and macroscale simulations, memory consumption on individual compute nodes becomes a critical bottleneck. This is especially relevant on modern supercomputers such as the Bluegene/P architecture where the available working memory per CPU core is rather limited. We develop a simple linear model to analyze the memory consumption of the constituent components of a neuronal simulator as a function of network size and the number of cores used. This approach has multiple benefits. The model enables identification of key contributing components to memory saturation and prediction of the effects of potential improvements to code before any implementation takes place.

Clinical Significance of Cerebrovascular Biomarkers and White Matter Tract Integrity in Alzheimer Disease: Clinical correlations With Neurobehavioral Data in Cross-Sectional and After 18 Months Follow-ups.

Science.gov (United States)

Wu, Ming-Kung; Lu, Yan-Ting; Huang, Chi-Wei; Lin, Pin-Hsuan; Chen, Nai-Ching; Lui, Chun-Chung; Chang, Wen-Neng; Lee, Chen-Chang; Chang, Ya-Ting; Chen, Sz-Fan; Chang, Chiung-Chih

2015-07-01

Cerebrovascular risk factors and white matter (WM) damage lead to worse cognitive performance in Alzheimer dementia (AD). This study investigated WM microstructure using diffusion tensor imaging in patients with mild to moderate AD and investigated specific fiber tract involvement with respect to predefined cerebrovascular risk factors and neurobehavioral data prediction cross-sectionally and after 18 months. To identify the primary pathoanatomic relationships of risk biomarkers to fiber tract integrity, we predefined 11 major association tracts and calculated tract specific fractional anisotropy (FA) values. Eighty-five patients with AD underwent neurobehavioral assessments including the minimental state examination (MMSE) and 12-item neuropsychiatric inventory twice with a 1.5-year interval to represent major outcome factors. In the cross-sectional data, total cholesterol, low-density lipoprotein, vitamin B12, and homocysteine levels correlated variably with WM FA values. After entering the biomarkers and WM FA into a regression model to predict neurobehavioral outcomes, only fiber tract FA or homocysteine level predicted the MMSE score, and fiber tract FA or age predicted the neuropsychiatric inventory total scores and subdomains of apathy, disinhibition, and aberrant motor behavior. In the follow-up neurobehavioral data, the mean global FA value predicted the MMSE and aberrant motor behavior subdomain, while age predicted the anxiety and elation subdomains. Cerebrovascular risk biomarkers may modify WM microstructural organization, while the association with fiber integrity showed greater clinical significance to the prediction of neurobehavioral outcomes both cross-sectionally and longitudinally.

Emergence of fractal scale-free networks from stochastic evolution on the Cayley tree

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Chełminiak, Przemysław, E-mail: geronimo@amu.edu.pl

2013-11-29

An unexpected recognition of fractal topology in some real-world scale-free networks has evoked again an interest in the mechanisms stimulating their evolution. To explain this phenomenon a few models of a deterministic construction as well as a probabilistic growth controlled by a tunable parameter have been proposed so far. A quite different approach based on the fully stochastic evolution of the fractal scale-free networks presented in this Letter counterpoises these former ideas. It is argued that the diffusive evolution of the network on the Cayley tree shapes its fractality, self-similarity and the branching number criticality without any control parameter. The last attribute of the scale-free network is an intrinsic property of the skeleton, a special type of spanning tree which determines its fractality.

Cascading failure in the wireless sensor scale-free networks

Science.gov (United States)

Liu, Hao-Ran; Dong, Ming-Ru; Yin, Rong-Rong; Han, Li

2015-05-01

In the practical wireless sensor networks (WSNs), the cascading failure caused by a failure node has serious impact on the network performance. In this paper, we deeply research the cascading failure of scale-free topology in WSNs. Firstly, a cascading failure model for scale-free topology in WSNs is studied. Through analyzing the influence of the node load on cascading failure, the critical load triggering large-scale cascading failure is obtained. Then based on the critical load, a control method for cascading failure is presented. In addition, the simulation experiments are performed to validate the effectiveness of the control method. The results show that the control method can effectively prevent cascading failure. Project supported by the Natural Science Foundation of Hebei Province, China (Grant No. F2014203239), the Autonomous Research Fund of Young Teacher in Yanshan University (Grant No. 14LGB017) and Yanshan University Doctoral Foundation, China (Grant No. B867).

Neurocomportamento de recém-nascidos a termo, pequenos para a idade gestacional, filhos de mães adolescentes Neurobehavior of full-term small for gestational age newborn infants of adolescent mothers

Directory of Open Access Journals (Sweden)

Marina C. de Moraes Barros

2008-06-01

Full Text Available OBJETIVO: Comparar o neurocomportamento de recém-nascidos a termo pequenos (PIG e adequados (AIG para a idade gestacional, filhos de mães adolescentes. MÉTODOS: Estudo transversal prospectivo de nascidos a termo AIG e PIG, com 24-72 horas de vida, sem afecções do sistema nervoso central. Os neonatos foram avaliados por meio da Neonatal Intensive Care Unit Network Neurobehavioral Scale (NNNS para: habituação, atenção, despertar, controle, manobras para a orientação, qualidade dos movimentos, excitabilidade, letargia, reflexos não ótimos, assimetria, hipertonia, hipotonia e sinais de estresse e abstinência. A comparação dos grupos AIG e PIG foi feita por análise de variância e teste do qui-quadrado. Aplicou-se a regressão multivariada para analisar os fatores associados ao escore de cada variável do NNNS. RESULTADOS: Dos 3.685 nascidos no local do estudo, 928 (25% eram de mães adolescentes. Desses, 477 satisfizeram os critérios de inclusão, sendo 419 (88% AIG e 58 (12% PIG. A análise univariada não mostrou diferença em nenhuma das variáveis da NNNS entre os PIG e os AIG. Na análise multivariada, os PIG nascidos de parto vaginal apresentaram menor escore na variável qualidade de movimentos do que os nascidos por cesárea. Os PIG nascidos com anestesia local ou sem anestesia apresentaram maior escore na variável excitabilidade do que os nascidos sob anestesia loco-regional. Os PIG femininos tiveram menor escore na variável sinais de estresse/abstinência que os masculinos. CONCLUSÃO: Os recém-nascidos PIG de mães adolescentes mostraram menor qualidade de movimento, mais excitabilidade e mais sinais de estresse, em associação com o sexo do neonato e com variáveis relacionadas ao parto.OBJECTIVE: To compare the neurobehavior of small (SGA and adequate (AGA for gestational age full-term neonates born to adolescent mothers. METHODS: This prospective cross-sectional study included full-term newborn infants aged 24

Network modularity reveals critical scales for connectivity in ecology and evolution

Science.gov (United States)

Fletcher, Robert J.; Revell, Andre; Reichert, Brian E.; Kitchens, Wiley M.; Dixon, J.; Austin, James D.

2013-01-01

For nearly a century, biologists have emphasized the profound importance of spatial scale for ecology, evolution and conservation. Nonetheless, objectively identifying critical scales has proven incredibly challenging. Here we extend new techniques from physics and social sciences that estimate modularity on networks to identify critical scales for movement and gene flow in animals. Using four species that vary widely in dispersal ability and include both mark-recapture and population genetic data, we identify significant modularity in three species, two of which cannot be explained by geographic distance alone. Importantly, the inclusion of modularity in connectivity and population viability assessments alters conclusions regarding patch importance to connectivity and suggests higher metapopulation viability than when ignoring this hidden spatial scale. We argue that network modularity reveals critical meso-scales that are probably common in populations, providing a powerful means of identifying fundamental scales for biology and for conservation strategies aimed at recovering imperilled species.

Network-state modulation of power-law frequency-scaling in visual cortical neurons.

Science.gov (United States)

El Boustani, Sami; Marre, Olivier; Béhuret, Sébastien; Baudot, Pierre; Yger, Pierre; Bal, Thierry; Destexhe, Alain; Frégnac, Yves

2009-09-01

Various types of neural-based signals, such as EEG, local field potentials and intracellular synaptic potentials, integrate multiple sources of activity distributed across large assemblies. They have in common a power-law frequency-scaling structure at high frequencies, but it is still unclear whether this scaling property is dominated by intrinsic neuronal properties or by network activity. The latter case is particularly interesting because if frequency-scaling reflects the network state it could be used to characterize the functional impact of the connectivity. In intracellularly recorded neurons of cat primary visual cortex in vivo, the power spectral density of V(m) activity displays a power-law structure at high frequencies with a fractional scaling exponent. We show that this exponent is not constant, but depends on the visual statistics used to drive the network. To investigate the determinants of this frequency-scaling, we considered a generic recurrent model of cortex receiving a retinotopically organized external input. Similarly to the in vivo case, our in computo simulations show that the scaling exponent reflects the correlation level imposed in the input. This systematic dependence was also replicated at the single cell level, by controlling independently, in a parametric way, the strength and the temporal decay of the pairwise correlation between presynaptic inputs. This last model was implemented in vitro by imposing the correlation control in artificial presynaptic spike trains through dynamic-clamp techniques. These in vitro manipulations induced a modulation of the scaling exponent, similar to that observed in vivo and predicted in computo. We conclude that the frequency-scaling exponent of the V(m) reflects stimulus-driven correlations in the cortical network activity. Therefore, we propose that the scaling exponent could be used to read-out the "effective" connectivity responsible for the dynamical signature of the population signals measured

Synchronization in scale-free networks: The role of finite-size effects

Science.gov (United States)

Torres, D.; Di Muro, M. A.; La Rocca, C. E.; Braunstein, L. A.

2015-06-01

Synchronization problems in complex networks are very often studied by researchers due to their many applications to various fields such as neurobiology, e-commerce and completion of tasks. In particular, scale-free networks with degree distribution P(k)∼ k-λ , are widely used in research since they are ubiquitous in Nature and other real systems. In this paper we focus on the surface relaxation growth model in scale-free networks with 2.5< λ <3 , and study the scaling behavior of the fluctuations, in the steady state, with the system size N. We find a novel behavior of the fluctuations characterized by a crossover between two regimes at a value of N=N* that depends on λ: a logarithmic regime, found in previous research, and a constant regime. We propose a function that describes this crossover, which is in very good agreement with the simulations. We also find that, for a system size above N* , the fluctuations decrease with λ, which means that the synchronization of the system improves as λ increases. We explain this crossover analyzing the role of the network's heterogeneity produced by the system size N and the exponent of the degree distribution.

A multi-scale network method for two-phase flow in porous media

Energy Technology Data Exchange (ETDEWEB)

Khayrat, Karim, E-mail: khayratk@ifd.mavt.ethz.ch; Jenny, Patrick

2017-08-01

Pore-network models of porous media are useful in the study of pore-scale flow in porous media. In order to extract macroscopic properties from flow simulations in pore-networks, it is crucial the networks are large enough to be considered representative elementary volumes. However, existing two-phase network flow solvers are limited to relatively small domains. For this purpose, a multi-scale pore-network (MSPN) method, which takes into account flow-rate effects and can simulate larger domains compared to existing methods, was developed. In our solution algorithm, a large pore network is partitioned into several smaller sub-networks. The algorithm to advance the fluid interfaces within each subnetwork consists of three steps. First, a global pressure problem on the network is solved approximately using the multiscale finite volume (MSFV) method. Next, the fluxes across the subnetworks are computed. Lastly, using fluxes as boundary conditions, a dynamic two-phase flow solver is used to advance the solution in time. Simulation results of drainage scenarios at different capillary numbers and unfavourable viscosity ratios are presented and used to validate the MSPN method against solutions obtained by an existing dynamic network flow solver.

A multi-scale network method for two-phase flow in porous media

International Nuclear Information System (INIS)

Khayrat, Karim; Jenny, Patrick

2017-01-01

Pore-network models of porous media are useful in the study of pore-scale flow in porous media. In order to extract macroscopic properties from flow simulations in pore-networks, it is crucial the networks are large enough to be considered representative elementary volumes. However, existing two-phase network flow solvers are limited to relatively small domains. For this purpose, a multi-scale pore-network (MSPN) method, which takes into account flow-rate effects and can simulate larger domains compared to existing methods, was developed. In our solution algorithm, a large pore network is partitioned into several smaller sub-networks. The algorithm to advance the fluid interfaces within each subnetwork consists of three steps. First, a global pressure problem on the network is solved approximately using the multiscale finite volume (MSFV) method. Next, the fluxes across the subnetworks are computed. Lastly, using fluxes as boundary conditions, a dynamic two-phase flow solver is used to advance the solution in time. Simulation results of drainage scenarios at different capillary numbers and unfavourable viscosity ratios are presented and used to validate the MSPN method against solutions obtained by an existing dynamic network flow solver.

Large-scale network dynamics of beta-band oscillations underlie auditory perceptual decision-making

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Mohsen Alavash

2017-06-01

Full Text Available Perceptual decisions vary in the speed at which we make them. Evidence suggests that translating sensory information into perceptual decisions relies on distributed interacting neural populations, with decision speed hinging on power modulations of the neural oscillations. Yet the dependence of perceptual decisions on the large-scale network organization of coupled neural oscillations has remained elusive. We measured magnetoencephalographic signals in human listeners who judged acoustic stimuli composed of carefully titrated clouds of tone sweeps. These stimuli were used in two task contexts, in which the participants judged the overall pitch or direction of the tone sweeps. We traced the large-scale network dynamics of the source-projected neural oscillations on a trial-by-trial basis using power-envelope correlations and graph-theoretical network discovery. In both tasks, faster decisions were predicted by higher segregation and lower integration of coupled beta-band (∼16–28 Hz oscillations. We also uncovered the brain network states that promoted faster decisions in either lower-order auditory or higher-order control brain areas. Specifically, decision speed in judging the tone sweep direction critically relied on the nodal network configurations of anterior temporal, cingulate, and middle frontal cortices. Our findings suggest that global network communication during perceptual decision-making is implemented in the human brain by large-scale couplings between beta-band neural oscillations. The speed at which we make perceptual decisions varies. This translation of sensory information into perceptual decisions hinges on dynamic changes in neural oscillatory activity. However, the large-scale neural-network embodiment supporting perceptual decision-making is unclear. We addressed this question by experimenting two auditory perceptual decision-making situations. Using graph-theoretical network discovery, we traced the large-scale network

A Hybrid Testbed for Performance Evaluation of Large-Scale Datacenter Networks

DEFF Research Database (Denmark)

Pilimon, Artur; Ruepp, Sarah Renée

2018-01-01

Datacenters (DC) as well as their network interconnects are growing in scale and complexity. They are constantly being challenged in terms of energy and resource utilization efficiency, scalability, availability, reliability and performance requirements. Therefore, these resource-intensive enviro......Datacenters (DC) as well as their network interconnects are growing in scale and complexity. They are constantly being challenged in terms of energy and resource utilization efficiency, scalability, availability, reliability and performance requirements. Therefore, these resource......-intensive environments must be properly tested and analyzed in order to make timely upgrades and transformations. However, a limited number of academic institutions and Research and Development companies have access to production scale DC Network (DCN) testing facilities, and resource-limited studies can produce...... misleading or inaccurate results. To address this problem, we introduce an alternative solution, which forms a solid base for a more realistic and comprehensive performance evaluation of different aspects of DCNs. It is based on the System-in-the-loop (SITL) concept, where real commercial DCN equipment...

multi scale analysis of a function by neural networks elementary derivatives functions

International Nuclear Information System (INIS)

Chikhi, A.; Gougam, A.; Chafa, F.

2006-01-01

Recently, the wavelet network has been introduced as a special neural network supported by the wavelet theory . Such networks constitute a tool for function approximation problems as it has been already proved in reference . Our present work deals with this model, treating a multi scale analysis of a function. We have then used a linear expansion of a given function in wavelets, neglecting the usual translation parameters. We investigate two training operations. The first one consists on an optimization of the output synaptic layer, the second one, optimizing the output function with respect to scale parameters. We notice a temporary merging of the scale parameters leading to some interesting results : new elementary derivatives units emerge, representing a new elementary task, which is the derivative of the output task

Epidemic mitigation via awareness propagation in communication networks: the role of time scales

Science.gov (United States)

Wang, Huijuan; Chen, Chuyi; Qu, Bo; Li, Daqing; Havlin, Shlomo

2017-07-01

The participation of individuals in multi-layer networks allows for feedback between network layers, opening new possibilities to mitigate epidemic spreading. For instance, the spread of a biological disease such as Ebola in a physical contact network may trigger the propagation of the information related to this disease in a communication network, e.g. an online social network. The information propagated in the communication network may increase the awareness of some individuals, resulting in them avoiding contact with their infected neighbors in the physical contact network, which might protect the population from the infection. In this work, we aim to understand how the time scale γ of the information propagation (speed that information is spread and forgotten) in the communication network relative to that of the epidemic spread (speed that an epidemic is spread and cured) in the physical contact network influences such mitigation using awareness information. We begin by proposing a model of the interaction between information propagation and epidemic spread, taking into account the relative time scale γ. We analytically derive the average fraction of infected nodes in the meta-stable state for this model (i) by developing an individual-based mean-field approximation (IBMFA) method and (ii) by extending the microscopic Markov chain approach (MMCA). We show that when the time scale γ of the information spread relative to the epidemic spread is large, our IBMFA approximation is better compared to MMCA near the epidemic threshold, whereas MMCA performs better when the prevalence of the epidemic is high. Furthermore, we find that an optimal mitigation exists that leads to a minimal fraction of infected nodes. The optimal mitigation is achieved at a non-trivial relative time scale γ, which depends on the rate at which an infected individual becomes aware. Contrary to our intuition, information spread too fast in the communication network could reduce the

Effect of clustering on attack vulnerability of interdependent scale-free networks

International Nuclear Information System (INIS)

Li, Rui-qi; Sun, Shi-wen; Ma, Yi-lin; Wang, Li; Xia, Cheng-yi

2015-01-01

In order to deeply understand the complex interdependent systems, it is of great concern to take clustering coefficient, which is an important feature of many real-world systems, into account. Previous study mainly focused on the impact of clustering on interdependent networks under random attacks, while we extend the study to the case of the more realistic attacking strategy, targeted attack. A system composed of two interdependent scale-free networks with tunable clustering is provided. The effects of coupling strength and coupling preference on attack vulnerability are explored. Numerical simulation results demonstrate that interdependent links between two networks make the entire system much more fragile to attacks. Also, it is found that clustering significantly increases the vulnerability of interdependent scale-free networks. Moreover, for fully coupled network, disassortative coupling is found to be most vulnerable to random attacks, while the random and assortative coupling have little difference. Additionally, enhancing coupling strength can greatly enhance the fragility of interdependent networks against targeted attacks. These results can not only improve the deep understanding of structural complexity of complex systems, but also provide insights into the guidance of designing resilient infrastructures.

Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models.

Directory of Open Access Journals (Sweden)

Ryan C Williamson

2016-12-01

Full Text Available Recent studies have applied dimensionality reduction methods to understand how the multi-dimensional structure of neural population activity gives rise to brain function. It is unclear, however, how the results obtained from dimensionality reduction generalize to recordings with larger numbers of neurons and trials or how these results relate to the underlying network structure. We address these questions by applying factor analysis to recordings in the visual cortex of non-human primates and to spiking network models that self-generate irregular activity through a balance of excitation and inhibition. We compared the scaling trends of two key outputs of dimensionality reduction-shared dimensionality and percent shared variance-with neuron and trial count. We found that the scaling properties of networks with non-clustered and clustered connectivity differed, and that the in vivo recordings were more consistent with the clustered network. Furthermore, recordings from tens of neurons were sufficient to identify the dominant modes of shared variability that generalize to larger portions of the network. These findings can help guide the interpretation of dimensionality reduction outputs in regimes of limited neuron and trial sampling and help relate these outputs to the underlying network structure.

Opinion formation on multiplex scale-free networks

Science.gov (United States)

Nguyen, Vu Xuan; Xiao, Gaoxi; Xu, Xin-Jian; Li, Guoqi; Wang, Zhen

2018-01-01

Most individuals, if not all, live in various social networks. The formation of opinion systems is an outcome of social interactions and information propagation occurring in such networks. We study the opinion formation with a new rule of pairwise interactions in the novel version of the well-known Deffuant model on multiplex networks composed of two layers, each of which is a scale-free network. It is found that in a duplex network composed of two identical layers, the presence of the multiplexity helps either diminish or enhance opinion diversity depending on the relative magnitudes of tolerance ranges characterizing the degree of openness/tolerance on both layers: there is a steady separation between different regions of tolerance range values on two network layers where multiplexity plays two different roles, respectively. Additionally, the two critical tolerance ranges follow a one-sum rule; that is, each of the layers reaches a complete consensus only if the sum of the tolerance ranges on the two layers is greater than a constant approximately equaling 1, the double of the critical bound on a corresponding isolated network. A further investigation of the coupling between constituent layers quantified by a link overlap parameter reveals that as the layers are loosely coupled, the two opinion systems co-evolve independently, but when the inter-layer coupling is sufficiently strong, a monotonic behavior is observed: an increase in the tolerance range of a layer causes a decline in the opinion diversity on the other layer regardless of the magnitudes of tolerance ranges associated with the layers in question.

Cerebral oxygenation in the beach chair position for shoulder surgery in regional anesthesia: impact on cerebral blood flow and neurobehavioral outcome.

Science.gov (United States)

Aguirre, José A; Märzendorfer, Olivia; Brada, Muriel; Saporito, Andrea; Borgeat, Alain; Bühler, Philipp

2016-12-01

Beach chair position is considered a potential risk factor for central neurological events particularly if combined with low blood pressure. The aim of this study was to assess the impact of regional anesthesia on cerebral blood flow and neurobehavioral outcome. This is a prospective, assessor-blinded observational study evaluating patients in the beach chair position undergoing shoulder surgery under regional anesthesia. University hospital operating room. Forty patients with American Society of Anesthesiologists classes I-II physical status scheduled for elective shoulder surgery. Cerebral saturation and blood flow of the middle cerebral artery were measured prior to anesthesia and continued after beach chair positioning until discharge to the postanesthesia care unit. The anesthesiologist was blinded for these values. Controlled hypotension with systolic blood pressure≤100mm Hg was maintained during surgery. Neurobehavioral tests and values of regional cerebral saturation, bispectral index, the mean maximal blood flow of the middle cerebral artery, and invasive blood pressure were measured prior to regional anesthesia, and measurements were repeated after placement of the patient on the beach chair position and every 20 minutes thereafter until discharge to postanesthesia care unit. The neurobehavioral tests were repeated the day after surgery. The incidence of cerebral desaturation events was 5%. All patients had a significant blood pressure drop 5 minutes after beach chair positioning, measured at the heart as well as the acoustic meatus levels, when compared with baseline values (Psurgery (Pshoulder surgery had no major impact on cerebral blood flow and cerebral oxygenation. However, some impact on neurobehavioral outcome 24 hours after surgery was observed. Copyright © 2016 Elsevier Inc. All rights reserved.

Development of large-scale functional brain networks in children.

Directory of Open Access Journals (Sweden)

Kaustubh Supekar

2009-07-01

Full Text Available The ontogeny of large-scale functional organization of the human brain is not well understood. Here we use network analysis of intrinsic functional connectivity to characterize the organization of brain networks in 23 children (ages 7-9 y and 22 young-adults (ages 19-22 y. Comparison of network properties, including path-length, clustering-coefficient, hierarchy, and regional connectivity, revealed that although children and young-adults' brains have similar "small-world" organization at the global level, they differ significantly in hierarchical organization and interregional connectivity. We found that subcortical areas were more strongly connected with primary sensory, association, and paralimbic areas in children, whereas young-adults showed stronger cortico-cortical connectivity between paralimbic, limbic, and association areas. Further, combined analysis of functional connectivity with wiring distance measures derived from white-matter fiber tracking revealed that the development of large-scale brain networks is characterized by weakening of short-range functional connectivity and strengthening of long-range functional connectivity. Importantly, our findings show that the dynamic process of over-connectivity followed by pruning, which rewires connectivity at the neuronal level, also operates at the systems level, helping to reconfigure and rebalance subcortical and paralimbic connectivity in the developing brain. Our study demonstrates the usefulness of network analysis of brain connectivity to elucidate key principles underlying functional brain maturation, paving the way for novel studies of disrupted brain connectivity in neurodevelopmental disorders such as autism.

Development of large-scale functional brain networks in children.

Science.gov (United States)

Supekar, Kaustubh; Musen, Mark; Menon, Vinod

2009-07-01

The ontogeny of large-scale functional organization of the human brain is not well understood. Here we use network analysis of intrinsic functional connectivity to characterize the organization of brain networks in 23 children (ages 7-9 y) and 22 young-adults (ages 19-22 y). Comparison of network properties, including path-length, clustering-coefficient, hierarchy, and regional connectivity, revealed that although children and young-adults' brains have similar "small-world" organization at the global level, they differ significantly in hierarchical organization and interregional connectivity. We found that subcortical areas were more strongly connected with primary sensory, association, and paralimbic areas in children, whereas young-adults showed stronger cortico-cortical connectivity between paralimbic, limbic, and association areas. Further, combined analysis of functional connectivity with wiring distance measures derived from white-matter fiber tracking revealed that the development of large-scale brain networks is characterized by weakening of short-range functional connectivity and strengthening of long-range functional connectivity. Importantly, our findings show that the dynamic process of over-connectivity followed by pruning, which rewires connectivity at the neuronal level, also operates at the systems level, helping to reconfigure and rebalance subcortical and paralimbic connectivity in the developing brain. Our study demonstrates the usefulness of network analysis of brain connectivity to elucidate key principles underlying functional brain maturation, paving the way for novel studies of disrupted brain connectivity in neurodevelopmental disorders such as autism.

Scaling-Laws of Flow Entropy with Topological Metrics of Water Distribution Networks

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Giovanni Francesco Santonastaso

2018-01-01

Full Text Available Robustness of water distribution networks is related to their connectivity and topological structure, which also affect their reliability. Flow entropy, based on Shannon’s informational entropy, has been proposed as a measure of network redundancy and adopted as a proxy of reliability in optimal network design procedures. In this paper, the scaling properties of flow entropy of water distribution networks with their size and other topological metrics are studied. To such aim, flow entropy, maximum flow entropy, link density and average path length have been evaluated for a set of 22 networks, both real and synthetic, with different size and topology. The obtained results led to identify suitable scaling laws of flow entropy and maximum flow entropy with water distribution network size, in the form of power–laws. The obtained relationships allow comparing the flow entropy of water distribution networks with different size, and provide an easy tool to define the maximum achievable entropy of a specific water distribution network. An example of application of the obtained relationships to the design of a water distribution network is provided, showing how, with a constrained multi-objective optimization procedure, a tradeoff between network cost and robustness is easily identified.

Animal Models of Virus-Induced Neurobehavioral Sequelae: Recent Advances, Methodological Issues, and Future Prospects

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Marco Bortolato

2010-01-01

Full Text Available Converging lines of clinical and epidemiological evidence suggest that viral infections in early developmental stages may be a causal factor in neuropsychiatric disorders such as schizophrenia, bipolar disorder, and autism-spectrum disorders. This etiological link, however, remains controversial in view of the lack of consistent and reproducible associations between viruses and mental illness. Animal models of virus-induced neurobehavioral disturbances afford powerful tools to test etiological hypotheses and explore pathophysiological mechanisms. Prenatal or neonatal inoculations of neurotropic agents (such as herpes-, influenza-, and retroviruses in rodents result in a broad spectrum of long-term alterations reminiscent of psychiatric abnormalities. Nevertheless, the complexity of these sequelae often poses methodological and interpretational challenges and thwarts their characterization. The recent conceptual advancements in psychiatric nosology and behavioral science may help determine new heuristic criteria to enhance the translational value of these models. A particularly critical issue is the identification of intermediate phenotypes, defined as quantifiable factors representing single neurochemical, neuropsychological, or neuroanatomical aspects of a diagnostic category. In this paper, we examine how the employment of these novel concepts may lead to new methodological refinements in the study of virus-induced neurobehavioral sequelae through animal models.

Large-scale modeling of epileptic seizures: scaling properties of two parallel neuronal network simulation algorithms.

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Pesce, Lorenzo L; Lee, Hyong C; Hereld, Mark; Visser, Sid; Stevens, Rick L; Wildeman, Albert; van Drongelen, Wim

2013-01-01

Our limited understanding of the relationship between the behavior of individual neurons and large neuronal networks is an important limitation in current epilepsy research and may be one of the main causes of our inadequate ability to treat it. Addressing this problem directly via experiments is impossibly complex; thus, we have been developing and studying medium-large-scale simulations of detailed neuronal networks to guide us. Flexibility in the connection schemas and a complete description of the cortical tissue seem necessary for this purpose. In this paper we examine some of the basic issues encountered in these multiscale simulations. We have determined the detailed behavior of two such simulators on parallel computer systems. The observed memory and computation-time scaling behavior for a distributed memory implementation were very good over the range studied, both in terms of network sizes (2,000 to 400,000 neurons) and processor pool sizes (1 to 256 processors). Our simulations required between a few megabytes and about 150 gigabytes of RAM and lasted between a few minutes and about a week, well within the capability of most multinode clusters. Therefore, simulations of epileptic seizures on networks with millions of cells should be feasible on current supercomputers.

Large-Scale Modeling of Epileptic Seizures: Scaling Properties of Two Parallel Neuronal Network Simulation Algorithms

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Lorenzo L. Pesce

2013-01-01

Full Text Available Our limited understanding of the relationship between the behavior of individual neurons and large neuronal networks is an important limitation in current epilepsy research and may be one of the main causes of our inadequate ability to treat it. Addressing this problem directly via experiments is impossibly complex; thus, we have been developing and studying medium-large-scale simulations of detailed neuronal networks to guide us. Flexibility in the connection schemas and a complete description of the cortical tissue seem necessary for this purpose. In this paper we examine some of the basic issues encountered in these multiscale simulations. We have determined the detailed behavior of two such simulators on parallel computer systems. The observed memory and computation-time scaling behavior for a distributed memory implementation were very good over the range studied, both in terms of network sizes (2,000 to 400,000 neurons and processor pool sizes (1 to 256 processors. Our simulations required between a few megabytes and about 150 gigabytes of RAM and lasted between a few minutes and about a week, well within the capability of most multinode clusters. Therefore, simulations of epileptic seizures on networks with millions of cells should be feasible on current supercomputers.

Coarse-Grain Bandwidth Estimation Scheme for Large-Scale Network

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Cheung, Kar-Ming; Jennings, Esther H.; Sergui, John S.

2013-01-01

A large-scale network that supports a large number of users can have an aggregate data rate of hundreds of Mbps at any time. High-fidelity simulation of a large-scale network might be too complicated and memory-intensive for typical commercial-off-the-shelf (COTS) tools. Unlike a large commercial wide-area-network (WAN) that shares diverse network resources among diverse users and has a complex topology that requires routing mechanism and flow control, the ground communication links of a space network operate under the assumption of a guaranteed dedicated bandwidth allocation between specific sparse endpoints in a star-like topology. This work solved the network design problem of estimating the bandwidths of a ground network architecture option that offer different service classes to meet the latency requirements of different user data types. In this work, a top-down analysis and simulation approach was created to size the bandwidths of a store-and-forward network for a given network topology, a mission traffic scenario, and a set of data types with different latency requirements. These techniques were used to estimate the WAN bandwidths of the ground links for different architecture options of the proposed Integrated Space Communication and Navigation (SCaN) Network. A new analytical approach, called the "leveling scheme," was developed to model the store-and-forward mechanism of the network data flow. The term "leveling" refers to the spreading of data across a longer time horizon without violating the corresponding latency requirement of the data type. Two versions of the leveling scheme were developed: 1. A straightforward version that simply spreads the data of each data type across the time horizon and doesn't take into account the interactions among data types within a pass, or between data types across overlapping passes at a network node, and is inherently sub-optimal. 2. Two-state Markov leveling scheme that takes into account the second order behavior of

Green Supply Chain Network Design with Economies of Scale and Environmental Concerns

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Dezhi Zhang

2017-01-01

Full Text Available This study considers a design problem in the supply chain network of an assembly manufacturing enterprise with economies of scale and environmental concerns. The study aims to obtain a rational tradeoff between environmental influence and total cost. A mixed-integer nonlinear programming model is developed to determine the optimal location and size of regional distribution centers (RDCs and the investment of environmental facilities considering the effects of economies of scale and CO2 emission taxes. Numerical examples are provided to illustrate the applications of the proposed model. Moreover, comparative analysis of the related key parameters is conducted (i.e., carbon emission tax, logistics demand of customers, and economies of scale of RDC, to explore the corresponding effects on the network design of a green supply chain. Moreover, the proposed model is applied in an actual case—network design of a supply chain of an electric meter company in China. Findings show that (i the optimal location of RDCs is affected by the demand of customers and the level of economies of scale and that (ii the introduction of CO2 emission taxes will change the structure of a supply chain network, which will decrease CO2 emissions per unit shipment.

Dynamic circadian modulation in a biomathematical model for the effects of sleep and sleep loss on waking neurobehavioral performance.

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McCauley, Peter; Kalachev, Leonid V; Mollicone, Daniel J; Banks, Siobhan; Dinges, David F; Van Dongen, Hans P A

2013-12-01

Recent experimental observations and theoretical advances have indicated that the homeostatic equilibrium for sleep/wake regulation--and thereby sensitivity to neurobehavioral impairment from sleep loss--is modulated by prior sleep/wake history. This phenomenon was predicted by a biomathematical model developed to explain changes in neurobehavioral performance across days in laboratory studies of total sleep deprivation and sustained sleep restriction. The present paper focuses on the dynamics of neurobehavioral performance within days in this biomathematical model of fatigue. Without increasing the number of model parameters, the model was updated by incorporating time-dependence in the amplitude of the circadian modulation of performance. The updated model was calibrated using a large dataset from three laboratory experiments on psychomotor vigilance test (PVT) performance, under conditions of sleep loss and circadian misalignment; and validated using another large dataset from three different laboratory experiments. The time-dependence of circadian amplitude resulted in improved goodness-of-fit in night shift schedules, nap sleep scenarios, and recovery from prior sleep loss. The updated model predicts that the homeostatic equilibrium for sleep/wake regulation--and thus sensitivity to sleep loss--depends not only on the duration but also on the circadian timing of prior sleep. This novel theoretical insight has important implications for predicting operator alertness during work schedules involving circadian misalignment such as night shift work.

A characterization of scale invariant responses in enzymatic networks.

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Maja Skataric

Full Text Available An ubiquitous property of biological sensory systems is adaptation: a step increase in stimulus triggers an initial change in a biochemical or physiological response, followed by a more gradual relaxation toward a basal, pre-stimulus level. Adaptation helps maintain essential variables within acceptable bounds and allows organisms to readjust themselves to an optimum and non-saturating sensitivity range when faced with a prolonged change in their environment. Recently, it was shown theoretically and experimentally that many adapting systems, both at the organism and single-cell level, enjoy a remarkable additional feature: scale invariance, meaning that the initial, transient behavior remains (approximately the same even when the background signal level is scaled. In this work, we set out to investigate under what conditions a broadly used model of biochemical enzymatic networks will exhibit scale-invariant behavior. An exhaustive computational study led us to discover a new property of surprising simplicity and generality, uniform linearizations with fast output (ULFO, whose validity we show is both necessary and sufficient for scale invariance of three-node enzymatic networks (and sufficient for any number of nodes. Based on this study, we go on to develop a mathematical explanation of how ULFO results in scale invariance. Our work provides a surprisingly consistent, simple, and general framework for understanding this phenomenon, and results in concrete experimental predictions.

Precision Scaling of Neural Networks for Efficient Audio Processing

OpenAIRE

Ko, Jong Hwan; Fromm, Josh; Philipose, Matthai; Tashev, Ivan; Zarar, Shuayb

2017-01-01

While deep neural networks have shown powerful performance in many audio applications, their large computation and memory demand has been a challenge for real-time processing. In this paper, we study the impact of scaling the precision of neural networks on the performance of two common audio processing tasks, namely, voice-activity detection and single-channel speech enhancement. We determine the optimal pair of weight/neuron bit precision by exploring its impact on both the performance and ...

A Networked Sensor System for the Analysis of Plot-Scale Hydrology.

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Villalba, German; Plaza, Fernando; Zhong, Xiaoyang; Davis, Tyler W; Navarro, Miguel; Li, Yimei; Slater, Thomas A; Liang, Yao; Liang, Xu

2017-03-20

This study presents the latest updates to the Audubon Society of Western Pennsylvania (ASWP) testbed, a $50,000 USD, 104-node outdoor multi-hop wireless sensor network (WSN). The network collects environmental data from over 240 sensors, including the EC-5, MPS-1 and MPS-2 soil moisture and soil water potential sensors and self-made sap flow sensors, across a heterogeneous deployment comprised of MICAz, IRIS and TelosB wireless motes. A low-cost sensor board and software driver was developed for communicating with the analog and digital sensors. Innovative techniques (e.g., balanced energy efficient routing and heterogeneous over-the-air mote reprogramming) maintained high success rates (>96%) and enabled effective software updating, throughout the large-scale heterogeneous WSN. The edaphic properties monitored by the network showed strong agreement with data logger measurements and were fitted to pedotransfer functions for estimating local soil hydraulic properties. Furthermore, sap flow measurements, scaled to tree stand transpiration, were found to be at or below potential evapotranspiration estimates. While outdoor WSNs still present numerous challenges, the ASWP testbed proves to be an effective and (relatively) low-cost environmental monitoring solution and represents a step towards developing a platform for monitoring and quantifying statistically relevant environmental parameters from large-scale network deployments.

Software-defined optical network for metro-scale geographically distributed data centers.

Science.gov (United States)

Samadi, Payman; Wen, Ke; Xu, Junjie; Bergman, Keren

2016-05-30

The emergence of cloud computing and big data has rapidly increased the deployment of small and mid-sized data centers. Enterprises and cloud providers require an agile network among these data centers to empower application reliability and flexible scalability. We present a software-defined inter data center network to enable on-demand scale out of data centers on a metro-scale optical network. The architecture consists of a combined space/wavelength switching platform and a Software-Defined Networking (SDN) control plane equipped with a wavelength and routing assignment module. It enables establishing transparent and bandwidth-selective connections from L2/L3 switches, on-demand. The architecture is evaluated in a testbed consisting of 3 data centers, 5-25 km apart. We successfully demonstrated end-to-end bulk data transfer and Virtual Machine (VM) migrations across data centers with less than 100 ms connection setup time and close to full link capacity utilization.

A Chronic Longitudinal Characterization of Neurobehavioral and Neuropathological Cognitive Impairment in a Mouse Model of Gulf War Agent Exposure

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Zakirova, Zuchra; Crynen, Gogce; Hassan, Samira; Abdullah, Laila; Horne, Lauren; Mathura, Venkatarajan; Crawford, Fiona; Ait-Ghezala, Ghania

2016-01-01

Gulf War Illness (GWI) is a chronic multisymptom illness with a central nervous system component that includes memory impairment as well as neurological and musculoskeletal deficits. Previous studies have shown that in the First Persian Gulf War conflict (1990–1991) exposure to Gulf War (GW) agents, such as pyridostigmine bromide (PB) and permethrin (PER), were key contributors to the etiology of GWI. For this study, we used our previously established mouse model of GW agent exposure (10 days PB+PER) and undertook an extensive lifelong neurobehavioral characterization of the mice from 11 days to 22.5 months post exposure in order to address the persistence and chronicity of effects suffered by the current GWI patient population, 24 years post-exposure. Mice were evaluated using a battery of neurobehavioral testing paradigms, including Open Field Test (OFT), Elevated Plus Maze (EPM), Three Chamber Testing, Radial Arm Water Maze (RAWM), and Barnes Maze (BM) Test. We also carried out neuropathological analyses at 22.5 months post exposure to GW agents after the final behavioral testing. Our results demonstrate that PB+PER exposed mice exhibit neurobehavioral deficits beginning at the 13 months post exposure time point and continuing trends through the 22.5 month post exposure time point. Furthermore, neuropathological changes, including an increase in GFAP staining in the cerebral cortices of exposed mice, were noted 22.5 months post exposure. Thus, the persistent neuroinflammation evident in our model presents a platform with which to identify novel biological pathways, correlating with emergent outcomes that may be amenable to therapeutic targeting. Furthermore, in this work we confirmed our previous findings that GW agent exposure causes neuropathological changes, and have presented novel data which demonstrate increased disinhibition, and lack of social preference in PB+PER exposed mice at 13 months after exposure. We also extended upon our previous work to

Active self-testing noise measurement sensors for large-scale environmental sensor networks.

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Domínguez, Federico; Cuong, Nguyen The; Reinoso, Felipe; Touhafi, Abdellah; Steenhaut, Kris

2013-12-13

Large-scale noise pollution sensor networks consist of hundreds of spatially distributed microphones that measure environmental noise. These networks provide historical and real-time environmental data to citizens and decision makers and are therefore a key technology to steer environmental policy. However, the high cost of certified environmental microphone sensors render large-scale environmental networks prohibitively expensive. Several environmental network projects have started using off-the-shelf low-cost microphone sensors to reduce their costs, but these sensors have higher failure rates and produce lower quality data. To offset this disadvantage, we developed a low-cost noise sensor that actively checks its condition and indirectly the integrity of the data it produces. The main design concept is to embed a 13 mm speaker in the noise sensor casing and, by regularly scheduling a frequency sweep, estimate the evolution of the microphone's frequency response over time. This paper presents our noise sensor's hardware and software design together with the results of a test deployment in a large-scale environmental network in Belgium. Our middle-range-value sensor (around €50) effectively detected all experienced malfunctions, in laboratory tests and outdoor deployments, with a few false positives. Future improvements could further lower the cost of our sensor below €10.

Scale free effects in world currency exchange network

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Górski, A. Z.; Drożdż, S.; Kwapień, J.

2008-11-01

A large collection of daily time series for 60 world currencies' exchange rates is considered. The correlation matrices are calculated and the corresponding Minimal Spanning Tree (MST) graphs are constructed for each of those currencies used as reference for the remaining ones. It is shown that multiplicity of the MST graphs' nodes to a good approximation develops a power like, scale free distribution with the scaling exponent similar as for several other complex systems studied so far. Furthermore, quantitative arguments in favor of the hierarchical organization of the world currency exchange network are provided by relating the structure of the above MST graphs and their scaling exponents to those that are derived from an exactly solvable hierarchical network model. A special status of the USD during the period considered can be attributed to some departures of the MST features, when this currency (or some other tied to it) is used as reference, from characteristics typical to such a hierarchical clustering of nodes towards those that correspond to the random graphs. Even though in general the basic structure of the MST is robust with respect to changing the reference currency some trace of a systematic transition from somewhat dispersed - like the USD case - towards more compact MST topology can be observed when correlations increase.

REAL-TIME VIDEO SCALING BASED ON CONVOLUTION NEURAL NETWORK ARCHITECTURE

OpenAIRE

S Safinaz; A V Ravi Kumar

2017-01-01

In recent years, video super resolution techniques becomes mandatory requirements to get high resolution videos. Many super resolution techniques researched but still video super resolution or scaling is a vital challenge. In this paper, we have presented a real-time video scaling based on convolution neural network architecture to eliminate the blurriness in the images and video frames and to provide better reconstruction quality while scaling of large datasets from lower resolution frames t...

Complex networks with scale-free nature and hierarchical modularity

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Shekatkar, Snehal M.; Ambika, G.

2015-09-01

Generative mechanisms which lead to empirically observed structure of networked systems from diverse fields like biology, technology and social sciences form a very important part of study of complex networks. The structure of many networked systems like biological cell, human society and World Wide Web markedly deviate from that of completely random networks indicating the presence of underlying processes. Often the main process involved in their evolution is the addition of links between existing nodes having a common neighbor. In this context we introduce an important property of the nodes, which we call mediating capacity, that is generic to many networks. This capacity decreases rapidly with increase in degree, making hubs weak mediators of the process. We show that this property of nodes provides an explanation for the simultaneous occurrence of the observed scale-free structure and hierarchical modularity in many networked systems. This also explains the high clustering and small-path length seen in real networks as well as non-zero degree-correlations. Our study also provides insight into the local process which ultimately leads to emergence of preferential attachment and hence is also important in understanding robustness and control of real networks as well as processes happening on real networks.

Network-state modulation of power-law frequency-scaling in visual cortical neurons.

Directory of Open Access Journals (Sweden)

Sami El Boustani

2009-09-01

Full Text Available Various types of neural-based signals, such as EEG, local field potentials and intracellular synaptic potentials, integrate multiple sources of activity distributed across large assemblies. They have in common a power-law frequency-scaling structure at high frequencies, but it is still unclear whether this scaling property is dominated by intrinsic neuronal properties or by network activity. The latter case is particularly interesting because if frequency-scaling reflects the network state it could be used to characterize the functional impact of the connectivity. In intracellularly recorded neurons of cat primary visual cortex in vivo, the power spectral density of V(m activity displays a power-law structure at high frequencies with a fractional scaling exponent. We show that this exponent is not constant, but depends on the visual statistics used to drive the network. To investigate the determinants of this frequency-scaling, we considered a generic recurrent model of cortex receiving a retinotopically organized external input. Similarly to the in vivo case, our in computo simulations show that the scaling exponent reflects the correlation level imposed in the input. This systematic dependence was also replicated at the single cell level, by controlling independently, in a parametric way, the strength and the temporal decay of the pairwise correlation between presynaptic inputs. This last model was implemented in vitro by imposing the correlation control in artificial presynaptic spike trains through dynamic-clamp techniques. These in vitro manipulations induced a modulation of the scaling exponent, similar to that observed in vivo and predicted in computo. We conclude that the frequency-scaling exponent of the V(m reflects stimulus-driven correlations in the cortical network activity. Therefore, we propose that the scaling exponent could be used to read-out the "effective" connectivity responsible for the dynamical signature of the population

Inter-subject FDG PET Brain Networks Exhibit Multi-scale Community Structure with Different Normalization Techniques.

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Sperry, Megan M; Kartha, Sonia; Granquist, Eric J; Winkelstein, Beth A

2018-07-01

Inter-subject networks are used to model correlations between brain regions and are particularly useful for metabolic imaging techniques, like 18F-2-deoxy-2-(18F)fluoro-D-glucose (FDG) positron emission tomography (PET). Since FDG PET typically produces a single image, correlations cannot be calculated over time. Little focus has been placed on the basic properties of inter-subject networks and if they are affected by group size and image normalization. FDG PET images were acquired from rats (n = 18), normalized by whole brain, visual cortex, or cerebellar FDG uptake, and used to construct correlation matrices. Group size effects on network stability were investigated by systematically adding rats and evaluating local network connectivity (node strength and clustering coefficient). Modularity and community structure were also evaluated in the differently normalized networks to assess meso-scale network relationships. Local network properties are stable regardless of normalization region for groups of at least 10. Whole brain-normalized networks are more modular than visual cortex- or cerebellum-normalized network (p network resolutions where modularity differs most between brain and randomized networks. Hierarchical analysis reveals consistent modules at different scales and clustering of spatially-proximate brain regions. Findings suggest inter-subject FDG PET networks are stable for reasonable group sizes and exhibit multi-scale modularity.

Adaptive local routing strategy on a scale-free network

International Nuclear Information System (INIS)

Feng, Liu; Han, Zhao; Ming, Li; Yan-Bo, Zhu; Feng-Yuan, Ren

2010-01-01

Due to the heterogeneity of the structure on a scale-free network, making the betweennesses of all nodes become homogeneous by reassigning the weights of nodes or edges is very difficult. In order to take advantage of the important effect of high degree nodes on the shortest path communication and preferentially deliver packets by them to increase the probability to destination, an adaptive local routing strategy on a scale-free network is proposed, in which the node adjusts the forwarding probability with the dynamical traffic load (packet queue length) and the degree distribution of neighbouring nodes. The critical queue length of a node is set to be proportional to its degree, and the node with high degree has a larger critical queue length to store and forward more packets. When the queue length of a high degree node is shorter than its critical queue length, it has a higher probability to forward packets. After higher degree nodes are saturated (whose queue lengths are longer than their critical queue lengths), more packets will be delivered by the lower degree nodes around them. The adaptive local routing strategy increases the probability of a packet finding its destination quickly, and improves the transmission capacity on the scale-free network by reducing routing hops. The simulation results show that the transmission capacity of the adaptive local routing strategy is larger than that of three previous local routing strategies. (general)

Environmental versatility promotes modularity in large scale metabolic networks

OpenAIRE

Samal A.; Wagner Andreas; Martin O.C.

2011-01-01

Abstract Background The ubiquity of modules in biological networks may result from an evolutionary benefit of a modular organization. For instance, modularity may increase the rate of adaptive evolution, because modules can be easily combined into new arrangements that may benefit their carrier. Conversely, modularity may emerge as a by-product of some trait. We here ask whether this last scenario may play a role in genome-scale metabolic networks that need to sustain life in one or more chem...

Trajectory Control of Scale-Free Dynamical Networks with Exogenous Disturbances

International Nuclear Information System (INIS)

Yang Hongyong; Zhang Shun; Zong Guangdeng

2011-01-01

In this paper, the trajectory control of multi-agent dynamical systems with exogenous disturbances is studied. Suppose multiple agents composing of a scale-free network topology, the performance of rejecting disturbances for the low degree node and high degree node is analyzed. Firstly, the consensus of multi-agent systems without disturbances is studied by designing a pinning control strategy on a part of agents, where this pinning control can bring multiple agents' states to an expected consensus track. Then, the influence of the disturbances is considered by developing disturbance observers, and disturbance observers based control (DOBC) are developed for disturbances generated by an exogenous system to estimate the disturbances. Asymptotical consensus of the multi-agent systems with disturbances under the composite controller can be achieved for scale-free network topology. Finally, by analyzing examples of multi-agent systems with scale-free network topology and exogenous disturbances, the verities of the results are proved. Under the DOBC with the designed parameters, the trajectory convergence of multi-agent systems is researched by pinning two class of the nodes. We have found that it has more stronger robustness to exogenous disturbances for the high degree node pinned than that of the low degree node pinned. (interdisciplinary physics and related areas of science and technology)

Influence of the Time Scale on the Construction of Financial Networks

OpenAIRE

Emmert-Streib, Frank; Dehmer, Matthias

2010-01-01

BACKGROUND: In this paper we investigate the definition and formation of financial networks. Specifically, we study the influence of the time scale on their construction. METHODOLOGY/PRINCIPAL FINDINGS: For our analysis we use correlation-based networks obtained from the daily closing prices of stock market data. More precisely, we use the stocks that currently comprise the Dow Jones Industrial Average (DJIA) and estimate financial networks where nodes correspond to stocks and edges correspon...

Large-scale simulations of plastic neural networks on neuromorphic hardware

Directory of Open Access Journals (Sweden)

James Courtney Knight

2016-04-01

Full Text Available SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Rather than using bespoke analog or digital hardware, the basic computational unit of a SpiNNaker system is a general-purpose ARM processor, allowing it to be programmed to simulate a wide variety of neuron and synapse models. This flexibility is particularly valuable in the study of biological plasticity phenomena. A recently proposed learning rule based on the Bayesian Confidence Propagation Neural Network (BCPNN paradigm offers a generic framework for modeling the interaction of different plasticity mechanisms using spiking neurons. However, it can be computationally expensive to simulate large networks with BCPNN learning since it requires multiple state variables for each synapse, each of which needs to be updated every simulation time-step. We discuss the trade-offs in efficiency and accuracy involved in developing an event-based BCPNN implementation for SpiNNaker based on an analytical solution to the BCPNN equations, and detail the steps taken to fit this within the limited computational and memory resources of the SpiNNaker architecture. We demonstrate this learning rule by learning temporal sequences of neural activity within a recurrent attractor network which we simulate at scales of up to 20000 neurons and 51200000 plastic synapses: the largest plastic neural network ever to be simulated on neuromorphic hardware. We also run a comparable simulation on a Cray XC-30 supercomputer system and find that, if it is to match the run-time of our SpiNNaker simulation, the super computer system uses approximately more power. This suggests that cheaper, more power efficient neuromorphic systems are becoming useful discovery tools in the study of plasticity in large-scale brain models.

Reproductive and neurobehavioral effects of clothianidin administered to mice in the diet.

Science.gov (United States)

Tanaka, Toyohito

2012-04-01

Clothianidin was given in the diet to provide levels of 0% (control), 0.003%, 0.006%, and 0.012% from 5 weeks of age of the F(0) generation to 11 weeks of age of the F(1) generation in mice. Selected reproductive and neurobehavioral parameters were measured. In exploratory behavior in the F(0) generation, average time of movement, number of rearing, and rearing time of adult males increased significantly in a dose-related manner. There was no adverse effect of clothianidin on litter size, litter weight, or sex ratio at birth. The average body weight of male and female offspring was increased significantly in a dose-related manner during the early lactation period. With respect to behavioral developmental parameters, swimming head angle at postnatal day (PND) 7 of male offspring was accelerated significantly in a dose-related manner. Negative geotaxis at PND 7 of female offspring was accelerated significantly in a dose-related manner. For movement activity of exploratory behavior in the F(1) generation, number of rearing of female offspring increased significantly in a dose-related manner. Movement time of adult males increased significantly in a dose-related manner. The dose levels of clothianidin in the present study produced several adverse effects in neurobehavioral parameters in mice. Nevertheless, it would appear that the levels of the actual dietary intake of clothianidin are unlikely to produce adverse effects in humans. © 2012 Wiley Periodicals, Inc.

Toward the automated generation of genome-scale metabolic networks in the SEED.

Science.gov (United States)

DeJongh, Matthew; Formsma, Kevin; Boillot, Paul; Gould, John; Rycenga, Matthew; Best, Aaron

2007-04-26

Current methods for the automated generation of genome-scale metabolic networks focus on genome annotation and preliminary biochemical reaction network assembly, but do not adequately address the process of identifying and filling gaps in the reaction network, and verifying that the network is suitable for systems level analysis. Thus, current methods are only sufficient for generating draft-quality networks, and refinement of the reaction network is still largely a manual, labor-intensive process. We have developed a method for generating genome-scale metabolic networks that produces substantially complete reaction networks, suitable for systems level analysis. Our method partitions the reaction space of central and intermediary metabolism into discrete, interconnected components that can be assembled and verified in isolation from each other, and then integrated and verified at the level of their interconnectivity. We have developed a database of components that are common across organisms, and have created tools for automatically assembling appropriate components for a particular organism based on the metabolic pathways encoded in the organism's genome. This focuses manual efforts on that portion of an organism's metabolism that is not yet represented in the database. We have demonstrated the efficacy of our method by reverse-engineering and automatically regenerating the reaction network from a published genome-scale metabolic model for Staphylococcus aureus. Additionally, we have verified that our method capitalizes on the database of common reaction network components created for S. aureus, by using these components to generate substantially complete reconstructions of the reaction networks from three other published metabolic models (Escherichia coli, Helicobacter pylori, and Lactococcus lactis). We have implemented our tools and database within the SEED, an open-source software environment for comparative genome annotation and analysis. Our method sets the

Toward the automated generation of genome-scale metabolic networks in the SEED

Directory of Open Access Journals (Sweden)

Gould John

2007-04-01

Full Text Available Abstract Background Current methods for the automated generation of genome-scale metabolic networks focus on genome annotation and preliminary biochemical reaction network assembly, but do not adequately address the process of identifying and filling gaps in the reaction network, and verifying that the network is suitable for systems level analysis. Thus, current methods are only sufficient for generating draft-quality networks, and refinement of the reaction network is still largely a manual, labor-intensive process. Results We have developed a method for generating genome-scale metabolic networks that produces substantially complete reaction networks, suitable for systems level analysis. Our method partitions the reaction space of central and intermediary metabolism into discrete, interconnected components that can be assembled and verified in isolation from each other, and then integrated and verified at the level of their interconnectivity. We have developed a database of components that are common across organisms, and have created tools for automatically assembling appropriate components for a particular organism based on the metabolic pathways encoded in the organism's genome. This focuses manual efforts on that portion of an organism's metabolism that is not yet represented in the database. We have demonstrated the efficacy of our method by reverse-engineering and automatically regenerating the reaction network from a published genome-scale metabolic model for Staphylococcus aureus. Additionally, we have verified that our method capitalizes on the database of common reaction network components created for S. aureus, by using these components to generate substantially complete reconstructions of the reaction networks from three other published metabolic models (Escherichia coli, Helicobacter pylori, and Lactococcus lactis. We have implemented our tools and database within the SEED, an open-source software environment for comparative

An Integrative Bioinformatics Framework for Genome-scale Multiple Level Network Reconstruction of Rice

Directory of Open Access Journals (Sweden)

Liu Lili

2013-06-01

Full Text Available Understanding how metabolic reactions translate the genome of an organism into its phenotype is a grand challenge in biology. Genome-wide association studies (GWAS statistically connect genotypes to phenotypes, without any recourse to known molecular interactions, whereas a molecular mechanistic description ties gene function to phenotype through gene regulatory networks (GRNs, protein-protein interactions (PPIs and molecular pathways. Integration of different regulatory information levels of an organism is expected to provide a good way for mapping genotypes to phenotypes. However, the lack of curated metabolic model of rice is blocking the exploration of genome-scale multi-level network reconstruction. Here, we have merged GRNs, PPIs and genome-scale metabolic networks (GSMNs approaches into a single framework for rice via omics’ regulatory information reconstruction and integration. Firstly, we reconstructed a genome-scale metabolic model, containing 4,462 function genes, 2,986 metabolites involved in 3,316 reactions, and compartmentalized into ten subcellular locations. Furthermore, 90,358 pairs of protein-protein interactions, 662,936 pairs of gene regulations and 1,763 microRNA-target interactions were integrated into the metabolic model. Eventually, a database was developped for systematically storing and retrieving the genome-scale multi-level network of rice. This provides a reference for understanding genotype-phenotype relationship of rice, and for analysis of its molecular regulatory network.

Emergence of super cooperation of prisoner's dilemma games on scale-free networks.

Directory of Open Access Journals (Sweden)

Angsheng Li

Full Text Available Recently, the authors proposed a quantum prisoner's dilemma game based on the spatial game of Nowak and May, and showed that the game can be played classically. By using this idea, we proposed three generalized prisoner's dilemma (GPD, for short games based on the weak Prisoner's dilemma game, the full prisoner's dilemma game and the normalized Prisoner's dilemma game, written by GPDW, GPDF and GPDN respectively. Our games consist of two players, each of which has three strategies: cooperator (C, defector (D and super cooperator (denoted by Q, and have a parameter γ to measure the entangled relationship between the two players. We found that our generalised prisoner's dilemma games have new Nash equilibrium principles, that entanglement is the principle of emergence and convergence (i.e., guaranteed emergence of super cooperation in evolutions of our generalised prisoner's dilemma games on scale-free networks, that entanglement provides a threshold for a phase transition of super cooperation in evolutions of our generalised prisoner's dilemma games on scale-free networks, that the role of heterogeneity of the scale-free networks in cooperations and super cooperations is very limited, and that well-defined structures of scale-free networks allow coexistence of cooperators and super cooperators in the evolutions of the weak version of our generalised prisoner's dilemma games.

Foundational perspectives on causality in large-scale brain networks

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Mannino, Michael; Bressler, Steven L.

2015-12-01

A profusion of recent work in cognitive neuroscience has been concerned with the endeavor to uncover causal influences in large-scale brain networks. However, despite the fact that many papers give a nod to the important theoretical challenges posed by the concept of causality, this explosion of research has generally not been accompanied by a rigorous conceptual analysis of the nature of causality in the brain. This review provides both a descriptive and prescriptive account of the nature of causality as found within and between large-scale brain networks. In short, it seeks to clarify the concept of causality in large-scale brain networks both philosophically and scientifically. This is accomplished by briefly reviewing the rich philosophical history of work on causality, especially focusing on contributions by David Hume, Immanuel Kant, Bertrand Russell, and Christopher Hitchcock. We go on to discuss the impact that various interpretations of modern physics have had on our understanding of causality. Throughout all this, a central focus is the distinction between theories of deterministic causality (DC), whereby causes uniquely determine their effects, and probabilistic causality (PC), whereby causes change the probability of occurrence of their effects. We argue that, given the topological complexity of its large-scale connectivity, the brain should be considered as a complex system and its causal influences treated as probabilistic in nature. We conclude that PC is well suited for explaining causality in the brain for three reasons: (1) brain causality is often mutual; (2) connectional convergence dictates that only rarely is the activity of one neuronal population uniquely determined by another one; and (3) the causal influences exerted between neuronal populations may not have observable effects. A number of different techniques are currently available to characterize causal influence in the brain. Typically, these techniques quantify the statistical

Scaling-Laws of Flow Entropy with Topological Metrics of Water Distribution Networks

OpenAIRE

Giovanni Francesco Santonastaso; Armando Di Nardo; Michele Di Natale; Carlo Giudicianni; Roberto Greco

2018-01-01

Robustness of water distribution networks is related to their connectivity and topological structure, which also affect their reliability. Flow entropy, based on Shannon’s informational entropy, has been proposed as a measure of network redundancy and adopted as a proxy of reliability in optimal network design procedures. In this paper, the scaling properties of flow entropy of water distribution networks with their size and other topological metrics are studied. To such aim, flow entropy, ma...

Self-Organized Criticality in a Simple Neuron Model Based on Scale-Free Networks

International Nuclear Information System (INIS)

Lin Min; Wang Gang; Chen Tianlun

2006-01-01

A simple model for a set of interacting idealized neurons in scale-free networks is introduced. The basic elements of the model are endowed with the main features of a neuron function. We find that our model displays power-law behavior of avalanche sizes and generates long-range temporal correlation. More importantly, we find different dynamical behavior for nodes with different connectivity in the scale-free networks.

Effects of melatonin on aluminium-induced neurobehavioral and neurochemical changes in aging rats.

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Allagui, M S; Feriani, A; Saoudi, M; Badraoui, R; Bouoni, Z; Nciri, R; Murat, J C; Elfeki, A

2014-08-01

This study aimed to investigate the potential protective effects of melatonin (Mel) against aluminium-induced neurodegenerative changes in aging Wistar rats (24-28months old). Herein, aluminium chloride (AlCl3) (50mg/kg BW/day) was administered by gavage, and melatonin (Mel) was co-administered to a group of Al-treated rats by an intra-peritoneal injection at a daily dose of 10mg/kg BW for four months. The findings revealed that aluminium administration induced a significant decrease in body weight associated with marked mortality for the old group of rats, which was more pronounced in old Al-treated rats. Behavioural alterations were assessed by 'open fields', 'elevated plus maze' and 'Radial 8-arms maze' tests. The results demonstrated that Mel co-administration alleviated neurobehavioral changes in both old and old Al-treated rats. Melatonin was noted to play a good neuroprotective role, reducing lipid peroxidation (TBARs), and enhancing enzymatic (SOD, CAT and GPx) activities in the brain organs of old control and old Al-treated rats. Mel treatment also reversed the decrease of AChE activity in the brain tissues, which was confirmed by histological sections. Overall, the results showed that Mel administration can induce beneficial effects for the treatment of Al-induced neurobehavioral and neurochemical changes in the central nervous system (CNS). Copyright © 2014 Elsevier Ltd. All rights reserved.

Non-equilibrium mean-field theories on scale-free networks

International Nuclear Information System (INIS)

Caccioli, Fabio; Dall'Asta, Luca

2009-01-01

Many non-equilibrium processes on scale-free networks present anomalous critical behavior that is not explained by standard mean-field theories. We propose a systematic method to derive stochastic equations for mean-field order parameters that implicitly account for the degree heterogeneity. The method is used to correctly predict the dynamical critical behavior of some binary spin models and reaction–diffusion processes. The validity of our non-equilibrium theory is further supported by showing its relation with the generalized Landau theory of equilibrium critical phenomena on networks

Contextual Multi-Scale Region Convolutional 3D Network for Activity Detection

KAUST Repository

Bai, Yancheng

2018-01-28

Activity detection is a fundamental problem in computer vision. Detecting activities of different temporal scales is particularly challenging. In this paper, we propose the contextual multi-scale region convolutional 3D network (CMS-RC3D) for activity detection. To deal with the inherent temporal scale variability of activity instances, the temporal feature pyramid is used to represent activities of different temporal scales. On each level of the temporal feature pyramid, an activity proposal detector and an activity classifier are learned to detect activities of specific temporal scales. Temporal contextual information is fused into activity classifiers for better recognition. More importantly, the entire model at all levels can be trained end-to-end. Our CMS-RC3D detector can deal with activities at all temporal scale ranges with only a single pass through the backbone network. We test our detector on two public activity detection benchmarks, THUMOS14 and ActivityNet. Extensive experiments show that the proposed CMS-RC3D detector outperforms state-of-the-art methods on THUMOS14 by a substantial margin and achieves comparable results on ActivityNet despite using a shallow feature extractor.

Contextual Multi-Scale Region Convolutional 3D Network for Activity Detection

KAUST Repository

Bai, Yancheng; Xu, Huijuan; Saenko, Kate; Ghanem, Bernard

2018-01-01

Activity detection is a fundamental problem in computer vision. Detecting activities of different temporal scales is particularly challenging. In this paper, we propose the contextual multi-scale region convolutional 3D network (CMS-RC3D) for activity detection. To deal with the inherent temporal scale variability of activity instances, the temporal feature pyramid is used to represent activities of different temporal scales. On each level of the temporal feature pyramid, an activity proposal detector and an activity classifier are learned to detect activities of specific temporal scales. Temporal contextual information is fused into activity classifiers for better recognition. More importantly, the entire model at all levels can be trained end-to-end. Our CMS-RC3D detector can deal with activities at all temporal scale ranges with only a single pass through the backbone network. We test our detector on two public activity detection benchmarks, THUMOS14 and ActivityNet. Extensive experiments show that the proposed CMS-RC3D detector outperforms state-of-the-art methods on THUMOS14 by a substantial margin and achieves comparable results on ActivityNet despite using a shallow feature extractor.

Age- and gender-dependent impairments of neurobehaviors in mice whose mothers were exposed to lipopolysaccharide during pregnancy.

Science.gov (United States)

Wang, Hua; Meng, Xiu-Hong; Ning, Huan; Zhao, Xian-Feng; Wang, Qun; Liu, Ping; Zhang, Heng; Zhang, Cheng; Chen, Gui-Hai; Xu, De-Xiang

2010-02-01

Lipopolysaccharide (LPS)-induced intrauterine infection has been associated with neurodevelopmental injury in rodents. The purpose of the present study was to analyze the dynamic changes of neurobehaviors in mice whose mothers were exposed to LPS during pregnancy. The pregnant mice were intraperitoneally (i.p.) injected with LPS (8 microg/kg) daily from gestational day (gd) 8 to gd 15. A battery of neurobehavioral tasks was performed in mice at postnatal day (PND) 70, 200, 400 and 600. Results showed that the spatial learning and memory ability, determined by radial six-arm water maze (RAWM), were obviously impaired in two hundred-day-old female mice and four hundred-day-old male mice whose mothers were exposed to LPS during pregnancy. Open field test showed that the number of squares crossed and peripheral time, a marker of anxiety and exploration activity, were markedly increased in two hundred-day-old female mice following prenatal LPS exposure. In addition, prenatal LPS exposure significantly shortened the latency to the first grid crossing in six hundred-day-old female offspring. Moreover, sensorimotor impairment in the beam walking was observed in two hundred-day-old female mice whose mothers were exposed to LPS during pregnancy. Species-typical behavior examination showed that prenatal LPS exposure markedly increased weight burrowed in seventy-day-old male offspring and six hundred-day-old female offspring. Correspondingly, prenatal LPS exposure significantly reduced weight hoarded in two hundred-day-old female offspring. Taken together, these results suggest that prenatal LPS exposure induces neurobehavioral impairments at adulthood in an age- and gender-dependent manner. 2009 Elsevier Ireland Ltd. All rights reserved.

Optimal knockout strategies in genome-scale metabolic networks using particle swarm optimization.

Science.gov (United States)

Nair, Govind; Jungreuthmayer, Christian; Zanghellini, Jürgen

2017-02-01

Knockout strategies, particularly the concept of constrained minimal cut sets (cMCSs), are an important part of the arsenal of tools used in manipulating metabolic networks. Given a specific design, cMCSs can be calculated even in genome-scale networks. We would however like to find not only the optimal intervention strategy for a given design but the best possible design too. Our solution (PSOMCS) is to use particle swarm optimization (PSO) along with the direct calculation of cMCSs from the stoichiometric matrix to obtain optimal designs satisfying multiple objectives. To illustrate the working of PSOMCS, we apply it to a toy network. Next we show its superiority by comparing its performance against other comparable methods on a medium sized E. coli core metabolic network. PSOMCS not only finds solutions comparable to previously published results but also it is orders of magnitude faster. Finally, we use PSOMCS to predict knockouts satisfying multiple objectives in a genome-scale metabolic model of E. coli and compare it with OptKnock and RobustKnock. PSOMCS finds competitive knockout strategies and designs compared to other current methods and is in some cases significantly faster. It can be used in identifying knockouts which will force optimal desired behaviors in large and genome scale metabolic networks. It will be even more useful as larger metabolic models of industrially relevant organisms become available.

Changing delay discounting in the light of the competing neurobehavioral decision systems theory: a review.

Science.gov (United States)

Koffarnus, Mikhail N; Jarmolowicz, David P; Mueller, E Terry; Bickel, Warren K

2013-01-01

Excessively devaluing delayed reinforcers co-occurs with a wide variety of clinical conditions such as drug dependence, obesity, and excessive gambling. If excessive delay discounting is a trans-disease process that underlies the choice behavior leading to these and other negative health conditions, efforts to change an individual's discount rate are arguably important. Although discount rate is often regarded as a relatively stable trait, descriptions of interventions and environmental manipulations that successfully alter discount rate have begun to appear in the literature. In this review, we compare published examples of procedures that change discount rate and classify them into categories of procedures, including therapeutic interventions, direct manipulation of the executive decision-making system, framing effects, physiological state effects, and acute drug effects. These changes in discount rate are interpreted from the perspective of the competing neurobehavioral decision systems theory, which describes a combination of neurological and behavioral processes that account for delay discounting. We also suggest future directions that researchers could take to identify the mechanistic processes that allow for changes in discount rate and to test whether the competing neurobehavioral decision systems view of delay discounting is correct. © Society for the Experimental Analysis of Behavior.

Comparing Existing Pipeline Networks with the Potential Scale of Future U.S. CO2 Pipeline Networks

Energy Technology Data Exchange (ETDEWEB)

Dooley, James J.; Dahowski, Robert T.; Davidson, Casie L.

2008-02-29

There is growing interest regarding the potential size of a future U.S. dedicated CO2 pipeline infrastructure if carbon dioxide capture and storage (CCS) technologies are commercially deployed on a large scale. In trying to understand the potential scale of a future national CO2 pipeline network, comparisons are often made to the existing pipeline networks used to deliver natural gas and liquid hydrocarbons to markets within the U.S. This paper assesses the potential scale of the CO2 pipeline system needed under two hypothetical climate policies and compares this to the extant U.S. pipeline infrastructures used to deliver CO2 for enhanced oil recovery (EOR), and to move natural gas and liquid hydrocarbons from areas of production and importation to markets. The data presented here suggest that the need to increase the size of the existing dedicated CO2 pipeline system should not be seen as a significant obstacle for the commercial deployment of CCS technologies.

Moods as ups and downs of the motivation pendulum: Revisiting Reinforcement Sensitivity Theory (RST in Bipolar Disorder

Directory of Open Access Journals (Sweden)

Tal eGonen

2014-11-01

Full Text Available Motivation is a key neurobehavioral concept underlying adaptive responses to environmental incentives and threats. As such, dysregulation of motivational processes may be critical in the formation of abnormal behavioral patterns/tendencies. According to the long standing model of the Reinforcement Sensitivity Theory (RST, motivation behaviors are driven by three neurobehavioral systems mediating the sensitivity to punishment, reward or goal-conflict. Corresponding to current neurobehavioral theories in psychiatry, this theory links abnormal motivational drives to abnormal behavior; viewing depression and mania as two abnormal extremes of reward driven processes leading to either under or over approach tendencies, respectively. We revisit the RST framework in the context of bipolar disorder (BD and challenge this concept by suggesting that dysregulated interactions of both punishment and reward related processes better account for the psychological and neural abnormalities observed in BD. We further present an integrative model positing that the three parallel motivation systems currently proposed by the RST model, can be viewed as subsystems in a large-scale neurobehavioral network of motivational decision making.

Heuristic algorithm for determination of local properties of scale-free networks

CERN Document Server

Mitrovic, M

2006-01-01

Complex networks are everywhere. Many phenomena in nature can be modeled as networks: - brain structures - protein-protein interaction networks - social interactions - the Internet and WWW. They can be represented in terms of nodes and edges connecting them. Important characteristics: - these networks are not random; they have a structured architecture. Structure of different networks are similar: - all have power law degree distribution (scale-free property) - despite large size there is usually relatively short path between any two nodes (small world property). Global characteristics: - degree distribution, clustering coefficient and the diameter. Local structure: - frequency of subgraphs of given type (subgraph of order k is a part of the network consisting of k nodes and edges between them). There are different types of subgraphs of the same order.

Episodic memory in aspects of large-scale brain networks

Science.gov (United States)

Jeong, Woorim; Chung, Chun Kee; Kim, June Sic

2015-01-01

Understanding human episodic memory in aspects of large-scale brain networks has become one of the central themes in neuroscience over the last decade. Traditionally, episodic memory was regarded as mostly relying on medial temporal lobe (MTL) structures. However, recent studies have suggested involvement of more widely distributed cortical network and the importance of its interactive roles in the memory process. Both direct and indirect neuro-modulations of the memory network have been tried in experimental treatments of memory disorders. In this review, we focus on the functional organization of the MTL and other neocortical areas in episodic memory. Task-related neuroimaging studies together with lesion studies suggested that specific sub-regions of the MTL are responsible for specific components of memory. However, recent studies have emphasized that connectivity within MTL structures and even their network dynamics with other cortical areas are essential in the memory process. Resting-state functional network studies also have revealed that memory function is subserved by not only the MTL system but also a distributed network, particularly the default-mode network (DMN). Furthermore, researchers have begun to investigate memory networks throughout the entire brain not restricted to the specific resting-state network (RSN). Altered patterns of functional connectivity (FC) among distributed brain regions were observed in patients with memory impairments. Recently, studies have shown that brain stimulation may impact memory through modulating functional networks, carrying future implications of a novel interventional therapy for memory impairment. PMID:26321939

Episodic memory in aspects of large-scale brain networks

Directory of Open Access Journals (Sweden)

Woorim eJeong

2015-08-01

Full Text Available Understanding human episodic memory in aspects of large-scale brain networks has become one of the central themes in neuroscience over the last decade. Traditionally, episodic memory was regarded as mostly relying on medial temporal lobe (MTL structures. However, recent studies have suggested involvement of more widely distributed cortical network and the importance of its interactive roles in the memory process. Both direct and indirect neuro-modulations of the memory network have been tried in experimental treatments of memory disorders. In this review, we focus on the functional organization of the MTL and other neocortical areas in episodic memory. Task-related neuroimaging studies together with lesion studies suggested that specific sub-regions of the MTL are responsible for specific components of memory. However, recent studies have emphasized that connectivity within MTL structures and even their network dynamics with other cortical areas are essential in the memory process. Resting-state functional network studies also have revealed that memory function is subserved by not only the MTL system but also a distributed network, particularly the default-mode network. Furthermore, researchers have begun to investigate memory networks throughout the entire brain not restricted to the specific resting-state network. Altered patterns of functional connectivity among distributed brain regions were observed in patients with memory impairments. Recently, studies have shown that brain stimulation may impact memory through modulating functional networks, carrying future implications of a novel interventional therapy for memory impairment.

Multidimensional Scaling Localization Algorithm in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Zhang Dongyang

2014-02-01

Full Text Available Due to the localization algorithm in large-scale wireless sensor network exists shortcomings both in positioning accuracy and time complexity compared to traditional localization algorithm, this paper presents a fast multidimensional scaling location algorithm. By positioning algorithm for fast multidimensional scaling, fast mapping initialization, fast mapping and coordinate transform can get schematic coordinates of node, coordinates Initialize of MDS algorithm, an accurate estimate of the node coordinates and using the PRORUSTES to analysis alignment of the coordinate and final position coordinates of nodes etc. There are four steps, and the thesis gives specific implementation steps of the algorithm. Finally, compared with stochastic algorithms and classical MDS algorithm experiment, the thesis takes application of specific examples. Experimental results show that: the proposed localization algorithm has fast multidimensional scaling positioning accuracy in ensuring certain circumstances, but also greatly improves the speed of operation.

An efficient method based on the uniformity principle for synthesis of large-scale heat exchanger networks

International Nuclear Information System (INIS)

Zhang, Chunwei; Cui, Guomin; Chen, Shang

2016-01-01

Highlights: • Two dimensionless uniformity factors are presented to heat exchange network. • The grouping of process streams reduces the computational complexity of large-scale HENS problems. • The optimal sub-network can be obtained by Powell particle swarm optimization algorithm. • The method is illustrated by a case study involving 39 process streams, with a better solution. - Abstract: The optimal design of large-scale heat exchanger networks is a difficult task due to the inherent non-linear characteristics and the combinatorial nature of heat exchangers. To solve large-scale heat exchanger network synthesis (HENS) problems, two dimensionless uniformity factors to describe the heat exchanger network (HEN) uniformity in terms of the temperature difference and the accuracy of process stream grouping are deduced. Additionally, a novel algorithm that combines deterministic and stochastic optimizations to obtain an optimal sub-network with a suitable heat load for a given group of streams is proposed, and is named the Powell particle swarm optimization (PPSO). As a result, the synthesis of large-scale heat exchanger networks is divided into two corresponding sub-parts, namely, the grouping of process streams and the optimization of sub-networks. This approach reduces the computational complexity and increases the efficiency of the proposed method. The robustness and effectiveness of the proposed method are demonstrated by solving a large-scale HENS problem involving 39 process streams, and the results obtained are better than those previously published in the literature.

Reorganizing Complex Network to Improve Large-Scale Multiagent Teamwork

Directory of Open Access Journals (Sweden)

Yang Xu

2014-01-01

Full Text Available Large-scale multiagent teamwork has been popular in various domains. Similar to human society infrastructure, agents only coordinate with some of the others, with a peer-to-peer complex network structure. Their organization has been proven as a key factor to influence their performance. To expedite team performance, we have analyzed that there are three key factors. First, complex network effects may be able to promote team performance. Second, coordination interactions coming from their sources are always trying to be routed to capable agents. Although they could be transferred across the network via different paths, their sources and sinks depend on the intrinsic nature of the team which is irrelevant to the network connections. In addition, the agents involved in the same plan often form a subteam and communicate with each other more frequently. Therefore, if the interactions between agents can be statistically recorded, we are able to set up an integrated network adjustment algorithm by combining the three key factors. Based on our abstracted teamwork simulations and the coordination statistics, we implemented the adaptive reorganization algorithm. The experimental results briefly support our design that the reorganized network is more capable of coordinating heterogeneous agents.

Low frequency steady-state brain responses modulate large scale functional networks in a frequency-specific means.

Science.gov (United States)

Wang, Yi-Feng; Long, Zhiliang; Cui, Qian; Liu, Feng; Jing, Xiu-Juan; Chen, Heng; Guo, Xiao-Nan; Yan, Jin H; Chen, Hua-Fu

2016-01-01

Neural oscillations are essential for brain functions. Research has suggested that the frequency of neural oscillations is lower for more integrative and remote communications. In this vein, some resting-state studies have suggested that large scale networks function in the very low frequency range (frequency characteristics of brain networks because both resting-state studies and conventional frequency tagging approaches cannot simultaneously capture multiple large scale networks in controllable cognitive activities. In this preliminary study, we aimed to examine whether large scale networks can be modulated by task-induced low frequency steady-state brain responses (lfSSBRs) in a frequency-specific pattern. In a revised attention network test, the lfSSBRs were evoked in the triple network system and sensory-motor system, indicating that large scale networks can be modulated in a frequency tagging way. Furthermore, the inter- and intranetwork synchronizations as well as coherence were increased at the fundamental frequency and the first harmonic rather than at other frequency bands, indicating a frequency-specific modulation of information communication. However, there was no difference among attention conditions, indicating that lfSSBRs modulate the general attention state much stronger than distinguishing attention conditions. This study provides insights into the advantage and mechanism of lfSSBRs. More importantly, it paves a new way to investigate frequency-specific large scale brain activities. © 2015 Wiley Periodicals, Inc.

Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories.

Science.gov (United States)

Chockalingam, Sriram; Aluru, Maneesha; Aluru, Srinivas

2016-09-19

Pre-processing of microarray data is a well-studied problem. Furthermore, all popular platforms come with their own recommended best practices for differential analysis of genes. However, for genome-scale network inference using microarray data collected from large public repositories, these methods filter out a considerable number of genes. This is primarily due to the effects of aggregating a diverse array of experiments with different technical and biological scenarios. Here we introduce a pre-processing pipeline suitable for inferring genome-scale gene networks from large microarray datasets. We show that partitioning of the available microarray datasets according to biological relevance into tissue- and process-specific categories significantly extends the limits of downstream network construction. We demonstrate the effectiveness of our pre-processing pipeline by inferring genome-scale networks for the model plant Arabidopsis thaliana using two different construction methods and a collection of 11,760 Affymetrix ATH1 microarray chips. Our pre-processing pipeline and the datasets used in this paper are made available at http://alurulab.cc.gatech.edu/microarray-pp.

Impact of interaction style and degree on the evolution of cooperation on Barabási-Albert scale-free network.

Directory of Open Access Journals (Sweden)

Fengjie Xie

Full Text Available In this work, we study an evolutionary prisoner's dilemma game (PDG on Barabási-Albert scale-free networks with limited player interactions, and explore the effect of interaction style and degree on cooperation. The results show that high-degree preference interaction, namely the most applicable interaction in the real world, is less beneficial for emergence of cooperation on scale-free networks than random interaction. Besides, cooperation on scale-free networks is enhanced with the increase of interaction degree regardless whether the interaction is high-degree preference or random. If the interaction degree is very low, the cooperation level on scale-free networks is much lower than that on regular ring networks, which is against the common belief that scale-free networks must be more beneficial for cooperation. Our analysis indicates that the interaction relations, the strategy and the game payoff of high-connectivity players play important roles in the evolution of cooperation on scale-free networks. A certain number of interactions are necessary for scale-free networks to exhibit strong capability of facilitating cooperation. Our work provides important insight for members on how to interact with others in a social organization.

Dynamic scaling, data-collapse and self-similarity in Barabasi-Albert networks

Energy Technology Data Exchange (ETDEWEB)

Hassan, M Kamrul; Pavel, Neeaj I [Theoretical Physics Group, Department of Physics, University of Dhaka, Dhaka 1000 (Bangladesh); Hassan, M Zahedul, E-mail: khassan@univdhaka.edu [Institute of Computer Science, Bangladesh Atomic Energy Commission, Dhaka 1000 (Bangladesh)

2011-04-29

In this paper, we show that if each node of the Barabasi-Albert (BA) network is characterized by the generalized degree q, i.e. the product of their degree k and the square root of their respective birth time, then the distribution function F(q, t) exhibits dynamic scaling F(q, t {yields} {infinity}) {approx} t{sup -1/2}{phi}(q/t{sup 1/2}) where {phi}(x) is the scaling function. We verified it by showing that a series of distinct F(q, t) versus q curves for different network sizes N collapse onto a single universal curve if we plot t{sup 1/2}F(q, t) versus q/t{sup 1/2} instead. Finally, we show that the BA network falls into two universality classes depending on whether new nodes arrive with single edge (m = 1) or with multiple edges (m > 1).

Locating inefficient links in a large-scale transportation network

Science.gov (United States)

Sun, Li; Liu, Like; Xu, Zhongzhi; Jie, Yang; Wei, Dong; Wang, Pu

2015-02-01

Based on data from geographical information system (GIS) and daily commuting origin destination (OD) matrices, we estimated the distribution of traffic flow in the San Francisco road network and studied Braess's paradox in a large-scale transportation network with realistic travel demand. We measured the variation of total travel time Δ T when a road segment is closed, and found that | Δ T | follows a power-law distribution if Δ T 0. This implies that most roads have a negligible effect on the efficiency of the road network, while the failure of a few crucial links would result in severe travel delays, and closure of a few inefficient links would counter-intuitively reduce travel costs considerably. Generating three theoretical networks, we discovered that the heterogeneously distributed travel demand may be the origin of the observed power-law distributions of | Δ T | . Finally, a genetic algorithm was used to pinpoint inefficient link clusters in the road network. We found that closing specific road clusters would further improve the transportation efficiency.

A probabilistic approach to quantifying spatial patterns of flow regimes and network-scale connectivity

Science.gov (United States)

Garbin, Silvia; Alessi Celegon, Elisa; Fanton, Pietro; Botter, Gianluca

2017-04-01

The temporal variability of river flow regime is a key feature structuring and controlling fluvial ecological communities and ecosystem processes. In particular, streamflow variability induced by climate/landscape heterogeneities or other anthropogenic factors significantly affects the connectivity between streams with notable implication for river fragmentation. Hydrologic connectivity is a fundamental property that guarantees species persistence and ecosystem integrity in riverine systems. In riverine landscapes, most ecological transitions are flow-dependent and the structure of flow regimes may affect ecological functions of endemic biota (i.e., fish spawning or grazing of invertebrate species). Therefore, minimum flow thresholds must be guaranteed to support specific ecosystem services, like fish migration, aquatic biodiversity and habitat suitability. In this contribution, we present a probabilistic approach aiming at a spatially-explicit, quantitative assessment of hydrologic connectivity at the network-scale as derived from river flow variability. Dynamics of daily streamflows are estimated based on catchment-scale climatic and morphological features, integrating a stochastic, physically based approach that accounts for the stochasticity of rainfall with a water balance model and a geomorphic recession flow model. The non-exceedance probability of ecologically meaningful flow thresholds is used to evaluate the fragmentation of individual stream reaches, and the ensuing network-scale connectivity metrics. A multi-dimensional Poisson Process for the stochastic generation of rainfall is used to evaluate the impact of climate signature on reach-scale and catchment-scale connectivity. The analysis shows that streamflow patterns and network-scale connectivity are influenced by the topology of the river network and the spatial variability of climatic properties (rainfall, evapotranspiration). The framework offers a robust basis for the prediction of the impact of

Epidemic spreading on adaptively weighted scale-free networks.

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Sun, Mengfeng; Zhang, Haifeng; Kang, Huiyan; Zhu, Guanghu; Fu, Xinchu

2017-04-01

We introduce three modified SIS models on scale-free networks that take into account variable population size, nonlinear infectivity, adaptive weights, behavior inertia and time delay, so as to better characterize the actual spread of epidemics. We develop new mathematical methods and techniques to study the dynamics of the models, including the basic reproduction number, and the global asymptotic stability of the disease-free and endemic equilibria. We show the disease-free equilibrium cannot undergo a Hopf bifurcation. We further analyze the effects of local information of diseases and various immunization schemes on epidemic dynamics. We also perform some stochastic network simulations which yield quantitative agreement with the deterministic mean-field approach.

Cascading Dynamics of Heterogenous Scale-Free Networks with Recovery Mechanism

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Shudong Li

2013-01-01

Full Text Available In network security, how to use efficient response methods against cascading failures of complex networks is very important. In this paper, concerned with the highest-load attack (HL and random attack (RA on one edge, we define five kinds of weighting strategies to assign the external resources for recovering the edges from cascading failures in heterogeneous scale-free (SF networks. The influence of external resources, the tolerance parameter, and the different weighting strategies on SF networks against cascading failures is investigated carefully. We find that, under HL attack, the fourth kind of weighting method can more effectively improve the integral robustness of SF networks, simultaneously control the spreading velocity, and control the outburst of cascading failures in SF networks than other methods. Moreover, the third method is optimal if we only knew the local structure of SF networks and the uniform assignment is the worst. The simulations of the real-world autonomous system in, Internet have also supported our findings. The results are useful for using efficient response strategy against the emergent accidents and controlling the cascading failures in the real-world networks.

Sensitivity of the Positive and Negative Syndrome Scale (PANSS) in Detecting Treatment Effects via Network Analysis.

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Esfahlani, Farnaz Zamani; Sayama, Hiroki; Visser, Katherine Frost; Strauss, Gregory P

2017-12-01

Objective: The Positive and Negative Syndrome Scale is a primary outcome measure in clinical trials examining the efficacy of antipsychotic medications. Although the Positive and Negative Syndrome Scale has demonstrated sensitivity as a measure of treatment change in studies using traditional univariate statistical approaches, its sensitivity to detecting network-level changes in dynamic relationships among symptoms has yet to be demonstrated using more sophisticated multivariate analyses. In the current study, we examined the sensitivity of the Positive and Negative Syndrome Scale to detecting antipsychotic treatment effects as revealed through network analysis. Design: Participants included 1,049 individuals diagnosed with psychotic disorders from the Phase I portion of the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study. Of these participants, 733 were clinically determined to be treatment-responsive and 316 were found to be treatment-resistant. Item level data from the Positive and Negative Syndrome Scale were submitted to network analysis, and macroscopic, mesoscopic, and microscopic network properties were evaluated for the treatment-responsive and treatment-resistant groups at baseline and post-phase I antipsychotic treatment. Results: Network analysis indicated that treatment-responsive patients had more densely connected symptom networks after antipsychotic treatment than did treatment-responsive patients at baseline, and that symptom centralities increased following treatment. In contrast, symptom networks of treatment-resistant patients behaved more randomly before and after treatment. Conclusions: These results suggest that the Positive and Negative Syndrome Scale is sensitive to detecting treatment effects as revealed through network analysis. Its findings also provide compelling new evidence that strongly interconnected symptom networks confer an overall greater probability of treatment responsiveness in patients with

Emergence of Scale-Free Syntax Networks

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Corominas-Murtra, Bernat; Valverde, Sergi; Solé, Ricard V.

The evolution of human language allowed the efficient propagation of nongenetic information, thus creating a new form of evolutionary change. Language development in children offers the opportunity of exploring the emergence of such complex communication system and provides a window to understanding the transition from protolanguage to language. Here we present the first analysis of the emergence of syntax in terms of complex networks. A previously unreported, sharp transition is shown to occur around two years of age from a (pre-syntactic) tree-like structure to a scale-free, small world syntax network. The observed combinatorial patterns provide valuable data to understand the nature of the cognitive processes involved in the acquisition of syntax, introducing a new ingredient to understand the possible biological endowment of human beings which results in the emergence of complex language. We explore this problem by using a minimal, data-driven model that is able to capture several statistical traits, but some key features related to the emergence of syntactic complexity display important divergences.

Large-scale functional networks connect differently for processing words and symbol strings.

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Liljeström, Mia; Vartiainen, Johanna; Kujala, Jan; Salmelin, Riitta

2018-01-01

Reconfigurations of synchronized large-scale networks are thought to be central neural mechanisms that support cognition and behavior in the human brain. Magnetoencephalography (MEG) recordings together with recent advances in network analysis now allow for sub-second snapshots of such networks. In the present study, we compared frequency-resolved functional connectivity patterns underlying reading of single words and visual recognition of symbol strings. Word reading emphasized coherence in a left-lateralized network with nodes in classical perisylvian language regions, whereas symbol processing recruited a bilateral network, including connections between frontal and parietal regions previously associated with spatial attention and visual working memory. Our results illustrate the flexible nature of functional networks, whereby processing of different form categories, written words vs. symbol strings, leads to the formation of large-scale functional networks that operate at distinct oscillatory frequencies and incorporate task-relevant regions. These results suggest that category-specific processing should be viewed not so much as a local process but as a distributed neural process implemented in signature networks. For words, increased coherence was detected particularly in the alpha (8-13 Hz) and high gamma (60-90 Hz) frequency bands, whereas increased coherence for symbol strings was observed in the high beta (21-29 Hz) and low gamma (30-45 Hz) frequency range. These findings attest to the role of coherence in specific frequency bands as a general mechanism for integrating stimulus-dependent information across brain regions.

Probing Rubber Cross-Linking Generation of Industrial Polymer Networks at Nanometer Scale.

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Gabrielle, Brice; Gomez, Emmanuel; Korb, Jean-Pierre

2016-06-23

We present improved analyses of rheometric torque measurements as well as (1)H double-quantum (DQ) nuclear magnetic resonance (NMR) buildup data on polymer networks of industrial compounds. This latter DQ NMR analysis allows finding the distribution of an orientation order parameter (Dres) resulting from the noncomplete averaging of proton dipole-dipole couplings within the cross-linked polymer chains. We investigate the influence of the formulation (filler and vulcanization systems) as well as the process (curing temperature) ending to the final polymer network. We show that DQ NMR follows the generation of the polymer network during the vulcanization process from a heterogeneous network to a very homogeneous one. The time variations of microscopic Dres and macroscopic rheometric torques present power-law behaviors above a threshold time scale with characteristic exponents of the percolation theory. We observe also a very good linear correlation between the kinetics of Dres and rheometric data routinely performed in industry. All these observations confirm the description of the polymer network generation as a critical phenomenon. On the basis of all these results, we believe that DQ NMR could become a valuable tool for investigating in situ the cross-linking of industrial polymer networks at the nanometer scale.

Large scale silver nanowires network fabricated by MeV hydrogen (H+) ion beam irradiation

International Nuclear Information System (INIS)

S, Honey; S, Naseem; A, Ishaq; M, Maaza; M T, Bhatti; D, Wan

2016-01-01

A random two-dimensional large scale nano-network of silver nanowires (Ag-NWs) is fabricated by MeV hydrogen (H + ) ion beam irradiation. Ag-NWs are irradiated under H +  ion beam at different ion fluences at room temperature. The Ag-NW network is fabricated by H + ion beam-induced welding of Ag-NWs at intersecting positions. H +  ion beam induced welding is confirmed by transmission electron microscopy (TEM) and scanning electron microscopy (SEM). Moreover, the structure of Ag NWs remains stable under H +  ion beam, and networks are optically transparent. Morphology also remains stable under H +  ion beam irradiation. No slicings or cuttings of Ag-NWs are observed under MeV H +  ion beam irradiation. The results exhibit that the formation of Ag-NW network proceeds through three steps: ion beam induced thermal spikes lead to the local heating of Ag-NWs, the formation of simple junctions on small scale, and the formation of a large scale network. This observation is useful for using Ag-NWs based devices in upper space where protons are abandoned in an energy range from MeV to GeV. This high-quality Ag-NW network can also be used as a transparent electrode for optoelectronics devices. (paper)

Network Partitioning Domain Knowledge Multiobjective Application Mapping for Large-Scale Network-on-Chip

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Yin Zhen Tei

2014-01-01

Full Text Available This paper proposes a multiobjective application mapping technique targeted for large-scale network-on-chip (NoC. As the number of intellectual property (IP cores in multiprocessor system-on-chip (MPSoC increases, NoC application mapping to find optimum core-to-topology mapping becomes more challenging. Besides, the conflicting cost and performance trade-off makes multiobjective application mapping techniques even more complex. This paper proposes an application mapping technique that incorporates domain knowledge into genetic algorithm (GA. The initial population of GA is initialized with network partitioning (NP while the crossover operator is guided with knowledge on communication demands. NP reduces the large-scale application mapping complexity and provides GA with a potential mapping search space. The proposed genetic operator is compared with state-of-the-art genetic operators in terms of solution quality. In this work, multiobjective optimization of energy and thermal-balance is considered. Through simulation, knowledge-based initial mapping shows significant improvement in Pareto front compared to random initial mapping that is widely used. The proposed knowledge-based crossover also shows better Pareto front compared to state-of-the-art knowledge-based crossover.

Large-Scale Analysis of Network Bistability for Human Cancers

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Shiraishi, Tetsuya; Matsuyama, Shinako; Kitano, Hiroaki

2010-01-01

Protein–protein interaction and gene regulatory networks are likely to be locked in a state corresponding to a disease by the behavior of one or more bistable circuits exhibiting switch-like behavior. Sets of genes could be over-expressed or repressed when anomalies due to disease appear, and the circuits responsible for this over- or under-expression might persist for as long as the disease state continues. This paper shows how a large-scale analysis of network bistability for various human cancers can identify genes that can potentially serve as drug targets or diagnosis biomarkers. PMID:20628618

Road network selection for small-scale maps using an improved centrality-based algorithm

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Roy Weiss

2014-12-01

Full Text Available The road network is one of the key feature classes in topographic maps and databases. In the task of deriving road networks for products at smaller scales, road network selection forms a prerequisite for all other generalization operators, and is thus a fundamental operation in the overall process of topographic map and database production. The objective of this work was to develop an algorithm for automated road network selection from a large-scale (1:10,000 to a small-scale database (1:200,000. The project was pursued in collaboration with swisstopo, the national mapping agency of Switzerland, with generic mapping requirements in mind. Preliminary experiments suggested that a selection algorithm based on betweenness centrality performed best for this purpose, yet also exposed problems. The main contribution of this paper thus consists of four extensions that address deficiencies of the basic centrality-based algorithm and lead to a significant improvement of the results. The first two extensions improve the formation of strokes concatenating the road segments, which is crucial since strokes provide the foundation upon which the network centrality measure is computed. Thus, the first extension ensures that roundabouts are detected and collapsed, thus avoiding interruptions of strokes by roundabouts, while the second introduces additional semantics in the process of stroke formation, allowing longer and more plausible strokes to built. The third extension detects areas of high road density (i.e., urban areas using density-based clustering and then locally increases the threshold of the centrality measure used to select road segments, such that more thinning takes place in those areas. Finally, since the basic algorithm tends to create dead-ends—which however are not tolerated in small-scale maps—the fourth extension reconnects these dead-ends to the main network, searching for the best path in the main heading of the dead-end.

Relationship Between Iodine Concentration in Maternal Colostrum and Neurobehavioral Development of Infants in Shanghai, China.

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Wu, Meiqin; Wu, Deqing; Wu, Wei; Li, Hui; Cao, Lulu; Xu, Jian; Yu, Xiaodan; Bian, Xiaoyan; Yan, Chonghuai; Wang, Weiye

2016-08-01

It is well known that iodine plays an important role in the process of early growth and development of most organs, especially the brain. However, iodine concentration in the colostrum and its association with the neurobehavioral development of infants remains unclear. Colostrums from 150 women were collected, and their iodine concentrations were measured. The median colostrum iodine level was 187.8 μg/L. The Bayley Scales of Infant and Toddler Development-III test was performed when the infants were about 18 months. The mean cognitive, language, and motor composite scores were 105.3 ± 9.8, 105.2 ± 11.1, and 104.6 ± 6.7, respectively. And the mean scores of the 5 subtests were 11.1 ± 2.0, 9.3 ± 2.0, 12.4 ± 2.3, 11.1 ± 1.2, and 10.4 ± 1.2, respectively. No statistically significant difference was observed in the cognition, language, or motor development of infants across different levels of colostrum iodine. After adjusting for a range of confounding factors, colostrum iodine concentration was a predictor of motor development, specifically gross motor development. © The Author(s) 2016.

Multilayer network modeling creates opportunities for novel network statistics. Comment on "Network science of biological systems at different scales: A review" by Gosak et al.

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Muldoon, Sarah Feldt

2018-03-01

As described in the review by Gosak et al., the field of network science has had enormous success in providing new insights into the structure and function of biological systems [1]. In the complex networks framework, system elements are network nodes, and connections between nodes represent some form of interaction between system elements [2]. The flexibility to define network nodes and edges to represent different aspects of biological systems has been employed to model numerous diverse systems at multiple scales.

The developmental neurobehavioral effects of fenugreek seeds on prenatally exposed mice.

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Khalki, Loubna; Bennis, Mohamed; Sokar, Zahra; Ba-M'hamed, Saâdia

2012-01-31

Fenugreek (Trigonella foenum graecum (L.)), is a medicinal plant whose seeds and leaves are widely used in Moroccan traditional medicine. Consumption of fenugreek seeds during pregnancy has been associated with a range of congenital malformations, including hydrocephalus, anencephaly and spina bifida. In previous work we have shown that exposure of pregnant mice to aqueous extract of fenugreek seeds (AEFS) leads to reduced litter size, intrauterine growth retardation, and malformations. However, there have been no studies to date of its longer-term neurobehavioral effects. We investigated these effects in prenatally exposed mice. Pregnant females were exposed to 0, 500 or 1000 mg/kg/day AEFS, by gavage, for the whole period of gestation. Pups body weight was measured at 1, 7, 14, 21 and 28 day of age. Behavior of progeny was evaluated three weeks after birth using the open field, the rotarod test and the continuous alternation task by the T-maze. At 28 postnatal day age, brain of progeny was removed and cut for histological evaluation. The progeny of exposed mice displayed reduced body weight at birth (1000 mg/kg group: 27%; 500 mg/kg group: 32%) and reduced brain weight (10% in both treated groups). Both males and females mice prenatally exposed to AEFS displayed a significant decrease in the locomotor activity, in the boli deposits during the open field test and in motor coordination. These results seem to show that exposure to AEFS induces a depressive effect in the offspring. Assessment on a continuous alternation T-maze test showed a significant reduction in successful spontaneous alternations in males and females but only in the 1000 mg/kg group. These results suggest that prenatal exposure of mice to high dose of fenugreek seeds causes growth retardation and altered neurobehavioral performance in the post-weaning period in both male and female. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

A local adaptive algorithm for emerging scale-free hierarchical networks

International Nuclear Information System (INIS)

Gomez Portillo, I J; Gleiser, P M

2010-01-01

In this work we study a growing network model with chaotic dynamical units that evolves using a local adaptive rewiring algorithm. Using numerical simulations we show that the model allows for the emergence of hierarchical networks. First, we show that the networks that emerge with the algorithm present a wide degree distribution that can be fitted by a power law function, and thus are scale-free networks. Using the LaNet-vi visualization tool we present a graphical representation that reveals a central core formed only by hubs, and also show the presence of a preferential attachment mechanism. In order to present a quantitative analysis of the hierarchical structure we analyze the clustering coefficient. In particular, we show that as the network grows the clustering becomes independent of system size, and also presents a power law decay as a function of the degree. Finally, we compare our results with a similar version of the model that has continuous non-linear phase oscillators as dynamical units. The results show that local interactions play a fundamental role in the emergence of hierarchical networks.

Dual Temporal Scale Convolutional Neural Network for Micro-Expression Recognition

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Min Peng

2017-10-01

Full Text Available Facial micro-expression is a brief involuntary facial movement and can reveal the genuine emotion that people try to conceal. Traditional methods of spontaneous micro-expression recognition rely excessively on sophisticated hand-crafted feature design and the recognition rate is not high enough for its practical application. In this paper, we proposed a Dual Temporal Scale Convolutional Neural Network (DTSCNN for spontaneous micro-expressions recognition. The DTSCNN is a two-stream network. Different of stream of DTSCNN is used to adapt to different frame rate of micro-expression video clips. Each stream of DSTCNN consists of independent shallow network for avoiding the overfitting problem. Meanwhile, we fed the networks with optical-flow sequences to ensure that the shallow networks can further acquire higher-level features. Experimental results on spontaneous micro-expression databases (CASME I/II showed that our method can achieve a recognition rate almost 10% higher than what some state-of-the-art method can achieve.

Dual Temporal Scale Convolutional Neural Network for Micro-Expression Recognition.

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Peng, Min; Wang, Chongyang; Chen, Tong; Liu, Guangyuan; Fu, Xiaolan

2017-01-01

Facial micro-expression is a brief involuntary facial movement and can reveal the genuine emotion that people try to conceal. Traditional methods of spontaneous micro-expression recognition rely excessively on sophisticated hand-crafted feature design and the recognition rate is not high enough for its practical application. In this paper, we proposed a Dual Temporal Scale Convolutional Neural Network (DTSCNN) for spontaneous micro-expressions recognition. The DTSCNN is a two-stream network. Different of stream of DTSCNN is used to adapt to different frame rate of micro-expression video clips. Each stream of DSTCNN consists of independent shallow network for avoiding the overfitting problem. Meanwhile, we fed the networks with optical-flow sequences to ensure that the shallow networks can further acquire higher-level features. Experimental results on spontaneous micro-expression databases (CASME I/II) showed that our method can achieve a recognition rate almost 10% higher than what some state-of-the-art method can achieve.

Dual Temporal Scale Convolutional Neural Network for Micro-Expression Recognition

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Peng, Min; Wang, Chongyang; Chen, Tong; Liu, Guangyuan; Fu, Xiaolan

2017-01-01

Facial micro-expression is a brief involuntary facial movement and can reveal the genuine emotion that people try to conceal. Traditional methods of spontaneous micro-expression recognition rely excessively on sophisticated hand-crafted feature design and the recognition rate is not high enough for its practical application. In this paper, we proposed a Dual Temporal Scale Convolutional Neural Network (DTSCNN) for spontaneous micro-expressions recognition. The DTSCNN is a two-stream network. Different of stream of DTSCNN is used to adapt to different frame rate of micro-expression video clips. Each stream of DSTCNN consists of independent shallow network for avoiding the overfitting problem. Meanwhile, we fed the networks with optical-flow sequences to ensure that the shallow networks can further acquire higher-level features. Experimental results on spontaneous micro-expression databases (CASME I/II) showed that our method can achieve a recognition rate almost 10% higher than what some state-of-the-art method can achieve. PMID:29081753

Scalable and Fully Distributed Localization in Large-Scale Sensor Networks

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Miao Jin

2017-06-01

Full Text Available This work proposes a novel connectivity-based localization algorithm, well suitable for large-scale sensor networks with complex shapes and a non-uniform nodal distribution. In contrast to current state-of-the-art connectivity-based localization methods, the proposed algorithm is highly scalable with linear computation and communication costs with respect to the size of the network; and fully distributed where each node only needs the information of its neighbors without cumbersome partitioning and merging process. The algorithm is theoretically guaranteed and numerically stable. Moreover, the algorithm can be readily extended to the localization of networks with a one-hop transmission range distance measurement, and the propagation of the measurement error at one sensor node is limited within a small area of the network around the node. Extensive simulations and comparison with other methods under various representative network settings are carried out, showing the superior performance of the proposed algorithm.

Large-Scale Cooperative Task Distribution on Peer-to-Peer Networks

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2012-01-01

SUBTITLE Large-scale cooperative task distribution on peer-to-peer networks 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6...disadvantages of ML- Chord are its fixed size (two layers), and limited scala - bility for large-scale systems. RC-Chord extends ML- D. Karrels et al...configurable before runtime. This can be improved by incorporating a distributed learning algorithm to tune the number and range of the DLoE tracking

Global forward-predicting dynamic routing for traffic concurrency space stereo multi-layer scale-free network

International Nuclear Information System (INIS)

Xie Wei-Hao; Zhou Bin; Liu En-Xiao; Lu Wei-Dang; Zhou Ting

2015-01-01

Many real communication networks, such as oceanic monitoring network and land environment observation network, can be described as space stereo multi-layer structure, and the traffic in these networks is concurrent. Understanding how traffic dynamics depend on these real communication networks and finding an effective routing strategy that can fit the circumstance of traffic concurrency and enhance the network performance are necessary. In this light, we propose a traffic model for space stereo multi-layer complex network and introduce two kinds of global forward-predicting dynamic routing strategies, global forward-predicting hybrid minimum queue (HMQ) routing strategy and global forward-predicting hybrid minimum degree and queue (HMDQ) routing strategy, for traffic concurrency space stereo multi-layer scale-free networks. By applying forward-predicting strategy, the proposed routing strategies achieve better performances in traffic concurrency space stereo multi-layer scale-free networks. Compared with the efficient routing strategy and global dynamic routing strategy, HMDQ and HMQ routing strategies can optimize the traffic distribution, alleviate the number of congested packets effectively and reach much higher network capacity. (paper)

Network Events on Multiple Space and Time Scales in Cultured Neural Networks and in a Stochastic Rate Model.

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Guido Gigante

2015-11-01

Full Text Available Cortical networks, in-vitro as well as in-vivo, can spontaneously generate a variety of collective dynamical events such as network spikes, UP and DOWN states, global oscillations, and avalanches. Though each of them has been variously recognized in previous works as expression of the excitability of the cortical tissue and the associated nonlinear dynamics, a unified picture of the determinant factors (dynamical and architectural is desirable and not yet available. Progress has also been partially hindered by the use of a variety of statistical measures to define the network events of interest. We propose here a common probabilistic definition of network events that, applied to the firing activity of cultured neural networks, highlights the co-occurrence of network spikes, power-law distributed avalanches, and exponentially distributed 'quasi-orbits', which offer a third type of collective behavior. A rate model, including synaptic excitation and inhibition with no imposed topology, synaptic short-term depression, and finite-size noise, accounts for all these different, coexisting phenomena. We find that their emergence is largely regulated by the proximity to an oscillatory instability of the dynamics, where the non-linear excitable behavior leads to a self-amplification of activity fluctuations over a wide range of scales in space and time. In this sense, the cultured network dynamics is compatible with an excitation-inhibition balance corresponding to a slightly sub-critical regime. Finally, we propose and test a method to infer the characteristic time of the fatigue process, from the observed time course of the network's firing rate. Unlike the model, possessing a single fatigue mechanism, the cultured network appears to show multiple time scales, signalling the possible coexistence of different fatigue mechanisms.

Mobile user forecast and power-law acceleration invariance of scale-free networks

International Nuclear Information System (INIS)

Guo Jin-Li; Guo Zhao-Hua; Liu Xue-Jiao

2011-01-01

This paper studies and predicts the number growth of China's mobile users by using the power-law regression. We find that the number growth of the mobile users follows a power law. Motivated by the data on the evolution of the mobile users, we consider scenarios of self-organization of accelerating growth networks into scale-free structures and propose a directed network model, in which the nodes grow following a power-law acceleration. The expressions for the transient and the stationary average degree distributions are obtained by using the Poisson process. This result shows that the model generates appropriate power-law connectivity distributions. Therefore, we find a power-law acceleration invariance of the scale-free networks. The numerical simulations of the models agree with the analytical results well. (interdisciplinary physics and related areas of science and technology)

Improved routing strategies for data traffic in scale-free networks

International Nuclear Information System (INIS)

Wu, Zhi-Xi; Peng, Gang; Wong, Wing-Ming; Yeung, Kai-Hau

2008-01-01

We study the information packet routing process in scale-free networks by mimicking Internet traffic delivery. We incorporate both the global shortest paths information and local degree information of the network in the dynamic process, via two tunable parameters, α and β, to guide the packet routing. We measure the performance of the routing method by both the average transit times of packets and the critical packet generation rate (above which packet aggregation occurs in the network). We found that the routing strategies which integrate ingredients of both global and local topological information of the underlying networks perform much better than the traditional shortest path routing protocol taking into account the global topological information only. Moreover, by doing comparative studies with some related works, we found that the performance of our proposed method shows universal efficiency characteristic against the amount of traffic

Validation of the Social Networking Activity Intensity Scale among Junior Middle School Students in China.

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Li, Jibin; Lau, Joseph T F; Mo, Phoenix K H; Su, Xuefen; Wu, Anise M S; Tang, Jie; Qin, Zuguo

2016-01-01

Online social networking use has been integrated into adolescents' daily life and the intensity of online social networking use may have important consequences on adolescents' well-being. However, there are few validated instruments to measure social networking use intensity. The present study aims to develop the Social Networking Activity Intensity Scale (SNAIS) and validate it among junior middle school students in China. A total of 910 students who were social networking users were recruited from two junior middle schools in Guangzhou, and 114 students were retested after two weeks to examine the test-retest reliability. The psychometrics of the SNAIS were estimated using appropriate statistical methods. Two factors, Social Function Use Intensity (SFUI) and Entertainment Function Use Intensity (EFUI), were clearly identified by both exploratory and confirmatory factor analyses. No ceiling or floor effects were observed for the SNAIS and its two subscales. The SNAIS and its two subscales exhibited acceptable reliability (Cronbach's alpha = 0.89, 0.90 and 0.60, and test-retest Intra-class Correlation Coefficient = 0.85, 0.87 and 0.67 for Overall scale, SFUI and EFUI subscale, respectively, psocial networking, social networking addiction, Internet addiction, and characteristics related to social networking use. The SNAIS is an easily self-administered scale with good psychometric properties. It would facilitate more research in this field worldwide and specifically in the Chinese population.

Validation of the Social Networking Activity Intensity Scale among Junior Middle School Students in China

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Li, Jibin; Lau, Joseph T. F.; Mo, Phoenix K. H.; Su, Xuefen; Wu, Anise M. S.; Tang, Jie; Qin, Zuguo

2016-01-01

Background Online social networking use has been integrated into adolescents’ daily life and the intensity of online social networking use may have important consequences on adolescents’ well-being. However, there are few validated instruments to measure social networking use intensity. The present study aims to develop the Social Networking Activity Intensity Scale (SNAIS) and validate it among junior middle school students in China. Methods A total of 910 students who were social networking users were recruited from two junior middle schools in Guangzhou, and 114 students were retested after two weeks to examine the test-retest reliability. The psychometrics of the SNAIS were estimated using appropriate statistical methods. Results Two factors, Social Function Use Intensity (SFUI) and Entertainment Function Use Intensity (EFUI), were clearly identified by both exploratory and confirmatory factor analyses. No ceiling or floor effects were observed for the SNAIS and its two subscales. The SNAIS and its two subscales exhibited acceptable reliability (Cronbach’s alpha = 0.89, 0.90 and 0.60, and test-retest Intra-class Correlation Coefficient = 0.85, 0.87 and 0.67 for Overall scale, SFUI and EFUI subscale, respectively, psocial networking, social networking addiction, Internet addiction, and characteristics related to social networking use. Conclusions The SNAIS is an easily self-administered scale with good psychometric properties. It would facilitate more research in this field worldwide and specifically in the Chinese population. PMID:27798699

Environmental versatility promotes modularity in genome-scale metabolic networks.

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Samal, Areejit; Wagner, Andreas; Martin, Olivier C

2011-08-24

The ubiquity of modules in biological networks may result from an evolutionary benefit of a modular organization. For instance, modularity may increase the rate of adaptive evolution, because modules can be easily combined into new arrangements that may benefit their carrier. Conversely, modularity may emerge as a by-product of some trait. We here ask whether this last scenario may play a role in genome-scale metabolic networks that need to sustain life in one or more chemical environments. For such networks, we define a network module as a maximal set of reactions that are fully coupled, i.e., whose fluxes can only vary in fixed proportions. This definition overcomes limitations of purely graph based analyses of metabolism by exploiting the functional links between reactions. We call a metabolic network viable in a given chemical environment if it can synthesize all of an organism's biomass compounds from nutrients in this environment. An organism's metabolism is highly versatile if it can sustain life in many different chemical environments. We here ask whether versatility affects the modularity of metabolic networks. Using recently developed techniques to randomly sample large numbers of viable metabolic networks from a vast space of metabolic networks, we use flux balance analysis to study in silico metabolic networks that differ in their versatility. We find that highly versatile networks are also highly modular. They contain more modules and more reactions that are organized into modules. Most or all reactions in a module are associated with the same biochemical pathways. Modules that arise in highly versatile networks generally involve reactions that process nutrients or closely related chemicals. We also observe that the metabolism of E. coli is significantly more modular than even our most versatile networks. Our work shows that modularity in metabolic networks can be a by-product of functional constraints, e.g., the need to sustain life in multiple

Environmental versatility promotes modularity in genome-scale metabolic networks

Directory of Open Access Journals (Sweden)

Wagner Andreas

2011-08-01

Full Text Available Abstract Background The ubiquity of modules in biological networks may result from an evolutionary benefit of a modular organization. For instance, modularity may increase the rate of adaptive evolution, because modules can be easily combined into new arrangements that may benefit their carrier. Conversely, modularity may emerge as a by-product of some trait. We here ask whether this last scenario may play a role in genome-scale metabolic networks that need to sustain life in one or more chemical environments. For such networks, we define a network module as a maximal set of reactions that are fully coupled, i.e., whose fluxes can only vary in fixed proportions. This definition overcomes limitations of purely graph based analyses of metabolism by exploiting the functional links between reactions. We call a metabolic network viable in a given chemical environment if it can synthesize all of an organism's biomass compounds from nutrients in this environment. An organism's metabolism is highly versatile if it can sustain life in many different chemical environments. We here ask whether versatility affects the modularity of metabolic networks. Results Using recently developed techniques to randomly sample large numbers of viable metabolic networks from a vast space of metabolic networks, we use flux balance analysis to study in silico metabolic networks that differ in their versatility. We find that highly versatile networks are also highly modular. They contain more modules and more reactions that are organized into modules. Most or all reactions in a module are associated with the same biochemical pathways. Modules that arise in highly versatile networks generally involve reactions that process nutrients or closely related chemicals. We also observe that the metabolism of E. coli is significantly more modular than even our most versatile networks. Conclusions Our work shows that modularity in metabolic networks can be a by-product of functional

Multiple synchronization transitions in scale-free neuronal networks with electrical and chemical hybrid synapses

International Nuclear Information System (INIS)

Liu, Chen; Wang, Jiang; Wang, Lin; Yu, Haitao; Deng, Bin; Wei, Xile; Tsang, Kaiming; Chan, Wailok

2014-01-01

Highlights: • Synchronization transitions in hybrid scale-free neuronal networks are investigated. • Multiple synchronization transitions can be induced by the time delay. • Effect of synchronization transitions depends on the ratio of the electrical and chemical synapses. • Coupling strength and the density of inter-neuronal links can enhance the synchronization. -- Abstract: The impacts of information transmission delay on the synchronization transitions in scale-free neuronal networks with electrical and chemical hybrid synapses are investigated. Numerical results show that multiple appearances of synchronization regions transitions can be induced by different information transmission delays. With the time delay increasing, the synchronization of neuronal activities can be enhanced or destroyed, irrespective of the probability of chemical synapses in the whole hybrid neuronal network. In particular, for larger probability of electrical synapses, the regions of synchronous activities appear broader with stronger synchronization ability of electrical synapses compared with chemical ones. Moreover, it can be found that increasing the coupling strength can promote synchronization monotonously, playing the similar role of the increasing the probability of the electrical synapses. Interestingly, the structures and parameters of the scale-free neuronal networks, especially the structural evolvement plays a more subtle role in the synchronization transitions. In the network formation process, it is found that every new vertex is attached to the more old vertices already present in the network, the more synchronous activities will be emerge

Nonlinear dynamics of the complex multi-scale network

Science.gov (United States)

Makarov, Vladimir V.; Kirsanov, Daniil; Goremyko, Mikhail; Andreev, Andrey; Hramov, Alexander E.

2018-04-01

In this paper, we study the complex multi-scale network of nonlocally coupled oscillators for the appearance of chimera states. Chimera is a special state in which, in addition to the asynchronous cluster, there are also completely synchronous parts in the system. We show that the increase of nodes in subgroups leads to the destruction of the synchronous interaction within the common ring and to the narrowing of the chimera region.

Rotation and scale change invariant point pattern relaxation matching by the Hopfield neural network

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Sang, Nong; Zhang, Tianxu

1997-12-01

Relaxation matching is one of the most relevant methods for image matching. The original relaxation matching technique using point patterns is sensitive to rotations and scale changes. We improve the original point pattern relaxation matching technique to be invariant to rotations and scale changes. A method that makes the Hopfield neural network perform this matching process is discussed. An advantage of this is that the relaxation matching process can be performed in real time with the neural network's massively parallel capability to process information. Experimental results with large simulated images demonstrate the effectiveness and feasibility of the method to perform point patten relaxation matching invariant to rotations and scale changes and the method to perform this matching by the Hopfield neural network. In addition, we show that the method presented can be tolerant to small random error.

Tradeoffs between quality-of-control and quality-of-service in large-scale nonlinear networked control systems

NARCIS (Netherlands)

Borgers, D. P.; Geiselhart, R.; Heemels, W. P. M. H.

2017-01-01

In this paper we study input-to-state stability (ISS) of large-scale networked control systems (NCSs) in which sensors, controllers and actuators are connected via multiple (local) communication networks which operate asynchronously and independently of each other. We model the large-scale NCS as an

Neurobehavioral function in school-age children exposed to manganese in drinking water.

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Oulhote, Youssef; Mergler, Donna; Barbeau, Benoit; Bellinger, David C; Bouffard, Thérèse; Brodeur, Marie-Ève; Saint-Amour, Dave; Legrand, Melissa; Sauvé, Sébastien; Bouchard, Maryse F

2014-12-01

Manganese neurotoxicity is well documented in individuals occupationally exposed to airborne particulates, but few data are available on risks from drinking-water exposure. We examined associations of exposure from concentrations of manganese in water and hair with memory, attention, motor function, and parent- and teacher-reported hyperactive behaviors. We recruited 375 children and measured manganese in home tap water (MnW) and hair (MnH). We estimated manganese intake from water ingestion. Using structural equation modeling, we estimated associations between neurobehavioral functions and MnH, MnW, and manganese intake from water. We evaluated exposure-response relationships using generalized additive models. After adjusting for potential confounders, a 1-SD increase in log10 MnH was associated with a significant difference of -24% (95% CI: -36, -12%) SD in memory and -25% (95% CI: -41, -9%) SD in attention. The relations between log10 MnH and poorer memory and attention were linear. A 1-SD increase in log10 MnW was associated with a significant difference of -14% (95% CI: -24, -4%) SD in memory, and this relation was nonlinear, with a steeper decline in performance at MnW > 100 μg/L. A 1-SD increase in log10 manganese intake from water was associated with a significant difference of -11% (95% CI: -21, -0.4%) SD in motor function. The relation between log10 manganese intake and poorer motor function was linear. There was no significant association between manganese exposure and hyperactivity. Exposure to manganese in water was associated with poorer neurobehavioral performances in children, even at low levels commonly encountered in North America.

Division III Collision Sports Are Not Associated with Neurobehavioral Quality of Life.

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Meehan, William P; Taylor, Alex M; Berkner, Paul; Sandstrom, Noah J; Peluso, Mark W; Kurtz, Matthew M; Pascual-Leone, Alvaro; Mannix, Rebekah

2016-01-15

We sought to determine whether the exposure to the sub-concussive blows that occur during division III collegiate collision sports affect later life neurobehavioral quality-of-life measures. We conducted a cross-sectional study of alumni from four division III colleges, targeting those between the ages of 40-70 years, using several well-validated quality-of-life measures for executive function, general concerns, anxiety, depression, emotional and behavior dyscontrol, fatigue, positive affect, sleep disturbance, and negative consequences of alcohol use. We used multivariable linear regression to assess for associations between collision sport participation and quality-of-life measures while adjusting for covariates including age, gender, race, annual income, highest educational degree, college grades, exercise frequency, and common medical conditions. We obtained data from 3702 alumni, more than half of whom (2132) had participated in collegiate sports, 23% in collision sports, 23% in non-contact sports. Respondents with a history of concussion had worse self-reported health on several measures. When subjects with a history of concussion were removed from the analyses in order to assess for any potential effect of sub-concussive blows alone, negative consequences of alcohol use remained higher among collision sport athletes (β-coefficient 1.957, 95% CI 0.827-3.086). There were, however, no other significant associations between exposure to collision sports during college and any other quality-of-life measures. Our results suggest that, in the absence of a history of concussions, participation in collision sports at the Division III collegiate level is not a risk factor for worse long-term neurobehavioral outcomes, despite exposure to repeated sub-concussive blows.

Large-Scale Brain Networks Supporting Divided Attention across Spatial Locations and Sensory Modalities.

Science.gov (United States)

Santangelo, Valerio

2018-01-01

Higher-order cognitive processes were shown to rely on the interplay between large-scale neural networks. However, brain networks involved with the capability to split attentional resource over multiple spatial locations and multiple stimuli or sensory modalities have been largely unexplored to date. Here I re-analyzed data from Santangelo et al. (2010) to explore the causal interactions between large-scale brain networks during divided attention. During fMRI scanning, participants monitored streams of visual and/or auditory stimuli in one or two spatial locations for detection of occasional targets. This design allowed comparing a condition in which participants monitored one stimulus/modality (either visual or auditory) in two spatial locations vs. a condition in which participants monitored two stimuli/modalities (both visual and auditory) in one spatial location. The analysis of the independent components (ICs) revealed that dividing attentional resources across two spatial locations necessitated a brain network involving the left ventro- and dorso-lateral prefrontal cortex plus the posterior parietal cortex, including the intraparietal sulcus (IPS) and the angular gyrus, bilaterally. The analysis of Granger causality highlighted that the activity of lateral prefrontal regions were predictive of the activity of all of the posteriors parietal nodes. By contrast, dividing attention across two sensory modalities necessitated a brain network including nodes belonging to the dorsal frontoparietal network, i.e., the bilateral frontal eye-fields (FEF) and IPS, plus nodes belonging to the salience network, i.e., the anterior cingulated cortex and the left and right anterior insular cortex (aIC). The analysis of Granger causality highlights a tight interdependence between the dorsal frontoparietal and salience nodes in trials requiring divided attention between different sensory modalities. The current findings therefore highlighted a dissociation among brain networks

Large-Scale Brain Networks Supporting Divided Attention across Spatial Locations and Sensory Modalities

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Valerio Santangelo

2018-02-01

Full Text Available Higher-order cognitive processes were shown to rely on the interplay between large-scale neural networks. However, brain networks involved with the capability to split attentional resource over multiple spatial locations and multiple stimuli or sensory modalities have been largely unexplored to date. Here I re-analyzed data from Santangelo et al. (2010 to explore the causal interactions between large-scale brain networks during divided attention. During fMRI scanning, participants monitored streams of visual and/or auditory stimuli in one or two spatial locations for detection of occasional targets. This design allowed comparing a condition in which participants monitored one stimulus/modality (either visual or auditory in two spatial locations vs. a condition in which participants monitored two stimuli/modalities (both visual and auditory in one spatial location. The analysis of the independent components (ICs revealed that dividing attentional resources across two spatial locations necessitated a brain network involving the left ventro- and dorso-lateral prefrontal cortex plus the posterior parietal cortex, including the intraparietal sulcus (IPS and the angular gyrus, bilaterally. The analysis of Granger causality highlighted that the activity of lateral prefrontal regions were predictive of the activity of all of the posteriors parietal nodes. By contrast, dividing attention across two sensory modalities necessitated a brain network including nodes belonging to the dorsal frontoparietal network, i.e., the bilateral frontal eye-fields (FEF and IPS, plus nodes belonging to the salience network, i.e., the anterior cingulated cortex and the left and right anterior insular cortex (aIC. The analysis of Granger causality highlights a tight interdependence between the dorsal frontoparietal and salience nodes in trials requiring divided attention between different sensory modalities. The current findings therefore highlighted a dissociation among

Equation Chapter 1 Section 1Cross Layer Design for Localization in Large-Scale Underwater Sensor Networks

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Yuanfeng ZHANG

2014-02-01

Full Text Available There are many technical challenges for designing large-scale underwater sensor networks, especially the sensor node localization. Although many papers studied for large-scale sensor node localization, previous studies mainly study the location algorithm without the cross layer design for localization. In this paper, by utilizing the network hierarchical structure of underwater sensor networks, we propose a new large-scale underwater acoustic localization scheme based on cross layer design. In this scheme, localization is performed in a hierarchical way, and the whole localization process focused on the physical layer, data link layer and application layer. We increase the pipeline parameters which matched the acoustic channel, added in MAC protocol to increase the authenticity of the large-scale underwater sensor networks, and made analysis of different location algorithm. We conduct extensive simulations, and our results show that MAC layer protocol and the localization algorithm all would affect the result of localization which can balance the trade-off between localization accuracy, localization coverage, and communication cost.

Truncation of power law behavior in 'scale-free' network models due to information filtering

International Nuclear Information System (INIS)

Mossa, Stefano; Barthelemy, Marc; Eugene Stanley, H.; Nunes Amaral, Luis A.

2002-01-01

We formulate a general model for the growth of scale-free networks under filtering information conditions--that is, when the nodes can process information about only a subset of the existing nodes in the network. We find that the distribution of the number of incoming links to a node follows a universal scaling form, i.e., that it decays as a power law with an exponential truncation controlled not only by the system size but also by a feature not previously considered, the subset of the network 'accessible' to the node. We test our model with empirical data for the World Wide Web and find agreement

Multi-GNSS PPP-RTK: From Large- to Small-Scale Networks

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Nandakumaran Nadarajah

2018-04-01

Full Text Available Precise point positioning (PPP and its integer ambiguity resolution-enabled variant, PPP-RTK (real-time kinematic, can benefit enormously from the integration of multiple global navigation satellite systems (GNSS. In such a multi-GNSS landscape, the positioning convergence time is expected to be reduced considerably as compared to the one obtained by a single-GNSS setup. It is therefore the goal of the present contribution to provide numerical insights into the role taken by the multi-GNSS integration in delivering fast and high-precision positioning solutions (sub-decimeter and centimeter levels using PPP-RTK. To that end, we employ the Curtin PPP-RTK platform and process data-sets of GPS, BeiDou Navigation Satellite System (BDS and Galileo in stand-alone and combined forms. The data-sets are collected by various receiver types, ranging from high-end multi-frequency geodetic receivers to low-cost single-frequency mass-market receivers. The corresponding stations form a large-scale (Australia-wide network as well as a small-scale network with inter-station distances less than 30 km. In case of the Australia-wide GPS-only ambiguity-float setup, 90% of the horizontal positioning errors (kinematic mode are shown to become less than five centimeters after 103 min. The stated required time is reduced to 66 min for the corresponding GPS + BDS + Galieo setup. The time is further reduced to 15 min by applying single-receiver ambiguity resolution. The outcomes are supported by the positioning results of the small-scale network.

Topology of the Italian airport network: A scale-free small-world network with a fractal structure?

International Nuclear Information System (INIS)

Guida, Michele; Maria, Funaro

2007-01-01

In this paper, for the first time we analyze the structure of the Italian Airport Network (IAN) looking at it as a mathematical graph and investigate its topological properties. We find that it has very remarkable features, being like a scale-free network, since both the degree and the 'betweenness centrality' distributions follow a typical power-law known in literature as a Double Pareto Law. From a careful analysis of the data, the Italian Airport Network turns out to have a self-similar structure. In short, it is characterized by a fractal nature, whose typical dimensions can be easily determined from the values of the power-law scaling exponents. Moreover, we show that, according to the period examined, these distributions exhibit a number of interesting features, such as the existence of some 'hubs', i.e. in the graph theory's jargon, nodes with a very large number of links, and others most probably associated with geographical constraints. Also, we find that the IAN can be classified as a small-world network because the average distance between reachable pairs of airports grows at most as the logarithm of the number of airports. The IAN does not show evidence of 'communities' and this result could be the underlying reason behind the smallness of the value of the clustering coefficient, which is related to the probability that two nearest neighbors of a randomly chosen airport are connected

Final Technical Report for Terabit-scale hybrid networking project.

Energy Technology Data Exchange (ETDEWEB)

Veeraraghavan, Malathi [Univ. of Virginia, Charlottesville, VA (United States)

2015-12-12

This report describes our accomplishments and activities for the project titled Terabit-Scale Hybrid Networking. The key accomplishment is that we developed, tested and deployed an Alpha Flow Characterization System (AFCS) in ESnet. It is being run in production mode since Sept. 2015. Also, a new QoS class was added to ESnet5 to support alpha flows.

Output regulation of large-scale hydraulic networks with minimal steady state power consumption

NARCIS (Netherlands)

Jensen, Tom Nørgaard; Wisniewski, Rafał; De Persis, Claudio; Kallesøe, Carsten Skovmose

2014-01-01

An industrial case study involving a large-scale hydraulic network is examined. The hydraulic network underlies a district heating system, with an arbitrary number of end-users. The problem of output regulation is addressed along with a optimization criterion for the control. The fact that the

The medical consultation viewed as a value chain: a neurobehavioral approach to emotion regulation in doctor-patient interaction.

Science.gov (United States)

Finset, Arnstein; Mjaaland, Trond A

2009-03-01

To present a model of the medical consultation as a value chain, and to apply a neurobehavioral perspective to analyze each element in the chain with relevance for emotion regulation. Current knowledge on four elements in medical consultations and neuroscientific evidence on corresponding basic processes are selectively reviewed. The four elements of communication behaviours presented as steps in a value chain model are: (1) establishing rapport, (2) patient disclosure of emotional cues and concerns, (3) the doctor's expression of empathy, and (4) positive reappraisal of concerns. The metaphor of the value chain, with emphasis on goal orientation, helps to understand the impact of each communicative element on the outcome of the consultation. Added value at each step is proposed in terms of effects on outcome indicators; in this case patients affect regulation. Neurobehavioral mechanisms are suggested to explain the association between communication behaviour and affect regulation outcome. The value chain metaphor and the emphasis on behaviour-outcome-mechanisms associations may be of interest as conceptualizations for communications skills training.

An Efficient Causal Group Communication Protocol for Free Scale Peer-to-Peer Networks

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Grigory Evropeytsev

2016-08-01

Full Text Available In peer-to-peer (P2P overlay networks, a group of n (≥2 peer processes have to cooperate with each other. Each peer sends messages to every peer and receives messages from every peer in a group. In group communications, each message sent by a peer is required to be causally delivered to every peer. Most of the protocols designed to ensure causal message order are designed for networks with a plain architecture. These protocols can be adapted to use in free scale and hierarchical topologies; however, the amount of control information is O(n, where n is the number of peers in the system. Some protocols are designed for a free scale or hierarchical networks, but in general they force the whole system to accomplish the same order viewed by a super peer. In this paper, we present a protocol that is specifically designed to work with a free scale peer-to-peer network. By using the information about the network’s architecture and by representing message dependencies on a bit level, the proposed protocol ensures causal message ordering without enforcing super peers order. The designed protocol is simulated and compared with the Immediate Dependency Relation and the Dependency Sequences protocols to show its lower overhead.

Large-Scale Brain Network Coupling Predicts Total Sleep Deprivation Effects on Cognitive Capacity.

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Yu Lei

Full Text Available Interactions between large-scale brain networks have received most attention in the study of cognitive dysfunction of human brain. In this paper, we aimed to test the hypothesis that the coupling strength of large-scale brain networks will reflect the pressure for sleep and will predict cognitive performance, referred to as sleep pressure index (SPI. Fourteen healthy subjects underwent this within-subject functional magnetic resonance imaging (fMRI study during rested wakefulness (RW and after 36 h of total sleep deprivation (TSD. Self-reported scores of sleepiness were higher for TSD than for RW. A subsequent working memory (WM task showed that WM performance was lower after 36 h of TSD. Moreover, SPI was developed based on the coupling strength of salience network (SN and default mode network (DMN. Significant increase of SPI was observed after 36 h of TSD, suggesting stronger pressure for sleep. In addition, SPI was significantly correlated with both the visual analogue scale score of sleepiness and the WM performance. These results showed that alterations in SN-DMN coupling might be critical in cognitive alterations that underlie the lapse after TSD. Further studies may validate the SPI as a potential clinical biomarker to assess the impact of sleep deprivation.

Scaling of peak flows with constant flow velocity in random self-similar networks

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Troutman, Brent M.; Mantilla, Ricardo; Gupta, Vijay K.

2011-01-01

A methodology is presented to understand the role of the statistical self-similar topology of real river networks on scaling, or power law, in peak flows for rainfall-runoff events. We created Monte Carlo generated sets of ensembles of 1000 random self-similar networks (RSNs) with geometrically distributed interior and exterior generators having parameters pi and pe, respectively. The parameter values were chosen to replicate the observed topology of real river networks. We calculated flow hydrographs in each of these networks by numerically solving the link-based mass and momentum conservation equation under the assumption of constant flow velocity. From these simulated RSNs and hydrographs, the scaling exponents β and φ characterizing power laws with respect to drainage area, and corresponding to the width functions and flow hydrographs respectively, were estimated. We found that, in general, φ > β, which supports a similar finding first reported for simulations in the river network of the Walnut Gulch basin, Arizona. Theoretical estimation of β and φ in RSNs is a complex open problem. Therefore, using results for a simpler problem associated with the expected width function and expected hydrograph for an ensemble of RSNs, we give heuristic arguments for theoretical derivations of the scaling exponents β(E) and φ(E) that depend on the Horton ratios for stream lengths and areas. These ratios in turn have a known dependence on the parameters of the geometric distributions of RSN generators. Good agreement was found between the analytically conjectured values of β(E) and φ(E) and the values estimated by the simulated ensembles of RSNs and hydrographs. The independence of the scaling exponents φ(E) and φ with respect to the value of flow velocity and runoff intensity implies an interesting connection between unit hydrograph theory and flow dynamics. Our results provide a reference framework to study scaling exponents under more complex scenarios

Global Exponential Stability of Delayed Cohen-Grossberg BAM Neural Networks with Impulses on Time Scales

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Yongkun Li

2009-01-01

Full Text Available Based on the theory of calculus on time scales, the homeomorphism theory, Lyapunov functional method, and some analysis techniques, sufficient conditions are obtained for the existence, uniqueness, and global exponential stability of the equilibrium point of Cohen-Grossberg bidirectional associative memory (BAM neural networks with distributed delays and impulses on time scales. This is the first time applying the time-scale calculus theory to unify the discrete-time and continuous-time Cohen-Grossberg BAM neural network with impulses under the same framework.

Reconceptualizing antisocial deviance in neurobehavioral terms.

Science.gov (United States)

Patrick, Christopher J; Durbin, C Emily; Moser, Jason S

2012-08-01

We propose that neuroscientific understanding of antisocial behavior can be advanced by focusing programmatic efforts on neurobehavioral trait constructs, that is, individual difference constructs with direct referents in neurobiology as well as behavior. As specific examples, we highlight inhibitory control and defensive reactivity as two such constructs with clear relevance for understanding antisocial behavior in the context of development. Variations in inhibitory control are theorized to reflect individual differences in the functioning of brain systems that operate to guide and inhibit behavior and regulate emotional response in the service of nonimmediate goals. Variations in defensive reactivity are posited to reflect individual differences in the sensitivity of the brain's aversive motivational (fear) system. We describe how these constructs have been conceptualized in the adult and child literatures and review work pertaining to traditional psychometric (rating and behaviorally based) assessment of these constructs and their known physiological correlates at differing ages as well as evidence linking these constructs to antisocial behavior problems in children and adults. We outline a psychoneurometric approach, which entails systematic development of neurobiological measures of target trait constructs through reference to psychological phenotypes, as a paradigm for linking clinical disorders to neurobiological systems. We provide a concrete illustration of this approach in the domain of externalizing proneness and discuss its broader implications for research on conduct disorder, antisocial personality, and psychopathy.

Expectation propagation for large scale Bayesian inference of non-linear molecular networks from perturbation data.

Science.gov (United States)

Narimani, Zahra; Beigy, Hamid; Ahmad, Ashar; Masoudi-Nejad, Ali; Fröhlich, Holger

2017-01-01

Inferring the structure of molecular networks from time series protein or gene expression data provides valuable information about the complex biological processes of the cell. Causal network structure inference has been approached using different methods in the past. Most causal network inference techniques, such as Dynamic Bayesian Networks and ordinary differential equations, are limited by their computational complexity and thus make large scale inference infeasible. This is specifically true if a Bayesian framework is applied in order to deal with the unavoidable uncertainty about the correct model. We devise a novel Bayesian network reverse engineering approach using ordinary differential equations with the ability to include non-linearity. Besides modeling arbitrary, possibly combinatorial and time dependent perturbations with unknown targets, one of our main contributions is the use of Expectation Propagation, an algorithm for approximate Bayesian inference over large scale network structures in short computation time. We further explore the possibility of integrating prior knowledge into network inference. We evaluate the proposed model on DREAM4 and DREAM8 data and find it competitive against several state-of-the-art existing network inference methods.

Neurobehavioral toxicity of total body irradiation: a follow-up in long-term survivors

International Nuclear Information System (INIS)

Peper, Martin; Steinvorth, Sarah; Schraube, Peter; Fruehauf, Stefan; Haas, Rainer; Kimmig, Bernhard N.; Lohr, Frank; Wenz, Frederik; Wannenmacher, Michael

2000-01-01

Purpose: Total body irradiation (TBI) in preparation for bone marrow transplantation (BMT) is a routine treatment of hematological malignancy. A retrospective and a prospective group study of long-term cerebral side effects was performed, with a special emphasis on neurobehavioral toxicity effects. Methods and Materials: Twenty disease-free patients treated with hyperfractionated TBI (14.4 Gy, 12 x 1.2 Gy, 4 days), 50 mg/kg cyclophosphamide, and autologous BMT (mean age 38 years, range 17-52 years; age at TBI 35 years, 16-50 years; follow-up time 32 months, 9-65 months) participated in a neuropsychological, neuroradiological, and neurological examination. Data were compared to 14 patients who were investigated prior to TBI. Eleven patients with renal insufficiencies matched for sex and age (38 years, 20-52 years) served as controls. In a longitudinal approach, neuropsychological follow-up data were assessed in 12 long-term survivors (45 years, 23-59 years; follow-up time 8.8 years, 7-10.8 years; time since diagnosis 10.1 years, 7.5-14.2 years). Results: No evidence of neurological deficits was found in post-TBI patients except one case of peripheral movement disorder of unknown origin. Some patients showed moderate brain atrophy. Neuropsychological assessment showed a subtle reduction of memory performance of about one standard deviation. Cognitive decline in individual patients appeared to be associated with pretreatment (brain irradiation, intrathecal methotrexate). Ten-years post disease onset, survivors without pretreatment showed behavioral improvement up to the premorbid level. Conclusion: The incidence of long-term neurobehavioral toxicity was very low for the present TBI/BMT regimen

Validation of the Social Networking Activity Intensity Scale among Junior Middle School Students in China.

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Jibin Li

Full Text Available Online social networking use has been integrated into adolescents' daily life and the intensity of online social networking use may have important consequences on adolescents' well-being. However, there are few validated instruments to measure social networking use intensity. The present study aims to develop the Social Networking Activity Intensity Scale (SNAIS and validate it among junior middle school students in China.A total of 910 students who were social networking users were recruited from two junior middle schools in Guangzhou, and 114 students were retested after two weeks to examine the test-retest reliability. The psychometrics of the SNAIS were estimated using appropriate statistical methods.Two factors, Social Function Use Intensity (SFUI and Entertainment Function Use Intensity (EFUI, were clearly identified by both exploratory and confirmatory factor analyses. No ceiling or floor effects were observed for the SNAIS and its two subscales. The SNAIS and its two subscales exhibited acceptable reliability (Cronbach's alpha = 0.89, 0.90 and 0.60, and test-retest Intra-class Correlation Coefficient = 0.85, 0.87 and 0.67 for Overall scale, SFUI and EFUI subscale, respectively, p<0.001. As expected, the SNAIS and its subscale scores were correlated significantly with emotional connection to social networking, social networking addiction, Internet addiction, and characteristics related to social networking use.The SNAIS is an easily self-administered scale with good psychometric properties. It would facilitate more research in this field worldwide and specifically in the Chinese population.

100 nm scale low-noise sensors based on aligned carbon nanotube networks: overcoming the fundamental limitation of network-based sensors

Science.gov (United States)

Lee, Minbaek; Lee, Joohyung; Kim, Tae Hyun; Lee, Hyungwoo; Lee, Byung Yang; Park, June; Jhon, Young Min; Seong, Maeng-Je; Hong, Seunghun

2010-02-01

Nanoscale sensors based on single-walled carbon nanotube (SWNT) networks have been considered impractical due to several fundamental limitations such as a poor sensitivity and small signal-to-noise ratio. Herein, we present a strategy to overcome these fundamental problems and build highly-sensitive low-noise nanoscale sensors simply by controlling the structure of the SWNT networks. In this strategy, we prepared nanoscale width channels based on aligned SWNT networks using a directed assembly strategy. Significantly, the aligned network-based sensors with narrower channels exhibited even better signal-to-noise ratio than those with wider channels, which is opposite to conventional random network-based sensors. As a proof of concept, we demonstrated 100 nm scale low-noise sensors to detect mercury ions with the detection limit of ~1 pM, which is superior to any state-of-the-art portable detection system and is below the allowable limit of mercury ions in drinking water set by most government environmental protection agencies. This is the first demonstration of 100 nm scale low-noise sensors based on SWNT networks. Considering the increased interests in high-density sensor arrays for healthcare and environmental protection, our strategy should have a significant impact on various industrial applications.

100 nm scale low-noise sensors based on aligned carbon nanotube networks: overcoming the fundamental limitation of network-based sensors

Energy Technology Data Exchange (ETDEWEB)

Lee, Minbaek; Lee, Joohyung; Kim, Tae Hyun; Lee, Hyungwoo; Lee, Byung Yang; Hong, Seunghun [Department of Physics and Astronomy, Seoul National University, Shilim-Dong, Kwanak-Gu, Seoul 151-742 (Korea, Republic of); Park, June; Seong, Maeng-Je [Department of Physics, Chung-Ang University, Heukseok-Dong, Dongjak-Gu, Seoul 156-756 (Korea, Republic of); Jhon, Young Min, E-mail: mseong@cau.ac.kr, E-mail: shong@phya.snu.ac.kr [Korea Institute of Science and Technology, Hawolgok-Dong, Seongbuk-Gu, Seoul 136-791 (Korea, Republic of)

2010-02-05

Nanoscale sensors based on single-walled carbon nanotube (SWNT) networks have been considered impractical due to several fundamental limitations such as a poor sensitivity and small signal-to-noise ratio. Herein, we present a strategy to overcome these fundamental problems and build highly-sensitive low-noise nanoscale sensors simply by controlling the structure of the SWNT networks. In this strategy, we prepared nanoscale width channels based on aligned SWNT networks using a directed assembly strategy. Significantly, the aligned network-based sensors with narrower channels exhibited even better signal-to-noise ratio than those with wider channels, which is opposite to conventional random network-based sensors. As a proof of concept, we demonstrated 100 nm scale low-noise sensors to detect mercury ions with the detection limit of {approx}1 pM, which is superior to any state-of-the-art portable detection system and is below the allowable limit of mercury ions in drinking water set by most government environmental protection agencies. This is the first demonstration of 100 nm scale low-noise sensors based on SWNT networks. Considering the increased interests in high-density sensor arrays for healthcare and environmental protection, our strategy should have a significant impact on various industrial applications.

100 nm scale low-noise sensors based on aligned carbon nanotube networks: overcoming the fundamental limitation of network-based sensors

International Nuclear Information System (INIS)

Lee, Minbaek; Lee, Joohyung; Kim, Tae Hyun; Lee, Hyungwoo; Lee, Byung Yang; Hong, Seunghun; Park, June; Seong, Maeng-Je; Jhon, Young Min

2010-01-01

Nanoscale sensors based on single-walled carbon nanotube (SWNT) networks have been considered impractical due to several fundamental limitations such as a poor sensitivity and small signal-to-noise ratio. Herein, we present a strategy to overcome these fundamental problems and build highly-sensitive low-noise nanoscale sensors simply by controlling the structure of the SWNT networks. In this strategy, we prepared nanoscale width channels based on aligned SWNT networks using a directed assembly strategy. Significantly, the aligned network-based sensors with narrower channels exhibited even better signal-to-noise ratio than those with wider channels, which is opposite to conventional random network-based sensors. As a proof of concept, we demonstrated 100 nm scale low-noise sensors to detect mercury ions with the detection limit of ∼1 pM, which is superior to any state-of-the-art portable detection system and is below the allowable limit of mercury ions in drinking water set by most government environmental protection agencies. This is the first demonstration of 100 nm scale low-noise sensors based on SWNT networks. Considering the increased interests in high-density sensor arrays for healthcare and environmental protection, our strategy should have a significant impact on various industrial applications.

Stream-groundwater exchange and hydrologic turnover at the network scale

Science.gov (United States)

Covino, Tim; McGlynn, Brian; Mallard, John

2011-12-01

The exchange of water between streams and groundwater can influence stream water quality, hydrologic mass balances, and attenuate solute export from watersheds. We used conservative tracer injections (chloride, Cl-) across 10 stream reaches to investigate stream water gains and losses from and to groundwater at larger spatial and temporal scales than typically associated with hyporheic exchanges. We found strong relationships between reach discharge, median tracer velocity, and gross hydrologic loss across a range of stream morphologies and sizes in the 11.4 km2 Bull Trout Watershed of central ID. We implemented these empirical relationships in a numerical network model and simulated stream water gains and losses and subsequent fractional hydrologic turnover across the stream network. We found that stream gains and losses from and to groundwater can influence source water contributions and stream water compositions across stream networks. Quantifying proportional influences of source water contributions from runoff generation locations across the network on stream water composition can provide insight into the internal mechanisms that partially control the hydrologic and biogeochemical signatures observed along networks and at watershed outlets.

Collaborative Catchment-Scale Water Quality Management using Integrated Wireless Sensor Networks

Science.gov (United States)

Zia, Huma; Harris, Nick; Merrett, Geoff

2013-04-01

Electronics and Computer Science, University of Southampton, United Kingdom Summary The challenge of improving water quality (WQ) is a growing global concern [1]. Poor WQ is mainly attributed to poor water management and outdated agricultural activities. We propose that collaborative sensor networks spread across an entire catchment can allow cooperation among individual activities for integrated WQ monitoring and management. We show that sharing information on critical parameters among networks of water bodies and farms can enable identification and quantification of the contaminant sources, enabling better decision making for agricultural practices and thereby reducing contaminants fluxes. Motivation and results Nutrient losses from land to water have accelerated due to agricultural and urban pursuits [2]. In many cases, the application of fertiliser can be reduced by 30-50% without any loss of yield [3]. Thus information about nutrient levels and trends around the farm can improve agricultural practices and thereby reduce water contamination. The use of sensor networks for monitoring WQ in a catchment is in its infancy, but more applications are being tested [4]. However, these are focussed on local requirements and are mostly limited to water bodies. They have yet to explore the use of this technology for catchment-scale monitoring and management decisions, in an autonomous and dynamic manner. For effective and integrated WQ management, we propose a system that utilises local monitoring networks across a catchment, with provision for collaborative information sharing. This system of networks shares information about critical events, such as rain or flooding. Higher-level applications make use of this information to inform decisions about nutrient management, improving the quality of monitoring through the provision of richer datasets of catchment information to local networks. In the full paper, we present example scenarios and analyse how the benefits of

Perspectives on stress resilience and adolescent neurobehavioral function.

Science.gov (United States)

Romeo, Russell D

2015-01-01

Interest in adolescence as a crucial stage of neurobehavioral maturation is growing, as is the concern of how stress may perturb this critical period of development. Though it is well recognized that stress-related vulnerabilities increase during adolescence, not all adolescent individuals are uniformly affected by stress nor do stressful experiences inevitability lead to negative outcomes. Indeed, many adolescents show resilience to stress-induced dysfunctions. However, relatively little is known regarding the mechanisms that may mediate resilience to stress in adolescence. The goal of this brief review is to bring together a few separate, yet related lines of research that highlight specific variables that may influence stress resilience during adolescence, including early life programming of the hypothalamic-pituitary-adrenal (HPA) axis, stress inoculation, and genetic predisposition. Though we are far from a clear understanding of the factors that mediate resistance to stress-induced dysfunctions, it is imperative that we identify and delineate these aspects of resilience to help adolescents reach their full potential, even in the face of adversity.

Perspectives on stress resilience and adolescent neurobehavioral function

Directory of Open Access Journals (Sweden)

Russell D. Romeo

2015-01-01

Full Text Available Interest in adolescence as a crucial stage of neurobehavioral maturation is growing, as is the concern of how stress may perturb this critical period of development. Though it is well recognized that stress-related vulnerabilities increase during adolescence, not all adolescent individuals are uniformly affected by stress nor do stressful experiences inevitability lead to negative outcomes. Indeed, many adolescents show resilience to stress-induced dysfunctions. However, relatively little is known regarding the mechanisms that may mediate resilience to stress in adolescence. The goal of this brief review is to bring together a few separate, yet related lines of research that highlight specific variables that may influence stress resilience during adolescence, including early life programming of the hypothalamic-pituitary-adrenal (HPA axis, stress inoculation, and genetic predisposition. Though we are far from a clear understanding of the factors that mediate resistance to stress-induced dysfunctions, it is imperative that we identify and delineate these aspects of resilience to help adolescents reach their full potential, even in the face of adversity.

On the Effects of Frequency Scaling over Capacity Scaling in Underwater Networks

DEFF Research Database (Denmark)

Shin, Won-Yong; Roetter, Daniel Enrique Lucani; Médard, Muriel

2013-01-01

that there exists either a bandwidth or power limitation, or both, according to the operating regimes (i.e., path-loss attenuation regimes), thus yielding the upper bound that follows three fundamentally different information transfer arguments. In addition, an achievability result based on the multi-hop (MH......) transmission is presented for dense networks. MH is shown to guarantee the order optimality under certain operating regimes. More specifically, it turns out that scaling the carrier frequency faster than or as is instrumental towards achieving the order optimality of the MH protocol....

Identifying influential nodes in large-scale directed networks: the role of clustering.

Science.gov (United States)

Chen, Duan-Bing; Gao, Hui; Lü, Linyuan; Zhou, Tao

2013-01-01

Identifying influential nodes in very large-scale directed networks is a big challenge relevant to disparate applications, such as accelerating information propagation, controlling rumors and diseases, designing search engines, and understanding hierarchical organization of social and biological networks. Known methods range from node centralities, such as degree, closeness and betweenness, to diffusion-based processes, like PageRank and LeaderRank. Some of these methods already take into account the influences of a node's neighbors but do not directly make use of the interactions among it's neighbors. Local clustering is known to have negative impacts on the information spreading. We further show empirically that it also plays a negative role in generating local connections. Inspired by these facts, we propose a local ranking algorithm named ClusterRank, which takes into account not only the number of neighbors and the neighbors' influences, but also the clustering coefficient. Subject to the susceptible-infected-recovered (SIR) spreading model with constant infectivity, experimental results on two directed networks, a social network extracted from delicious.com and a large-scale short-message communication network, demonstrate that the ClusterRank outperforms some benchmark algorithms such as PageRank and LeaderRank. Furthermore, ClusterRank can also be applied to undirected networks where the superiority of ClusterRank is significant compared with degree centrality and k-core decomposition. In addition, ClusterRank, only making use of local information, is much more efficient than global methods: It takes only 191 seconds for a network with about [Formula: see text] nodes, more than 15 times faster than PageRank.

Identifying influential nodes in large-scale directed networks: the role of clustering.

Directory of Open Access Journals (Sweden)

Duan-Bing Chen

Full Text Available Identifying influential nodes in very large-scale directed networks is a big challenge relevant to disparate applications, such as accelerating information propagation, controlling rumors and diseases, designing search engines, and understanding hierarchical organization of social and biological networks. Known methods range from node centralities, such as degree, closeness and betweenness, to diffusion-based processes, like PageRank and LeaderRank. Some of these methods already take into account the influences of a node's neighbors but do not directly make use of the interactions among it's neighbors. Local clustering is known to have negative impacts on the information spreading. We further show empirically that it also plays a negative role in generating local connections. Inspired by these facts, we propose a local ranking algorithm named ClusterRank, which takes into account not only the number of neighbors and the neighbors' influences, but also the clustering coefficient. Subject to the susceptible-infected-recovered (SIR spreading model with constant infectivity, experimental results on two directed networks, a social network extracted from delicious.com and a large-scale short-message communication network, demonstrate that the ClusterRank outperforms some benchmark algorithms such as PageRank and LeaderRank. Furthermore, ClusterRank can also be applied to undirected networks where the superiority of ClusterRank is significant compared with degree centrality and k-core decomposition. In addition, ClusterRank, only making use of local information, is much more efficient than global methods: It takes only 191 seconds for a network with about [Formula: see text] nodes, more than 15 times faster than PageRank.

Identifying Influential Nodes in Large-Scale Directed Networks: The Role of Clustering

Science.gov (United States)

Chen, Duan-Bing; Gao, Hui; Lü, Linyuan; Zhou, Tao

2013-01-01

Identifying influential nodes in very large-scale directed networks is a big challenge relevant to disparate applications, such as accelerating information propagation, controlling rumors and diseases, designing search engines, and understanding hierarchical organization of social and biological networks. Known methods range from node centralities, such as degree, closeness and betweenness, to diffusion-based processes, like PageRank and LeaderRank. Some of these methods already take into account the influences of a node’s neighbors but do not directly make use of the interactions among it’s neighbors. Local clustering is known to have negative impacts on the information spreading. We further show empirically that it also plays a negative role in generating local connections. Inspired by these facts, we propose a local ranking algorithm named ClusterRank, which takes into account not only the number of neighbors and the neighbors’ influences, but also the clustering coefficient. Subject to the susceptible-infected-recovered (SIR) spreading model with constant infectivity, experimental results on two directed networks, a social network extracted from delicious.com and a large-scale short-message communication network, demonstrate that the ClusterRank outperforms some benchmark algorithms such as PageRank and LeaderRank. Furthermore, ClusterRank can also be applied to undirected networks where the superiority of ClusterRank is significant compared with degree centrality and k-core decomposition. In addition, ClusterRank, only making use of local information, is much more efficient than global methods: It takes only 191 seconds for a network with about nodes, more than 15 times faster than PageRank. PMID:24204833

Prediction of Full-Scale Propulsion Power using Artificial Neural Networks

DEFF Research Database (Denmark)

Pedersen, Benjamin Pjedsted; Larsen, Jan

2009-01-01

Full scale measurements of the propulsion power, ship speed, wind speed and direction, sea and air temperature from four different loading conditions, together with hind cast data of wind and sea properties; and noon report data has been used to train an Artificial Neural Network for prediction...

Networking for large-scale science: infrastructure, provisioning, transport and application mapping

International Nuclear Information System (INIS)

Rao, Nageswara S; Carter, Steven M; Wu Qishi; Wing, William R; Zhu Mengxia; Mezzacappa, Anthony; Veeraraghavan, Malathi; Blondin, John M

2005-01-01

Large-scale science computations and experiments require unprecedented network capabilities in the form of large bandwidth and dynamically stable connections to support data transfers, interactive visualizations, and monitoring and steering operations. A number of component technologies dealing with the infrastructure, provisioning, transport and application mappings must be developed and/or optimized to achieve these capabilities. We present a brief account of the following technologies that contribute toward achieving these network capabilities: (a) DOE UltraScienceNet and NSF CHEETAH network testbeds that provide on-demand and scheduled dedicated network connections; (b) experimental results on transport protocols that achieve close to 100% utilization on dedicated 1Gbps wide-area channels; (c) a scheme for optimally mapping a visualization pipeline onto a network to minimize the end-to-end delays; and (d) interconnect configuration and protocols that provides multiple Gbps flows from Cray X1 to external hosts

Networking for large-scale science: infrastructure, provisioning, transport and application mapping

Energy Technology Data Exchange (ETDEWEB)

Rao, Nageswara S [Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Carter, Steven M [Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Wu Qishi [Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Wing, William R [Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Zhu Mengxia [Department of Computer Science, Louisiana State University, Baton Rouge, LA 70803 (United States); Mezzacappa, Anthony [Physics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Veeraraghavan, Malathi [Department of Computer Science, University of Virginia, Charlottesville, VA 22904 (United States); Blondin, John M [Department of Physics, North Carolina State University, Raleigh, NC 27695 (United States)

2005-01-01

Large-scale science computations and experiments require unprecedented network capabilities in the form of large bandwidth and dynamically stable connections to support data transfers, interactive visualizations, and monitoring and steering operations. A number of component technologies dealing with the infrastructure, provisioning, transport and application mappings must be developed and/or optimized to achieve these capabilities. We present a brief account of the following technologies that contribute toward achieving these network capabilities: (a) DOE UltraScienceNet and NSF CHEETAH network testbeds that provide on-demand and scheduled dedicated network connections; (b) experimental results on transport protocols that achieve close to 100% utilization on dedicated 1Gbps wide-area channels; (c) a scheme for optimally mapping a visualization pipeline onto a network to minimize the end-to-end delays; and (d) interconnect configuration and protocols that provides multiple Gbps flows from Cray X1 to external hosts.

SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation.

Science.gov (United States)

Xue, Yuan; Xu, Tao; Zhang, Han; Long, L Rodney; Huang, Xiaolei

2018-05-03

Inspired by classic Generative Adversarial Networks (GANs), we propose a novel end-to-end adversarial neural network, called SegAN, for the task of medical image segmentation. Since image segmentation requires dense, pixel-level labeling, the single scalar real/fake output of a classic GAN's discriminator may be ineffective in producing stable and sufficient gradient feedback to the networks. Instead, we use a fully convolutional neural network as the segmentor to generate segmentation label maps, and propose a novel adversarial critic network with a multi-scale L 1 loss function to force the critic and segmentor to learn both global and local features that capture long- and short-range spatial relationships between pixels. In our SegAN framework, the segmentor and critic networks are trained in an alternating fashion in a min-max game: The critic is trained by maximizing a multi-scale loss function, while the segmentor is trained with only gradients passed along by the critic, with the aim to minimize the multi-scale loss function. We show that such a SegAN framework is more effective and stable for the segmentation task, and it leads to better performance than the state-of-the-art U-net segmentation method. We tested our SegAN method using datasets from the MICCAI BRATS brain tumor segmentation challenge. Extensive experimental results demonstrate the effectiveness of the proposed SegAN with multi-scale loss: on BRATS 2013 SegAN gives performance comparable to the state-of-the-art for whole tumor and tumor core segmentation while achieves better precision and sensitivity for Gd-enhance tumor core segmentation; on BRATS 2015 SegAN achieves better performance than the state-of-the-art in both dice score and precision.

The “Double-Edge Sword” of Human Empathy: A Unifying Neurobehavioral Theory of Compassion Stress Injury

Directory of Open Access Journals (Sweden)

Mark Russell

2015-11-01

Full Text Available An integrative neurobehavioral model for “compassion stress injury” is offered to explain the “double-edge sword” of empathy and inherent vulnerability of helping professionals and care-givers. One of the most strikingly robust, yet largely invisible scientific findings to emerge over the past decade is identifying the neurophysiological mechanisms enabling human beings to understand and feel what another is feeling. The compelling convergence of evidence from multi-disciplinary lines of primary research and studies of paired-deficits has revealed that the phenomenon of human beings witnessing the pain and suffering of others is clearly associated with activation of neural structures used during first-hand experience. Moreover, it is now evident that a large part of the neural activation shared between self- and other-related experiences occurs automatically, outside the observer’s conscious awareness or control. However, it is also well established that full blown human empathic capacity and altruistic behavior is regulated by neural pathways responsible for flexible consciously controlled actions of the observer. We review the history, prevalence, and etiological models of “compassion stress injury” such as burnout, secondary traumatic stress, vicarious traumatization, compassion fatigue, and empathic distress fatigue, along with implications of the neurobehavioral approach in future research.

Non-parametric co-clustering of large scale sparse bipartite networks on the GPU

DEFF Research Database (Denmark)

Hansen, Toke Jansen; Mørup, Morten; Hansen, Lars Kai

2011-01-01

of row and column clusters from a hypothesis space of an infinite number of clusters. To reach large scale applications of co-clustering we exploit that parameter inference for co-clustering is well suited for parallel computing. We develop a generic GPU framework for efficient inference on large scale...... sparse bipartite networks and achieve a speedup of two orders of magnitude compared to estimation based on conventional CPUs. In terms of scalability we find for networks with more than 100 million links that reliable inference can be achieved in less than an hour on a single GPU. To efficiently manage...

Postnatal penile growth concurrent with mini-puberty predicts later sex-typed play behavior: Evidence for neurobehavioral effects of the postnatal androgen surge in typically developing boys.

Science.gov (United States)

Pasterski, Vickie; Acerini, Carlo L; Dunger, David B; Ong, Ken K; Hughes, Ieuan A; Thankamony, Ajay; Hines, Melissa

2015-03-01

The masculinizing effects of prenatal androgens on human neurobehavioral development are well established. Also, the early postnatal surge of androgens in male infants, or mini-puberty, has been well documented and is known to influence physiological development, including penile growth. However, neurobehavioral effects of androgen exposure during mini-puberty are largely unknown. The main aim of the current study was to evaluate possible neurobehavioral consequences of mini-puberty by relating penile growth in the early postnatal period to subsequent behavior. Using multiple linear regression, we demonstrated that penile growth between birth and three months postnatal, concurrent with mini-puberty, significantly predicted increased masculine/decreased feminine behavior assessed using the Pre-school Activities Inventory (PSAI) in 81 healthy boys at 3 to 4years of age. When we controlled for other potential influences on masculine/feminine behavior and/or penile growth, including variance in androgen exposure prenatally and body growth postnally, the predictive value of penile growth in the early postnatal period persisted. More specifically, prenatal androgen exposure, reflected in the measurement of anogenital distance (AGD), and early postnatal androgen exposure, reflected in penile growth from birth to 3months, were significant predictors of increased masculine/decreased feminine behavior, with each accounting for unique variance. Our findings suggest that independent associations of PSAI with AGD at birth and with penile growth during mini-puberty reflect prenatal and early postnatal androgen exposures respectively. Thus, we provide a novel and readily available approach for assessing effects of early androgen exposures, as well as novel evidence that early postnatal aes human neurobehavioral development. Copyright © 2015. Published by Elsevier Inc.

Validation of the Social Networking Activity Intensity Scale among Junior Middle School Students in China

OpenAIRE

Li, Jibin; Lau, Joseph T. F.; Mo, Phoenix K. H.; Su, Xuefen; Wu, Anise M. S.; Tang, Jie; Qin, Zuguo

2016-01-01

Background Online social networking use has been integrated into adolescents? daily life and the intensity of online social networking use may have important consequences on adolescents? well-being. However, there are few validated instruments to measure social networking use intensity. The present study aims to develop the Social Networking Activity Intensity Scale (SNAIS) and validate it among junior middle school students in China. Methods A total of 910 students who were social networking...

ProteoLens: a visual analytic tool for multi-scale database-driven biological network data mining.

Science.gov (United States)

Huan, Tianxiao; Sivachenko, Andrey Y; Harrison, Scott H; Chen, Jake Y

2008-08-12

New systems biology studies require researchers to understand how interplay among myriads of biomolecular entities is orchestrated in order to achieve high-level cellular and physiological functions. Many software tools have been developed in the past decade to help researchers visually navigate large networks of biomolecular interactions with built-in template-based query capabilities. To further advance researchers' ability to interrogate global physiological states of cells through multi-scale visual network explorations, new visualization software tools still need to be developed to empower the analysis. A robust visual data analysis platform driven by database management systems to perform bi-directional data processing-to-visualizations with declarative querying capabilities is needed. We developed ProteoLens as a JAVA-based visual analytic software tool for creating, annotating and exploring multi-scale biological networks. It supports direct database connectivity to either Oracle or PostgreSQL database tables/views, on which SQL statements using both Data Definition Languages (DDL) and Data Manipulation languages (DML) may be specified. The robust query languages embedded directly within the visualization software help users to bring their network data into a visualization context for annotation and exploration. ProteoLens supports graph/network represented data in standard Graph Modeling Language (GML) formats, and this enables interoperation with a wide range of other visual layout tools. The architectural design of ProteoLens enables the de-coupling of complex network data visualization tasks into two distinct phases: 1) creating network data association rules, which are mapping rules between network node IDs or edge IDs and data attributes such as functional annotations, expression levels, scores, synonyms, descriptions etc; 2) applying network data association rules to build the network and perform the visual annotation of graph nodes and edges

Autonomous management of a recursive area hierarchy for large scale wireless sensor networks using multiple parents

Energy Technology Data Exchange (ETDEWEB)

Cree, Johnathan Vee [Washington State Univ., Pullman, WA (United States); Delgado-Frias, Jose [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

2016-03-01

Large scale wireless sensor networks have been proposed for applications ranging from anomaly detection in an environment to vehicle tracking. Many of these applications require the networks to be distributed across a large geographic area while supporting three to five year network lifetimes. In order to support these requirements large scale wireless sensor networks of duty-cycled devices need a method of efficient and effective autonomous configuration/maintenance. This method should gracefully handle the synchronization tasks duty-cycled networks. Further, an effective configuration solution needs to recognize that in-network data aggregation and analysis presents significant benefits to wireless sensor network and should configure the network in a way such that said higher level functions benefit from the logically imposed structure. NOA, the proposed configuration and maintenance protocol, provides a multi-parent hierarchical logical structure for the network that reduces the synchronization workload. It also provides higher level functions with significant inherent benefits such as but not limited to: removing network divisions that are created by single-parent hierarchies, guarantees for when data will be compared in the hierarchy, and redundancies for communication as well as in-network data aggregation/analysis/storage.

Shortwave surface radiation network for observing small-scale cloud inhomogeneity fields

Science.gov (United States)

Lakshmi Madhavan, Bomidi; Kalisch, John; Macke, Andreas

2016-03-01

As part of the High Definition Clouds and Precipitation for advancing Climate Prediction Observational Prototype Experiment (HOPE), a high-density network of 99 silicon photodiode pyranometers was set up around Jülich (10 km × 12 km area) from April to July 2013 to capture the small-scale variability of cloud-induced radiation fields at the surface. In this paper, we provide the details of this unique setup of the pyranometer network, data processing, quality control, and uncertainty assessment under variable conditions. Some exemplary days with clear, broken cloudy, and overcast skies were explored to assess the spatiotemporal observations from the network along with other collocated radiation and sky imager measurements available during the HOPE period.

Detection of large-scale concentric gravity waves from a Chinese airglow imager network

Science.gov (United States)

Lai, Chang; Yue, Jia; Xu, Jiyao; Yuan, Wei; Li, Qinzeng; Liu, Xiao

2018-06-01

Concentric gravity waves (CGWs) contain a broad spectrum of horizontal wavelengths and periods due to their instantaneous localized sources (e.g., deep convection, volcanic eruptions, or earthquake, etc.). However, it is difficult to observe large-scale gravity waves of >100 km wavelength from the ground for the limited field of view of a single camera and local bad weather. Previously, complete large-scale CGW imagery could only be captured by satellite observations. In the present study, we developed a novel method that uses assembling separate images and applying low-pass filtering to obtain temporal and spatial information about complete large-scale CGWs from a network of all-sky airglow imagers. Coordinated observations from five all-sky airglow imagers in Northern China were assembled and processed to study large-scale CGWs over a wide area (1800 km × 1 400 km), focusing on the same two CGW events as Xu et al. (2015). Our algorithms yielded images of large-scale CGWs by filtering out the small-scale CGWs. The wavelengths, wave speeds, and periods of CGWs were measured from a sequence of consecutive assembled images. Overall, the assembling and low-pass filtering algorithms can expand the airglow imager network to its full capacity regarding the detection of large-scale gravity waves.

Large scale network management. Condition indicators for network stations, high voltage power conductions and cables

International Nuclear Information System (INIS)

Eggen, Arnt Ove; Rolfseng, Lars; Langdal, Bjoern Inge

2006-02-01

In the Strategic Institute Programme (SIP) 'Electricity Business enters e-business (eBee)' SINTEF Energy research has developed competency that can help the energy business employ ICT systems and computer technology in an improved way. Large scale network management is now a reality, and it is characterized by large entities with increasing demands on efficiency and quality. These are goals that can only be reached by using ICT systems and computer technology in a more clever way than what is the case today. At the same time it is important that knowledge held by experienced co-workers is consulted when formal rules for evaluations and decisions in ICT systems are developed. In this project an analytical concept for evaluation of networks based information in different ICT systems has been developed. The method estimating the indicators to describe different conditions in a network is general, and indicators can be made to fit different levels of decision and network levels, for example network station, transformer circuit, distribution network and regional network. Moreover, the indicators can contain information about technical aspects, economy and HSE. An indicator consists of an indicator name, an indicator value, and an indicator colour based on a traffic-light analogy to indicate a condition or a quality for the indicator. Values on one or more indicators give an impression of important conditions in the network, and make up the basis for knowing where more detailed evaluations have to be conducted before a final decision on for example maintenance or renewal is made. A prototype has been developed for testing the new method. The prototype has been developed in Excel, and especially designed for analysing transformer circuits in a distribution network. However, the method is a general one, and well suited for implementation in a commercial computer system (ml)

Scaling of peak flows with constant flow velocity in random self-similar networks

Directory of Open Access Journals (Sweden)

R. Mantilla

2011-07-01

Full Text Available A methodology is presented to understand the role of the statistical self-similar topology of real river networks on scaling, or power law, in peak flows for rainfall-runoff events. We created Monte Carlo generated sets of ensembles of 1000 random self-similar networks (RSNs with geometrically distributed interior and exterior generators having parameters p_i and p_e, respectively. The parameter values were chosen to replicate the observed topology of real river networks. We calculated flow hydrographs in each of these networks by numerically solving the link-based mass and momentum conservation equation under the assumption of constant flow velocity. From these simulated RSNs and hydrographs, the scaling exponents β and φ characterizing power laws with respect to drainage area, and corresponding to the width functions and flow hydrographs respectively, were estimated. We found that, in general, φ > β, which supports a similar finding first reported for simulations in the river network of the Walnut Gulch basin, Arizona. Theoretical estimation of β and φ in RSNs is a complex open problem. Therefore, using results for a simpler problem associated with the expected width function and expected hydrograph for an ensemble of RSNs, we give heuristic arguments for theoretical derivations of the scaling exponents β^(E and φ^(E that depend on the Horton ratios for stream lengths and areas. These ratios in turn have a known dependence on the parameters of the geometric distributions of RSN generators. Good agreement was found between the analytically conjectured values of β^(E and φ^(E and the values estimated by the simulated ensembles of RSNs and hydrographs. The independence of the scaling exponents φ^(E and φ with respect to the value of flow velocity and runoff intensity implies an interesting connection between unit

Edaravone alleviates cisplatin-induced neurobehavioral deficits via modulation of oxidative stress and inflammatory mediators in the rat hippocampus.

Science.gov (United States)

Jangra, Ashok; Kwatra, Mohit; Singh, Tavleen; Pant, Rajat; Kushwah, Pawan; Ahmed, Sahabuddin; Dwivedi, Durgesh; Saroha, Babita; Lahkar, Mangala

2016-11-15

Cisplatin is a chemotherapeutic agent used in the treatment of malignant tumors. A major clinical limitation of cisplatin is its potential toxic effects, including neurotoxicity. Edaravone, a potent free radical scavenger, has been reported to have the neuroprotective effect against neurological deficits. The aim of the present study was to determine the neuroprotective effect of edaravone against cisplatin-induced behavioral and biochemical anomalies in male Wistar rats. Our results showed that cisplatin (5mg/kg/week, i.p.) administration for seven weeks caused marked cognitive deficits and motor incoordination in rats. This was accompanied by oxido-nitrosative stress, neuroinflammation, NF-κB activation and down-regulation of Nrf2/HO-1 gene expression level in the hippocampus. Edaravone (10mg/kg/week, i.p.) treatment for seven weeks inhibited the aforementioned neurobehavioral and neurochemical deficits. Furthermore, edaravone was found to up-regulate the gene expression level of Nrf2/HO-1 and prevented the cisplatin-induced NF-κB activation. These findings demonstrated that oxido-nitrosative stress and inflammatory signaling mediators play a key role in the development of cisplatin-induced neurobehavioral deficits which were prevented by edaravone treatment. Copyright © 2016 Elsevier B.V. All rights reserved.

Local, distributed topology control for large-scale wireless ad-hoc networks

NARCIS (Netherlands)

Nieberg, T.; Hurink, Johann L.

In this document, topology control of a large-scale, wireless network by a distributed algorithm that uses only locally available information is presented. Topology control algorithms adjust the transmission power of wireless nodes to create a desired topology. The algorithm, named local power

Cardinality Estimation Algorithm in Large-Scale Anonymous Wireless Sensor Networks

KAUST Repository

Douik, Ahmed

2017-08-30

Consider a large-scale anonymous wireless sensor network with unknown cardinality. In such graphs, each node has no information about the network topology and only possesses a unique identifier. This paper introduces a novel distributed algorithm for cardinality estimation and topology discovery, i.e., estimating the number of node and structure of the graph, by querying a small number of nodes and performing statistical inference methods. While the cardinality estimation allows the design of more efficient coding schemes for the network, the topology discovery provides a reliable way for routing packets. The proposed algorithm is shown to produce a cardinality estimate proportional to the best linear unbiased estimator for dense graphs and specific running times. Simulation results attest the theoretical results and reveal that, for a reasonable running time, querying a small group of nodes is sufficient to perform an estimation of 95% of the whole network. Applications of this work include estimating the number of Internet of Things (IoT) sensor devices, online social users, active protein cells, etc.

Complex Quantum Network Manifolds in Dimension d > 2 are Scale-Free

Science.gov (United States)

Bianconi, Ginestra; Rahmede, Christoph

2015-09-01

In quantum gravity, several approaches have been proposed until now for the quantum description of discrete geometries. These theoretical frameworks include loop quantum gravity, causal dynamical triangulations, causal sets, quantum graphity, and energetic spin networks. Most of these approaches describe discrete spaces as homogeneous network manifolds. Here we define Complex Quantum Network Manifolds (CQNM) describing the evolution of quantum network states, and constructed from growing simplicial complexes of dimension . We show that in d = 2 CQNM are homogeneous networks while for d > 2 they are scale-free i.e. they are characterized by large inhomogeneities of degrees like most complex networks. From the self-organized evolution of CQNM quantum statistics emerge spontaneously. Here we define the generalized degrees associated with the -faces of the -dimensional CQNMs, and we show that the statistics of these generalized degrees can either follow Fermi-Dirac, Boltzmann or Bose-Einstein distributions depending on the dimension of the -faces.

Time-Varying, Multi-Scale Adaptive System Reliability Analysis of Lifeline Infrastructure Networks

Energy Technology Data Exchange (ETDEWEB)

Gearhart, Jared Lee [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Kurtz, Nolan Scot [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2014-09-01

The majority of current societal and economic needs world-wide are met by the existing networked, civil infrastructure. Because the cost of managing such infrastructure is high and increases with time, risk-informed decision making is essential for those with management responsibilities for these systems. To address such concerns, a methodology that accounts for new information, deterioration, component models, component importance, group importance, network reliability, hierarchical structure organization, and efficiency concerns has been developed. This methodology analyzes the use of new information through the lens of adaptive Importance Sampling for structural reliability problems. Deterioration, multi-scale bridge models, and time-variant component importance are investigated for a specific network. Furthermore, both bridge and pipeline networks are studied for group and component importance, as well as for hierarchical structures in the context of specific networks. Efficiency is the primary driver throughout this study. With this risk-informed approach, those responsible for management can address deteriorating infrastructure networks in an organized manner.

2B-Alert Web: An Open-Access Tool for Predicting the Effects of Sleep/Wake Schedules and Caffeine Consumption on Neurobehavioral Performance.

Science.gov (United States)

Reifman, Jaques; Kumar, Kamal; Wesensten, Nancy J; Tountas, Nikolaos A; Balkin, Thomas J; Ramakrishnan, Sridhar

2016-12-01

Computational tools that predict the effects of daily sleep/wake amounts on neurobehavioral performance are critical components of fatigue management systems, allowing for the identification of periods during which individuals are at increased risk for performance errors. However, none of the existing computational tools is publicly available, and the commercially available tools do not account for the beneficial effects of caffeine on performance, limiting their practical utility. Here, we introduce 2B-Alert Web, an open-access tool for predicting neurobehavioral performance, which accounts for the effects of sleep/wake schedules, time of day, and caffeine consumption, while incorporating the latest scientific findings in sleep restriction, sleep extension, and recovery sleep. We combined our validated Unified Model of Performance and our validated caffeine model to form a single, integrated modeling framework instantiated as a Web-enabled tool. 2B-Alert Web allows users to input daily sleep/wake schedules and caffeine consumption (dosage and time) to obtain group-average predictions of neurobehavioral performance based on psychomotor vigilance tasks. 2B-Alert Web is accessible at: https://2b-alert-web.bhsai.org. The 2B-Alert Web tool allows users to obtain predictions for mean response time, mean reciprocal response time, and number of lapses. The graphing tool allows for simultaneous display of up to seven different sleep/wake and caffeine schedules. The schedules and corresponding predicted outputs can be saved as a Microsoft Excel file; the corresponding plots can be saved as an image file. The schedules and predictions are erased when the user logs off, thereby maintaining privacy and confidentiality. The publicly accessible 2B-Alert Web tool is available for operators, schedulers, and neurobehavioral scientists as well as the general public to determine the impact of any given sleep/wake schedule, caffeine consumption, and time of day on performance of a

Self-Organization in Coupled Map Scale-Free Networks

International Nuclear Information System (INIS)

Xiao-Ming, Liang; Zong-Hua, Liu; Hua-Ping, Lü

2008-01-01

We study the self-organization of phase synchronization in coupled map scale-free networks with chaotic logistic map at each node and find that a variety of ordered spatiotemporal patterns emerge spontaneously in a regime of coupling strength. These ordered behaviours will change with the increase of the average links and are robust to both the system size and parameter mismatch. A heuristic theory is given to explain the mechanism of self-organization and to figure out the regime of coupling for the ordered spatiotemporal patterns

Modeling a full-scale primary sedimentation tank using artificial neural networks.

Science.gov (United States)

Gamal El-Din, A; Smith, D W

2002-05-01

Modeling the performance of full-scale primary sedimentation tanks has been commonly done using regression-based models, which are empirical relationships derived strictly from observed daily average influent and effluent data. Another approach to model a sedimentation tank is using a hydraulic efficiency model that utilizes tracer studies to characterize the performance of model sedimentation tanks based on eddy diffusion. However, the use of hydraulic efficiency models to predict the dynamic behavior of a full-scale sedimentation tank is very difficult as the development of such models has been done using controlled studies of model tanks. In this paper, another type of model, namely artificial neural network modeling approach, is used to predict the dynamic response of a full-scale primary sedimentation tank. The neuralmodel consists of two separate networks, one uses flow and influent total suspended solids data in order to predict the effluent total suspended solids from the tank, and the other makes predictions of the effluent chemical oxygen demand using data of the flow and influent chemical oxygen demand as inputs. An extensive sampling program was conducted in order to collect a data set to be used in training and validating the networks. A systematic approach was used in the building process of the model which allowed the identification of a parsimonious neural model that is able to learn (and not memorize) from past data and generalize very well to unseen data that were used to validate the model. Theresults seem very promising. The potential of using the model as part of a real-time process control system isalso discussed.

78 FR 70076 - Large Scale Networking (LSN)-Middleware and Grid Interagency Coordination (MAGIC) Team

Science.gov (United States)

2013-11-22

... projects. The MAGIC Team reports to the Large Scale Networking (LSN) Coordinating Group (CG). Public... Coordination (MAGIC) Team AGENCY: The Networking and Information Technology Research and Development (NITRD... MAGIC Team meetings are held on the first Wednesday of each month, 2:00-4:00 p.m., at the National...

Automatic Generation of Connectivity for Large-Scale Neuronal Network Models through Structural Plasticity.

Science.gov (United States)

Diaz-Pier, Sandra; Naveau, Mikaël; Butz-Ostendorf, Markus; Morrison, Abigail

2016-01-01

With the emergence of new high performance computation technology in the last decade, the simulation of large scale neural networks which are able to reproduce the behavior and structure of the brain has finally become an achievable target of neuroscience. Due to the number of synaptic connections between neurons and the complexity of biological networks, most contemporary models have manually defined or static connectivity. However, it is expected that modeling the dynamic generation and deletion of the links among neurons, locally and between different regions of the brain, is crucial to unravel important mechanisms associated with learning, memory and healing. Moreover, for many neural circuits that could potentially be modeled, activity data is more readily and reliably available than connectivity data. Thus, a framework that enables networks to wire themselves on the basis of specified activity targets can be of great value in specifying network models where connectivity data is incomplete or has large error margins. To address these issues, in the present work we present an implementation of a model of structural plasticity in the neural network simulator NEST. In this model, synapses consist of two parts, a pre- and a post-synaptic element. Synapses are created and deleted during the execution of the simulation following local homeostatic rules until a mean level of electrical activity is reached in the network. We assess the scalability of the implementation in order to evaluate its potential usage in the self generation of connectivity of large scale networks. We show and discuss the results of simulations on simple two population networks and more complex models of the cortical microcircuit involving 8 populations and 4 layers using the new framework.

A mixed-integer linear programming approach to the reduction of genome-scale metabolic networks.

Science.gov (United States)

Röhl, Annika; Bockmayr, Alexander

2017-01-03

Constraint-based analysis has become a widely used method to study metabolic networks. While some of the associated algorithms can be applied to genome-scale network reconstructions with several thousands of reactions, others are limited to small or medium-sized models. In 2015, Erdrich et al. introduced a method called NetworkReducer, which reduces large metabolic networks to smaller subnetworks, while preserving a set of biological requirements that can be specified by the user. Already in 2001, Burgard et al. developed a mixed-integer linear programming (MILP) approach for computing minimal reaction sets under a given growth requirement. Here we present an MILP approach for computing minimum subnetworks with the given properties. The minimality (with respect to the number of active reactions) is not guaranteed by NetworkReducer, while the method by Burgard et al. does not allow specifying the different biological requirements. Our procedure is about 5-10 times faster than NetworkReducer and can enumerate all minimum subnetworks in case there exist several ones. This allows identifying common reactions that are present in all subnetworks, and reactions appearing in alternative pathways. Applying complex analysis methods to genome-scale metabolic networks is often not possible in practice. Thus it may become necessary to reduce the size of the network while keeping important functionalities. We propose a MILP solution to this problem. Compared to previous work, our approach is more efficient and allows computing not only one, but even all minimum subnetworks satisfying the required properties.

Phase transitions in scale-free neural networks: Departure from the standard mean-field universality class

International Nuclear Information System (INIS)

Aldana, Maximino; Larralde, Hernan

2004-01-01

We investigate the nature of the phase transition from an ordered to a disordered state that occurs in a family of neural network models with noise. These models are closely related to the majority voter model, where a ferromagneticlike interaction between the elements prevails. Each member of the family is distinguished by the network topology, which is determined by the probability distribution of the number of incoming links. We show that for homogeneous random topologies, the phase transition belongs to the standard mean-field universality class, characterized by the order parameter exponent β=1/2. However, for scale-free networks we obtain phase transition exponents ranging from 1/2 to infinity. Furthermore, we show the existence of a phase transition even for values of the scale-free exponent in the interval (1.5,2], where the average network connectivity diverges

Upgrading Wood-Based Industries: Harnessing the Social Network of Small-Scale Furniture Producers and Their Institutions

Directory of Open Access Journals (Sweden)

Melati ,

2011-05-01

Full Text Available Furniture is a major export commodity in Indonesia with a total value of USD 1.96 million in 2007.  Jepara District is one of the key location for wood furniture production with 15,271 furniture related business units employing 176,469 workers.  However, inefficiencies and power imbalances throughout the furniture value chain have resulted in overharvesting and uneven distribution of gains among the industry’s actors.  In contrast to price-setting international furniture retailers, small-scale producers enjoy the least value from their products.  In order to increase added value and competitiveness, small-scale furniture producers have made efforts to upgrade by harnessing their social network and institutions.  This paper describes small-scale furniture producers’ efforts to upgrade by utilising their social network and institutions in Jepara.  Data was collected through in-depth interviews with members of the small-scale furniture producers’ association.  The research provides insight into the nature of social networks and information flow and develops future scenarios to upgrade.  The scenarios will not only benefit the furniture industry in Jepara, but may also be adopted for similar industries throughout Indonesia and the world, and potentially improve many people’s economies and livelihoods.Keywords: wood-based industry, furniture, small-scale, social network, institution

Identifying all moiety conservation laws in genome-scale metabolic networks.

Science.gov (United States)

De Martino, Andrea; De Martino, Daniele; Mulet, Roberto; Pagnani, Andrea

2014-01-01

The stoichiometry of a metabolic network gives rise to a set of conservation laws for the aggregate level of specific pools of metabolites, which, on one hand, pose dynamical constraints that cross-link the variations of metabolite concentrations and, on the other, provide key insight into a cell's metabolic production capabilities. When the conserved quantity identifies with a chemical moiety, extracting all such conservation laws from the stoichiometry amounts to finding all non-negative integer solutions of a linear system, a programming problem known to be NP-hard. We present an efficient strategy to compute the complete set of integer conservation laws of a genome-scale stoichiometric matrix, also providing a certificate for correctness and maximality of the solution. Our method is deployed for the analysis of moiety conservation relationships in two large-scale reconstructions of the metabolism of the bacterium E. coli, in six tissue-specific human metabolic networks, and, finally, in the human reactome as a whole, revealing that bacterial metabolism could be evolutionarily designed to cover broader production spectra than human metabolism. Convergence to the full set of moiety conservation laws in each case is achieved in extremely reduced computing times. In addition, we uncover a scaling relation that links the size of the independent pool basis to the number of metabolites, for which we present an analytical explanation.

Identifying all moiety conservation laws in genome-scale metabolic networks.

Directory of Open Access Journals (Sweden)

Andrea De Martino

Full Text Available The stoichiometry of a metabolic network gives rise to a set of conservation laws for the aggregate level of specific pools of metabolites, which, on one hand, pose dynamical constraints that cross-link the variations of metabolite concentrations and, on the other, provide key insight into a cell's metabolic production capabilities. When the conserved quantity identifies with a chemical moiety, extracting all such conservation laws from the stoichiometry amounts to finding all non-negative integer solutions of a linear system, a programming problem known to be NP-hard. We present an efficient strategy to compute the complete set of integer conservation laws of a genome-scale stoichiometric matrix, also providing a certificate for correctness and maximality of the solution. Our method is deployed for the analysis of moiety conservation relationships in two large-scale reconstructions of the metabolism of the bacterium E. coli, in six tissue-specific human metabolic networks, and, finally, in the human reactome as a whole, revealing that bacterial metabolism could be evolutionarily designed to cover broader production spectra than human metabolism. Convergence to the full set of moiety conservation laws in each case is achieved in extremely reduced computing times. In addition, we uncover a scaling relation that links the size of the independent pool basis to the number of metabolites, for which we present an analytical explanation.

A Mountain-Scale Monitoring Network for Yucca Mountain Performance Confirmation

International Nuclear Information System (INIS)

Freifeld, Barry; Tsang, Yvonne

2006-01-01

Confirmation of the performance of Yucca Mountain is required by 10 CFR Part 63.131 to indicate, where practicable, that the natural system acts as a barrier, as intended. Hence, performance confirmation monitoring and testing would provide data for continued assessment during the pre-closure period. In general, to carry out testing at a relevant scale is always important, and in the case of performance confirmation, it is particularly important to be able to test at the scale of the repository. We view the large perturbation caused by construction of the repository at Yucca Mountain as a unique opportunity to study the large-scale behavior of the natural barrier system. Repository construction would necessarily introduce traced fluids and result in the creation of leachates. A program to monitor traced fluids and construction leachates permits evaluation of transport through the unsaturated zone and potentially downgradient through the saturated zone. A robust sampling and monitoring network for continuous measurement of important parameters, and for periodic collection of agrochemical samples, is proposed to observe thermo-hydrogeochemical changes near the repository horizon and down to the water table. The sampling and monitoring network can be used to provide data to (1) assess subsurface conditions encountered and changes in those conditions during construction and waste emplacement operations; and (2) for modeling to determine that the natural system is functioning as intended

Distributed and Cooperative Link Scheduling for Large-Scale Multihop Wireless Networks

Directory of Open Access Journals (Sweden)

Swami Ananthram

2007-01-01

Full Text Available A distributed and cooperative link-scheduling (DCLS algorithm is introduced for large-scale multihop wireless networks. With this algorithm, each and every active link in the network cooperatively calibrates its environment and converges to a desired link schedule for data transmissions within a time frame of multiple slots. This schedule is such that the entire network is partitioned into a set of interleaved subnetworks, where each subnetwork consists of concurrent cochannel links that are properly separated from each other. The desired spacing in each subnetwork can be controlled by a tuning parameter and the number of time slots specified for each frame. Following the DCLS algorithm, a distributed and cooperative power control (DCPC algorithm can be applied to each subnetwork to ensure a desired data rate for each link with minimum network transmission power. As shown consistently by simulations, the DCLS algorithm along with a DCPC algorithm yields significant power savings. The power savings also imply an increased feasible region of averaged link data rates for the entire network.

Distributed and Cooperative Link Scheduling for Large-Scale Multihop Wireless Networks

Directory of Open Access Journals (Sweden)

Ananthram Swami

2007-12-01

Full Text Available A distributed and cooperative link-scheduling (DCLS algorithm is introduced for large-scale multihop wireless networks. With this algorithm, each and every active link in the network cooperatively calibrates its environment and converges to a desired link schedule for data transmissions within a time frame of multiple slots. This schedule is such that the entire network is partitioned into a set of interleaved subnetworks, where each subnetwork consists of concurrent cochannel links that are properly separated from each other. The desired spacing in each subnetwork can be controlled by a tuning parameter and the number of time slots specified for each frame. Following the DCLS algorithm, a distributed and cooperative power control (DCPC algorithm can be applied to each subnetwork to ensure a desired data rate for each link with minimum network transmission power. As shown consistently by simulations, the DCLS algorithm along with a DCPC algorithm yields significant power savings. The power savings also imply an increased feasible region of averaged link data rates for the entire network.

Scale-dependent genetic structure of the Idaho giant salamander (Dicamptodon aterrimus) in stream networks

Science.gov (United States)

Lindy B. Mullen; H. Arthur Woods; Michael K. Schwartz; Adam J. Sepulveda; Winsor H. Lowe

2010-01-01

The network architecture of streams and rivers constrains evolutionary, demographic and ecological processes of freshwater organisms. This consistent architecture also makes stream networks useful for testing general models of population genetic structure and the scaling of gene flow. We examined genetic structure and gene flow in the facultatively paedomorphic Idaho...

77 FR 58416 - Large Scale Networking (LSN); Middleware and Grid Interagency Coordination (MAGIC) Team

Science.gov (United States)

2012-09-20

..., Grid, and cloud projects. The MAGIC Team reports to the Large Scale Networking (LSN) Coordinating Group... Coordination (MAGIC) Team AGENCY: The Networking and Information Technology Research and Development (NITRD.... Dates/Location: The MAGIC Team meetings are held on the first Wednesday of each month, 2:00-4:00pm, at...

Measuring large-scale social networks with high resolution.

Directory of Open Access Journals (Sweden)

Arkadiusz Stopczynski

Full Text Available This paper describes the deployment of a large-scale study designed to measure human interactions across a variety of communication channels, with high temporal resolution and spanning multiple years-the Copenhagen Networks Study. Specifically, we collect data on face-to-face interactions, telecommunication, social networks, location, and background information (personality, demographics, health, politics for a densely connected population of 1000 individuals, using state-of-the-art smartphones as social sensors. Here we provide an overview of the related work and describe the motivation and research agenda driving the study. Additionally, the paper details the data-types measured, and the technical infrastructure in terms of both backend and phone software, as well as an outline of the deployment procedures. We document the participant privacy procedures and their underlying principles. The paper is concluded with early results from data analysis, illustrating the importance of multi-channel high-resolution approach to data collection.

Increased signaling entropy in cancer requires the scale-free property of protein interaction networks

Science.gov (United States)

Teschendorff, Andrew E.; Banerji, Christopher R. S.; Severini, Simone; Kuehn, Reimer; Sollich, Peter

2015-01-01

One of the key characteristics of cancer cells is an increased phenotypic plasticity, driven by underlying genetic and epigenetic perturbations. However, at a systems-level it is unclear how these perturbations give rise to the observed increased plasticity. Elucidating such systems-level principles is key for an improved understanding of cancer. Recently, it has been shown that signaling entropy, an overall measure of signaling pathway promiscuity, and computable from integrating a sample's gene expression profile with a protein interaction network, correlates with phenotypic plasticity and is increased in cancer compared to normal tissue. Here we develop a computational framework for studying the effects of network perturbations on signaling entropy. We demonstrate that the increased signaling entropy of cancer is driven by two factors: (i) the scale-free (or near scale-free) topology of the interaction network, and (ii) a subtle positive correlation between differential gene expression and node connectivity. Indeed, we show that if protein interaction networks were random graphs, described by Poisson degree distributions, that cancer would generally not exhibit an increased signaling entropy. In summary, this work exposes a deep connection between cancer, signaling entropy and interaction network topology. PMID:25919796

Modeling Reservoir-River Networks in Support of Optimizing Seasonal-Scale Reservoir Operations

Science.gov (United States)

Villa, D. L.; Lowry, T. S.; Bier, A.; Barco, J.; Sun, A.

2011-12-01

HydroSCOPE (Hydropower Seasonal Concurrent Optimization of Power and the Environment) is a seasonal time-scale tool for scenario analysis and optimization of reservoir-river networks. Developed in MATLAB, HydroSCOPE is an object-oriented model that simulates basin-scale dynamics with an objective of optimizing reservoir operations to maximize revenue from power generation, reliability in the water supply, environmental performance, and flood control. HydroSCOPE is part of a larger toolset that is being developed through a Department of Energy multi-laboratory project. This project's goal is to provide conventional hydropower decision makers with better information to execute their day-ahead and seasonal operations and planning activities by integrating water balance and operational dynamics across a wide range of spatial and temporal scales. This presentation details the modeling approach and functionality of HydroSCOPE. HydroSCOPE consists of a river-reservoir network model and an optimization routine. The river-reservoir network model simulates the heat and water balance of river-reservoir networks for time-scales up to one year. The optimization routine software, DAKOTA (Design Analysis Kit for Optimization and Terascale Applications - dakota.sandia.gov), is seamlessly linked to the network model and is used to optimize daily volumetric releases from the reservoirs to best meet a set of user-defined constraints, such as maximizing revenue while minimizing environmental violations. The network model uses 1-D approximations for both the reservoirs and river reaches and is able to account for surface and sediment heat exchange as well as ice dynamics for both models. The reservoir model also accounts for inflow, density, and withdrawal zone mixing, and diffusive heat exchange. Routing for the river reaches is accomplished using a modified Muskingum-Cunge approach that automatically calculates the internal timestep and sub-reach lengths to match the conditions of

Large-scale brain network coupling predicts acute nicotine abstinence effects on craving and cognitive function.

Science.gov (United States)

Lerman, Caryn; Gu, Hong; Loughead, James; Ruparel, Kosha; Yang, Yihong; Stein, Elliot A

2014-05-01

Interactions of large-scale brain networks may underlie cognitive dysfunctions in psychiatric and addictive disorders. To test the hypothesis that the strength of coupling among 3 large-scale brain networks--salience, executive control, and default mode--will reflect the state of nicotine withdrawal (vs smoking satiety) and will predict abstinence-induced craving and cognitive deficits and to develop a resource allocation index (RAI) that reflects the combined strength of interactions among the 3 large-scale networks. A within-subject functional magnetic resonance imaging study in an academic medical center compared resting-state functional connectivity coherence strength after 24 hours of abstinence and after smoking satiety. We examined the relationship of abstinence-induced changes in the RAI with alterations in subjective, behavioral, and neural functions. We included 37 healthy smoking volunteers, aged 19 to 61 years, for analyses. Twenty-four hours of abstinence vs smoking satiety. Inter-network connectivity strength (primary) and the relationship with subjective, behavioral, and neural measures of nicotine withdrawal during abstinence vs smoking satiety states (secondary). The RAI was significantly lower in the abstinent compared with the smoking satiety states (left RAI, P = .002; right RAI, P = .04), suggesting weaker inhibition between the default mode and salience networks. Weaker inter-network connectivity (reduced RAI) predicted abstinence-induced cravings to smoke (r = -0.59; P = .007) and less suppression of default mode activity during performance of a subsequent working memory task (ventromedial prefrontal cortex, r = -0.66, P = .003; posterior cingulate cortex, r = -0.65, P = .001). Alterations in coupling of the salience and default mode networks and the inability to disengage from the default mode network may be critical in cognitive/affective alterations that underlie nicotine dependence.

Neuro-behavioral pattern of sleep bruxism in wakefulness

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Marila Rezende Azevedo

2018-02-01

Full Text Available AbstractIntroduction: Sleep Bruxism (SB is a non-functional rhythmic movement of the mandible with multifactorial aetiology and complex diagnose. It has been the subject of various studies over the past decades and it is considered a result of actions of the Central Nervous System modulated by Autonomous Nervous System. In this work, we test the hypothesis that SB subjects present a typical and defined neurobehavioral pattern that can be distinct from that of non-bruxers subjects and can be measured during wakefulness. Methods Fifteen sleep bruxers (experimental-group EG and fifteen non-bruxers (control-group CG took part in the experiments. To verify the presence and severity of SB, clinical examinations, anamneses and questionnaires, including Visual Analogic Scale - faces (VAS-f and State-Trait Anxiety Inventory (STAI were applied. To legitimate the diagnoses of SB, a disposable instrument (Bitestrip® to assess the masseter activity during sleep was employed. All subjects were submitted to a set of experiments for measuring various visual evoked responses during the presentation of visual stimuli (pleasant, unpleasant and neutral images. Events in Visual Evoked Potential (VEP were used to compare the neural responses of both CG and EG. Results VAS-f showed EG with higher perception of stress than CG (trait: p=0.05, and lower quality of life for (state: p=0.007. STAI I and II showed significant differences of anxiety between CG and EG (p=0.013 and p=0.004, respectively, being EG the highest. The EG Bitestrip scores confirmed that 100% of subjects were sleep bruxers. Significant differences were found between EG and CG for events associated with emotional (pleasant and unpleasant images in the first 250 ms after stimulation. In general, EG subjects showed higher amplitude and shorter latency of VEP events. Conclusion It is possible to distinguish between SB and non-bruxers subjects during wakefulness, based on differences in amplitude and

Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network

OpenAIRE

Mart?n-Jim?nez, Cynthia A.; Salazar-Barreto, Diego; Barreto, George E.; Gonz?lez, Janneth

2017-01-01

Astrocytes are the most abundant cells of the central nervous system; they have a predominant role in maintaining brain metabolism. In this sense, abnormal metabolic states have been found in different neuropathological diseases. Determination of metabolic states of astrocytes is difficult to model using current experimental approaches given the high number of reactions and metabolites present. Thus, genome-scale metabolic networks derived from transcriptomic data can be used as a framework t...

Adult neurobehavioral outcome of hyperbilirubinemia in full term neonates-a 30 year prospective follow-up study.

Science.gov (United States)

Hokkanen, Laura; Launes, Jyrki; Michelsson, Katarina

2014-01-01

Background. Neonatal hyperbilirubinemia (HB) may cause severe neurological damage, but serious consequences are effectively controlled by phototherapy and blood exchange transfusion. HB is still a serious health problem in economically compromised parts of the world. The long term outcome has been regarded favorable based on epidemiological data, but has not been confirmed in prospective follow-up studies extending to adulthood. Methods. We studied the long term consequences of HB in a prospective birth cohort of 128 HB cases and 82 controls. The cases are part of a neonatal at-risk cohort (n = 1196) that has been followed up to 30 years of age. HB cases were newborns ≥ 2500 g birth weight and ≥ 37 weeks of gestation who had bilirubin concentrations > 340 µmol/l or required blood exchange transfusion. Subjects with HB were divided into subgroups based on the presence (affected HB) or absence (unaffected HB) of diagnosed neurobehavioral disorders in childhood, and compared with healthy controls. Subjects were seen at discharge, 5, 9 and 16 years of life and parent's and teacher's assessments were recorded. At 30 years they filled a questionnaire about academic and occupational achievement, life satisfaction, somatic and psychiatric symptoms including a ADHD self-rating score. Cognitive functioning was tested using ITPA, WISC, and reading and writing tests at 9 years of life. Results. Compared to controls, the odds for a child with HB having neurobehavioral symptoms at 9 years was elevated (OR = 4.68). Forty-five per cent of the HB group were affected by cognitive abnormalities in childhood and continued to experience problems in adulthood. This was apparent in academic achievement (p mathematics. Childhood symptoms of hyperactivity/impulsivity (p < 0.0001) and inattention (p < 0.02) were more common in HB groups, but in adulthood the symptoms were equal. The affected HB had lower scores in parameters reflecting life satisfaction, less controlled drinking, but

Early neurobehavioral development of preterm infants Desenvolvimento neurocomportamental inicial de bebês prematuros

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Paula Stefaneli Ziotti Gabriel

2013-01-01

Full Text Available The aim of the present study was to assess the very early neurobehavioral development of preterm infants and to examine differences regarding sex. Two-hundred and two preterm infants were assessed by the Neurobehavioral Assessment of the Preterm Infant (NAPI, which was carried out at 32-37 weeks post-conceptional age in the hospital setting. The infants' performance was compared to a norm-referenced sample and a comparison between groups regarding sex was also done. In comparison to the NAPI norm-reference, the preterm infants showed less muscular tonicity on the scarf sign, less vigor and spontaneous movement, higher alertness and orientation, weaker cry, and more sleep state. There was no statistical difference between males and females preterm infants at NAPI performances.O objetivo do estudo foi avaliar o desenvolvimento neurocomportamental inicial de bebês prematuros e examinar as diferenças quanto ao sexo. Foram avaliados 202 bebês nascidos pré-termo pela Avaliação Neurocomportamental para Prematuros (NAPI, que foi realizada na fase de 32-37 semanas de idade pós-concepcional no contexto hospitalar. O desempenho dos bebês no NAPI foi comparado com a amostra de padronização do instrumento e também foi feita a comparação entre grupos diferenciados pelo sexo. Em relação à amostra de padronização, os bebês deste estudo apresentaram menor tonicidade muscular no sinal de cachecol, menor vigor e movimento espontâneo, mais alerta e orientação, choro mais fraco e mais estado de sono. Houve um padrão semelhante de desempenho neurocomportamental dos meninos e meninas nascidos prematuros.

Multi-scale structure and topological anomaly detection via a new network statistic: The onion decomposition.

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Hébert-Dufresne, Laurent; Grochow, Joshua A; Allard, Antoine

2016-08-18

We introduce a network statistic that measures structural properties at the micro-, meso-, and macroscopic scales, while still being easy to compute and interpretable at a glance. Our statistic, the onion spectrum, is based on the onion decomposition, which refines the k-core decomposition, a standard network fingerprinting method. The onion spectrum is exactly as easy to compute as the k-cores: It is based on the stages at which each vertex gets removed from a graph in the standard algorithm for computing the k-cores. Yet, the onion spectrum reveals much more information about a network, and at multiple scales; for example, it can be used to quantify node heterogeneity, degree correlations, centrality, and tree- or lattice-likeness. Furthermore, unlike the k-core decomposition, the combined degree-onion spectrum immediately gives a clear local picture of the network around each node which allows the detection of interesting subgraphs whose topological structure differs from the global network organization. This local description can also be leveraged to easily generate samples from the ensemble of networks with a given joint degree-onion distribution. We demonstrate the utility of the onion spectrum for understanding both static and dynamic properties on several standard graph models and on many real-world networks.

Reduced linear noise approximation for biochemical reaction networks with time-scale separation: The stochastic tQSSA+

Science.gov (United States)

Herath, Narmada; Del Vecchio, Domitilla

2018-03-01

Biochemical reaction networks often involve reactions that take place on different time scales, giving rise to "slow" and "fast" system variables. This property is widely used in the analysis of systems to obtain dynamical models with reduced dimensions. In this paper, we consider stochastic dynamics of biochemical reaction networks modeled using the Linear Noise Approximation (LNA). Under time-scale separation conditions, we obtain a reduced-order LNA that approximates both the slow and fast variables in the system. We mathematically prove that the first and second moments of this reduced-order model converge to those of the full system as the time-scale separation becomes large. These mathematical results, in particular, provide a rigorous justification to the accuracy of LNA models derived using the stochastic total quasi-steady state approximation (tQSSA). Since, in contrast to the stochastic tQSSA, our reduced-order model also provides approximations for the fast variable stochastic properties, we term our method the "stochastic tQSSA+". Finally, we demonstrate the application of our approach on two biochemical network motifs found in gene-regulatory and signal transduction networks.

Corrections to scaling in random resistor networks and diluted continuous spin models near the percolation threshold.

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Janssen, Hans-Karl; Stenull, Olaf

2004-02-01

We investigate corrections to scaling induced by irrelevant operators in randomly diluted systems near the percolation threshold. The specific systems that we consider are the random resistor network and a class of continuous spin systems, such as the x-y model. We focus on a family of least irrelevant operators and determine the corrections to scaling that originate from this family. Our field theoretic analysis carefully takes into account that irrelevant operators mix under renormalization. It turns out that long standing results on corrections to scaling are respectively incorrect (random resistor networks) or incomplete (continuous spin systems).

Functional inference of complex anatomical tendinous networks at a macroscopic scale via sparse experimentation.

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Saxena, Anupam; Lipson, Hod; Valero-Cuevas, Francisco J

2012-01-01

In systems and computational biology, much effort is devoted to functional identification of systems and networks at the molecular-or cellular scale. However, similarly important networks exist at anatomical scales such as the tendon network of human fingers: the complex array of collagen fibers that transmits and distributes muscle forces to finger joints. This network is critical to the versatility of the human hand, and its function has been debated since at least the 16(th) century. Here, we experimentally infer the structure (both topology and parameter values) of this network through sparse interrogation with force inputs. A population of models representing this structure co-evolves in simulation with a population of informative future force inputs via the predator-prey estimation-exploration algorithm. Model fitness depends on their ability to explain experimental data, while the fitness of future force inputs depends on causing maximal functional discrepancy among current models. We validate our approach by inferring two known synthetic Latex networks, and one anatomical tendon network harvested from a cadaver's middle finger. We find that functionally similar but structurally diverse models can exist within a narrow range of the training set and cross-validation errors. For the Latex networks, models with low training set error [functional structure of complex anatomical networks. This work expands current bioinformatics inference approaches by demonstrating that sparse, yet informative interrogation of biological specimens holds significant computational advantages in accurate and efficient inference over random testing, or assuming model topology and only inferring parameters values. These findings also hold clues to both our evolutionary history and the development of versatile machines.

Effect of prenatal exposure to low dose beta radiation from tritiated water on postnatal growth and neurobehavior of rats

International Nuclear Information System (INIS)

Gao Weimin; Zhou Xiangyan

1998-01-01

Objective: Effects of prenatal exposure to HTO (tritiated water) on postnatal growth and neurobehavior of rats were studied by determination of multiple parameters. Methods: Pregnant adult Wistar rats were randomly assigned to 4 groups, of which 3 groups were irradiated with beta-rays from tritiated water (HTO) by one single intraperitoneal injection on the 13th day of gestation. Offspring of these rats received cumulative doses of 0.000, 0.044, 0.088 and 0.264 Gy utero, respectively, and were observed for the appearance of three physiologic markers (eye opening, pinna detachment, incisor eruption), the age of acquisition of two reflexes (surface righting, negative geotaxis) and sensuous function (auditory startle), movement and coordination functions and activity (forelimb hanging, continuous corridor activity), and learning and memory (electric avoidance reflex in Y-maze, conditional reflex). Results: Results for most parameters in the 0.044 and 0.088 Gy groups were different significantly from those in the controls and for most parameters a dose-dependent effect was found. Conclusion: Offspring of rats having received prenatal low dose irradiation from HTO showed delayed growth and abnormal neurobehavior

How Did the Information Flow in the #AlphaGo Hashtag Network? A Social Network Analysis of the Large-Scale Information Network on Twitter.

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Kim, Jinyoung

2017-12-01

As it becomes common for Internet users to use hashtags when posting and searching information on social media, it is important to understand who builds a hashtag network and how information is circulated within the network. This article focused on unlocking the potential of the #AlphaGo hashtag network by addressing the following questions. First, the current study examined whether traditional opinion leadership (i.e., the influentials hypothesis) or grassroot participation by the public (i.e., the interpersonal hypothesis) drove dissemination of information in the hashtag network. Second, several unique patterns of information distribution by key users were identified. Finally, the association between attributes of key users who exerted great influence on information distribution (i.e., the number of followers and follows) and their central status in the network was tested. To answer the proffered research questions, a social network analysis was conducted using a large-scale hashtag network data set from Twitter (n = 21,870). The results showed that the leading actors in the network were actively receiving information from their followers rather than serving as intermediaries between the original information sources and the public. Moreover, the leading actors played several roles (i.e., conversation starters, influencers, and active engagers) in the network. Furthermore, the number of their follows and followers were significantly associated with their central status in the hashtag network. Based on the results, the current research explained how the information was exchanged in the hashtag network by proposing the reciprocal model of information flow.

Congenital blindness is associated with large-scale reorganization of anatomical networks.

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Hasson, Uri; Andric, Michael; Atilgan, Hicret; Collignon, Olivier

2016-03-01

Blindness is a unique model for understanding the role of experience in the development of the brain's functional and anatomical architecture. Documenting changes in the structure of anatomical networks for this population would substantiate the notion that the brain's core network-level organization may undergo neuroplasticity as a result of life-long experience. To examine this issue, we compared whole-brain networks of regional cortical-thickness covariance in early blind and matched sighted individuals. This covariance is thought to reflect signatures of integration between systems involved in similar perceptual/cognitive functions. Using graph-theoretic metrics, we identified a unique mode of anatomical reorganization in the blind that differed from that found for sighted. This was seen in that network partition structures derived from subgroups of blind were more similar to each other than they were to partitions derived from sighted. Notably, after deriving network partitions, we found that language and visual regions tended to reside within separate modules in sighted but showed a pattern of merging into shared modules in the blind. Our study demonstrates that early visual deprivation triggers a systematic large-scale reorganization of whole-brain cortical-thickness networks, suggesting changes in how occipital regions interface with other functional networks in the congenitally blind. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Birth of scale-free molecular networks and the number of distinct DNA and protein domains per genome.

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Rzhetsky, A; Gomez, S M

2001-10-01

Current growth in the field of genomics has provided a number of exciting approaches to the modeling of evolutionary mechanisms within the genome. Separately, dynamical and statistical analyses of networks such as the World Wide Web and the social interactions existing between humans have shown that these networks can exhibit common fractal properties-including the property of being scale-free. This work attempts to bridge these two fields and demonstrate that the fractal properties of molecular networks are linked to the fractal properties of their underlying genomes. We suggest a stochastic model capable of describing the evolutionary growth of metabolic or signal-transduction networks. This model generates networks that share important statistical properties (so-called scale-free behavior) with real molecular networks. In particular, the frequency of vertices connected to exactly k other vertices follows a power-law distribution. The shape of this distribution remains invariant to changes in network scale: a small subgraph has the same distribution as the complete graph from which it is derived. Furthermore, the model correctly predicts that the frequencies of distinct DNA and protein domains also follow a power-law distribution. Finally, the model leads to a simple equation linking the total number of different DNA and protein domains in a genome with both the total number of genes and the overall network topology. MatLab (MathWorks, Inc.) programs described in this manuscript are available on request from the authors. ar345@columbia.edu.

Coordinated SLNR based Precoding in Large-Scale Heterogeneous Networks

KAUST Repository

Boukhedimi, Ikram

2017-03-06

This work focuses on the downlink of large-scale two-tier heterogeneous networks composed of a macro-cell overlaid by micro-cell networks. Our interest is on the design of coordinated beamforming techniques that allow to mitigate the inter-cell interference. Particularly, we consider the case in which the coordinating base stations (BSs) have imperfect knowledge of the channel state information. Under this setting, we propose a regularized SLNR based precoding design in which the regularization factor is used to allow better resilience with respect to the channel estimation errors. Based on tools from random matrix theory, we provide an analytical analysis of the SINR and SLNR performances. These results are then exploited to propose a proper setting of the regularization factor. Simulation results are finally provided in order to validate our findings and to confirm the performance of the proposed precoding scheme.

Coordinated SLNR based Precoding in Large-Scale Heterogeneous Networks

KAUST Repository

Boukhedimi, Ikram; Kammoun, Abla; Alouini, Mohamed-Slim

2017-01-01

This work focuses on the downlink of large-scale two-tier heterogeneous networks composed of a macro-cell overlaid by micro-cell networks. Our interest is on the design of coordinated beamforming techniques that allow to mitigate the inter-cell interference. Particularly, we consider the case in which the coordinating base stations (BSs) have imperfect knowledge of the channel state information. Under this setting, we propose a regularized SLNR based precoding design in which the regularization factor is used to allow better resilience with respect to the channel estimation errors. Based on tools from random matrix theory, we provide an analytical analysis of the SINR and SLNR performances. These results are then exploited to propose a proper setting of the regularization factor. Simulation results are finally provided in order to validate our findings and to confirm the performance of the proposed precoding scheme.

Optical network scaling: roles of spectral and spatial aggregation.

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Arık, Sercan Ö; Ho, Keang-Po; Kahn, Joseph M

2014-12-01

As the bit rates of routed data streams exceed the throughput of single wavelength-division multiplexing channels, spectral and spatial traffic aggregation become essential for optical network scaling. These aggregation techniques reduce network routing complexity by increasing spectral efficiency to decrease the number of fibers, and by increasing switching granularity to decrease the number of switching components. Spectral aggregation yields a modest decrease in the number of fibers but a substantial decrease in the number of switching components. Spatial aggregation yields a substantial decrease in both the number of fibers and the number of switching components. To quantify routing complexity reduction, we analyze the number of multi-cast and wavelength-selective switches required in a colorless, directionless and contentionless reconfigurable optical add-drop multiplexer architecture. Traffic aggregation has two potential drawbacks: reduced routing power and increased switching component size.

Multirelational organization of large-scale social networks in an online world.

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Szell, Michael; Lambiotte, Renaud; Thurner, Stefan

2010-08-03

The capacity to collect fingerprints of individuals in online media has revolutionized the way researchers explore human society. Social systems can be seen as a nonlinear superposition of a multitude of complex social networks, where nodes represent individuals and links capture a variety of different social relations. Much emphasis has been put on the network topology of social interactions, however, the multidimensional nature of these interactions has largely been ignored, mostly because of lack of data. Here, for the first time, we analyze a complete, multirelational, large social network of a society consisting of the 300,000 odd players of a massive multiplayer online game. We extract networks of six different types of one-to-one interactions between the players. Three of them carry a positive connotation (friendship, communication, trade), three a negative (enmity, armed aggression, punishment). We first analyze these types of networks as separate entities and find that negative interactions differ from positive interactions by their lower reciprocity, weaker clustering, and fatter-tail degree distribution. We then explore how the interdependence of different network types determines the organization of the social system. In particular, we study correlations and overlap between different types of links and demonstrate the tendency of individuals to play different roles in different networks. As a demonstration of the power of the approach, we present the first empirical large-scale verification of the long-standing structural balance theory, by focusing on the specific multiplex network of friendship and enmity relations.

Root Systems Biology: Integrative Modeling across Scales, from Gene Regulatory Networks to the Rhizosphere1

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Hill, Kristine; Porco, Silvana; Lobet, Guillaume; Zappala, Susan; Mooney, Sacha; Draye, Xavier; Bennett, Malcolm J.

2013-01-01

Genetic and genomic approaches in model organisms have advanced our understanding of root biology over the last decade. Recently, however, systems biology and modeling have emerged as important approaches, as our understanding of root regulatory pathways has become more complex and interpreting pathway outputs has become less intuitive. To relate root genotype to phenotype, we must move beyond the examination of interactions at the genetic network scale and employ multiscale modeling approaches to predict emergent properties at the tissue, organ, organism, and rhizosphere scales. Understanding the underlying biological mechanisms and the complex interplay between systems at these different scales requires an integrative approach. Here, we describe examples of such approaches and discuss the merits of developing models to span multiple scales, from network to population levels, and to address dynamic interactions between plants and their environment. PMID:24143806

Impact of Sleep Restriction on Neurobehavioral Functioning of Children with Attention Deficit Hyperactivity Disorder

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Gruber, Reut; Wiebe, Sabrina; Montecalvo, Lisa; Brunetti, Bianca; Amsel, Rhonda; Carrier, Julie

2011-01-01

Study Objectives: The objective of this study was to assess the cumulative impact of 1 hour of nightly sleep restriction over the course of 6 nights on the neurobehavioral functioning (NBF) of children with attention deficit hyperactivity disorder (ADHD) and healthy controls. Design: Following 6 nights of actigraphic monitoring of sleep to determine baseline sleep duration, children were asked to restrict sleep duration by 1 hour for 6 consecutive nights. NBF was assessed at baseline (Day 6) and following sleep manipulation (Day 12). Setting: A quiet location within their home environments. Participants: Forty-three children (11 ADHD, 32 Controls, mean age = 8.7 years, SD = 1.3) between the ages of 7 and 11 years. Interventions: NA Measurements: Sleep was monitored using actigraphy. In addition, parents were asked to complete nightly sleep logs. Sleepiness was evaluated using a questionnaire. The Conners' Continuous Performance Test (CPT) was used to assess NBF. Results: Restricted sleep led to poorer CPT scores on two-thirds of CPT outcome measures in both healthy controls and children with ADHD. The performance of children with ADHD following sleep restriction deteriorated from subclinical levels to the clinical range of inattention on two-thirds of CPT outcome measures. Conclusions: Moderate sleep restriction leads to a detectable negative impact on the NBF of children with ADHD and healthy controls, leading to a clinical level of impairment in children with ADHD. Citation: Gruber R; Wiebe S; Montecalvo L; Brunetti B; Amsel R; Carrier J. Impact of sleep restriction on neurobehavioral functioning of children with attention deficit hyperactivity disorder. SLEEP 2011;34(3):315-323. PMID:21358848

Directed partial correlation: inferring large-scale gene regulatory network through induced topology disruptions.

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Yinyin Yuan

Full Text Available Inferring regulatory relationships among many genes based on their temporal variation in transcript abundance has been a popular research topic. Due to the nature of microarray experiments, classical tools for time series analysis lose power since the number of variables far exceeds the number of the samples. In this paper, we describe some of the existing multivariate inference techniques that are applicable to hundreds of variables and show the potential challenges for small-sample, large-scale data. We propose a directed partial correlation (DPC method as an efficient and effective solution to regulatory network inference using these data. Specifically for genomic data, the proposed method is designed to deal with large-scale datasets. It combines the efficiency of partial correlation for setting up network topology by testing conditional independence, and the concept of Granger causality to assess topology change with induced interruptions. The idea is that when a transcription factor is induced artificially within a gene network, the disruption of the network by the induction signifies a genes role in transcriptional regulation. The benchmarking results using GeneNetWeaver, the simulator for the DREAM challenges, provide strong evidence of the outstanding performance of the proposed DPC method. When applied to real biological data, the inferred starch metabolism network in Arabidopsis reveals many biologically meaningful network modules worthy of further investigation. These results collectively suggest DPC is a versatile tool for genomics research. The R package DPC is available for download (http://code.google.com/p/dpcnet/.

Ozone and atmospheric pollution at synoptic scale: the monitoring network Paes

International Nuclear Information System (INIS)

Gheusi, F.; Chevalier, A.; Delmas, R.; Athier, G.; Bouchou, P.; Cousin, J.M.; Meyerfeld, Y.; Laj, P.; Sellegri, K.; Ancellet, G.

2007-01-01

Ozone as an environmental concern extends beyond the questions usually covered by media - stratospheric ozone depletion and urban pollution peaks. Strong expositions to this pollutant are frequent even far from pollution sources, and the background tropospheric content of ozone has been growing fivefold over the last century. In response to this concern at the French national scale, formerly independent monitoring stations have been coordinated since 2004 in a structured network: Paes (French acronym for atmospheric pollution at synoptic scale). The data are put in free access online. (authors)

From local to central: a network analysis of who manages plant pest and disease outbreaks across scales

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Ryan R. J. McAllister

2015-03-01

Full Text Available One of the key determinants of success in managing natural resources is "institutional fit," i.e., how well the suite of required actions collectively match the scale of the environmental problem. The effective management of pest and pathogen threats to plants is a natural resource problem of particular economic, social, and environmental importance. Responses to incursions are managed by a network of decision makers and managers acting at different spatial and temporal scales. We applied novel network theoretical methods to assess the propensity of growers, local industry, local state government, and state and national government head offices to foster either within- or across-scale coordination during the successful 2001 Australian response to the outbreak of the fungal pathogen black sigatoka (Mycosphaerella fijiensis. We also reconstructed the response network to proxy what that network would look like today under the Australian government's revised response system. We illustrate a structural move in the plant biosecurity response system from one that was locally driven to the current top-down system, in which the national government leads coordination of a highly partitioned engagement process. For biological incursions that spread widely across regions, nationally rather than locally managed responses may improve coordination of diverse tasks. However, in dealing with such challenges of institutional fit, local engagement will always be critical in deploying flexible and adaptive local responses based on a national system. The methods we propose detect where and how network structures foster cross-scale interactions, which will contribute to stronger empirical studies of cross-scale environmental governance.

Large Scale Environmental Monitoring through Integration of Sensor and Mesh Networks

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Raja Jurdak

2008-11-01

Full Text Available Monitoring outdoor environments through networks of wireless sensors has received interest for collecting physical and chemical samples at high spatial and temporal scales. A central challenge to environmental monitoring applications of sensor networks is the short communication range of the sensor nodes, which increases the complexity and cost of monitoring commodities that are located in geographically spread areas. To address this issue, we propose a new communication architecture that integrates sensor networks with medium range wireless mesh networks, and provides users with an advanced web portal for managing sensed information in an integrated manner. Our architecture adopts a holistic approach targeted at improving the user experience by optimizing the system performance for handling data that originates at the sensors, traverses the mesh network, and resides at the server for user consumption. This holistic approach enables users to set high level policies that can adapt the resolution of information collected at the sensors, set the preferred performance targets for their application, and run a wide range of queries and analysis on both real-time and historical data. All system components and processes will be described in this paper.

Large Scale Environmental Monitoring through Integration of Sensor and Mesh Networks.

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Jurdak, Raja; Nafaa, Abdelhamid; Barbirato, Alessio

2008-11-24

Monitoring outdoor environments through networks of wireless sensors has received interest for collecting physical and chemical samples at high spatial and temporal scales. A central challenge to environmental monitoring applications of sensor networks is the short communication range of the sensor nodes, which increases the complexity and cost of monitoring commodities that are located in geographically spread areas. To address this issue, we propose a new communication architecture that integrates sensor networks with medium range wireless mesh networks, and provides users with an advanced web portal for managing sensed information in an integrated manner. Our architecture adopts a holistic approach targeted at improving the user experience by optimizing the system performance for handling data that originates at the sensors, traverses the mesh network, and resides at the server for user consumption. This holistic approach enables users to set high level policies that can adapt the resolution of information collected at the sensors, set the preferred performance targets for their application, and run a wide range of queries and analysis on both real-time and historical data. All system components and processes will be described in this paper.

Exploring network operations for data and information networks

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Yao, Bing; Su, Jing; Ma, Fei; Wang, Xiaomin; Zhao, Xiyang; Yao, Ming

2017-01-01

Barabási and Albert, in 1999, formulated scale-free models based on some real networks: World-Wide Web, Internet, metabolic and protein networks, language or sexual networks. Scale-free networks not only appear around us, but also have high qualities in the world. As known, high quality information networks can transfer feasibly and efficiently data, clearly, their topological structures are very important for data safety. We build up network operations for constructing large scale of dynamic networks from smaller scale of network models having good property and high quality. We focus on the simplest operators to formulate complex operations, and are interesting on the closeness of operations to desired network properties.

Large-scale transportation network congestion evolution prediction using deep learning theory.

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Ma, Xiaolei; Yu, Haiyang; Wang, Yunpeng; Wang, Yinhai

2015-01-01

Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS) and Internet of Things (IoT), transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS) data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU)-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation.

Temporal and Latitudinal Variations of the Length-Scales and Relative Intensities of the Chromospheric Network

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Raju, K. P.

2018-05-01

The Calcium K spectroheliograms of the Sun from Kodaikanal have a data span of about 100 years and covers over 9 solar cycles. The Ca line is a strong chromospheric line dominated by chromospheric network and plages which are good indicators of solar activity. Length-scales and relative intensities of the chromospheric network have been obtained in the solar latitudes from 50 degree N to 50 degree S from the spectroheliograms. The length-scale was obtained from the half-width of the two-dimensional autocorrelation of the latitude strip which gives a measure of the width of the network boundary. As reported earlier for the transition region extreme ultraviolet (EUV) network, relative intensity and width of the chromospheric network boundary are found to be dependent on the solar cycle. A varying phase difference has been noticed in the quantities in different solar latitudes. A cross-correlation analysis of the quantities from other latitudes with ±30 degree latitude revealed an interesting phase difference pattern indicating flux transfer. Evidence of equatorward flux transfer has been observed. The average equatorward flux transfer was estimated to be 5.8 ms-1. The possible reasons of the drift could be meridional circulation, torsional oscillations, or the bright point migration. Cross-correlation of intensity and length-scale from the same latitude showed increasing phase difference with increasing latitude. We have also obtained the cross correlation of the quantities across the equator to see the possible phase lags in the two hemispheres. Signatures of lags are seen in the length scales of southern hemisphere near the equatorial latitudes, but no such lags in the intensity are observed. The results have important implications on the flux transfer over the solar surface and hence on the solar activity and dynamo.

Multi-Scale Residual Convolutional Neural Network for Haze Removal of Remote Sensing Images

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