WorldWideScience

Sample records for network mood scales

  1. The effect of music-induced mood on attentional networks.

    Science.gov (United States)

    Jiang, Jun; Scolaro, Ashley J; Bailey, Kira; Chen, Antao

    2011-06-01

    Attention network theory suggests that there are three separate neural networks that execute the discrete functions of alerting, orienting, and executive attention. Previous research on the influence of mood on attention has shown subtle and inconsistent results. The attention network theory may aid in clarifying the influence of mood on attention. The present study investigated the influence of mood on attentional networks in a normal population. Participants performed the Attention Network Test (ANT), which provides functional measures of alerting, orienting, and executive attention. Positive or negative mood was induced by listening to music with a positive or negative valence, respectively; neutral mood was induced by reading a collection of basic facts about China. The results revealed that negative mood led to a significantly higher alerting efficiency relative to other moods, while there were no significant mood effects on orienting or executive attention efficiency. According to the algorithm underlying the ANT, the higher alerting efficiency in the negative mood condition can be attributed to relatively greater benefits of cueing effects. The findings are discussed in the context of the noradrenergic system and of evolutionary significance. Specifically, the increase in the alerting function during negative mood states may be due to the modulation effect of negative mood on the noradrenergic system, and/or to the survival benefit resulting from an increase in automatic vigilance towards negative information. The current results suggest that as the influence of negative mood on attention appears to specifically consist in an enhanced alerting function, it may not be found in studies where the three attentional networks are not dissociated.

  2. Examining the Feasibility and Acceptability of an Online Yoga Class for Mood Disorders: A MoodNetwork Study.

    Science.gov (United States)

    Uebelacker, Lisa; Dufour, Steven C; Dinerman, Jacob G; Walsh, Samantha L; Hearing, Casey; Gillette, Lee T; Deckersbach, Thilo; Nierenberg, Andrew A; Weinstock, Lauren; Sylvia, Louisa G

    2018-01-01

    Despite ongoing advances in the treatment of mood disorders, a substantial proportion of people diagnosed with major depression or bipolar disorder remain symptomatic over time. Yoga, which has been shown to reduce stress and depressive symptoms, as well as to improve overall quality of life, shows promise as an adjunctive treatment. However, dissemination of yoga for clinical populations remains challenging. The purpose of this pilot study was to test the feasibility and acceptability of an online yoga intervention for individuals with mood disorders. In total, 56 adults who reported being diagnosed with a mood disorder (bipolar disorder, major depressive disorder, cyclothymia, or schizoaffective disorder) were recruited from MoodNetwork, an online community of individuals with mood disorders. A feedback survey and a measure of positive and negative affect were administered before and after a 30-minute online Hatha yoga class. In total, 44 individuals (78.6%) completed all components of the yoga class. The mean score on a 10-point Likert scale rating how much participants liked the online yoga class was 7.24 (SD=2.40). Most participants (67.9%) reported that they would be "somewhat likely" or "very likely" to participate in an online yoga program again. There was a statistically significant decrease in negative affect after completing the class (t=-6.05; P0.10). These preliminary data support the utility of online yoga tailored specifically for people with mood disorders as a possible adjunctive intervention that warrants further investigation.

  3. Acupuncture-brain interactions as hypothesized by mood scale recordings.

    Science.gov (United States)

    Acker, Helmut; Schmidt-Rathjens, Claudia; Acker, Till; Fandrey, Joachim; Ehleben, Wilhelm

    2015-09-01

    Mood expressions encompassing positive scales like "activity, elation, contemplation, calmness" and negative scales like "anger, excitement, depression, fatigue" were applied for introducing a new tool to assess the effects of acupuncture on brain structures. Traditional acupuncture points defined in the literature for their effects on task negative and task positive brain structures were applied to chronic disease patients supposed to have dominant negative mood scales. Burn-out syndrome (n=10) and female chronic pain patients (n=22) showed a significant improvement on positive mood scales and a decline in negative mood scales after 10 acupuncture sessions. We observed a direct effect of acupuncture on brain structures in 5 burn-out syndrome patients showing an immediate, fast suppression of unusual slow high amplitude EEG waves in response to acupuncture needle rotation. These EEG waves described here for the first time in awake patients disappeared after 10 sessions but gradually returned after 1-1.5 years without acupuncture. This was accompanied with deterioration of positive mood scales and a return to negative mood scales. Both male (n=16) and female chronic pain patients reported a significant decrease of pain intensity after 10 sessions. Female patients only, however, showed a linear correlation between initial pain intensity and pain relief as well as a linear correlation between changes in pain intensity and mood scales accompanied by a drop of their heart rate during the acupuncture sessions. We hypothesized that mood scale recordings are a sensitive and specific new tool to reveal individual acupuncture-brain interaction. Copyright © 2015. Published by Elsevier Ltd.

  4. Measuring generalised expectancies for negative mood regulation in China: The Chinese language Negative Mood Regulation scale.

    Science.gov (United States)

    Wang, Guofang; Mearns, Jack; Yang, Xiaohui; Han, Peng; Catanzaro, Salvatore J

    2017-08-06

    Negative mood regulation expectancies (NMRE) represent people's confidence that they can alleviate their negative affect or induce a positive emotional state through thought or action. NMRE predict coping behaviour and mood outcomes for individuals under stress. Since 1990, much research documents the reliability and validity of the English language Negative Mood Regulation (NMR) scale as a measure of NMRE. The current research reports two studies developing a Chinese language translation of the NMR (NMR-C) scale that goes beyond literal translation to be a culturally sensitive measure of NMRE in China. In Study 1, 713 college students from both a major city and a rural setting in China were surveyed. Data support the resulting 32-item NMR-C's reliability (alpha = .88) and validity. The NMR-C showed both direct and indirect links to depression and anxiety; coping mediated the indirect effect. In Study 2, 331 prison police officers in three Chinese provinces participated. NMRE buffered the effect of high role pressure, moderating the relationship between prison police role stress and job engagement. Results of the two studies support the reliability and validity of the Chinese language NMR scale and parallel results found with measures of NMRE in the West and in other Asian countries. © 2017 International Union of Psychological Science.

  5. Comparative efficacy and acceptability of combined antipsychotics and mood stabilizers versus individual drug classes for acute mania: Network meta-analysis.

    Science.gov (United States)

    Glue, Paul; Herbison, Peter

    2015-12-01

    Recent network meta-analyses of drug treatments for acute mania have only evaluated the efficacy and acceptability of individual drug treatments. The relative efficacy and acceptability of combined drug treatment has not been assessed. Double-blind drug trials in acute mania were identified using a systematic search strategy. We recorded numbers of patients enrolled, endpoints for efficacy (changes in mania rating scales, numbers of responders) and acceptability (numbers of dropouts) and treatment administered (categorized as antipsychotic, mood stabilizer, combined antipsychotic/mood stabilizer or placebo). Data were analyzed using a random effects frequentist network meta-analysis. All three drug categories were more effective than placebo. Antipsychotics and combined antipsychotic/mood stabilizer were significantly more effective than mood stabilizers for changes in mania rating scales. Combined antipsychotic/mood stabilizer was significantly more effective than mood stabilizers and antipsychotics for responder rate. Dropout rates were significantly lower for antipsychotics compared with placebo and mood stabilizers. Combined antipsychotic/mood stabilizer had the highest probability of being the best treatment based on change in mania rating scales (96.1% for all mania scales; 85.5% for Young Mania Rating Scale), and 99.3% for being the best treatment for responders. Antipsychotics had 82.0% probability as the best treatment to minimize dropouts. Combined antipsychotic/mood stabilizer appears to have efficacy advantages over antipsychotic or mood stabilizer monotherapy in acute mania, and should be considered as first line therapy. © The Royal Australian and New Zealand College of Psychiatrists 2015.

  6. Glancing up or down: Mood management and selective social comparisons on social networking sites.

    NARCIS (Netherlands)

    Johnson, B.K.; Knobloch-Westerwick, S.

    2014-01-01

    Social networking sites (SNS) provide opportunities for mood management through selective exposure. This study tested the prediction that negative mood fosters self-enhancing social comparisons to SNS profiles. Participants were induced into positive or negative moods and then browsed manipulated

  7. Introducing a Clinical Course-Graphing Scale for DSM-5 Mood Disorders.

    Science.gov (United States)

    Mccullough, James P; Clark, Sarah W; Klein, Daniel N; First, Michael B

    2016-12-31

    Assessment of clinical course to aid in the diagnosis of patients and to guide treatment planning has gained momentum in recent years. A course-graphing scale for the DSM-5 Mood Disorders is presented to facilitate clinical history-taking and diagnosis of the mood disorders during the screening interview. The scale can be administered in the more traditional historytaking portion of the screening interview. The only difference is that it is a more systematic approach especially when the clinician suspects the presence of a mood disorder. The Timeline Course Graphing Scale for the DSM-5 Mood Disorders (TCGS) is described and accompanied with guidelines for administration.

  8. Autism Spectrum Disorder Scale Scores in Pediatric Mood and Anxiety Disorders

    Science.gov (United States)

    Pine, Daniel S.; Guyer, Amanda E.; Goldwin, Michelle; Towbin, Kenneth A.; Leibenluft, Ellen

    2008-01-01

    A study compares the scores on autism spectrum disorder (ASD) symptom scales in healthy children and in children with mood or anxiety disorders. It is observed that children with mood or anxiety disorders obtained higher scores on ASD symptom scales than healthy children.

  9. The Brunel Mood Scale: A South African norm study

    African Journals Online (AJOL)

    classroom setting and adolescent athletes before competition. The ... and athletic sport injuries,34,35 the effect of hormones on mood36 and mood changes .... fatigue (p<0.1 for all). No consistent differences were observed between provinces. The only interpretable difference noted was that the participants from. Gauteng ...

  10. Development and Validation of the Brief Music in Mood Regulation Scale (B-MMR)

    OpenAIRE

    Saarikallio, Suvi

    2012-01-01

    mood regulation has been shown to be among of the most important reasons for musical engagement, but there has been a lack of a concise measurement instrument for this behavior. The current study focused on developing and testing the Brief Music in Mood Regulation scale (BMMR), a 21-item self-report instrument for assessing the use of seven different music-related mood-regulation strategies. Two survey studies (N = 1515 and N = 526) were conducted to first develop and ...

  11. Excitable scale free networks

    Science.gov (United States)

    Copelli, M.; Campos, P. R. A.

    2007-04-01

    When a simple excitable system is continuously stimulated by a Poissonian external source, the response function (mean activity versus stimulus rate) generally shows a linear saturating shape. This is experimentally verified in some classes of sensory neurons, which accordingly present a small dynamic range (defined as the interval of stimulus intensity which can be appropriately coded by the mean activity of the excitable element), usually about one or two decades only. The brain, on the other hand, can handle a significantly broader range of stimulus intensity, and a collective phenomenon involving the interaction among excitable neurons has been suggested to account for the enhancement of the dynamic range. Since the role of the pattern of such interactions is still unclear, here we investigate the performance of a scale-free (SF) network topology in this dynamic range problem. Specifically, we study the transfer function of disordered SF networks of excitable Greenberg-Hastings cellular automata. We observe that the dynamic range is maximum when the coupling among the elements is critical, corroborating a general reasoning recently proposed. Although the maximum dynamic range yielded by general SF networks is slightly worse than that of random networks, for special SF networks which lack loops the enhancement of the dynamic range can be dramatic, reaching nearly five decades. In order to understand the role of loops on the transfer function we propose a simple model in which the density of loops in the network can be gradually increased, and show that this is accompanied by a gradual decrease of dynamic range.

  12. Autism spectrum disorder scale scores in pediatric mood and anxiety disorders.

    Science.gov (United States)

    Pine, Daniel S; Guyer, Amanda E; Goldwin, Michelle; Towbin, Kenneth A; Leibenluft, Ellen

    2008-06-01

    To compare scores on autism spectrum disorder (ASD) symptom scales in healthy youths and youths with mood or anxiety disorders. A total of 352 youths were recruited (107 healthy participants, 88 with an anxiety disorder, 32 with major depressive disorder, 62 with bipolar disorder, and 63 with a mood disorder characterized by severe nonepisodic irritability). Participants received structured psychiatric interviews and parent ratings on at least one of three ASD symptom scales: Children's Communication Checklist, Social Communication Questionnaire, and Social Responsiveness Scale. Relative to healthy youths, youths with mood or anxiety disorders exhibited higher scores on each ASD symptom scale. ASD symptom scale scores also showed an association with impairment severity and attention-deficit/hyperactivity disorder. Among patients with mood disorders but not those with anxiety disorders, consistent, statistically significant associations between diagnosis and ASD symptom scale scores remained even after controlling for potential confounders. Patients with mood disorders exhibit higher scores on ASD symptom scales than healthy youths or youths with anxiety disorders. These data should alert clinicians to the importance of assessing ASD symptoms to identify social reciprocity and communication deficits as possible treatment targets in pediatric mood and anxiety disorders.

  13. SocialMood: an information visualization tool to measure the mood of the people in social networks

    Science.gov (United States)

    Amorim, Guilherme; Franco, Roberto; Moraes, Rodolfo; Figueiredo, Bruno; Miranda, João.; Dobrões, José; Afonso, Ricardo; Meiguins, Bianchi

    2013-12-01

    Based on the arena of social networks, the tool developed in this study aims to identify trends mood among undergraduate students. Combining the methodology Self-Assessment Manikin (SAM), which originated in the field of Psychology, the system filters the content provided on the Web and isolates certain words, establishing a range of values as perceived positive, negative or neutral. A Big Data summarizing the results, assisting in the construction and visualization of behavioral profiles generic, so we have a guideline for the development of information visualization tools for social networks.

  14. Facial expression (mood) recognition from facial images using committee neural networks

    OpenAIRE

    Kulkarni, Saket S; Reddy, Narender P; Hariharan, SI

    2009-01-01

    Abstract Background Facial expressions are important in facilitating human communication and interactions. Also, they are used as an important tool in behavioural studies and in medical rehabilitation. Facial image based mood detection techniques may provide a fast and practical approach for non-invasive mood detection. The purpose of the present study was to develop an intelligent system for facial image based expression classification using committee neural networks. Methods Several facial ...

  15. Loneliness, social support networks, mood and wellbeing in community-dwelling elderly.

    Science.gov (United States)

    Golden, Jeannette; Conroy, Ronán M; Bruce, Irene; Denihan, Aisling; Greene, Elaine; Kirby, Michael; Lawlor, Brian A

    2009-07-01

    Both loneliness and social networks have been linked with mood and wellbeing. However, few studies have examined these factors simultaneously in community-dwelling participants. The aim of this study was to examine the relationship between social network, loneliness, depression, anxiety and quality of life in community dwelling older people living in Dublin. One thousand two hundred and ninety-nine people aged 65 and over, recruited through primary care practices, were interviewed in their own homes using the GMS-AGECAT. Social network was assessed using Wenger's typology. 35% of participants were lonely, with 9% describing it as painful and 6% as intrusive. Similarly, 34% had a non-integrated social network. However, the two constructs were distinct: 32% of participants with an integrated social network reported being lonely. Loneliness was higher in women, the widowed and those with physical disability and increased with age, but when age-related variables were controlled for this association was non-significant. Wellbeing, depressed mood and hopelessness were all independently associated with both loneliness and non-integrated social network. In particular, loneliness explained the excess risk of depression in the widowed. The population attributable risk (PAR) associated with loneliness was 61%, compared with 19% for non-integrated social network. Taken together they had a PAR of 70% Loneliness and social networks both independently affect mood and wellbeing in the elderly, underlying a very significant proportion of depressed mood.

  16. Perceived emotional intelligence in nursing: psychometric properties of the Trait Meta-Mood Scale.

    Science.gov (United States)

    Aradilla-Herrero, Amor; Tomás-Sábado, Joaquín; Gómez-Benito, Juana

    2014-04-01

    To examine the psychometric properties of the Trait Meta-Mood Scale in the nursing context and to determine the relationships between emotional intelligence, self-esteem, alexithymia and death anxiety. The Trait Meta-Mood Scale is one of the most widely used self-report measures for assessing perceived emotional intelligence. However, in the nursing context, no extensive analysis has been conducted to examine its psychometric properties. Cross-sectional and observational study. A total of 1417 subjects participated in the study (1208 nursing students and 209 hospital nurses). The Trait Meta-Mood Scale, the Toronto Alexithymia Scale, the Rosenberg Self-Esteem Scale and the Death Anxiety Inventory were all applied to half of the sample (n = 707). A confirmatory factor analysis was carried out, and statistical analyses examined the internal consistency and test-retest reliability of the Trait Meta-Mood Scale, as well as its relationship with relevant variables. Confirmatory factor analysis confirmed the three dimensions of the original scale (Attention, Clarity and Repair). The instrument showed adequate internal consistency and temporal stability. Correlational results indicated that nurses with high scores on emotional Attention experience more death anxiety, report greater difficulties identifying feelings and have less self-esteem. By contrast, nurses with high levels of emotional Clarity and Repair showed less death anxiety and higher levels of self-esteem. The Trait Meta-Mood Scale is an effective, valid and reliable tool for measuring perceived emotional intelligence in the nursing context. Training programmes should seek to promote emotional abilities among nurses. Use of the Trait Meta-Mood Scale in the nursing context would provide information about nurses' perceived abilities to interpret and manage emotions when interacting with patients. © 2013 John Wiley & Sons Ltd.

  17. Examination of the Spanish Trait Meta-Mood Scale-24 Factor Structure in a Mexican Setting

    Science.gov (United States)

    Valdivia Vázquez, Juan Antonio; Rubio Sosa, Juan Carlos A.; French, Brian F.

    2015-01-01

    The Trait Meta-Mood Scale (TMMS) is an emotional intelligence (EI) assessment originally developed for the U.S. population. This scale measures three EI factors--attention, clarity, and repair--to evaluate how an individual perceives one's own EI skills. Although the TMMS has been adapted for use in several languages and cultures, the structure of…

  18. The use and performance of productivity scales to evaluate presenteeism in mood disorders.

    Science.gov (United States)

    Despiégel, Nicolas; Danchenko, Natalya; François, Clément; Lensberg, Benedikte; Drummond, Michael F

    2012-12-01

    Mood disorders are associated with a high societal cost, mainly due to presenteeism. The objective of this study was to review the use of 10 instruments that rate presenteeism in mood disorders and to provide recommendations regarding the appropriateness of instruments in different study settings. A systematic review of the literature was conducted to identify scales used to measure presenteeism, including the World Health Organization Health and Work Performance Questionnaire, the Lam Employment Absence and Productivity Scale, the Sheehan Disability Scale, the Work Limitation Questionnaire, and Work Productivity and Activity Impairment questionnaire. Study characteristics and major results (by symptom level, by treatment arm, correlation to other scales, and use of monetization) were data extracted. Twenty-nine studies were identified. The Sheehan Disability Scale, the Work Limitation Questionnaire, and Health and Work Performance Questionnaire were the most commonly used instruments. The majority (60%) of scales demonstrated higher presenteeism in individuals with mood disorders than in individuals without. The Lam Employment Absence and Productivity Scale, the Sheehan Disability Scale, and the Work Limitation Questionnaire showed that presenteeism increased with increasing severity of disease. Few studies reported results on presenteeism by treatment, with only small between-treatment differences observed. Good correlations between presenteeism instruments and clinical or quality-of-life scales were reported. Three studies converted results from presenteeism scales into monetary units. Limited experiential evidence exists comparing the performance of presenteeism scales in mood disorders. Therefore, recommendations for inclusion of a presenteeism tool must be driven by instrument properties (ease of administration, amenability to monetization) and the study type. Future research should focus on the responsiveness of the instrument and on how mood disorders

  19. Foulds' "general instability" and "psychopathy" 16PF scales and their relationship to psychiatric mood state.

    Science.gov (United States)

    Bedford, A; McIver, D

    1978-04-01

    Previous research examined "general instability" and "psychopathy" scales, derived from the 16PF, in terms of Foulds' criteria of content, group differentiation, change over time, and score distributions. When the external criterion of a newly validated measure of psychiatric mood state (the DSSI/sAD) was used, it was confirmed in both a patient and a normal group that the "general instability" scale is related significantly to symptomatology, while the "psychopathy" scale is relatively independent of present state.

  20. Negative mood-induction modulates default mode network resting-state functional connectivity in chronic depression

    NARCIS (Netherlands)

    Renner, F.; Siep, N.; Arntz, A.; van de Ven, V.; Peeters, F.P.M.L.; Quaedflieg, C.W.E.M.; Huibers, M.J.H.

    2017-01-01

    BACKGROUND: The aim of this study was to investigate the effects of sad mood on default mode network (DMN) resting-state connectivity in persons with chronic major depressive disorder (cMDD). METHODS: Participants with a diagnosis of cMDD (n=18) and age, gender and education level matched

  1. Reliability of a Scale Assessing Depressed Mood in the Context of Sleep.

    Science.gov (United States)

    Roane, Brandy M; Seifer, Ronald; Sharkey, Katherine M; Van Reen, Eliza; Bond, Tamara L Y; Raffray, Tifenn; Carskadon, Mary A

    2013-03-01

    The current study assessed the reliability of Kandel & Davies mood scale with and without sleep-related items. 178 Brown University first-year students (mean age=18.1 years; 108 females) completed online biweekly surveys after weeks 2, 6, 8, and 10 and on 2 consecutive days after weeks 4 and 12 of their first semester. The scale was examined as a 1) full 6-item scale, 2) 5-item scale excluding the sleep item, and 3) 4-item scale excluding the sleep and tired items. Intraclass correlations (ICC) values for consecutive-day assessments and 6 biweekly surveys were similar and not a function of the weeks evaluated. Total-item correlations and inter-measure correlations with the Center for Epidemiologic Studies - Depressed Mood Scale (CES-D) supported the removal of the sleep-related items from the 6-item scale. These analyses confirm the reliability of the original Kandel and Davies depressed mood scale as well as without the sleep-related items.

  2. Facial expression (mood recognition from facial images using committee neural networks

    Directory of Open Access Journals (Sweden)

    Hariharan SI

    2009-08-01

    Full Text Available Abstract Background Facial expressions are important in facilitating human communication and interactions. Also, they are used as an important tool in behavioural studies and in medical rehabilitation. Facial image based mood detection techniques may provide a fast and practical approach for non-invasive mood detection. The purpose of the present study was to develop an intelligent system for facial image based expression classification using committee neural networks. Methods Several facial parameters were extracted from a facial image and were used to train several generalized and specialized neural networks. Based on initial testing, the best performing generalized and specialized neural networks were recruited into decision making committees which formed an integrated committee neural network system. The integrated committee neural network system was then evaluated using data obtained from subjects not used in training or in initial testing. Results and conclusion The system correctly identified the correct facial expression in 255 of the 282 images (90.43% of the cases, from 62 subjects not used in training or in initial testing. Committee neural networks offer a potential tool for image based mood detection.

  3. Facial expression (mood) recognition from facial images using committee neural networks.

    Science.gov (United States)

    Kulkarni, Saket S; Reddy, Narender P; Hariharan, S I

    2009-08-05

    Facial expressions are important in facilitating human communication and interactions. Also, they are used as an important tool in behavioural studies and in medical rehabilitation. Facial image based mood detection techniques may provide a fast and practical approach for non-invasive mood detection. The purpose of the present study was to develop an intelligent system for facial image based expression classification using committee neural networks. Several facial parameters were extracted from a facial image and were used to train several generalized and specialized neural networks. Based on initial testing, the best performing generalized and specialized neural networks were recruited into decision making committees which formed an integrated committee neural network system. The integrated committee neural network system was then evaluated using data obtained from subjects not used in training or in initial testing. The system correctly identified the correct facial expression in 255 of the 282 images (90.43% of the cases), from 62 subjects not used in training or in initial testing. Committee neural networks offer a potential tool for image based mood detection.

  4. Multi-scale brain networks

    CERN Document Server

    Betzel, Richard F

    2016-01-01

    The network architecture of the human brain has become a feature of increasing interest to the neuroscientific community, largely because of its potential to illuminate human cognition, its variation over development and aging, and its alteration in disease or injury. Traditional tools and approaches to study this architecture have largely focused on single scales -- of topology, time, and space. Expanding beyond this narrow view, we focus this review on pertinent questions and novel methodological advances for the multi-scale brain. We separate our exposition into content related to multi-scale topological structure, multi-scale temporal structure, and multi-scale spatial structure. In each case, we recount empirical evidence for such structures, survey network-based methodological approaches to reveal these structures, and outline current frontiers and open questions. Although predominantly peppered with examples from human neuroimaging, we hope that this account will offer an accessible guide to any neuros...

  5. 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.

  6. A closer look at the relationship between the default network, mind wandering, negative mood, and depression.

    Science.gov (United States)

    Konjedi, Shaghayegh; Maleeh, Reza

    2017-08-01

    By a systematic analysis of the current literature on the neural correlates of mind wandering, that is, the default network (DN), and by shedding light on some determinative factors and conditions which affect the relationship between mind wandering and negative mood, we show that (1) mind wandering per se does not necessarily have a positive correlation with negative mood and, on the higher levels, depression. We propose that negative mood as a consequence of mind wandering generally depends on two determinative conditions, that is, whether mind wandering is with or without meta-awareness and whether mind wandering occurs during high or low vigilance states; (2) increased activity of the DN is not necessarily followed by an increase in unhappiness and depression. We argue that while in some kinds of meditation practices we witness an increase in the structure and in the activity of the DN, no increase in unhappiness and depression is observed.

  7. Scaling Effect In Trade Network

    Science.gov (United States)

    Konar, M.; Lin, X.; Rushforth, R.; Ruddell, B. L.; Reimer, J.

    2015-12-01

    Scaling is an important issue in the physical sciences. Economic trade is increasingly of interest to the scientific community due to the natural resources (e.g. water, carbon, nutrients, etc.) embodied in traded commodities. Trade refers to the spatial and temporal redistribution of commodities, and is typically measured annually between countries. However, commodity exchange networks occur at many different scales, though data availability at finer temporal and spatial resolution is rare. Exchange networks may prove an important adaptation measure to cope with future climate and economic shocks. As such, it is essential to understand how commodity exchange networks scale, so that we can understand opportunities and roadblocks to the spatial and temporal redistribution of goods and services. To this end, we present an empirical analysis of trade systems across three spatial scales: global, sub-national in the United States, and county-scale in the United States. We compare and contrast the network properties, the self-sufficiency ratio, and performance of the gravity model of trade for these three exchange systems.

  8. "You've got a friend in me": can social networks mediate the relationship between mood and MCI?

    Science.gov (United States)

    Yates, Jennifer A; Clare, Linda; Woods, Robert T

    2017-07-13

    Social networks can change with age, for reasons that are adaptive or unwanted. Social engagement is beneficial to both mental health and cognition, and represents a potentially modifiable factor. Consequently this study explored this association and assessed whether the relationship between mild cognitive impairment (MCI) and mood problems was mediated by social networks. This study includes an analysis of data from the Cognitive Function and Ageing Study Wales (CFAS Wales). CFAS Wales Phase 1 data were collected from 2010 to 2013 by conducting structured interviews with older people aged over 65 years of age living in urban and rural areas of Wales, and included questions that assessed cognitive functioning, mood, and social networks. Regression analyses were used to investigate the associations between individual variables and the mediating role of social networks. Having richer social networks was beneficial to both mood and cognition. Participants in the MCI category had weaker social networks than participants without cognitive impairment, whereas stronger social networks were associated with a decrease in the odds of experiencing mood problems, suggesting that they may offer a protective effect against anxiety and depression. Regression analyses revealed that social networks are a significant mediator of the relationship between MCI and mood problems. These findings are important, as mood problems are a risk factor for progression from MCI to dementia, so interventions that increase and strengthen social networks may have beneficial effects on slowing the progression of cognitive decline.

  9. Joining the Dark Side: A Validation of the Changing Moods Scale exploring how Emotional Intelligence can be used to worsen, as well as improve, the moods of others.

    OpenAIRE

    Holton, Joshua Steven

    2013-01-01

    A new scale (The Changing Moods Scale, Austin, unpublished) is validated to add to growing research that sees emotional intelligence as having a dark side rather than purely being of prosocial usage. The scale was found to have four principal factors (Reassurance, Emotional Machiavellianism, Emotional Concealment and Morally-Negative Manipulation). Although positive emotional manipulation was found to correlate positively with emotional intelligence, whilst negative emotional manipulation cor...

  10. Modeling mood variation and covariation among adolescent smokers: application of a bivariate location-scale mixed-effects model.

    Science.gov (United States)

    Pugach, Oksana; Hedeker, Donald; Richmond, Melanie J; Sokolovsky, Alexander; Mermelstein, Robin

    2014-05-01

    Ecological momentary assessments (EMAs) are useful for understanding both between- and within-subject dynamic changes in smoking and mood. Modeling 2 moods (positive affect [PA] and negative affect [NA], PA and NA) simultaneously will better enable researchers to explore the association between mood variables and what influences them at both the momentary and subject level. The EMA component of a natural history study of adolescent smoking was analyzed with a bivariate location-scale mixed-effects model. The proposed model separately estimates the between- and within-subject variances and jointly models the 2 mood constructs. A total of 461 adolescents completed the baseline EMA wave, which resulted in 14,105 random prompts. Smoking level, represented by the number of smoking events on EMA, entered the model as 2 predictors: one that compared nonsmokers during the EMA week to 1-cigarette smokers, and the second one that estimated the effect of smoking level on mood among smokers. Results suggest that nonsmokers had more consistent positive and negative moods compared to 1-cigarette smokers. Among those who smoked, both moods were more consistent at higher smoking levels. The effects of smoking level were greater for NA than for PA. The within-subject association between mood constructs was negative and strongest among 1-cigarette smokers; the within-subject association between positive and negative moods was negatively associated with smoking. Mood variation and association between mood constructs varied across smoking levels. The most infrequent smokers were characterized with more inconsistent moods, whereas mood was more consistent for subjects with higher smoking levels.

  11. Multi-scale brain networks.

    Science.gov (United States)

    Betzel, Richard F; Bassett, Danielle S

    2016-11-11

    The network architecture of the human brain has become a feature of increasing interest to the neuroscientific community, largely because of its potential to illuminate human cognition, its variation over development and aging, and its alteration in disease or injury. Traditional tools and approaches to study this architecture have largely focused on single scales-of topology, time, and space. Expanding beyond this narrow view, we focus this review on pertinent questions and novel methodological advances for the multi-scale brain. We separate our exposition into content related to multi-scale topological structure, multi-scale temporal structure, and multi-scale spatial structure. In each case, we recount empirical evidence for such structures, survey network-based methodological approaches to reveal these structures, and outline current frontiers and open questions. Although predominantly peppered with examples from human neuroimaging, we hope that this account will offer an accessible guide to any neuroscientist aiming to measure, characterize, and understand the full richness of the brain's multiscale network structure-irrespective of species, imaging modality, or spatial resolution. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  12. The effect of a musical mood induction procedure on mood state-dependent word retrieval.

    Science.gov (United States)

    De L'Etoile, Shannon K

    2002-01-01

    The purpose of this experiment was to replicate and expand upon an earlier study by Thaut and de l'Etoile (1993) by examining the effect of a musical mood induction procedure on mood state-dependent word retrieval. Participants (N = 45) completed a 2-day testing procedure. On day one, participants read a list of adjectives and wrote down an antonym for each one. On day two, participants recalled as many of the antonyms as possible. During the testing procedure, participants were placed in 1 of 4 conditions: (a) mood induction at encoding, (b) mood induction at recall, (c) no mood induction, and (d) mood induction at both encoding and recall. The mood induction procedure included 3 steps. Participants first assessed their current mood state using a visual analog scale. They then listened to music for 5 minutes, determined the mood of the piece while listening, and tried to match their mood to the music. Finally, participants again used the visual analog scale to indicate their mood. Results indicated that participants who received mood induction prior to both encoding and recall were able to retrieve significantly more words than participants who did not undergo any mood induction. The results are discussed in light of the associative network theory of memory and emotions and the treatment of mood disorders.

  13. Scale invariance in road networks.

    Science.gov (United States)

    Kalapala, Vamsi; Sanwalani, Vishal; Clauset, Aaron; Moore, Cristopher

    2006-02-01

    We study the topological and geographic structure of the national road networks of the United States, England, and Denmark. By transforming these networks into their dual representation, where roads are vertices and an edge connects two vertices if the corresponding roads ever intersect, we show that they exhibit both topological and geographic scale invariance. That is, we show that for sufficiently large geographic areas, the dual degree distribution follows a power law with exponent 2.2< or = alpha < or =2.4, and that journeys, regardless of their length, have a largely identical structure. To explain these properties, we introduce and analyze a simple fractal model of road placement that reproduces the observed structure, and suggests a testable connection between the scaling exponent and the fractal dimensions governing the placement of roads and intersections.

  14. A new faces scale in pain measurement: a test of bias from current mood, trait affectivity, and scale range.

    Science.gov (United States)

    Elfering, Achim; Grebner, Simone

    2012-01-01

    Faces pain rating scales used among children have been criticized to confound affective states with pain when smiling faces are included. This experimental study is an attempt to examine the possible confounding of affective states with pain when smiling faces are used as part of a faces scale. The meaning of the faces was tested to depend on current mood, current pain, trait affectivity, and inclusion versus exclusion of smiling faces. Sixty-four participants made 6,720 two-categorical pain judgments on faces with different mouth curvature. In multilevel regression analysis, current level of pain and negative trait affectivity biased faces' meaning only when the smiling faces were excluded from the scale. In adults, the new full range faces pain scale including a midpoint neutral face and smiling faces was more robust than the restricted scale. The faces scale that was tested in this study is not applicable for patient measurement but it is an interesting tool for psychological research.

  15. Psychometric properties of a revised version of the Visual Analog Mood Scales.

    Science.gov (United States)

    Kontou, E; Thomas, S A; Lincoln, N B

    2012-12-01

    To assess the internal consistency, validity and factor structure of a revised version of the Visual Analog Mood Scales (VAMS-R) in healthy older adults and aphasic stroke patients. Cross-sectional study. Fifty healthy older people and 71 aphasic stroke patients. Community and hospital. The healthy participants were asked to complete the Hospital Anxiety and Depression Scale (HADS) and the VAMS-R. The aphasic stroke patients completed the VAMS-R and Visual Analogue Self Esteem Scale (VASES) and the Stroke Aphasic Depression Questionnaire 21 (SADQH-21) was completed by a nurse or carer. The internal consistency of the scale was high (healthy adults alpha 0.74, aphasic stroke patients alpha 0.80). The VAMS-R correlated significantly with the HADS in healthy participants (HADS Anxiety r (s) = 0.59, P happiness. The three factors accounted 73% of the variance in healthy participants and 70% of the variance in aphasic stroke patients. The VAMS-R showed better psychometric properties than the original VAMS. Reversing the happy and energetic items improved the ability of the scale to assess mood states.

  16. Handbook of Large-Scale Random Networks

    CERN Document Server

    Bollobas, Bela; Miklos, Dezso

    2008-01-01

    Covers various aspects of large-scale networks, including mathematical foundations and rigorous results of random graph theory, modeling and computational aspects of large-scale networks, as well as areas in physics, biology, neuroscience, sociology and technical areas

  17. Development and Initial Validation of the Italian Mood Scale (ITAMS for Use in Sport and Exercise Contexts

    Directory of Open Access Journals (Sweden)

    Alessandro Quartiroli

    2017-09-01

    Full Text Available The current study presents initial validation statistics for the Italian Mood Scale (ITAMS, a culturally- and linguistically-validated Italian version of the Brunel Mood Scale (BRUMS: Terry and Lane, 2010. The ITAMS was administered to 950 sport participants (659 females, who ranged in age from 16 to 63 years (M = 25.03, SD = 7.62. In the first stage of the validation process, statistical procedures in Mplus were used to evaluate the measurement model. Multigroup exploratory structural equation modeling supported the hypothesized 6-factor measurement model for males and females separately and for the combined sample. Analysis of the scale scores using SPSS provided further support for the construct validity of the ITAMS with hypothesized relationships observed between ITAMS scores and measures of depression and affect. The development and validation of the ITAMS opens the way for mood-related research and sport or exercise interventions requiring mood assessments, in an Italian-language context.

  18. Network robustness under large-scale attacks

    CERN Document Server

    Zhou, Qing; Liu, Ruifang; Cui, Shuguang

    2014-01-01

    Network Robustness under Large-Scale Attacks provides the analysis of network robustness under attacks, with a focus on large-scale correlated physical attacks. The book begins with a thorough overview of the latest research and techniques to analyze the network responses to different types of attacks over various network topologies and connection models. It then introduces a new large-scale physical attack model coined as area attack, under which a new network robustness measure is introduced and applied to study the network responses. With this book, readers will learn the necessary tools to evaluate how a complex network responds to random and possibly correlated attacks.

  19. Negative mood influences default mode network functional connectivity in patients with chronic low back pain: implications for functional neuroimaging biomarkers.

    Science.gov (United States)

    Letzen, Janelle E; Robinson, Michael E

    2017-01-01

    The default mode network (DMN) has been proposed as a biomarker for several chronic pain conditions. Default mode network functional connectivity (FC) is typically examined during resting-state functional neuroimaging, in which participants are instructed to let thoughts wander. However, factors at the time of data collection (eg, negative mood) that might systematically impact pain perception and its brain activity, influencing the application of the DMN as a pain biomarker, are rarely reported. This study measured whether positive and negative moods altered DMN FC patterns in patients with chronic low back pain (CLBP), specifically focusing on negative mood because of its clinical relevance. Thirty-three participants (CLBP = 17) underwent resting-state functional magnetic resonance imaging scanning before and after sad and happy mood inductions, and rated levels of mood and pain intensity at the time of scanning. Two-way repeated-measures analysis of variances were conducted on resting-state functional connectivity data. Significant group (CLBP > healthy controls) × condition (sadness > baseline) interaction effects were identified in clusters spanning parietal operculum/postcentral gyrus, insular cortices, anterior cingulate cortex, frontal pole, and a portion of the cerebellum (PFDR baseline (PFDR negative mood in individuals with and without CLBP. It is possible that DMN FC seen in patients with chronic pain is related to an affective dimension of pain, which is important to consider in future neuroimaging biomarker development and implementation.

  20. Mapping the MMPI-2/MMPI-2-RF Restructured Clinical Scales Onto Mood Markers in an Israeli Sample.

    Science.gov (United States)

    Shkalim, Eleanor; Ben-Porath, Yossef S; Almagor, Moshe

    2016-01-01

    This study cross-culturally evaluated the Minnesota Multiphasic Personality Inventory-2/MMPI-2 Restructured Form (MMPI-2/MMPI-2-RF) emotion-focused Restructured Clinical (RC) Scales to examine whether their patterns of associations with positive affect (PA) and negative affect (NA) are as expected based on Tellegen, Watson, and Clark's ( 1999a , 1999b ) mood model. The sample was composed of 100 men and 133 women from psychiatric settings in Israel who completed the MMPI-2 and the Mood Check List (MCL; Zevon & Tellegen, 1982 ). Results indicated that RCd was substantially correlated with both PA and NA in opposite directions, and that RC2 and RC7 were more highly correlated with PA and NA, respectively. Further, when compared with their Clinical Scale counterparts, RC2 and RC7 exhibited comparable convergent validities and improved discriminant properties. Findings provide support for Tellegen et al.'s ( 2003 ) goal to link the RC scales to contemporary conceptualizations of mood.

  1. 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

  2. Scalable Virtual Network Mapping Algorithm for Internet-Scale Networks

    Science.gov (United States)

    Yang, Qiang; Wu, Chunming; Zhang, Min

    The proper allocation of network resources from a common physical substrate to a set of virtual networks (VNs) is one of the key technical challenges of network virtualization. While a variety of state-of-the-art algorithms have been proposed in an attempt to address this issue from different facets, the challenge still remains in the context of large-scale networks as the existing solutions mainly perform in a centralized manner which requires maintaining the overall and up-to-date information of the underlying substrate network. This implies the restricted scalability and computational efficiency when the network scale becomes large. This paper tackles the virtual network mapping problem and proposes a novel hierarchical algorithm in conjunction with a substrate network decomposition approach. By appropriately transforming the underlying substrate network into a collection of sub-networks, the hierarchical virtual network mapping algorithm can be carried out through a global virtual network mapping algorithm (GVNMA) and a local virtual network mapping algorithm (LVNMA) operated in the network central server and within individual sub-networks respectively with their cooperation and coordination as necessary. The proposed algorithm is assessed against the centralized approaches through a set of numerical simulation experiments for a range of network scenarios. The results show that the proposed hierarchical approach can be about 5-20 times faster for VN mapping tasks than conventional centralized approaches with acceptable communication overhead between GVNCA and LVNCA for all examined networks, whilst performs almost as well as the centralized solutions.

  3. Can a one-item mood scale do the trick? Predicting relapse over 5.5-years in recurrent depression

    NARCIS (Netherlands)

    van Rijsbergen, Gerard D.; Bockting, Claudi L. H.; Berking, Matthias; Koeter, Maarten W.J.; Schene, Aart H.

    2012-01-01

    BACKGROUND: To examine whether a simple Visual Analogue Mood Scale (VAMS) is able to predict time to relapse over 5.5-years. METHODOLOGY/PRINCIPAL FINDINGS: 187 remitted recurrently depressed out-patients were interviewed using the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I)

  4. 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.

  5. 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....

  6. Scaling Laws in Spatial Network Formation

    CERN Document Server

    Molkenthin, Nora

    2016-01-01

    Geometric constraints impact the formation of a broad range of spatial networks, from amino acid chains folding to proteins structures to rearranging particle aggregates. How the network of interactions dynamically self-organizes in such systems is far from fully understood. Here, we analyze a class of spatial network formation processes by introducing a mapping from geometric to graph-theoretic constraints. Combining stochastic and mean field analyses yields an algebraic scaling law for the extent (graph diameter) of the resulting networks with system size, in contrast to logarithmic scaling known for networks without constraints. Intriguingly, the exponent falls between that of self-avoiding random walks and that of space filling arrangements, consistent with experimentally observed scaling (of the spatial radius of gyration) for protein tertiary structures.

  7. Spatial Scaling of Land Cover Networks

    CERN Document Server

    Small, Christopher

    2015-01-01

    Spatial networks of land cover are well-described by power law rank-size distributions. Continuous field proxies for human settlements, agriculture and forest cover have similar spatial scaling properties spanning 4 to 5 orders of magnitude. Progressive segmentation of these continuous fields yields spatial networks with rank-size distributions having slopes near -1 for a wide range of thresholds. We consider a general explanation for this scaling that does not require different processes for each type of land cover. The same conditions that give rise to scale-free networks in general can produce power law distributions of component sizes for bounded spatial networks confined to a plane or surface. Progressive segmentation of a continuous field naturally results in growth of the network while the increasing perimeters of the growing components result in preferential attachment to the larger components with the longer perimeters. Progressive segmentation of two types of random continuous field results in progr...

  8. Spatial Structure and Scaling of Agricultural Networks

    CERN Document Server

    Sousa, Daniel

    2016-01-01

    Considering agricultural landscapes as networks can provide information about spatial connectivity relevant for a wide range of applications including pollination, pest management, and ecology. Global agricultural networks are well-described by power law rank-size distributions. However, regional analyses capture only a subset of the total global network. Most analyses are regional. In this paper, we seek to address the following questions: Does the globally observed scale-free property of agricultural networks hold over smaller spatial domains? Can similar properties be observed at kilometer to meter scales? We analyze 9 intensively cultivated Landsat scenes on 5 continents with a wide range of vegetation distributions. We find that networks of vegetation fraction within the domain of each of these Landsat scenes exhibit substantial variability - but still possess similar scaling properties to the global distribution of agriculture. We also find similar results using a 39 km2 IKONOS image. To illustrate an a...

  9. Heterogeneous multidimensional scaling for complex networks

    Science.gov (United States)

    Xuan, Qi; Ma, Xiaodi; Fu, Chenbo; Dong, Hui; Zhang, Guijun; Yu, Li

    2015-07-01

    Many real-world networks are essentially heterogeneous, where the nodes have different abilities to gain connections. Such networks are difficult to be embedded into low-dimensional Euclidean space if we ignore the heterogeneity and treat all the nodes equally. In this paper, based on a newly defined heterogeneous distance and a generalized network distance under the constraints of network and triangle inequalities, respectively, we propose a new heterogeneous multidimensional scaling method (HMDS) to embed different networks into proper Euclidean spaces. We find that HMDS behaves much better than the traditional multidimensional scaling method (MDS) in embedding different artificial and real-world networks into Euclidean spaces. Besides, we also propose a method to estimate the appropriate dimensions of Euclidean spaces for different networks, and find that the estimated dimensions are quite close to the real dimensions for those geometrical networks under study. These methods thus can help to better understand the evolution of real-world networks, and have practical importance in network visualization, community detection, link prediction and localization of wireless sensors.

  10. The Geriatric Depression Scale: does it measure depressive mood, depressive affect, or both?

    Science.gov (United States)

    Gana, Kamel; Bailly, Nathalie; Broc, Guillaume; Cazauvieilh, Christophe; Boudouda, Nedjem Eddine

    2017-10-01

    Self-report measures of depression are highly important tools used in research and in various healthcare settings for the diagnosis of different levels of depression. The Geriatric Depression Scale (GDS) is the first and the most popular scale used to screen for late-life depression. It is endorsed by the Royal College of Physicians and the British Geriatric Society (1992). The purpose of the present research was to investigate whether scores on the GDS15 capture depressive mood (i.e. trait depression), depressive affect (i.e. short-term depressive state), or both. For this purpose, a trait-state model (stable trait, autoregressive trait, and state model) was applied to GDS15 scores obtained at four time points over a 6-year period among a sample of community-dwelling older persons (N = 753). This model allows decomposing the GDS15 scores into three different variance components: stable trait variance, autoregressive trait variance, and state variance. Our findings revealed a general pattern of a major proportion of stable trait (69%) and autoregressive trait (22%) variance and a very smaller amount of state variance (9%) in the GDS scores across 6 years. Age and gender (i.e. being female) were shown to be positively linked to more stable trait variance. Depression, as assessed with the GDS15 , should be regarded as a relatively stable and enduring trait construct, reflecting a stable core of a person's depressivity. The negligible amount of state elements in the variation of the GDS15 scores provides evidence that changing the context will not be enough to cause significant changes in depressive symptoms. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  11. Emergence of Scaling in Random Networks

    Science.gov (United States)

    Barabási, Albert-László; Albert, Réka

    1999-10-01

    Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.

  12. Sample-Starved Large Scale Network Analysis

    Science.gov (United States)

    2016-05-05

    Applications to materials science 2.1 Foundational principles for large scale inference on structure of covariance We developed general principles for...concise but accessible format. These principles are applicable to large-scale complex network applications arising genomics , connectomics, eco-informatics...available to estimate or detect patterns in the matrix. 15. SUBJECT TERMS multivariate dependency structure multivariate spatio-temporal prediction

  13. Continuous Road Network Generalization throughout All Scales

    NARCIS (Netherlands)

    Suba, R.; Meijers, B.M.; van Oosterom, P.J.M.

    2016-01-01

    Until now, road network generalization has mainly been applied to the task of generalizing from one fixed source scale to another fixed target scale. These actions result in large differences in content and representation, e.g., a sudden change of the representation of road segments from areas to

  14. Integrative deficits in depression and in negative mood states as a result of fronto-parietal network dysfunctions.

    Science.gov (United States)

    Brzezicka, Aneta

    2013-01-01

    Depression is a disorder characterized not only by persistent negative mood, lack of motivation and a "ruminative" style of thinking, but also by specific deficits in cognitive functioning. These deficits are especially pronounced when integration of information is required. Previous research on linear syllogisms points to a clear pattern of cognitive disturbances present in people suffering from depressive disorders, as well as in people with elevated negative mood. Such disturbances are characterized by deficits in the integration of piecemeal information into coherent mental representations. In this review, I present evidence which suggests that the dysfunction of specific brain areas plays a crucial role in creating reasoning and information integration problems among people with depression and with heightened negative mood. As the increasingly prevalent systems neuroscience approach is spreading into the study of mental disorders, it is important to understand how and which brain networks are involved in creating certain symptoms of depression. Two large brain networks are of particular interest when considering depression: the default mode network (DMN) and the fronto-parietal (executive) network (FNP). The DMN network shows abnormally high activity in the depressed population, whereas FNP circuit activity is diminished. Disturbances within the FNP network seem to be strongly associated with cognitive problems in depression, especially those concerning executive functions. The dysfunctions within the fronto-parietal network are most probably connected to ineffective transmission of information between prefrontal and parietal regions, and also to an imbalance between FNP and DMN circuits. Inefficiency of this crucial circuits functioning may be a more general mechanism leading to problems with flexible cognition and executive functions, and could be the cause of more typical symptoms of depression like persistent rumination.

  15. Continuous Road Network Generalization throughout All Scales

    Directory of Open Access Journals (Sweden)

    Radan Šuba

    2016-08-01

    Full Text Available Until now, road network generalization has mainly been applied to the task of generalizing from one fixed source scale to another fixed target scale. These actions result in large differences in content and representation, e.g., a sudden change of the representation of road segments from areas to lines, which may confuse users. Therefore, we aim at the continuous generalization of a road network for the whole range, from the large scale, where roads are represented as areas, to mid- and small scales, where roads are represented progressively more frequently as lines. As a consequence of this process, there is an intermediate scale range where at the same time some roads will be represented as areas, while others will be represented as lines. We propose a new data model together with a specific data structure where for all map objects, a range of valid map scales is stored. This model is based on the integrated and explicit representation of: (1 a planar area partition; and (2 a linear road network. This enables the generalization process to include the knowledge and understanding of a linear network. This paper further discusses the actual generalization options and algorithms for populating this data structure with high quality vario-scale cartographic content.

  16. The International Mood Network (IMN) Nosology Project: differentiating borderline personality from bipolar illness.

    Science.gov (United States)

    Vöhringer, P A; Barroilhet, S A; Alvear, K; Medina, S; Espinosa, C; Alexandrovich, K; Riumallo, P; Leiva, F; Hurtado, M E; Cabrera, J; Sullivan, M; Holtzman, N; Ghaemi, S N

    2016-12-01

    The differential diagnosis of bipolar illness vs. borderline personality is controversial. Both conditions manifest impulsive behavior, unstable interpersonal relationships, and mood symptoms. This study examines whether and which mood clinical features can differentiate between both conditions. A total of 260 patients (mean ± standard deviation age 41 ± 13 years, 68% female) attending to a mood clinic were examined for diagnosis of bipolar illness and borderline personality disorder using SCID-I, SCID-II, and clinical mood criteria extracted from Mood Disorder Questionnaire (MDQ). They were analyzed using diagnoses as dependent variables. Predictors of bipolar and borderline diagnoses were identified by multivariable logistic regressions, and predictive validity of models was assessed using ROC curve analysis. Bipolar illness was strongly predicted by elevated mood (OR = 4.02, 95% CI: 1.80-9.15), increased goal-directed activities (OR = 3.90, 95% CI: 1.73-8.96), and episodicity of mood symptoms (OR = 3.48, 95% CI 1.49-8.39). This triad model predicted bipolar illness with 88.7% sensitivity, 81.4% specificity, and obtained an auROC of 0.91 (95% CI: 0.76-0.96) and a positive predictive value of 85.1%. For borderline personality disorder, only female gender was a statistically significant predictor (OR = 3.41, 95% CI: 1.29-13.7), and the predictive model obtained an auROC of 0.67 (95% CI: 0.53-0.74). In a mood disorder clinic setting, manic criteria and episodic mood course distinguished bipolar illness from borderline personality disorder. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  17. Large-scale Heterogeneous Network Data Analysis

    Science.gov (United States)

    2012-07-31

    Information Diffusion over Crowds with Social Network.” ACM SIGGRAPH 2012. (poster)  Wan-Yu Lin, Nanyun Peng, Chun-Chao Yen, Shou-De Lin. “Online Plagiarism ...Abstract: Large-scale network is a powerful data structure allowing the depiction of relationship information between entities. Recent...we propose an unsupervised tensor-based mechanism, considering higher-order relational information , to model the complex semantics of nodes. The

  18. 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...... main focus. Here the general perception of the nature and role in society of large scale networks as a fundamental infrastructure is analysed. This analysis focuses on the effects of the technical DDN projects and on the perception of network infrastructure as expressed by key decision makers....... A summary of the most pressing growth limits for the coming three decades is given....

  19. Scaling Laws in Supramolecular Polymer Networks

    Science.gov (United States)

    Xu, Donghua; Craig, Stephen L.

    2011-01-01

    The linear rheological properties of networks formed by adding bis-Pd(II) cross-linkers to poly(4-vinylpyridine) (PVP) solution are examined, and the scaling law relationships between the zero shear viscosity (η0) of the networks versus the concentration of PVP solution (CPVP), the concentration of cross-linkers (CX), and the number density of elastically active chains (vphantom) are experimentally determined. The scaling law relationships are compared to the theoretical expectations of the Sticky Rouse and Sticky Reptation models (Macromolecules 2001, 34, 1058-1068), and both qualitative and quantitative differences are observed. PMID:21765553

  20. Topological Routing in Large-Scale Networks

    DEFF Research Database (Denmark)

    Pedersen, Jens Myrup; Knudsen, Thomas Phillip; Madsen, Ole Brun

    2004-01-01

    Topological Routing to large-scale networks are discussed. Hierarchical extensions are presented along with schemes for shortest path routing, fault handling and path restoration. Further reserach in the area is discussed and perspectives on the prerequisites for practical deployment of Topological Routing...

  1. Topological Routing in Large-Scale Networks

    DEFF Research Database (Denmark)

    Pedersen, Jens Myrup; Knudsen, Thomas Phillip; Madsen, Ole Brun

    Topological Routing to large-scale networks are discussed. Hierarchical extensions are presented along with schemes for shortest path routing, fault handling and path restoration. Further reserach in the area is discussed and perspectives on the prerequisites for practical deployment of Topological Routing...

  2. [Construction and validation of a dimensional scale for mood disorders: multidimensional assessment of thymic states (MAThyS)].

    Science.gov (United States)

    Henry, C; M'baïlara, K; Poinsot, R; Falissard, B

    2007-10-01

    The heterogeneity of mood episodes in bipolar disorders makes it difficult in some cases to define appropriate therapeutic strategies. Therefore, we proposed a new tool based on a dimensional approach, with five a priori subscales (emotional reactivity, thought processes, psychomotricity, motivation and sense perception) expected to help define subgroups of mood episodes predictive of the response to treatment. This study was designed to validate this multidimensional assessment of thymic states (MAThyS) scale. One hundred and ninety six subjects were included: 44 controls and 152 bipolar patients in various states: euthymic, manic or depressed. The MAThyS is a visual analog scale consisting of 20 items, ranging from inhibition to excitation. These items corresponded to five dimensions, namely: emotional reactivity, thought processes, psychomotricity, motivation and sense perception. They were selected since they represent clinically relevant quantitative traits. Confirmatory analyses demonstrated a good validity for this scale, fair convergent and divergent validity (multitrait multimethod analyses (MTMM)), good internal consistency both at global and dimensional level (Alpha Cronbach ranging from 0.70 to 0.93). The MathyS scale is moderately correlated with both the Montgomery and Asberg depression rating scale (MADRS) (depression score; r=-0.45) and the mania rating scale (MAS) (manic score; r=0.56). Some dimensions were linked (emotional reactivity and thought processes, r=0.71; psychomotricity and motivation, r=0.70). Analysing the divergent validity of items led us to redefine three of them. Following this validation step, exploratory analyses suggest that a four-dimension factorial structure is more appropriate. However, the statistical model is very close to the clinically relevant five-dimensional model. Further studies are needed to explore the relevance of retaining this dimension as a useful descriptive element.

  3. Mood Disorders

    Science.gov (United States)

    ... they're in a bad mood. A mood disorder is different. It affects a person's everyday emotional ... ten people aged 18 and older have mood disorders. These include depression and bipolar disorder (also called ...

  4. Scale-free networks as entropy competition

    Science.gov (United States)

    Sanchirico, Antonio; Fiorentino, Mauro

    2008-10-01

    Complex networks describe several and different real-world systems consisting of a number of interacting elements. A very important characteristic of such networks is the degree distribution that strongly controls their behavior. Based on statistical mechanics, three classes of uncorrelated complex networks are identified here, depending on the role played by the connectivities amongst elements. In particular, by identifying the connectivities of a node with the number of its nearest neighbors, we show that the power law is the most probable degree distribution that both nodes and neighbors, in a reciprocal competition, assume when the respective entropy functions reach their maxima, under mutual constraint. As a result, we obtain scaling exponent values as a function of the structural characteristics of the whole network. Moreover, our approach sheds light on the exponential and Poissonian degree distributions, derived, respectively, when connectivities are thought of as degenerated connections or as half-edges. Thus, all three classes of degree distributions are derived, starting from a common principle and leading to a general and unified framework for investigating the network structure.

  5. Network connectivity modulates power spectrum scale invariance.

    Science.gov (United States)

    Rădulescu, Anca; Mujica-Parodi, Lilianne R

    2014-04-15

    Measures of complexity are sensitive in detecting disease, which has made them attractive candidates for diagnostic biomarkers; one complexity measure that has shown promise in fMRI is power spectrum scale invariance (PSSI). Even if scale-free features of neuroimaging turn out to be diagnostically useful, however, their underlying neurobiological basis is poorly understood. Using modeling and simulations of a schematic prefrontal-limbic meso-circuit, with excitatory and inhibitory networks of nodes, we present here a framework for how network density within a control system can affect the complexity of signal outputs. Our model demonstrates that scale-free behavior, similar to that observed in fMRI PSSI data, can be obtained for sufficiently large networks in a context as simple as a linear stochastic system of differential equations, although the scale-free range improves when introducing more realistic, nonlinear behavior in the system. PSSI values (reflective of complexity) vary as a function of both input type (excitatory, inhibitory) and input density (mean number of long-range connections, or strength), independent of their node-specific geometric distribution. Signals show pink noise (1/f) behavior when excitatory and inhibitory influences are balanced. As excitatory inputs are increased and decreased, signals shift towards white and brown noise, respectively. As inhibitory inputs are increased and decreased, signals shift towards brown and white noise, respectively. The results hold qualitatively at the hemodynamic scale, which we modeled by introducing a neurovascular component. Comparing hemodynamic simulation results to fMRI PSSI results from 96 individuals across a wide spectrum of anxiety-levels, we show how our model can generate concrete and testable hypotheses for understanding how connectivity affects regulation of meso-circuits in the brain. Copyright © 2013 Elsevier Inc. All rights reserved.

  6. Positive affect and negative affect correlate differently with distress and health-related quality of life in patients with cardiac conditions: Validation of the Danish Global Mood Scale

    DEFF Research Database (Denmark)

    Spindler, Helle; Denollet, Johan; Kruse, Charlotte

    2009-01-01

    The Global Mood Scale (GMS), assessing negative affect (NA) and positive affect (PA), is sensitive to tapping treatment-related changes in patients with cardiac conditions. We examined the psychometric properties of the Danish GMS and the influence of NA and PA on distress and health-related qual......The Global Mood Scale (GMS), assessing negative affect (NA) and positive affect (PA), is sensitive to tapping treatment-related changes in patients with cardiac conditions. We examined the psychometric properties of the Danish GMS and the influence of NA and PA on distress and health...

  7. Correlation of Social Network Attributes with Individuals’ Score on Bipolar Spectrum Diagnostic Scale

    Directory of Open Access Journals (Sweden)

    Amir Momeni Boroujeni

    2012-09-01

    Full Text Available Introduction: Bipolar Spectrum Disorders include a variety of mood disorders from bipolar II disorder to conditions characterized by hyperthymic mood states. It has been suggested that psychosocial factors also play an important role in bipolar disorders, in this study we have used social network analysis in order to better understand the social positions of those affected by bipolar spectrum disorders. Methods: In this cross sectional study 90 individuals within a bounded network were included and studied by using a standard questionnaire for bipolar spectrum disorder scale (BSDS and a sociometric questionnaire for analyzing the social network of those individuals.Results: This study showed that BSDS score is signi.cantly correlated with the Bonacich power of the participants (P= 0.009 as well as with their Outdegree Strength (P= 0.013.Discussion: The results of this study show that there is interplay between social attributes and Bipolar Spectrum Disorders. This emphasizes the need for understanding the role of social networks and performing further research into quantifying social aspects of psychiatric disorders.

  8. Correlation of Social Network Attributes with Individuals’ Score on Bipolar Spectrum Diagnostic Scale

    Directory of Open Access Journals (Sweden)

    Amir Momeni Boroujeni

    2012-12-01

    Full Text Available Bipolar Spectrum Disorders include a variety of mood disorders from bipolar II disorder to conditions characterized by hyperthymic mood states. It has been suggested that psychosocial factors also play an important role in bipolar disorders, in this study we have used social network analysis in order to better understand the social positions of those affected by bipolar spectrum disorders.Methods and Materials: In this cross sectional study 90 individuals within a bounded network were included and studied by using a standard questionnaire for bipolar spectrum disorder scale (BSDS and a sociometric questionnaire for analyzing the social network of those individuals.Results: This study showed that BSDS score is significantly correlated with the Bonacich power of the participants (P= 0.009 as well as with their Outdegree Strength (P= 0.013.Discussion:The results of this study show that there is interplay between social attributes and Bipolar Spectrum Disorders. This emphasizes the need for understanding the role of social networks and performing further research into quantifying social aspects of psychiatric disorders.

  9. Association between family history of mood disorders and clinical characteristics of bipolar disorder: results from the Brazilian bipolar research network.

    Science.gov (United States)

    Berutti, Mariangeles; Nery, Fabiano G; Sato, Rodrigo; Scippa, Angela; Kapczinski, Flavio; Lafer, Beny

    2014-06-01

    To compare clinical characteristics of bipolar disorder (BD) in patients with and without a family history of mood disorders (FHMD) in a large sample from the Brazilian Research Network of Bipolar Disorders. Four-hundred eighty-eight DSM-IV BD patients participating in the Brazilian Research Network of Bipolar Disorders were included. Participants were divided between those with FHMD (n=230) and without FHMD (n=258). We compared these two groups on demographic and clinical variables and performed a logistic regression to identify which variables were most strongly associated with positive family history of mood disorders. BD patients with FHMD presented with significantly higher lifetime prevalence of any anxiety disorder, obsessive-compulsive disorder, social phobia, substance abuse, and were more likely to present history of suicide attempts, family history of suicide attempts and suicide, and more psychiatric hospitalizations than BD patients without FHMD. Logistic regression showed that the variables most strongly associated with a positive FHMD were any comorbid anxiety disorder, comorbid substance abuse, and family history of suicide. Cross-sectional study and verification of FHMD by indirect information. BD patients with FHMD differ from BD patients without FHMD in rates of comorbid anxiety disorder and substance abuse, number of hospitalizations and suicide attempts. As FHMD is routinely assessed in clinical practice, these findings may help to identify patients at risk for particular manifestations of BD and may point to a common, genetically determined neurobiological substrate that increases the risk of conditions such as comorbidities and suicidality in BD patients. Copyright © 2014 Elsevier B.V. All rights reserved.

  10. An Evaluation Framework for Large-Scale Network Structures

    DEFF Research Database (Denmark)

    Pedersen, Jens Myrup; Knudsen, Thomas Phillip; Madsen, Ole Brun

    2004-01-01

    An evaluation framework for large-scale network structures is presented, which facilitates evaluations and comparisons of different physical network structures. A number of quantitative and qualitative parameters are presented, and their importance to networks discussed. Choosing a network...... is closed by an example of how the framework can be used. The framework supports network planners in decision-making and researchers in evaluation and development of network structures....

  11. Measuring Positive Emotion With the Mood and Anxiety Symptom Questionnaire: Psychometric Properties of the Anhedonic Depression Scale.

    Science.gov (United States)

    Kendall, Ashley D; Zinbarg, Richard E; Bobova, Lyuba; Mineka, Susan; Revelle, William; Prenoveau, Jason M; Craske, Michelle G

    2016-02-01

    Low positive emotion distinguishes depression from most types of anxiety. Formative work in this area employed the Anhedonic Depression scale from the Mood and Anxiety Symptom Questionnaire (MASQ-AD), and the MASQ-AD has since become a popular measure of positive emotion, often used independently of the full MASQ. However, two key assumptions about the MASQ-AD-that it should be represented by a total scale score, and that it measures time-variant experiences-have not been adequately tested. The present study factor analyzed MASQ-AD data collected annually over 3 years (n = 618, mean age = 17 years at baseline), and then decomposed its stable and unstable components. The results suggested the data were best represented by a hierarchical structure, and that less than one quarter of the variance in the general factor fluctuated over time. The implications for interpreting past findings from the MASQ-AD, and for conducting future research with the scale, are discussed. © The Author(s) 2015.

  12. Emergence of Scale-Free Syntax Networks

    Science.gov (United States)

    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.

  13. SentiHealth-Cancer: A sentiment analysis tool to help detecting mood of patients in online social networks.

    Science.gov (United States)

    Rodrigues, Ramon Gouveia; das Dores, Rafael Marques; Camilo-Junior, Celso G; Rosa, Thierson Couto

    2016-01-01

    Cancer is a critical disease that affects millions of people and families around the world. In 2012 about 14.1 million new cases of cancer occurred globally. Because of many reasons like the severity of some cases, the side effects of some treatments and death of other patients, cancer patients tend to be affected by serious emotional disorders, like depression, for instance. Thus, monitoring the mood of the patients is an important part of their treatment. Many cancer patients are users of online social networks and many of them take part in cancer virtual communities where they exchange messages commenting about their treatment or giving support to other patients in the community. Most of these communities are of public access and thus are useful sources of information about the mood of patients. Based on that, Sentiment Analysis methods can be useful to automatically detect positive or negative mood of cancer patients by analyzing their messages in these online communities. The objective of this work is to present a Sentiment Analysis tool, named SentiHealth-Cancer (SHC-pt), that improves the detection of emotional state of patients in Brazilian online cancer communities, by inspecting their posts written in Portuguese language. The SHC-pt is a sentiment analysis tool which is tailored specifically to detect positive, negative or neutral messages of patients in online communities of cancer patients. We conducted a comparative study of the proposed method with a set of general-purpose sentiment analysis tools adapted to this context. Different collections of posts were obtained from two cancer communities in Facebook. Additionally, the posts were analyzed by sentiment analysis tools that support the Portuguese language (Semantria and SentiStrength) and by the tool SHC-pt, developed based on the method proposed in this paper called SentiHealth. Moreover, as a second alternative to analyze the texts in Portuguese, the collected texts were automatically translated

  14. Scaling solutions for connectivity and conductivity of continuous random networks.

    Science.gov (United States)

    Galindo-Torres, S A; Molebatsi, T; Kong, X-Z; Scheuermann, A; Bringemeier, D; Li, L

    2015-10-01

    Connectivity and conductivity of two-dimensional fracture networks (FNs), as an important type of continuous random networks, are examined systematically through Monte Carlo simulations under a variety of conditions, including different power law distributions of the fracture lengths and domain sizes. The simulation results are analyzed using analogies of the percolation theory for discrete random networks. With a characteristic length scale and conductivity scale introduced, we show that the connectivity and conductivity of FNs can be well described by universal scaling solutions. These solutions shed light on previous observations of scale-dependent FN behavior and provide a powerful method for quantifying effective bulk properties of continuous random networks.

  15. Chaotic Modes in Scale Free Opinion Networks

    Science.gov (United States)

    Kusmartsev, Feo V.; Kürten, Karl E.

    2010-12-01

    In this paper, we investigate processes associated with formation of public opinion in varies directed random, scale free and small-world social networks. The important factor of the opinion formation is the existence of contrarians which were discovered by Granovetter in various social psychology experiments1,2,3 long ago and later introduced in sociophysics by Galam.4 When the density of contrarians increases the system behavior drastically changes at some critical value. At high density of contrarians the system can never arrive to a consensus state and periodically oscillates with different periods depending on specific structure of the network. At small density of the contrarians the behavior is manifold. It depends primary on the initial state of the system. If initially the majority of the population agrees with each other a state of stable majority may be easily reached. However when originally the population is divided in nearly equal parts consensus can never be reached. We model the emergence of collective decision making by considering N interacting agents, whose opinions are described by two state Ising spin variable associated with YES and NO. We show that the dynamical behaviors are very sensitive not only to the density of the contrarians but also to the network topology. We find that a phase of social chaos may arise in various dynamical processes of opinion formation in many realistic models. We compare the prediction of the theory with data describing the dynamics of the average opinion of the USA population collected on a day-by-day basis by varies media sources during the last six month before the final Obama-McCain election. The qualitative ouctome is in reasonable agreement with the prediction of our theory. In fact, the analyses of these data made within the paradigm of our theory indicates that even in this campaign there were chaotic elements where the public opinion migrated in an unpredictable chaotic way. The existence of such a phase

  16. Developing Large-Scale Bayesian Networks by Composition

    Data.gov (United States)

    National Aeronautics and Space Administration — In this paper, we investigate the use of Bayesian networks to construct large-scale diagnostic systems. In particular, we consider the development of large-scale...

  17. Positive and negative affect within the realm of depression, stress and fatigue: the two-factor distress model of the Global Mood Scale (GMS).

    Science.gov (United States)

    Denollet, Johan; De Vries, Jolanda

    2006-04-01

    The Global Mood Scale (GMS; [Denollet, J., 1993a. Emotional distress and fatigue in coronary heart disease: the Global Mood Scale (GMS). Psychol Med 23, 111-121., Denollet, J., 1993b. The sensitivity of outcome assessment in cardiac rehabilitation. J Consult Clin Psychol 61, 686-695.]) was originally developed as a measure of positive affect (PA) and negative affect (NA) in cardiac patients. The purpose of this study was to examine its two-factor affect model in the realm of stress, depression, and fatigue in working adults. Affect, stress, depression, and fatigue were assessed with validated questionnaires in a sample of 228 adults (49.6% male; mean = 41.4 +/- 9 years) from the working population. The GMS PA and NA scales were internally consistent (Cronbach's alpha = .94 and alpha = .93, respectively), and correlated in the expected direction with their corresponding mood scales from the Positive and Negative Affect Schedule (PANAS). Factor analyses of the 40 mood terms comprising the GMS and PANAS yielded one common PA-dimension, but two NA-dimensions reflecting emotional exhaustion (GMS) and anxious apprehension (PANAS) as different components of the stress process. A relatively high mean NA score of the GMS suggested that these working adults perceived terms that refer to malaise/deactivation as being relevant to describe their negative affective status. The GSM-NA scale was related to stress, depression and fatigue while the GMS-PA scale was positively associated with quality of life. This study is based on a cross-sectional design. The association between the PA (negative correlation) and NA (positive correlation) scales of the GMS and perceived stress, depressive symptoms, and fatigue supports the validity of its two-factor model. Assessment of both PA and NA may benefit a better understanding of emotional distress in adults from the working population.

  18. Retrieving positive memories to regulate negative mood: consequences for mood-congruent memory.

    Science.gov (United States)

    Rusting, C L; DeHart, T

    2000-04-01

    Several researchers have suggested that mood-incongruency effects are due to a mood-regulatory process in which people retrieve positive memories to repair negative moods. The present studies tested this idea by manipulating mood-repair strategies and examining their impact on positive and negative memory retrieval. Mood-congruent retrieval occurred when participants stayed focused on events associated with their negative mood; mood-incongruent retrieval occurred when participants engaged in positive reappraisal (when they reinterpreted events as having positive outcomes). The effects of these strategies on memory retrieval also interacted with personality traits related to negative mood regulation. Individuals high in such traits showed stronger mood-incongruent memory than did individuals low in negative mood-regulation traits. Discussion focuses on integrating mood-regulatory variables and personality variables into existing mood-congruency theories (e.g., associative network models).

  19. Scaling in topological properties of brain networks

    NARCIS (Netherlands)

    Singh, S.S.; Khundrakpam, B.S.; Reid, A.T.; Lewis, J.D.; Evans, A.C.; Ishrat, R.; Sharma, B.I.; Singh, R.K.B.

    2016-01-01

    The organization in brain networks shows highly modular features with weak inter-modular interaction. The topology of the networks involves emergence of modules and sub-modules at different levels of constitution governed by fractal laws that are signatures of self-organization in complex networks.

  20. Aggregation algorithm towards large-scale Boolean network analysis

    OpenAIRE

    Zhao, Y.; Kim, J.; Filippone, M.

    2013-01-01

    The analysis of large-scale Boolean network dynamics is of great importance in understanding complex phenomena where systems are characterized by a large number of components. The computational cost to reveal the number of attractors and the period of each attractor increases exponentially as the number of nodes in the networks increases. This paper presents an efficient algorithm to find attractors for medium to large-scale networks. This is achieved by analyzing subnetworks within the netwo...

  1. 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.

  2. Signaling in large-scale neural networks

    DEFF Research Database (Denmark)

    Berg, Rune W; Hounsgaard, Jørn

    2009-01-01

    We examine the recent finding that neurons in spinal motor circuits enter a high conductance state during functional network activity. The underlying concomitant increase in random inhibitory and excitatory synaptic activity leads to stochastic signal processing. The possible advantages of this m......We examine the recent finding that neurons in spinal motor circuits enter a high conductance state during functional network activity. The underlying concomitant increase in random inhibitory and excitatory synaptic activity leads to stochastic signal processing. The possible advantages...... of this metabolically costly organization are analyzed by comparing with synaptically less intense networks driven by the intrinsic response properties of the network neurons....

  3. A scale-free neural network for modelling neurogenesis

    Science.gov (United States)

    Perotti, Juan I.; Tamarit, Francisco A.; Cannas, Sergio A.

    2006-11-01

    In this work we introduce a neural network model for associative memory based on a diluted Hopfield model, which grows through a neurogenesis algorithm that guarantees that the final network is a small-world and scale-free one. We also analyze the storage capacity of the network and prove that its performance is larger than that measured in a randomly dilute network with the same connectivity.

  4. Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network

    DEFF Research Database (Denmark)

    Förster, Jochen; Famili, I.; Fu, P.

    2003-01-01

    and the environment were included. A total of 708 structural open reading frames (ORFs) were accounted for in the reconstructed network, corresponding to 1035 metabolic reactions. Further, 140 reactions were included on the basis of biochemical evidence resulting in a genome-scale reconstructed metabolic network...... with Escherichia coli. The reconstructed metabolic network is the first comprehensive network for a eukaryotic organism, and it may be used as the basis for in silico analysis of phenotypic functions....

  5. Correlation of brain default mode network activation with bipolarity index in youth with mood disorders.

    Science.gov (United States)

    Ford, Kristen A; Théberge, Jean; Neufeld, Richard J; Williamson, Peter C; Osuch, Elizabeth A

    2013-09-25

    Major Depressive Disorder (MDD) and Bipolar Disorder (BD) can be difficult to differentiate, as both feature depressive episodes. Here we have utilized fMRI and a measure of trait bipolarity to examine resting-state functional connectivity of brain activation in the default mode network in youth with MDD and BD to isolate trait-specific patterns. We collected resting-state fMRI scans from thirty youth (15 MDD; 15 BD, Type 1). The Bipolarity Index (BI) was completed by each patient's treating psychiatrist. Independent components analysis was used to extract a default mode network component from each participant, and then multiple regression was used to identify correlations between bipolarity and network activation. Activation in putamen/claustrum/insula correlated positively with BI; activation in the postcentral gyrus/posterior cingulate gyrus correlated negatively with BI. These correlations did not appear to be driven by movement in the scanner, state depression, gender or lithium use. There were group differences in state depression and sex that needed to be statistically covaried; differences in medication use existed between the groups; sample size was not large. The identification of the putamen/claustrum in our positive correlation may indicate a potential trait marker for the psychomotor activation unique to bipolar mania. The negative correlation in the postcentral gyrus/posterior cingulate suggests that this functional inactivation is more specific to MDD and is consistent with previous research. Ultimately, this approach may help to develop techniques to minimize the current clinical dilemma by facilitating the classification between BD and MDD. © 2013 Elsevier B.V. All rights reserved.

  6. New scaling relation for information transfer in biological networks

    Science.gov (United States)

    Kim, Hyunju; Davies, Paul; Walker, Sara Imari

    2015-01-01

    We quantify characteristics of the informational architecture of two representative biological networks: the Boolean network model for the cell-cycle regulatory network of the fission yeast Schizosaccharomyces pombe (Davidich et al. 2008 PLoS ONE 3, e1672 (doi:10.1371/journal.pone.0001672)) and that of the budding yeast Saccharomyces cerevisiae (Li et al. 2004 Proc. Natl Acad. Sci. USA 101, 4781–4786 (doi:10.1073/pnas.0305937101)). We compare our results for these biological networks with the same analysis performed on ensembles of two different types of random networks: Erdös–Rényi and scale-free. We show that both biological networks share features in common that are not shared by either random network ensemble. In particular, the biological networks in our study process more information than the random networks on average. Both biological networks also exhibit a scaling relation in information transferred between nodes that distinguishes them from random, where the biological networks stand out as distinct even when compared with random networks that share important topological properties, such as degree distribution, with the biological network. We show that the most biologically distinct regime of this scaling relation is associated with a subset of control nodes that regulate the dynamics and function of each respective biological network. Information processing in biological networks is therefore interpreted as an emergent property of topology (causal structure) and dynamics (function). Our results demonstrate quantitatively how the informational architecture of biologically evolved networks can distinguish them from other classes of network architecture that do not share the same informational properties. PMID:26701883

  7. 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

  8. Relationship between the clinical global impression of severity for schizoaffective disorder scale and established mood scales for mania and depression.

    Science.gov (United States)

    Turkoz, Ibrahim; Fu, Dong-Jing; Bossie, Cynthia A; Sheehan, John J; Alphs, Larry

    2013-08-15

    This analysis explored the relationship between ratings on HAM-D-17 or YMRS and those on the depressive or manic subscale of CGI-S for schizoaffective disorder (CGI-S-SCA). This post hoc analysis used the database (N=614) from two 6-week, randomized, placebo-controlled studies of paliperidone ER versus placebo in symptomatic subjects with schizoaffective disorder assessed using HAM-D-17, YMRS, and CGI-S-SCA scales. Parametric and nonparametric regression models explored the relationships between ratings on YMRS and HAM-D-17 and on depressive and manic domains of the CGI-S-SCA from baseline to the 6-week end point. A clinically meaningful improvement was defined as a change of 1 point in the CGI-S-SCA score. No adjustment was made for multiplicity. Multiple linear regression models suggested that a 1-point change in the depressive domain of CGI-S-SCA corresponded to an average 3.6-point (SE=0.2) change in HAM-D-17 score. Similarly, a 1-point change in the manic domain of CGI-S-SCA corresponded to an average 5.8-point (SE=0.2) change in YMRS score. Results were confirmed using local and cumulative logistic regression models in addition to equipercentile linking. Lack of subjects scoring over the complete range of possible scores may limit broad application of the analyses. Clinically meaningful score changes in depressive and manic domains of CGI-S-SCA corresponded to approximately 4- and 6-point score changes on HAM-D-17 and YMRS, respectively, in symptomatic subjects with schizoaffective disorder. Copyright © 2013 Elsevier B.V. All rights reserved.

  9. Scaling of dissolved organic carbon removal in river networks

    Science.gov (United States)

    Bertuzzo, Enrico; Helton, Ashley M.; Hall, , Robert O.; Battin, Tom J.

    2017-12-01

    Streams and rivers play a major role in the global carbon cycle as they collect, transform and deliver terrestrial organic carbon to the ocean. The rate of dissolved organic carbon (DOC) removal depends on hydrological factors (primarily water depth and residence time) that change predictably within the river network and local DOC concentration and composition is the result of transformation and removal processes in the whole upstream catchment. We thus combine theory of the form and scaling of river networks with a model of DOC removal from streamwater to investigate how the structure of river networks and the related hydrological drivers control DOC dynamics. We find that minimization of energy dissipation, the physical process that shapes the topological and metric properties of river networks, leads to structures that are more efficient in terms of total DOC removal per unit of streambed area. River network structure also induces a scaling of the DOC mass flux with the contributing area that does not depend on the particular network used for the simulation and is robust to spatial heterogeneity of model parameters. Such scaling enables the derivation of removal patterns across a river network in terms of clearly identified biological, hydrological and geomorphological factors. In particular, we derive how the fraction of terrestrial DOC load removed by the river network scales with the catchment area and with the area of a region drained by multiple river networks. Such results further our understanding of the impact of streams and rivers on carbon cycling at large scales.

  10. 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.

  11. Small-time Scale Network Traffic Prediction Based on Complex-valued Neural Network

    Science.gov (United States)

    Yang, Bin

    2017-07-01

    Accurate models play an important role in capturing the significant characteristics of the network traffic, analyzing the network dynamic, and improving the forecasting accuracy for system dynamics. In this study, complex-valued neural network (CVNN) model is proposed to further improve the accuracy of small-time scale network traffic forecasting. Artificial bee colony (ABC) algorithm is proposed to optimize the complex-valued and real-valued parameters of CVNN model. Small-scale traffic measurements data namely the TCP traffic data is used to test the performance of CVNN model. Experimental results reveal that CVNN model forecasts the small-time scale network traffic measurement data very accurately

  12. Triadic closure dynamics drives scaling laws in social multiplex networks

    Science.gov (United States)

    Klimek, Peter; Thurner, Stefan

    2013-06-01

    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.

  13. 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

    This is the second in a two-part series of papers on information-theoretic capacity scaling laws for an underwater acoustic network. Part II focuses on a dense network scenario, where nodes are deployed in a unit area. By deriving a cut-set upper bound on the capacity scaling, we first show...

  14. Canadian Network for Mood and Anxiety Treatments (CANMAT) 2016 Clinical Guidelines for the Management of Adults with Major Depressive Disorder

    Science.gov (United States)

    McIntosh, Diane; Wang, JianLi; Enns, Murray W.; Kolivakis, Theo; Michalak, Erin E.; Sareen, Jitender; Song, Wei-Yi; Kennedy, Sidney H.; MacQueen, Glenda M.; Milev, Roumen V.; Parikh, Sagar V.; Ravindran, Arun V.

    2016-01-01

    Background: The Canadian Network for Mood and Anxiety Treatments (CANMAT) conducted a revision of the 2009 guidelines by updating the evidence and recommendations. The scope of the 2016 guidelines remains the management of major depressive disorder (MDD) in adults, with a target audience of psychiatrists and other mental health professionals. Methods: Using the question-answer format, we conducted a systematic literature search focusing on systematic reviews and meta-analyses. Evidence was graded using CANMAT-defined criteria for level of evidence. Recommendations for lines of treatment were based on the quality of evidence and clinical expert consensus. This section is the first of six guidelines articles. Results: In Canada, the annual and lifetime prevalence of MDD was 4.7% and 11.3%, respectively. MDD represents the second leading cause of global disability, with high occupational and economic impact mainly attributable to indirect costs. DSM-5 criteria for depressive disorders remain relatively unchanged, but other clinical dimensions (sleep, cognition, physical symptoms) may have implications for depression management. e-Mental health is increasingly used to support clinical and self-management of MDD. In the 2-phase (acute and maintenance) treatment model, specific goals address symptom remission, functional recovery, improved quality of life, and prevention of recurrence. Conclusions: The burden attributed to MDD remains high, whether from individual distress, functional and relationship impairment, reduced quality of life, or societal economic cost. Applying core principles of care, including comprehensive assessment, therapeutic alliance, support of self-management, evidence-informed treatment, and measurement-based care, will optimize clinical, quality of life, and functional outcomes in MDD. PMID:27486151

  15. Reorganizing Complex Network to Improve Large-Scale Multiagent Teamwork

    National Research Council Canada - National Science Library

    Yang Xu; Pengfei Liu; Xiang Li

    2014-01-01

      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...

  16. Large Scale Evolution of Convolutional Neural Networks Using Volunteer Computing

    OpenAIRE

    Desell, Travis

    2017-01-01

    This work presents a new algorithm called evolutionary exploration of augmenting convolutional topologies (EXACT), which is capable of evolving the structure of convolutional neural networks (CNNs). EXACT is in part modeled after the neuroevolution of augmenting topologies (NEAT) algorithm, with notable exceptions to allow it to scale to large scale distributed computing environments and evolve networks with convolutional filters. In addition to multithreaded and MPI versions, EXACT has been ...

  17. New Visions for Large Scale Networks: Research and Applications

    Data.gov (United States)

    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...

  18. Large-Scale Road Network Vulnerability Analysis

    OpenAIRE

    Jenelius, Erik

    2010-01-01

    Disruptions in the transport system can have severe impacts for affected individuals, businesses and the society as a whole. In this research, vulnerability is seen as the risk of unplanned system disruptions, with a focus on large, rare events. Vulnerability analysis aims to provide decision support regarding preventive and restorative actions, ideally as an integrated part of the planning process.The thesis specifically develops the methodology for vulnerability analysis of road networks an...

  19. 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...

  20. 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.

  1. 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.

  2. Lower bound of assortativity coefficient in scale-free networks

    Science.gov (United States)

    Yang, Dan; Pan, Liming; Zhou, Tao

    2017-03-01

    The degree-degree correlation is important in understanding the structural organization of a network and dynamics upon a network. Such correlation is usually measured by the assortativity coefficient r, with natural bounds r ∈ [ - 1 , 1 ] . For scale-free networks with power-law degree distribution p ( k ) ˜ k - γ , we analytically obtain the lower bound of assortativity coefficient in the limit of large network size, which is not -1 but dependent on the power-law exponent γ. This work challenges the validation of the assortativity coefficient in heterogeneous networks, suggesting that one cannot judge whether a network is positively or negatively correlated just by looking at its assortativity coefficient alone.

  3. Experimental evidence of massive-scale emotional contagion through social networks.

    Science.gov (United States)

    Kramer, Adam D I; Guillory, Jamie E; Hancock, Jeffrey T

    2014-06-17

    Emotional states can be transferred to others via emotional contagion, leading people to experience the same emotions without their awareness. Emotional contagion is well established in laboratory experiments, with people transferring positive and negative emotions to others. Data from a large real-world social network, collected over a 20-y period suggests that longer-lasting moods (e.g., depression, happiness) can be transferred through networks [Fowler JH, Christakis NA (2008) BMJ 337:a2338], although the results are controversial. In an experiment with people who use Facebook, we test whether emotional contagion occurs outside of in-person interaction between individuals by reducing the amount of emotional content in the News Feed. When positive expressions were reduced, people produced fewer positive posts and more negative posts; when negative expressions were reduced, the opposite pattern occurred. These results indicate that emotions expressed by others on Facebook influence our own emotions, constituting experimental evidence for massive-scale contagion via social networks. This work also suggests that, in contrast to prevailing assumptions, in-person interaction and nonverbal cues are not strictly necessary for emotional contagion, and that the observation of others' positive experiences constitutes a positive experience for people.

  4. 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 distri......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....

  5. Identification of regulatory modules in genome scale transcription regulatory networks.

    Science.gov (United States)

    Song, Qi; Grene, Ruth; Heath, Lenwood S; Li, Song

    2017-12-15

    In gene regulatory networks, transcription factors often function as co-regulators to synergistically induce or inhibit expression of their target genes. However, most existing module-finding algorithms can only identify densely connected genes but not co-regulators in regulatory networks. We have developed a new computational method, CoReg, to identify transcription co-regulators in large-scale regulatory networks. CoReg calculates gene similarities based on number of common neighbors of any two genes. Using simulated and real networks, we compared the performance of different similarity indices and existing module-finding algorithms and we found CoReg outperforms other published methods in identifying co-regulatory genes. We applied CoReg to a large-scale network of Arabidopsis with more than 2.8 million edges and we analyzed more than 2,300 published gene expression profiles to charaterize co-expression patterns of gene moduled identified by CoReg. We identified three types of modules in the Arabidopsis network: regulator modules, target modules and intermediate modules. Regulator modules include genes with more than 90% edges as out-going edges; Target modules include genes with more than 90% edges as incoming edges. Other modules are classified as intermediate modules. We found that genes in target modules tend to be highly co-expressed under abiotic stress conditions, suggesting this network struture is robust against perturbation. Our analysis shows that the CoReg is an accurate method in identifying co-regulatory genes in large-scale networks. We provide CoReg as an R package, which can be applied in finding co-regulators in any organisms with genome-scale regulatory network data.

  6. 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.

  7. 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

  8. Using the mood disorder questionnaire and bipolar spectrum diagnostic scale to detect bipolar disorder and borderline personality disorder among eating disorder patients

    Science.gov (United States)

    2013-01-01

    Background Screening scales for bipolar disorder including the Mood Disorder Questionnaire (MDQ) and Bipolar Spectrum Diagnostic Scale (BSDS) have been plagued by high false positive rates confounded by presence of borderline personality disorder. This study examined the accuracy of these scales for detecting bipolar disorder among patients referred for eating disorders and explored the possibility of simultaneous assessment of co-morbid borderline personality disorder. Methods Participants were 78 consecutive female patients who were referred for evaluation of an eating disorder. All participants completed the mood and eating disorder sections of the SCID-I/P and the borderline personality disorder section of the SCID-II, in addition to the MDQ and BSDS. Predictive validity of the MDQ and BSDS was evaluated by Receiver Operating Characteristic analysis of the Area Under the Curve (AUC). Results Fifteen (19%) and twelve (15%) patients fulfilled criteria for bipolar II disorder and borderline personality disorder, respectively. The AUCs for bipolar II disorder were 0.78 (MDQ) and 0.78 (BDSD), and the AUCs for borderline personality disorder were 0.75 (MDQ) and 0.79 (BSDS). Conclusions Among patients being evaluated for eating disorders, the MDQ and BSDS show promise as screening questionnaires for both bipolar disorder and borderline personality disorder. PMID:23443034

  9. 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.

  10. 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).

  11. 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...

  12. An optimal routing strategy on scale-free networks

    Science.gov (United States)

    Yang, Yibo; Zhao, Honglin; Ma, Jinlong; Qi, Zhaohui; Zhao, Yongbin

    Traffic is one of the most fundamental dynamical processes in networked systems. With the traditional shortest path routing (SPR) protocol, traffic congestion is likely to occur on the hub nodes on scale-free networks. In this paper, we propose an improved optimal routing (IOR) strategy which is based on the betweenness centrality and the degree centrality of nodes in the scale-free networks. With the proposed strategy, the routing paths can accurately bypass hub nodes in the network to enhance the transport efficiency. Simulation results show that the traffic capacity as well as some other indexes reflecting transportation efficiency are further improved with the IOR strategy. Owing to the significantly improved traffic performance, this study is helpful to design more efficient routing strategies in communication or transportation systems.

  13. Metric clusters in evolutionary games on scale-free networks.

    Science.gov (United States)

    Kleineberg, Kaj-Kolja

    2017-12-01

    The evolution of cooperation in social dilemmas in structured populations has been studied extensively in recent years. Whereas many theoretical studies have found that a heterogeneous network of contacts favors cooperation, the impact of spatial effects in scale-free networks is still not well understood. In addition to being heterogeneous, real contact networks exhibit a high mean local clustering coefficient, which implies the existence of an underlying metric space. Here we show that evolutionary dynamics in scale-free networks self-organize into spatial patterns in the underlying metric space. The resulting metric clusters of cooperators are able to survive in social dilemmas as their spatial organization shields them from surrounding defectors, similar to spatial selection in Euclidean space. We show that under certain conditions these metric clusters are more efficient than the most connected nodes at sustaining cooperation and that heterogeneity does not always favor-but can even hinder-cooperation in social dilemmas.

  14. Evolution of vocabulary on scale-free and random networks

    Science.gov (United States)

    Kalampokis, Alkiviadis; Kosmidis, Kosmas; Argyrakis, Panos

    2007-06-01

    We examine the evolution of the vocabulary of a group of individuals (linguistic agents) on a scale-free network, using Monte Carlo simulations and assumptions from evolutionary game theory. It is known that when the agents are arranged in a two-dimensional lattice structure and interact by diffusion and encounter, then their final vocabulary size is the maximum possible. Knowing all available words is essential in order to increase the probability to “survive” by effective reproduction. On scale-free networks we find a different result. It is not necessary to learn the entire vocabulary available. Survival chances are increased by using the vocabulary of the “hubs” (nodes with high degree). The existence of the “hubs” in a scale-free network is the source of an additional important fitness generating mechanism.

  15. 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 ...

  16. Scaling in a Multispecies Network Model Ecosystem

    CERN Document Server

    Solé, R V; McKane, A; Sole, Ricard V.; Alonso, David; Kane, Alan Mc

    1999-01-01

    A new model ecosystem consisting of many interacting species is introduced. The species are connected through a random matrix with a given connectivity. It is shown that the system is organized close to a boundary of marginal stability in such a way that fluctuations follow power law distributions both in species abundance and their lifetimes for some slow-driving (immigration) regime. The connectivity and the number of species are linked through a scaling relation which is the one observed in real ecosystems. These results suggest that the basic macroscopic features of real, species-rich ecologies might be linked with a critical state. A natural link between lognormal and power law distributions of species abundances is suggested.

  17. How to measure mood in nutrition research.

    Science.gov (United States)

    Hammersley, Richard; Reid, Marie; Atkin, Stephen L

    2014-12-01

    Mood is widely assessed in nutrition research, usually with rating scales. A core assumption is that positive mood reinforces ingestion, so it is important to measure mood well. Four relevant theoretical issues are reviewed: (i) the distinction between protracted and transient mood; (ii) the distinction between mood and emotion; (iii) the phenomenology of mood as an unstable tint to consciousness rather than a distinct state of consciousness; (iv) moods can be caused by social and cognitive processes as well as physiological ones. Consequently, mood is difficult to measure and mood rating is easily influenced by non-nutritive aspects of feeding, the psychological, social and physical environment where feeding occurs, and the nature of the rating system employed. Some of the difficulties are illustrated by reviewing experiments looking at the impact of food on mood. The mood-rating systems in common use in nutrition research are then reviewed, the requirements of a better mood-rating system are described, and guidelines are provided for a considered choice of mood-rating system including that assessment should: have two main dimensions; be brief; balance simplicity and comprehensiveness; be easy to use repeatedly. Also mood should be assessed only under conditions where cognitive biases have been considered and controlled.

  18. 77 FR 58416 - Large Scale Networking (LSN); Middleware and Grid Interagency Coordination (MAGIC) Team

    Science.gov (United States)

    2012-09-20

    ... Large Scale Networking (LSN); Middleware and Grid Interagency Coordination (MAGIC) Team AGENCY: The Networking and Information Technology Research and Development (NITRD) National Coordination Office (NCO... to the Large Scale Networking (LSN) Coordinating Group (CG). Public Comments: The government seeks...

  19. 78 FR 7464 - Large Scale Networking (LSN)-Middleware And Grid Interagency Coordination (MAGIC) Team

    Science.gov (United States)

    2013-02-01

    ... Large Scale Networking (LSN)--Middleware And Grid Interagency Coordination (MAGIC) Team AGENCY: The Networking and Information Technology Research and Development (NITRD) National Coordination Office (NCO... Team reports to the Large Scale Networking (LSN) Coordinating Group (CG). Public Comments: The...

  20. Environmental versatility promotes modularity in genome-scale metabolic networks.

    Science.gov (United States)

    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

  1. Environmental versatility promotes modularity in genome-scale metabolic networks

    Science.gov (United States)

    2011-01-01

    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 constraints, e.g., the need to

  2. 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

  3. Evolution of Scale-Free Wireless Sensor Networks with Feature of Small-World Networks

    Directory of Open Access Journals (Sweden)

    Ying Duan

    2017-01-01

    Full Text Available Scale-free network and small-world network are the most impacting discoveries in the complex networks theories and have already been successfully proved to be highly effective in improving topology structures of wireless sensor networks. However, currently both theories are not jointly applied to have further improvements in the generation of WSN topologies. Therefore, this paper proposes a cluster-structured evolution model of WSNs considering the characteristics of both networks. With introduction of energy sensitivity and maximum limitation of degrees that a cluster head could have, the performance of our model can be ensured. In order to give an overall assessment of lifting effects of shortcuts, four placement schemes of shortcuts are analyzed. The characteristics of small-world network and scale-free network of our model are proved via theoretical derivation and simulations. Besides, we find that, by introducing shortcuts into scale-free wireless sensor network, the performance of the network can be improved concerning energy-saving and invulnerability, and we discover that the schemes constructing shortcuts between cluster heads and the sink node have better promoted effects than the scheme building shortcuts between pairs of cluster heads, and the schemes based on the preferential principle are superior to the schemes based on the random principle.

  4. Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks

    OpenAIRE

    Fabian Fröhlich; Barbara Kaltenbacher; Theis, Fabian J; Jan Hasenauer

    2017-01-01

    Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far. While individual simulations are feasible, the inference of the model ...

  5. Mood Disorders

    Science.gov (United States)

    ... a person’s risk for health complications. However, treating mood disorders can have positive effects on treatment outcomes and recovery from co-occurring disorders as well. Studies focusing on conditions that frequently co-occur and how they affect one another may lead to more targeted screening ...

  6. 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.

  7. 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.

  8. Quantifying the multi-scale performance of network inference algorithms.

    Science.gov (United States)

    Oates, Chris J; Amos, Richard; Spencer, Simon E F

    2014-10-01

    Graphical models are widely used to study complex multivariate biological systems. Network inference algorithms aim to reverse-engineer such models from noisy experimental data. It is common to assess such algorithms using techniques from classifier analysis. These metrics, based on ability to correctly infer individual edges, possess a number of appealing features including invariance to rank-preserving transformation. However, regulation in biological systems occurs on multiple scales and existing metrics do not take into account the correctness of higher-order network structure. In this paper novel performance scores are presented that share the appealing properties of existing scores, whilst capturing ability to uncover regulation on multiple scales. Theoretical results confirm that performance of a network inference algorithm depends crucially on the scale at which inferences are to be made; in particular strong local performance does not guarantee accurate reconstruction of higher-order topology. Applying these scores to a large corpus of data from the DREAM5 challenge, we undertake a data-driven assessment of estimator performance. We find that the "wisdom of crowds" network, that demonstrated superior local performance in the DREAM5 challenge, is also among the best performing methodologies for inference of regulation on multiple length scales.

  9. Energy scaling and reduction in controlling complex networks

    Science.gov (United States)

    Chen, Yu-Zhong; Wang, Le-Zhi; Wang, Wen-Xu; Lai, Ying-Cheng

    2016-01-01

    Recent works revealed that the energy required to control a complex network depends on the number of driving signals and the energy distribution follows an algebraic scaling law. If one implements control using a small number of drivers, e.g. as determined by the structural controllability theory, there is a high probability that the energy will diverge. We develop a physical theory to explain the scaling behaviour through identification of the fundamental structural elements, the longest control chains (LCCs), that dominate the control energy. Based on the LCCs, we articulate a strategy to drastically reduce the control energy (e.g. in a large number of real-world networks). Owing to their structural nature, the LCCs may shed light on energy issues associated with control of nonlinear dynamical networks. PMID:27152220

  10. The Multi-Scale Network Landscape of Collaboration

    CERN Document Server

    Bae, Arram; 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 comprehens...

  11. Formation control for a network of small-scale robots.

    Science.gov (United States)

    Kim, Yoonsoo

    2014-10-01

    In this paper, a network of small-scale robots (typically centimeter-scale robots) equipped with artificial actuators such as electric motors is considered. The purpose of this network is to have the robots keep a certain formation shape (or change to another formation shape) during maneuvers. The network has a fixed communication topology in the sense that robots have a fixed group of neighbors to communicate during maneuvers. Assuming that each robot and its actuator can be modeled as a linear system, a decentralized control law (such that each robot activates its actuator based on the information from its neighbors only) is introduced to achieve the purpose of formation keeping or change. A linear matrix inequality (LMI) for deriving the upper bound on the actuator's time constant is also presented. Simulation results are shown to demonstrate the merit of the introduced control law.

  12. 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......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...... 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....

  13. Meeting the memory challenges of brain-scale network simulation

    Directory of Open Access Journals (Sweden)

    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.

  14. The Multi-Scale Network Landscape of Collaboration.

    Directory of Open Access Journals (Sweden)

    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.

  15. Characterizing the intrinsic correlations of scale-free networks

    CERN Document Server

    de Brito, J B; Moreira, A A; Andrade, J S

    2015-01-01

    Very often, when studying topological or dynamical properties of random scale-free networks, it is tacitly assumed that degree-degree correlations are not present. However, simple constraints, such as the absence of multiple edges and self-loops, can give rise to intrinsic correlations in these structures. In the same way that Fermionic correlations in thermodynamic systems are relevant only in the limit of low temperature, the intrinsic correlations in scale-free networks are relevant only when the extreme values for the degrees grow faster than the square-root of the network size. In this situation, these correlations can significantly affect the dependence of the average degree of the nearest neighbors of a given vertice on this vertices's degree. Here, we introduce an analytical approach that is capable to predict the functional form of this property. Moreover, our results indicate that random scale-free networks models are not self-averaging, that is, the second moment of their degree distribution may va...

  16. 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.

  17. Inferring cell-scale signalling networks via compressive sensing.

    Directory of Open Access Journals (Sweden)

    Lei Nie

    Full Text Available Signalling network inference is a central problem in system biology. Previous studies investigate this problem by independently inferring local signalling networks and then linking them together via crosstalk. Since a cellular signalling system is in fact indivisible, this reductionistic approach may have an impact on the accuracy of the inference results. Preferably, a cell-scale signalling network should be inferred as a whole. However, the holistic approach suffers from three practical issues: scalability, measurement and overfitting. Here we make this approach feasible based on two key observations: 1 variations of concentrations are sparse due to separations of timescales; 2 several species can be measured together using cross-reactivity. We propose a method, CCELL, for cell-scale signalling network inference from time series generated by immunoprecipitation using Bayesian compressive sensing. A set of benchmark networks with varying numbers of time-variant species is used to demonstrate the effectiveness of our method. Instead of exhaustively measuring all individual species, high accuracy is achieved from relatively few measurements.

  18. Statistical mechanics of scale-free gene expression networks

    Science.gov (United States)

    Gross, Eitan

    2012-12-01

    The gene co-expression networks of many organisms including bacteria, mice and man exhibit scale-free distribution. This heterogeneous distribution of connections decreases the vulnerability of the network to random attacks and thus may confer the genetic replication machinery an intrinsic resilience to such attacks, triggered by changing environmental conditions that the organism may be subject to during evolution. This resilience to random attacks comes at an energetic cost, however, reflected by the lower entropy of the scale-free distribution compared to the more homogenous, random network. In this study we found that the cell cycle-regulated gene expression pattern of the yeast Saccharomyces cerevisiae obeys a power-law distribution with an exponent α = 2.1 and an entropy of 1.58. The latter is very close to the maximal value of 1.65 obtained from linear optimization of the entropy function under the constraint of a constant cost function, determined by the average degree connectivity . We further show that the yeast's gene expression network can achieve scale-free distribution in a process that does not involve growth but rather via re-wiring of the connections between nodes of an ordered network. Our results support the idea of an evolutionary selection, which acts at the level of the protein sequence, and is compatible with the notion of greater biological importance of highly connected nodes in the protein interaction network. Our constrained re-wiring model provides a theoretical framework for a putative thermodynamically driven evolutionary selection process.

  19. 77 FR 58415 - Large Scale Networking (LSN); Joint Engineering Team (JET)

    Science.gov (United States)

    2012-09-20

    ... Large Scale Networking (LSN); Joint Engineering Team (JET) AGENCY: The Networking and Information... agencies and non-Federal participants with interest in high performance research networking and networking to support science applications. The JET reports to the Large Scale Networking (LSN) Coordinating...

  20. 78 FR 70076 - Large Scale Networking (LSN)-Joint Engineering Team (JET)

    Science.gov (United States)

    2013-11-22

    ... Large Scale Networking (LSN)--Joint Engineering Team (JET) AGENCY: The Networking and Information... and non-Federal participants with interest in high performance research networking and networking to support science applications. The JET reports to the Large Scale Networking (LSN) Coordinating Group (CG...

  1. 78 FR 7464 - Large Scale Networking (LSN) ; Joint Engineering Team (JET)

    Science.gov (United States)

    2013-02-01

    ... Large Scale Networking (LSN) ; Joint Engineering Team (JET) AGENCY: The Networking and Information... research networking and networking to support science applications. The JET reports to the Large Scale Networking (LSN) Coordinating Group (CG). Public Comments: The government seeks individual input; attendees...

  2. Exact Solutions of a Generalized Weighted Scale Free Network

    Directory of Open Access Journals (Sweden)

    Li Tan

    2013-01-01

    Full Text Available We investigate a class of generalized weighted scale-free networks, where the new vertex connects to m pairs of vertices selected preferentially. The key contribution of this paper is that, from the standpoint of random processes, we provide rigorous analytic solutions for the steady state distributions, including the vertex degree distribution, the vertex strength distribution and the edge weight distribution. Numerical simulations indicate that this network model yields three power law distributions for the vertex degrees, vertex strengths and edge weights, respectively.

  3. 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.

  4. 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.

  5. Innovation diffusion equations on correlated scale-free networks

    Science.gov (United States)

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

    2016-07-01

    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.

  6. Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks.

    Science.gov (United States)

    Fröhlich, Fabian; Kaltenbacher, Barbara; Theis, Fabian J; Hasenauer, Jan

    2017-01-01

    Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology. Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis. The computational complexity is effectively independent of the number of parameters, enabling the analysis of large- and genome-scale models. Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods. The proposed method will facilitate mechanistic modeling of genome-scale cellular processes, as required in the age of omics.

  7. Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks.

    Directory of Open Access Journals (Sweden)

    Fabian Fröhlich

    2017-01-01

    Full Text Available Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology. Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis. The computational complexity is effectively independent of the number of parameters, enabling the analysis of large- and genome-scale models. Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods. The proposed method will facilitate mechanistic modeling of genome-scale cellular processes, as required in the age of omics.

  8. The scaling properties of dynamical fluctuations in temporal networks

    CERN Document Server

    Chi, Liping

    2015-01-01

    The factorial moments analyses are performed to study the scaling properties of the dynamical fluctuations of contacts and nodes in temporal networks based on empirical data sets. The intermittent behaviors are observed in the fluctuations for all orders of the moments. It indicates that the interaction has self-similarity structure in time interval and the fluctuations are not purely random but dynamical and correlated. The scaling exponents for contacts in Prostitution data and nodes in Conference data are very close to that for 2D Ising model undergoing a second-order phase transition.

  9. Computational tools for large-scale biological network analysis

    OpenAIRE

    Pinto, José Pedro Basto Gouveia Pereira

    2012-01-01

    Tese de doutoramento em Informática The surge of the field of Bioinformatics, among other contributions, provided biological researchers with powerful computational methods for processing and analysing the large amount of data coming from recent biological experimental techniques such as genome sequencing and other omics. Naturally, this led to the opening of new avenues of biological research among which is included the analysis of large-scale biological networks. The an...

  10. Metabolite coupling in genome-scale metabolic networks

    Directory of Open Access Journals (Sweden)

    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

  11. Autonomous and Decentralized Optimization of Large-Scale Heterogeneous Wireless Networks by Neural Network Dynamics

    Science.gov (United States)

    Hasegawa, Mikio; Tran, Ha Nguyen; Miyamoto, Goh; Murata, Yoshitoshi; Harada, Hiroshi; Kato, Shuzo

    We propose a neurodynamical approach to a large-scale optimization problem in Cognitive Wireless Clouds, in which a huge number of mobile terminals with multiple different air interfaces autonomously utilize the most appropriate infrastructure wireless networks, by sensing available wireless networks, selecting the most appropriate one, and reconfiguring themselves with seamless handover to the target networks. To deal with such a cognitive radio network, game theory has been applied in order to analyze the stability of the dynamical systems consisting of the mobile terminals' distributed behaviors, but it is not a tool for globally optimizing the state of the network. As a natural optimization dynamical system model suitable for large-scale complex systems, we introduce the neural network dynamics which converges to an optimal state since its property is to continually decrease its energy function. In this paper, we apply such neurodynamics to the optimization problem of radio access technology selection. We compose a neural network that solves the problem, and we show that it is possible to improve total average throughput simply by using distributed and autonomous neuron updates on the terminal side.

  12. Quantifying the Relationship Between Drainage Networks at Hillslope Scale and Particle Size Distribution at Pedon Scale

    Science.gov (United States)

    Cámara, Joaquín; Martín, Miguel Ángel; Gómez-Miguel, Vicente

    2015-02-01

    Nowadays, translating information about hydrologic and soil properties and processes across scales has emerged as a major theme in soil science and hydrology, and suitable theories for upscaling or downscaling hydrologic and soil information are being looked forward. The recognition of low-order catchments as self-organized systems suggests the existence of a great amount of links at different scales between their elements. The objective of this work was to research in areas of homogeneous bedrock material, the relationship between the hierarchical structure of the drainage networks at hillslope scale and the heterogeneity of the particle-size distribution at pedon scale. One of the most innovative elements in this work is the choice of the parameters to quantify the organization level of the studied features. The fractal dimension has been selected to measure the hierarchical structure of the drainage networks, while the Balanced Entropy Index (BEI) has been the chosen parameter to quantify the heterogeneity of the particle-size distribution from textural data. These parameters have made it possible to establish quantifiable relationships between two features attached to different steps in the scale range. Results suggest that the bedrock lithology of the landscape constrains the architecture of the drainage networks developed on it and the particle soil distribution resulting in the fragmentation processes.

  13. Developmental changes in large-scale network connectivity in autism.

    Science.gov (United States)

    Nomi, Jason S; Uddin, Lucina Q

    2015-01-01

    Disrupted cortical connectivity is thought to underlie the complex cognitive and behavior profile observed in individuals with autism spectrum disorder (ASD). Previous neuroimaging research has identified patterns of both functional hypo- and hyper-connectivity in individuals with ASD. A recent theory attempting to reconcile conflicting results in the literature proposes that hyper-connectivity of brain networks may be more characteristic of young children with ASD, while hypo-connectivity may be more prevalent in adolescents and adults with the disorder when compared to typical development (TD) (Uddin etal., 2013). Previous work has examined only young children, mixed groups of children and adolescents, or adult cohorts in separate studies, leaving open the question of developmental influences on functional brain connectivity in ASD. The current study tests this developmental hypothesis by examining within- and between-network resting state functional connectivity in a large sample of 26 children, 28 adolescents, and 18 adults with ASD and age- and IQ-matchedTD individuals for the first time using an entirely data-driven approach. Independent component analyses (ICA) and dual regression was applied to data from three age cohorts to examine the effects of participant age on patterns of within-networkwhole-brain functional connectivity in individuals with ASD compared with TD individuals. Between-network connectivity differences were examined for each age cohort by comparing correlations between ICA components across groups. We find that in the youngest cohort (age 11 and under), children with ASD exhibit hyper-connectivity within large-scale brain networks as well as decreased between-network connectivity compared with age-matchedTD children. In contrast, adolescents with ASD (age 11-18) do not differ from TD adolescents in within-network connectivity, yet show decreased between-network connectivity compared with TD adolescents. Adults with ASD show no within- or

  14. 78 FR 70076 - Large Scale Networking (LSN)-Middleware and Grid Interagency Coordination (MAGIC) Team

    Science.gov (United States)

    2013-11-22

    ... Large Scale Networking (LSN)--Middleware and Grid Interagency Coordination (MAGIC) Team AGENCY: The Networking and Information Technology Research and Development (NITRD) National Coordination Office (NCO... Networking (LSN) Coordinating Group (CG). Public Comments: The government seeks individual input; attendees...

  15. High Fidelity Simulations of Large-Scale Wireless Networks

    Energy Technology Data Exchange (ETDEWEB)

    Onunkwo, Uzoma [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Benz, Zachary [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2015-11-01

    The worldwide proliferation of wireless connected devices continues to accelerate. There are 10s of billions of wireless links across the planet with an additional explosion of new wireless usage anticipated as the Internet of Things develops. Wireless technologies do not only provide convenience for mobile applications, but are also extremely cost-effective to deploy. Thus, this trend towards wireless connectivity will only continue and Sandia must develop the necessary simulation technology to proactively analyze the associated emerging vulnerabilities. Wireless networks are marked by mobility and proximity-based connectivity. The de facto standard for exploratory studies of wireless networks is discrete event simulations (DES). However, the simulation of large-scale wireless networks is extremely difficult due to prohibitively large turnaround time. A path forward is to expedite simulations with parallel discrete event simulation (PDES) techniques. The mobility and distance-based connectivity associated with wireless simulations, however, typically doom PDES and fail to scale (e.g., OPNET and ns-3 simulators). We propose a PDES-based tool aimed at reducing the communication overhead between processors. The proposed solution will use light-weight processes to dynamically distribute computation workload while mitigating communication overhead associated with synchronizations. This work is vital to the analytics and validation capabilities of simulation and emulation at Sandia. We have years of experience in Sandia’s simulation and emulation projects (e.g., MINIMEGA and FIREWHEEL). Sandia’s current highly-regarded capabilities in large-scale emulations have focused on wired networks, where two assumptions prevent scalable wireless studies: (a) the connections between objects are mostly static and (b) the nodes have fixed locations.

  16. Entropies and Scaling Exponents of Street and Fracture Networks

    Directory of Open Access Journals (Sweden)

    Agust Gudmundsson

    2012-04-01

    Full Text Available Many natural and man-made lineaments form networks that can be analysed through entropy and energy considerations. Here we report the results of a detailed study of the variations in trends and lengths of 1554 named streets and 6004 street segments, forming a part of the evolving street network of the city of Dundee in East Scotland. Based on changes in the scaling exponents (ranging from 0.24 to 3.89, the streets can be divided into 21 populations. For comparison, we analysed 221 active crustal fractures in Iceland that (a are of similar lengths as the streets of Dundee; (b are composed of segments; and (c form evolving networks. The streets and fractures follow power-law size distributions (validated through various statistical tests that can be partly explained in terms of the energies needed for their formation. The entropies of the 21 street populations and 9 fracture populations show strong linear correlations with (1 the scaling exponents (R2 = 0.845–0.947 for streets, R2 = 0.859 for fractures and with (2 the length ranges, that is, the differences between the longest and shortest streets/fractures, (R2 = 0.845–0.906 for streets, R2 = 0.927 for fractures.

  17. Foundational perspectives on causality in large-scale brain networks

    Science.gov (United States)

    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

  18. Music and emotion / mood

    OpenAIRE

    古賀, 弘之

    2004-01-01

    The purpose of this article was to create a new kind of problem in the area of "music and emotion" research. Before surveying and reviewing articles about mood responses for music, I redefined "feeling" and "mood" for the purpose of this article. From the reviewed articles. I inferred that mood induction studies were effective to induce positive or negative moods in subjects. Recent studies, however, suggest that negative music not only induces negative mood but positive mood as well. Thus, f...

  19. Weighted social networks for a large scale artificial society

    Science.gov (United States)

    Fan, Zong Chen; Duan, Wei; Zhang, Peng; Qiu, Xiao Gang

    2016-12-01

    The method of artificial society has provided a powerful way to study and explain how individual behaviors at micro level give rise to the emergence of global social phenomenon. It also creates the need for an appropriate representation of social structure which usually has a significant influence on human behaviors. It has been widely acknowledged that social networks are the main paradigm to describe social structure and reflect social relationships within a population. To generate social networks for a population of interest, considering physical distance and social distance among people, we propose a generation model of social networks for a large-scale artificial society based on human choice behavior theory under the principle of random utility maximization. As a premise, we first build an artificial society through constructing a synthetic population with a series of attributes in line with the statistical (census) data for Beijing. Then the generation model is applied to assign social relationships to each individual in the synthetic population. Compared with previous empirical findings, the results show that our model can reproduce the general characteristics of social networks, such as high clustering coefficient, significant community structure and small-world property. Our model can also be extended to a larger social micro-simulation as an input initial. It will facilitate to research and predict some social phenomenon or issues, for example, epidemic transition and rumor spreading.

  20. 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.

  1. Measuring large-scale social networks with high resolution.

    Science.gov (United States)

    Stopczynski, Arkadiusz; Sekara, Vedran; Sapiezynski, Piotr; Cuttone, Andrea; Madsen, Mette My; Larsen, Jakob Eg; Lehmann, Sune

    2014-01-01

    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.

  2. 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.

  3. Qualitative and quantitative study on drainage networks at laboratory scale

    Science.gov (United States)

    Oliveto, G.; Palma, D.; di Domenico, A.

    2009-04-01

    Although simulated drainage networks at the laboratory scale would represent highly-simplified models of natural drainages, they would provide a significant contribute to the comprehension of the complex dynamics governing the fluvial systems. Laboratory experiments also give the advantage to detect transient growth phases shedding some light on the knowledge of temporal and spatial landform evolution. Perhaps, pioneering laboratory experiments on drainage network evolution were carried out in 1977 at REF (Rainfall Erosion Facility) of Colorado State University by Schumm and co-workers. This study deals with an analysis of physical experiments simulating the evolution and the development of drainage networks. To this purpose, some experiments were carried out at University of Basilicata by using a 1.5 m by 1.5 m box-basin-simulator with an outlet incised in the middle of the downslope-end side. The experimental landscape was made of a weakly cohesive soil mainly constituted by clay and silt. A system of microsprinklers generated an almost uniform artificial precipitation. Simulations were performed at a constant rainfall rate with intensity of 100 mm/h. In total four experiments were carried out. Three of those were conducted by ensuring consistent initial conditions except for the initial landscape planar slope of 9%, 5%, and 0.6%, respectively. The remaining experiment was performed with a landscape slope of 9% again, but with the (surface) base-level coinciding with the base of the outlet (i.e. streams could not erode below the base-level). Despite the central outlet constraint, the generated stream system for the 9% plane exhibited trellis-like drainage patterns with many short tributaries joining the main stream at nearly right angles. For the 5% experiment still sub-parallel drainage patterns were formed but mainly in the centre of the watershed. Channels were clearly shallower than those of the 9% experiment. For the gentler slope of 0.6% dendritic drainage

  4. Complex networks for rainfall modeling: Spatial connections, temporal scale, and network size

    Science.gov (United States)

    Jha, Sanjeev Kumar; Sivakumar, Bellie

    2017-11-01

    We apply the concepts of complex networks to investigate the properties of rainfall. Specifically, we examine the rainfall properties in terms of spatial connections, temporal scale, and network size. We employ the clustering coefficient method to rainfall data at six different temporal scales (daily, 2-day, 4-day, 8-day, 16-day, and monthly) from a large number of stations in the Murray-Darling basin in Australia. We consider different correlation thresholds to identify the existence of links between stations. To account for the influence of network size (i.e. number of stations) and length of data, we consider three different networks: (1) 430 stations with 30 years of daily data; (2) 383 stations with 30 years of daily data; and (3) 383 stations with 64 years of daily data. The results indicate that the nature of spatial connections changes with correlation threshold, with changes occurring at different temporal scales for different thresholds. Identification of an appropriate threshold is key to understand the rainfall connectivity properties.

  5. Generative models of rich clubs in Hebbian neuronal networks and large-scale human brain networks.

    Science.gov (United States)

    Vértes, Petra E; Alexander-Bloch, Aaron; Bullmore, Edward T

    2014-10-05

    Rich clubs arise when nodes that are 'rich' in connections also form an elite, densely connected 'club'. In brain networks, rich clubs incur high physical connection costs but also appear to be especially valuable to brain function. However, little is known about the selection pressures that drive their formation. Here, we take two complementary approaches to this question: firstly we show, using generative modelling, that the emergence of rich clubs in large-scale human brain networks can be driven by an economic trade-off between connection costs and a second, competing topological term. Secondly we show, using simulated neural networks, that Hebbian learning rules also drive the emergence of rich clubs at the microscopic level, and that the prominence of these features increases with learning time. These results suggest that Hebbian learning may provide a neuronal mechanism for the selection of complex features such as rich clubs. The neural networks that we investigate are explicitly Hebbian, and we argue that the topological term in our model of large-scale brain connectivity may represent an analogous connection rule. This putative link between learning and rich clubs is also consistent with predictions that integrative aspects of brain network organization are especially important for adaptive behaviour. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

  6. Marine Polysaccharide Networks and Diatoms at the Nanometric Scale

    Directory of Open Access Journals (Sweden)

    Tea Mišić Radić

    2013-10-01

    Full Text Available Despite many advances in research on photosynthetic carbon fixation in marine diatoms, the biophysical and biochemical mechanisms of extracellular polysaccharide production remain significant challenges to be resolved at the molecular scale in order to proceed toward an understanding of their functions at the cellular level, as well as their interactions and fate in the ocean. This review covers studies of diatom extracellular polysaccharides using atomic force microscopy (AFM imaging and the quantification of physical forces. Following a brief summary of the basic principle of the AFM experiment and the first AFM studies of diatom extracellular polymeric substance (EPS, we focus on the detection of supramolecular structures in polysaccharide systems produced by marine diatoms. Extracellular polysaccharide fibrils, attached to the diatom cell wall or released into the surrounding seawater, form distinct supramolecular assemblies best described as gel networks. AFM makes characterization of the diatom polysaccharide networks at the micro and nanometric scales and a clear distinction between the self-assembly and self-organization of these complex systems in marine environments possible.

  7. Brief Mental Training Reorganizes Large-Scale Brain Networks.

    Science.gov (United States)

    Tang, Yi-Yuan; Tang, Yan; Tang, Rongxiang; Lewis-Peacock, Jarrod A

    2017-01-01

    Emerging evidences have shown that one form of mental training-mindfulness meditation, can improve attention, emotion regulation and cognitive performance through changing brain activity and structural connectivity. However, whether and how the short-term mindfulness meditation alters large-scale brain networks are not well understood. Here, we applied a novel data-driven technique, the multivariate pattern analysis (MVPA) to resting-state fMRI (rsfMRI) data to identify changes in brain activity patterns and assess the neural mechanisms induced by a brief mindfulness training-integrative body-mind training (IBMT), which was previously reported in our series of randomized studies. Whole brain rsfMRI was performed on an undergraduate group who received 2 weeks of IBMT with 30 min per session (5 h training in total). Classifiers were trained on measures of functional connectivity in this fMRI data, and they were able to reliably differentiate (with 72% accuracy) patterns of connectivity from before vs. after the IBMT training. After training, an increase in positive functional connections (60 connections) were detected, primarily involving bilateral superior/middle occipital gyrus, bilateral frontale operculum, bilateral superior temporal gyrus, right superior temporal pole, bilateral insula, caudate and cerebellum. These results suggest that brief mental training alters the functional connectivity of large-scale brain networks at rest that may involve a portion of the neural circuitry supporting attention, cognitive and affective processing, awareness and sensory integration and reward processing.

  8. A hybrid queuing strategy for network traffic on scale-free networks

    Science.gov (United States)

    Cai, Kai-Quan; Yu, Lu; Zhu, Yan-Bo

    2017-02-01

    In this paper, a hybrid queuing strategy (HQS) is proposed in traffic dynamics model on scale-free networks, where the delivery priority of packets in the queue is related to their distance to destination and the queue length of next jump. We compare the performance of the proposed HQS with that of the traditional first-in-first-out (FIFO) queuing strategy and the shortest-remaining-path-first (SRPF) queuing strategy proposed by Du et al. It is observed that the network traffic efficiency utilizing HQS with suitable value of parameter h can be further improved in the congestion state. Our work provides new insights for the understanding of the networked-traffic systems.

  9. Mood, music, and caffeine

    NARCIS (Netherlands)

    Jolij, Jacob; Lorist, Monicque

    2014-01-01

    What we see is affected by how we feel: in positive moods, we are more sensitive to positive stimuli, such as happy faces, but in negative moods we are more sensitive to negative stimuli, such as sad faces. Caffeine is known to affect mood - a cup of coffee results in a more positive mood, but also

  10. Effects of Eating on Depressed Moods.

    Science.gov (United States)

    Grenier, Victoria; And Others

    Research has found that depressed moods increase eating among persons who are dieting and among those characterized by high levels of weight fluctuation. To determine whether eating improves depressed moods among persons who score high on the weight fluctuation factor on the Restraint Scale (Herman, et al, 1978), 72 college women consumed either a…

  11. The scaling structure of the global road network.

    Science.gov (United States)

    Strano, Emanuele; Giometto, Andrea; Shai, Saray; Bertuzzo, Enrico; Mucha, Peter J; Rinaldo, Andrea

    2017-10-01

    Because of increasing global urbanization and its immediate consequences, including changes in patterns of food demand, circulation and land use, the next century will witness a major increase in the extent of paved roads built worldwide. To model the effects of this increase, it is crucial to understand whether possible self-organized patterns are inherent in the global road network structure. Here, we use the largest updated database comprising all major roads on the Earth, together with global urban and cropland inventories, to suggest that road length distributions within croplands are indistinguishable from urban ones, once rescaled to account for the difference in mean road length. Such similarity extends to road length distributions within urban or agricultural domains of a given area. We find two distinct regimes for the scaling of the mean road length with the associated area, holding in general at small and at large values of the latter. In suitably large urban and cropland domains, we find that mean and total road lengths increase linearly with their domain area, differently from earlier suggestions. Scaling regimes suggest that simple and universal mechanisms regulate urban and cropland road expansion at the global scale. As such, our findings bear implications for global road infrastructure growth based on land-use change and for planning policies sustaining urban expansions.

  12. From network models to network responses: integration of thermodynamic and kinetic properties of yeast genome-scale metabolic networks.

    Science.gov (United States)

    Soh, Keng Cher; Miskovic, Ljubisa; Hatzimanikatis, Vassily

    2012-03-01

    Many important problems in cell biology arise from the dense nonlinear interactions between functional modules. The importance of mathematical modelling and computer simulation in understanding cellular processes is now indisputable and widely appreciated. Genome-scale metabolic models have gained much popularity and utility in helping us to understand and test hypotheses about these complex networks. However, there are some caveats that come with the use and interpretation of different types of metabolic models, which we aim to highlight here. We discuss and illustrate how the integration of thermodynamic and kinetic properties of the yeast metabolic networks in network analyses can help in understanding and utilizing this organism more successfully in the areas of metabolic engineering, synthetic biology and disease treatment. © 2011 Federation of European Microbiological Societies. Published by Blackwell Publishing Ltd. All rights reserved.

  13. Scale effects of nitrate sinks and sources in stream networks

    Science.gov (United States)

    Schuetz, Tobias; Weiler, Markus; Gascuel-Odoux, Chantal

    2014-05-01

    Increasing N-fertilizer applications in agricultural catchments are considered as one of the major sources for dissolved nitrate-nitrogen (NO3-N) in surface water. While NO3-N mobilization pathways depend on catchment's pedological and hydrogeological characteristics and its runoff generation processes, in-stream retention and removal processes depend on local/reach-scale conditions such as weather, discharge, channel morphology, vegetation, shading or hyporheic exchange and others. However, knowledge is still limited to scale up locally observable retention and removal processes to larger stream networks to understand the spatial and temporal dynamics of in-stream NO3-N concentrations. Relevant processes to consider explicitly are the effects of 'hot spots', dominant NO3-N sources (e.g. sub-catchments, 'critical source areas') or specific NO3-N sinks (e.g. riparian wetlands and stream reaches with high biogeochemical activity). We studied these processes in a 1.7 km2 agricultural headwater catchment, where distinct locations of groundwater inflow (a dense artificial drainage network) and a predominantly impervious streambed allowed separating mixing and dilution processes as well as in-stream retention and removal processes. During two summer seasons we conducted a set (25) of stream network wide (stream water and drainage water) synoptic sampling campaigns including climate parameters, discharge, channel geomorphology, vegetation, stream water chemistry and physical water parameters (dissolved oxygen concentration, water temperatures, electrical conductivity, pH). Analyzing these data sets we were able to determine a) time variant NO3-N concentrations and loads for all sub-catchments (sources), b) time variant in-stream removal rates for all stream reaches (sinks) and c) the hierarchical order of all contributing NO3-N sinks and sources and their time variant influence on total NO3-N export. Climate parameters, discharge, channel geomorphology, vegetation, stream

  14. Synchronization control for large-scale network systems

    CERN Document Server

    Wu, Yuanqing; Su, Hongye; Shi, Peng; Wu, Zheng-Guang

    2017-01-01

    This book provides recent advances in analysis and synthesis of Large-scale network systems (LSNSs) with sampled-data communication and non-identical nodes. In its first chapter of the book presents an introduction to Synchronization of LSNSs and Algebraic Graph Theory as well as an overview of recent developments of LSNSs with sampled data control or output regulation control. The main text of the book is organized into two main parts - Part I: LSNSs with sampled-data communication and Part II: LSNSs with non-identical nodes. This monograph provides up-to-date advances and some recent developments in the analysis and synthesis issues for LSNSs with sampled-data communication and non-identical nodes. It describes the constructions of the adaptive reference generators in the first stage and the robust regulators in the second stage. Examples are presented to show the effectiveness of the proposed design techniques.

  15. 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.

  16. Rasch analysis of the participation scale (P-scale): usefulness of the P-scale to a rehabilitation services network.

    Science.gov (United States)

    Souza, Mariana Angélica Peixoto; Coster, Wendy Jane; Mancini, Marisa Cotta; Dutra, Fabiana Caetano Martins Silva; Kramer, Jessica; Sampaio, Rosana Ferreira

    2017-12-08

    A person's participation is acknowledged as an important outcome of the rehabilitation process. The Participation Scale (P-Scale) is an instrument that was designed to assess the participation of individuals with a health condition or disability. The scale was developed in an effort to better describe the participation of people living in middle-income and low-income countries. The aim of this study was to use Rasch analysis to examine whether the Participation Scale is suitable to assess the perceived ability to take part in participation situations by patients with diverse levels of function. The sample was comprised by 302 patients from a public rehabilitation services network. Participants had orthopaedic or neurological health conditions, were at least 18 years old, and completed the Participation Scale. Rasch analysis was conducted using the Winsteps software. The mean age of all participants was 45.5 years (standard deviation = 14.4), 52% were male, 86% had orthopaedic conditions, and 52% had chronic symptoms. Rasch analysis was performed using a dichotomous rating scale, and only one item showed misfit. Dimensionality analysis supported the existence of only one Rasch dimension. The person separation index was 1.51, and the item separation index was 6.38. Items N2 and N14 showed Differential Item Functioning between men and women. Items N6 and N12 showed Differential Item Functioning between acute and chronic conditions. The item difficulty range was -1.78 to 2.09 logits, while the sample ability range was -2.41 to 4.61 logits. The P-Scale was found to be useful as a screening tool for participation problems reported by patients in a rehabilitation context, despite some issues that should be addressed to further improve the scale.

  17. Positive affect and negative affect correlate differently with distress and health-related quality of life in patients with cardiac conditions: validation of the Danish Global Mood Scale.

    Science.gov (United States)

    Spindler, Helle; Denollet, Johan; Kruse, Charlotte; Pedersen, Susanne S

    2009-07-01

    The Global Mood Scale (GMS), assessing negative affect (NA) and positive affect (PA), is sensitive to tapping treatment-related changes in patients with cardiac conditions. We examined the psychometric properties of the Danish GMS and the influence of NA and PA on distress and health-related quality of life (HRQL). A mixed group of patients with cardiac conditions (n=502) completed the GMS, the Hospital Anxiety and Depression Scale, the Type D Scale, and the 36-item Short-Form Health Survey. The two-factor model of the Danish GMS was confirmed, and the scale was shown to be valid, internally consistent (Cronbach's alpha NA/PA=.93/.85), and stable over 3 weeks (Pearson's r NA/PA=.82/.80). Unadjusted multiple linear regression analyses showed NA (beta=0.67, P<.001), PA (beta=-0.17, P=.001), and the interaction effect NA x PA (beta=-0.17, P=.015) to be associated with anxiety and depressive symptoms (NA:beta=0.99, P<.001; PA:beta=-0.12, P=.004; NA x PA:beta=-0.43, P<.001), as well as with physical HRQL (NA:beta=-0.37, P<.001; PA:beta=0.17, P=.001; NA x PA: beta=-0.27, P<.001) and mental HRQL (NA:beta=-0.72, P<.001; PA:beta=0.27, P=.004; NA x PA:beta=0.23, P<.001). When adjusting for demographic and clinical characteristics, only NA (beta=0.26, P=.003) was associated with anxiety, whereas NA (beta=0.75, P<.001) and NA x PA (beta=-0.34, P=.002) were associated with depressive symptoms. For physical HRQL, PA (beta=0.21, P=.03) and NA x PA (beta=-0.36, P=.005) remained significant, whereas NA (beta=-0.38, P<.001) and PA (beta=0.21, P=.002) remained significant for mental HRQL. The Danish GMS is a psychometrically sound measure of affect in patients with cardiac conditions. Future studies should examine changes in both PA and NA and their impact on health outcomes.

  18. A topology visualization early warning distribution algorithm for large-scale network security incidents.

    Science.gov (United States)

    He, Hui; Fan, Guotao; Ye, Jianwei; Zhang, Weizhe

    2013-01-01

    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.

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

    Science.gov (United States)

    He, Hui; Fan, Guotao; Ye, Jianwei; Zhang, Weizhe

    2013-01-01

    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. PMID:24191145

  20. Developing A Large-Scale, Collaborative, Productive Geoscience Education Network

    Science.gov (United States)

    Manduca, C. A.; Bralower, T. J.; Egger, A. E.; Fox, S.; Ledley, T. S.; Macdonald, H.; Mcconnell, D. A.; Mogk, D. W.; Tewksbury, B. J.

    2012-12-01

    Over the past 15 years, the geoscience education community has grown substantially and developed broad and deep capacity for collaboration and dissemination of ideas. While this community is best viewed as emergent from complex interactions among changing educational needs and opportunities, we highlight the role of several large projects in the development of a network within this community. In the 1990s, three NSF projects came together to build a robust web infrastructure to support the production and dissemination of on-line resources: On The Cutting Edge (OTCE), Earth Exploration Toolbook, and Starting Point: Teaching Introductory Geoscience. Along with the contemporaneous Digital Library for Earth System Education, these projects engaged geoscience educators nationwide in exploring professional development experiences that produced lasting on-line resources, collaborative authoring of resources, and models for web-based support for geoscience teaching. As a result, a culture developed in the 2000s in which geoscience educators anticipated that resources for geoscience teaching would be shared broadly and that collaborative authoring would be productive and engaging. By this time, a diverse set of examples demonstrated the power of the web infrastructure in supporting collaboration, dissemination and professional development . Building on this foundation, more recent work has expanded both the size of the network and the scope of its work. Many large research projects initiated collaborations to disseminate resources supporting educational use of their data. Research results from the rapidly expanding geoscience education research community were integrated into the Pedagogies in Action website and OTCE. Projects engaged faculty across the nation in large-scale data collection and educational research. The Climate Literacy and Energy Awareness Network and OTCE engaged community members in reviewing the expanding body of on-line resources. Building Strong

  1. Topology Management Algorithms for Large Scale Aerial High Capacity Directional Networks

    Science.gov (United States)

    2016-11-01

    Topology Management Algorithms for Large-Scale Aerial High Capacity Directional Networks Joy Wang, Thomas Shake, Patricia Deutsch, Andrea Coyle, Bow...airborne backbone network is large- scale topology management of directional links in a dynamic environment. In this paper, we present several... topology manage- ment algorithms for large scale airborne networks and evaluate the performance of these algorithms under various scenarios. In each case

  2. Critical behavior of the contact process in annealed scale-free networks

    OpenAIRE

    Noh, Jae Dong; Park, Hyunggyu

    2008-01-01

    Critical behavior of the contact process is studied in annealed scale-free networks by mapping it on the random walk problem. We obtain the analytic results for the critical scaling, using the event-driven dynamics approach. These results are confirmed by numerical simulations. The disorder fluctuation induced by the sampling disorder in annealed networks is also explored. Finally, we discuss over the discrepancy of the finite-size-scaling theory in annealed and quenched networks in spirit of...

  3. 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.

  4. Hubness of strategic planning and sociality influences depressive mood and anxiety in College Population.

    Science.gov (United States)

    Yun, Je-Yeon; Choi, Yoobin; Kwon, Yoonhee; Lee, Hwa Young; Choi, Soo-Hee; Jang, Joon Hwan

    2017-12-19

    Depressive mood and anxiety can reduce cognitive performance. Conversely, the presence of a biased cognitive tendency may serve as a trigger for depressive mood-anxiety. Previous studies have largely focused on group-wise correlations between clinical-neurocognitive variables. Using network analyses for intra-individual covariance, we sought to decipher the most influential clinical-neurocognitive hub in the differential severity of depressive-anxiety symptoms in a college population. Ninety college students were evaluated for depressive-anxiety symptoms, Minnesota multiphasic personality inventory-2(MMPI-2), and neuro-cognition. Weighted and undirected version of the intra-individual covariance networks, comprised of 18 clinical-neurocognitive variables satisfied small-worldness and modular organization in the sparsity range of K = 0.20-0.21. Furthermore, betweenness centrality of perseverative error for the Wisconsin card sorting test was reduced in more depressive individuals; higher anxiety was related to the increased betweenness centrality of MMPI-2 clinical scale 0(Si). Elevated edge-betweenness centrality of covariance between the MMPI-2 clinical scale 7(Pt) versus commission error of the continuous performance test predicted more anxiety higher than depressive mood. With intra-individual covariance network of clinical-neurocognitive variables, this study demonstrated critical drivers of depressive mood[attenuated influence of strategic planning] or anxiety[domination of social introversion/extroversion, in addition to the influence of compulsivity-impulsivity covariance as a shortcut component among various clinical-neurocognitive features].

  5. De-novo learning of genome-scale regulatory networks in S. cerevisiae.

    Directory of Open Access Journals (Sweden)

    Sisi Ma

    Full Text Available De-novo reverse-engineering of genome-scale regulatory networks is a fundamental problem of biological and translational research. One of the major obstacles in developing and evaluating approaches for de-novo gene network reconstruction is the absence of high-quality genome-scale gold-standard networks of direct regulatory interactions. To establish a foundation for assessing the accuracy of de-novo gene network reverse-engineering, we constructed high-quality genome-scale gold-standard networks of direct regulatory interactions in Saccharomyces cerevisiae that incorporate binding and gene knockout data. Then we used 7 performance metrics to assess accuracy of 18 statistical association-based approaches for de-novo network reverse-engineering in 13 different datasets spanning over 4 data types. We found that most reconstructed networks had statistically significant accuracies. We also determined which statistical approaches and datasets/data types lead to networks with better reconstruction accuracies. While we found that de-novo reverse-engineering of the entire network is a challenging problem, it is possible to reconstruct sub-networks around some transcription factors with good accuracy. The latter transcription factors can be identified by assessing their connectivity in the inferred networks. Overall, this study provides the gene network reverse-engineering community with a rigorous assessment of the accuracy of S. cerevisiae gene network reconstruction and variability in performance of various approaches for learning both the entire network and sub-networks around transcription factors.

  6. Stress and Mood

    Science.gov (United States)

    ... Relaxation Emotions & Relationships HealthyYouTXT Tools Home » Stress & Mood Stress & Mood Many people who go back to smoking ... story: Time Out Times 10 >> share What Causes Stress? Read full story: What Causes Stress? >> share The ...

  7. Canadian Network for Mood and Anxiety Treatments (CANMAT) 2016 Clinical Guidelines for the Management of Adults with Major Depressive Disorder: Section 1. Disease Burden and Principles of Care.

    Science.gov (United States)

    Lam, Raymond W; McIntosh, Diane; Wang, JianLi; Enns, Murray W; Kolivakis, Theo; Michalak, Erin E; Sareen, Jitender; Song, Wei-Yi; Kennedy, Sidney H; MacQueen, Glenda M; Milev, Roumen V; Parikh, Sagar V; Ravindran, Arun V

    2016-09-01

    The Canadian Network for Mood and Anxiety Treatments (CANMAT) conducted a revision of the 2009 guidelines by updating the evidence and recommendations. The scope of the 2016 guidelines remains the management of major depressive disorder (MDD) in adults, with a target audience of psychiatrists and other mental health professionals. Using the question-answer format, we conducted a systematic literature search focusing on systematic reviews and meta-analyses. Evidence was graded using CANMAT-defined criteria for level of evidence. Recommendations for lines of treatment were based on the quality of evidence and clinical expert consensus. This section is the first of six guidelines articles. In Canada, the annual and lifetime prevalence of MDD was 4.7% and 11.3%, respectively. MDD represents the second leading cause of global disability, with high occupational and economic impact mainly attributable to indirect costs. DSM-5 criteria for depressive disorders remain relatively unchanged, but other clinical dimensions (sleep, cognition, physical symptoms) may have implications for depression management. e-Mental health is increasingly used to support clinical and self-management of MDD. In the 2-phase (acute and maintenance) treatment model, specific goals address symptom remission, functional recovery, improved quality of life, and prevention of recurrence. The burden attributed to MDD remains high, whether from individual distress, functional and relationship impairment, reduced quality of life, or societal economic cost. Applying core principles of care, including comprehensive assessment, therapeutic alliance, support of self-management, evidence-informed treatment, and measurement-based care, will optimize clinical, quality of life, and functional outcomes in MDD. © The Author(s) 2016.

  8. Canadian Network for Mood and Anxiety Treatments (CANMAT) 2016 Clinical Guidelines for the Management of Adults with Major Depressive Disorder: Section 5. Complementary and Alternative Medicine Treatments.

    Science.gov (United States)

    Ravindran, Arun V; Balneaves, Lynda G; Faulkner, Guy; Ortiz, Abigail; McIntosh, Diane; Morehouse, Rachel L; Ravindran, Lakshmi; Yatham, Lakshmi N; Kennedy, Sidney H; Lam, Raymond W; MacQueen, Glenda M; Milev, Roumen V; Parikh, Sagar V

    2016-09-01

    The Canadian Network for Mood and Anxiety Treatments (CANMAT) conducted a revision of the 2009 guidelines by updating the evidence and recommendations. The scope of the 2016 guidelines remains the management of major depressive disorder (MDD) in adults, with a target audience of psychiatrists and other mental health professionals. Using the question-answer format, we conducted a systematic literature search focusing on systematic reviews and meta-analyses. Evidence was graded using CANMAT-defined criteria for level of evidence. Recommendations for lines of treatment were based on the quality of evidence and clinical expert consensus. "Complementary and Alternative Medicine Treatments" is the fifth of six sections of the 2016 guidelines. Evidence-informed responses were developed for 12 questions for 2 broad categories of complementary and alternative medicine (CAM) interventions: 1) physical and meditative treatments (light therapy, sleep deprivation, exercise, yoga, and acupuncture) and 2) natural health products (St. John's wort, omega-3 fatty acids; S-adenosyl-L-methionine [SAM-e], dehydroepiandrosterone, folate, Crocus sativus, and others). Recommendations were based on available data on efficacy, tolerability, and safety. For MDD of mild to moderate severity, exercise, light therapy, St. John's wort, omega-3 fatty acids, SAM-e, and yoga are recommended as first- or second-line treatments. Adjunctive exercise and adjunctive St. John's wort are second-line recommendations for moderate to severe MDD. Other physical treatments and natural health products have less evidence but may be considered as third-line treatments. CAM treatments are generally well tolerated. Caveats include methodological limitations of studies and paucity of data on long-term outcomes and drug interactions. © The Author(s) 2016.

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

    Directory of Open Access Journals (Sweden)

    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.

  10. ASH : Tackling node mobility in large-scale networks

    NARCIS (Netherlands)

    Pruteanu, A.; Dulman, S.

    2012-01-01

    With the increased adoption of technologies likewireless sensor networks by real-world applications, dynamic network topologies are becoming the rule rather than the exception. Node mobility, however, introduces a range of problems (communication interference, path uncertainty, low quality of

  11. 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

  12. Traffic properties for stochastic routings on scale-free networks

    CERN Document Server

    Hayashi, Yukio

    2011-01-01

    For realistic scale-free networks, we investigate the traffic properties of stochastic routing inspired by a zero-range process known in statistical physics. By parameters $\\alpha$ and $\\delta$, this model controls degree-dependent hopping of packets and forwarding of packets with higher performance at more busy nodes. Through a theoretical analysis and numerical simulations, we derive the condition for the concentration of packets at a few hubs. In particular, we show that the optimal $\\alpha$ and $\\delta$ are involved in the trade-off between a detour path for $\\alpha 0$; In the low-performance regime at a small $\\delta$, the wandering path for $\\alpha 0$ and $\\alpha < 0$ is small, neither the wandering long path with short wait trapped at nodes ($\\alpha = -1$), nor the short hopping path with long wait trapped at hubs ($\\alpha = 1$) is advisable. A uniformly random walk ($\\alpha = 0$) yields slightly better performance. We also discuss the congestion phenomena in a more complicated situation with pack...

  13. Examining the Emergence of Large-Scale Structures in Collaboration Networks: Methods in Sociological Analysis

    Science.gov (United States)

    Ghosh, Jaideep; Kshitij, Avinash

    2017-01-01

    This article introduces a number of methods that can be useful for examining the emergence of large-scale structures in collaboration networks. The study contributes to sociological research by investigating how clusters of research collaborators evolve and sometimes percolate in a collaboration network. Typically, we find that in our networks,…

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    Sudip Mandal

    2016-01-01

    Full Text Available 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.

  16. Chronobiology and Mood Disorders

    Directory of Open Access Journals (Sweden)

    Yavuz Selvi

    2011-09-01

    Full Text Available Living organizms show cyclic rhythmicity in a variety of physiological, hormonal, behavioral, and psychological processes. Sleep-wake cycles, body temperature, hormone levels, mood and cognition display a circadian rhythm in humans. Delays, advances or desynchronizations of circadian rhythm are known to be strongly associated with mental illness especially mood disorders such as bipolar disorder, major depression and seasonal affective disorder. Furthermore, some of the mood stabilizers, sleep deprivation and light treatment are employed to treat mood disorders by shifting circadian rhythm. This paper reviews the relationship between mood disorders and circadian rhythm, and describes treatment options by altering circadian rhythm.

  17. Environmentally induced amplitude death and firing provocation in large-scale networks of neuronal systems

    Science.gov (United States)

    Pankratova, Evgeniya V.; Kalyakulina, Alena I.

    2016-12-01

    We study the dynamics of multielement neuronal systems taking into account both the direct interaction between the cells via linear coupling and nondiffusive cell-to-cell communication via common environment. For the cells exhibiting individual bursting behavior, we have revealed the dependence of the network activity on its scale. Particularly, we show that small-scale networks demonstrate the inability to maintain complicated oscillations: for a small number of elements in an ensemble, the phenomenon of amplitude death is observed. The existence of threshold network scales and mechanisms causing firing in artificial and real multielement neural networks, as well as their significance for biological applications, are discussed.

  18. Two-scale cost efficiency optimization of 5G wireless backhaul networks

    OpenAIRE

    Ge, Xiaohu; Tu, Song; Mao, Guoqiang; Lau, Vincent K. N.; Pan, Linghui

    2016-01-01

    To cater for the demands of future fifth generation (5G) ultra-dense small cell networks, the wireless backhaul network is an attractive solution for the urban deployment of 5G wireless networks. Optimization of 5G wireless backhaul networks is a key issue. In this paper we propose a two-scale optimization solution to maximize the cost efficiency of 5G wireless backhaul networks. Specifically, the number and positions of gateways are optimized in the long time scale of 5G wireless backhaul ne...

  19. Approach and Avoidance of Emotional Faces in Happy and Sad Mood

    NARCIS (Netherlands)

    Vrijsen, J.N.; Oostrom, I.I.H. van; Speckens, A.; Becker, E.S.; Rinck, M.

    2013-01-01

    Since the introduction of the associative network theory, mood-congruent biases in emotional information processing have been established in individuals in a sad and happy mood. Research has concentrated on memory and attentional biases. According to the network theory, mood-congruent behavioral

  20. Reports of Perceived Adverse Events of Stimulant Medication on Cognition, Motivation, and Mood: Qualitative Investigation and the Generation of Items for the Medication and Cognition Rating Scale

    NARCIS (Netherlands)

    Kovshoff, H.; Banaschewski, T.; Buitelaar, J.K.; Carucci, S.; Coghill, D.; Danckaerts, M.; Dittmann, R.W.; Falissard, B.; Grimshaw, D.G.; Hollis, C.; Inglis, S.; Konrad, K.; Liddle, E.; McCarthy, S.; Nagy, P.; Thompson, M.; Wong, I.C.; Zuddas, A.; Sonuga-Barke, E.J.

    2016-01-01

    OBJECTIVE: There is no questionnaire to specifically monitor perceived adverse events of methylphenidate (MPH) on cognition, motivation, and mood. The current study therefore had two goals. First, to harvest accounts of such putative events from transcripts of interviews in samples enriched for such

  1. LARGE-SCALE TOPOLOGICAL PROPERTIES OF MOLECULAR NETWORKS.

    Energy Technology Data Exchange (ETDEWEB)

    MASLOV,S.SNEPPEN,K.

    2003-11-17

    Bio-molecular networks lack the top-down design. Instead, selective forces of biological evolution shape them from raw material provided by random events such as gene duplications and single gene mutations. As a result individual connections in these networks are characterized by a large degree of randomness. One may wonder which connectivity patterns are indeed random, while which arose due to the network growth, evolution, and/or its fundamental design principles and limitations? Here we introduce a general method allowing one to construct a random null-model version of a given network while preserving the desired set of its low-level topological features, such as, e.g., the number of neighbors of individual nodes, the average level of modularity, preferential connections between particular groups of nodes, etc. Such a null-model network can then be used to detect and quantify the non-random topological patterns present in large networks. In particular, we measured correlations between degrees of interacting nodes in protein interaction and regulatory networks in yeast. It was found that in both these networks, links between highly connected proteins are systematically suppressed. This effect decreases the likelihood of cross-talk between different functional modules of the cell, and increases the overall robustness of a network by localizing effects of deleterious perturbations. It also teaches us about the overall computational architecture of such networks and points at the origin of large differences in the number of neighbors of individual nodes.

  2. An Examination of Not-For-Profit Stakeholder Networks for Relationship Management: A Small-Scale Analysis on Social Media

    National Research Council Canada - National Science Library

    Wyllie, Jessica; Lucas, Benjamin; Carlson, Jamie; Kitchens, Brent; Kozary, Ben; Zaki, Mohamed

    2016-01-01

    Using a small-scale descriptive network analysis approach, this study highlights the importance of stakeholder networks for identifying valuable stakeholders and the management of existing stakeholder...

  3. Large-scale Network Monitoring for Visual Analysis of Attacks

    OpenAIRE

    Fischer, Fabian; Mansmann, Florian; Keim, Daniel A.; Pietzko, Stephan; Waldvogel, Marcel

    2008-01-01

    The importance of the Internet and our dependency on computer networks are steadily growing, which results in high costs and substantial consequences in case of successful intrusions, stolen data, and interrupted services. At the same time, a trend towards massive attacks against the network infrastructure is noticeable. Therefore, monitoring large networks has become an importatnt field in practice and research. Through monitoring systems, attacks can be detected and analyzed to gain knowled...

  4. 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.

  5. 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.

  6. Uncovering disassortativity in large scale-free networks

    NARCIS (Netherlands)

    Litvak, Nelli; van der Hofstad, Remco

    2013-01-01

    Mixing patterns in large self-organizing networks, such as the Internet, the World Wide Web, and social and biological networks, are often characterized by degree-degree dependencies between neighboring nodes. In this paper, we propose a new way of measuring degree-degree dependencies. One of the

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

    Science.gov (United States)

    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.

  8. MCF: a tool to find multi-scale community profiles in biological networks.

    Science.gov (United States)

    Gao, Shang; Chen, Alan; Rahmani, Ali; Jarada, Tamer; Alhajj, Reda; Demetrick, Doug; Zeng, Jia

    2013-12-01

    Recent developments of complex graph clustering methods have implicated the practical applications with biological networks in different settings. Multi-scale Community Finder (MCF) is a tool to profile network communities (i.e., clusters of nodes) with the control of community sizes. The controlling parameter is referred to as the scale of the network community profile. MCF is able to find communities in all major types of networks including directed, signed, bipartite, and multi-slice networks. The fast computation promotes the practicability of the tool for large-scaled analysis (e.g., protein-protein interaction and gene co-expression networks). MCF is distributed as an open-source C++ package for academic use with both command line and user interface options, and can be downloaded at http://bsdxd.cpsc.ucalgary.ca/MCF. Detailed user manual and sample data sets are also available at the project website. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  9. Mass balances of dissolved gases at river network scales across biomes.

    Science.gov (United States)

    Wollheim, W. M.; Stewart, R. J.; Sheehan, K.

    2016-12-01

    Estimating aquatic metabolism and gas fluxes at broad spatial scales is needed to evaluate the role of aquatic ecosystems in continental carbon cycles. We applied a river network model, FrAMES, to quantify the mass balances of dissolved oxygen at river network scales across five river networks in different biomes. The model accounts for hydrology; spatially varying re-aeration rates due to flow, slope, and water temperature; gas inputs via terrestrial runoff; variation in light due to canopy cover and water depth; benthic gross primary production; and benthic respiration. The model was parameterized using existing groundwater information and empirical relationships of GPP, R, and re-aeration, and was tested using dissolved oxygen patterns measured throughout river networks. We found that during summers, internal aquatic production dominates the river network mass balance of Kings Cr., Konza Prairie, KS (16.3 km2), whereas terrestrial inputs and aeration dominate the network mass balance at Coweeta Cr., Coweeta Forest, NC (15.7 km2). At network scales, both river networks are net heterotrophic, with Coweeta more so than Kings Cr. (P:R 0.6 vs. 0.7, respectively). The river network of Kings Creek showed higher network-scale GPP and R compared to Coweeta, despite having a lower drainage density because streams are on average wider so cumulative benthic surface areas are similar. Our findings suggest that the role of aquatic systems in watershed carbon balances will depend on interactions of drainage density, channel hydraulics, terrestrial vegetation, and biological activity.

  10. Progression of nicotine dependence, mood level, and mood variability in adolescent smokers.

    Science.gov (United States)

    Piasecki, Thomas M; Hedeker, Donald; Dierker, Lisa C; Mermelstein, Robin J

    2016-06-01

    Mood processes are theorized to play a role in the initiation and progression of smoking behavior. Available work using real-time assessments in samples of young smokers, including several reports from the Social and Emotional Contexts of Adolescent Smoking Patterns (SECASP) study, has indicated that smoking events acutely improve mood and that escalating smoking frequency may stabilize mood. However, prior analyses have not specifically evaluated within-person change in nicotine dependence, which is conceptually distinguishable from frequent smoking and may be associated with unique mood consequences. The current investigation addressed this question using data from 329 adolescent SECASP participants (9th or 10th grade at recruitment) who contributed mood reports via ecological momentary assessment in up to four 1-week bursts over the course of 24 months. Mixed-effects location scale analyses revealed that within-person increases in scores on the Nicotine Dependence Syndrome Scale were associated with elevations in negative mood level and increased variability of both positive and negative moods. These effects remained when within-person changes in smoking frequency were covaried and were not fully attributable to a subgroup of youth who rapidly escalated their smoking frequency over time. The findings indicate that adolescents tend to show increasing levels of positive mood states, decreasing levels of negative mood, and diminishing mood variability between ages 16 to 18, but progression of nicotine dependence may counteract some of these developmental gains. Emergence of withdrawal symptoms is a likely explanation for the adverse mood effects associated with dependence progression. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  11. Networks and landscapes: a framework for setting goals and evaluating performance at the large landscape scale

    Science.gov (United States)

    R Patrick Bixler; Shawn Johnson; Kirk Emerson; Tina Nabatchi; Melly Reuling; Charles Curtin; Michele Romolini; Morgan Grove

    2016-01-01

    The objective of large landscape conser vation is to mitigate complex ecological problems through interventions at multiple and overlapping scales. Implementation requires coordination among a diverse network of individuals and organizations to integrate local-scale conservation activities with broad-scale goals. This requires an understanding of the governance options...

  12. Scaling of transmission capacities in coarse-grained renewable electricity networks

    Science.gov (United States)

    Schäfer, M.; Bugge Siggaard, S.; Zhu, Kun; Risager Poulsen, C.; Greiner, M.

    2017-08-01

    Network models of large-scale electricity systems feature only a limited spatial resolution, either due to lack of data or in order to reduce the complexity of the problem with respect to numerical calculations. In such cases, both the network topology, the load and the generation patterns below a given spatial scale are aggregated into representative nodes. This coarse-graining affects power flows and thus the resulting transmission needs of the system. We derive analytical scaling laws for measures of network transmission capacity and cost in coarse-grained renewable electricity networks. For the cost measure only a very weak scaling with the spatial resolution of the system is found. The analytical results are shown to describe the scaling of the transmission infrastructure measures for a simplified, but data-driven and spatially detailed model of the European electricity system with a high share of fluctuating renewable generation.

  13. Changeability of mood

    OpenAIRE

    Brandstätter, Hermann

    1994-01-01

    Time sampling of emotional experience, several times a day over a period of several weeks (Brandstätter. 1977; Csikszentmihalyi, Larsen & Prescott, 1977; Diener, 1984), provides data which can be analyzed from many different perspectives. This paper focusses on the changeability of mood as a personality characteristic. Everybody would agree that mood changes all the time and that people differ in the frequency of mood changes. This intuitive idea was supported by a number of studies (for e...

  14. Managing Virtual Networks on Large-Scale Projects

    National Research Council Canada - National Science Library

    Noll, David

    2006-01-01

    The complexity of Boeing's 787 Program is too great for the formal planned information and communication network structure to fully meet the needs of companies, managers, and employees located throughout the world...

  15. Fuzziness and Overlapping Communities in Large-Scale Networks

    OpenAIRE

    Wang, Qinna; Fleury, Eric

    2012-01-01

    International audience; Overlapping community detection is a popular topic in complex networks. As compared to disjoint community structure, overlapping community structure is more suitable to describe networks at a macroscopic level. Overlaps shared by communities play an important role in combining different communities. In this paper, two methods are proposed to detect overlapping community structure. One is called clique optimization, and the other is named fuzzy detection. Clique optimiz...

  16. Large-Scale Analysis of Network Bistability for Human Cancers

    OpenAIRE

    Tetsuya Shiraishi; Shinako Matsuyama; Hiroaki Kitano

    2010-01-01

    Author Summary Since most disease states exhibit a certain level of resilience against therapeutic interventions, each disease state can be considered to be homeostatic to some extent. There must be one or more mechanisms that cause the gene-regulatory network to maintain a certain state, and one such mechanism is a bistable switch. In this work, bistable switch networks were constructed and their ON(upregulated)/OFF(downregulated) states were compared between human cancers and healthy contro...

  17. Reports of Perceived Adverse Events of Stimulant Medication on Cognition, Motivation, and Mood: Qualitative Investigation and the Generation of Items for the Medication and Cognition Rating Scale.

    Science.gov (United States)

    Kovshoff, Hanna; Banaschewski, Tobias; Buitelaar, Jan K; Carucci, Sara; Coghill, David; Danckaerts, Marina; Dittmann, Ralf W; Falissard, Bruno; Grimshaw, Dina Gojkovic; Hollis, Chris; Inglis, Sarah; Konrad, Kerstin; Liddle, Elizabeth; McCarthy, Suzanne; Nagy, Peter; Thompson, Margaret; Wong, Ian C K; Zuddas, Alessandro; Sonuga-Barke, Edmund J S

    2016-08-01

    There is no questionnaire to specifically monitor perceived adverse events of methylphenidate (MPH) on cognition, motivation, and mood. The current study therefore had two goals. First, to harvest accounts of such putative events from transcripts of interviews in samples enriched for such potential experiences. Second, to use the derived data to generate items for a new questionnaire that can be used for monitoring such events in medication trials or routine clinical care. Following a literature search aimed at identifying associations between MPH and cognition and/or motivation, a qualitative semistructured interview was designed to focus specifically on the domains of cognition (i.e., reasoning, depth/breadth of thinking, intellectual capacity, and creativity) and motivation (i.e., drive, effort, and attitudes toward rewards/incentives). Interviews were conducted with 45 participants drawn from the following four groups: (a) clinicians, child and adolescent psychiatrists, and pediatricians specializing in attention-deficit/hyperactivity disorder (ADHD) (n = 15); (2) teachers, with experience of teaching at least 10 medicated children with ADHD (n = 10); (3) parents of children with ADHD (n = 8) treated with MPH; and (4) adolescents/adults with ADHD (n = 12). Purposeful sampling was used to selectively recruit ADHD participants whose histories suggested a degree of vulnerability to MPH adverse events. Data were analyzed using a deductive approach to content analysis. While we probed purposefully for cognitive and motivational adverse events, a third domain, related to mood, emerged from the reports. Therefore, three domains, each with a number of subdomains, were identified from the interview accounts: (i) Cognition (six subdomains; attention/concentration, changes in thinking, reduced creativity, sensory overload, memory, slower processing speed); (ii) motivation (four subdomains; loss of intrinsic motivation for goal-directed activities, external

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

    Science.gov (United States)

    Brummer, Alexander Byers; Savage, Van M; Enquist, Brian J

    2017-03-01

    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.

  19. Aquatic Nitrate Retention at River Network Scales Across Flow Conditions Determined Using Nested In Situ Sensors

    Science.gov (United States)

    Wollheim, W. M.; Mulukutla, G. K.; Cook, C.; Carey, R. O.

    2017-11-01

    Nonpoint pollution sources are strongly influenced by hydrology and are therefore sensitive to climate variability. Some pollutants entering aquatic ecosystems, e.g., nitrate, can be mitigated by in-stream processes during transport through river networks. Whole river network nitrate retention is difficult to quantify with observations. High frequency, in situ nitrate sensors, deployed in nested locations within a single watershed, can improve estimates of both nonpoint inputs and aquatic retention at river network scales. We deployed a nested sensor network and associated sampling in the urbanizing Oyster River watershed in coastal New Hampshire, USA, to quantify storm event-scale loading and retention at network scales. An end member analysis used the relative behavior of reactive nitrate and conservative chloride to infer river network fate of nitrate. In the headwater catchments, nitrate and chloride concentrations are both increasingly diluted with increasing storm size. At the mouth of the watershed, chloride is also diluted, but nitrate tended to increase. The end member analysis suggests that this pattern is the result of high retention during small storms (51-78%) that declines to zero during large storms. Although high frequency nitrate sensors did not alter estimates of fluxes over seasonal time periods compared to less frequent grab sampling, they provide the ability to estimate nitrate flux versus storm size at event scales that is critical for such analyses. Nested sensor networks can improve understanding of the controls of both loading and network scale retention, and therefore also improve management of nonpoint source pollution.

  20. Spatio-temporal modeling of connectome-scale brain network interactions via time-evolving graphs.

    Science.gov (United States)

    Yuan, Jing; Li, Xiang; Zhang, Jinhe; Luo, Liao; Dong, Qinglin; Lv, Jinglei; Zhao, Yu; Jiang, Xi; Zhang, Shu; Zhang, Wei; Liu, Tianming

    2017-11-09

    Many recent literature studies have revealed interesting dynamics patterns of functional brain networks derived from fMRI data. However, it has been rarely explored how functional networks spatially overlap (or interact) and how such connectome-scale network interactions temporally evolve. To explore these unanswered questions, this paper presents a novel framework for spatio-temporal modeling of connectome-scale functional brain network interactions via two main effective computational methodologies. First, to integrate, pool and compare brain networks across individuals and their cognitive states under task performances, we designed a novel group-wise dictionary learning scheme to derive connectome-scale consistent brain network templates that can be used to define the common reference space of brain network interactions. Second, the temporal dynamics of spatial network interactions is modeled by a weighted time-evolving graph, and then a data-driven unsupervised learning algorithm based on the dynamic behavioral mixed-membership model (DBMM) is adopted to identify behavioral patterns of brain networks during the temporal evolution process of spatial overlaps/interactions. Experimental results on the Human Connectome Project (HCP) task fMRI data showed that our methods can reveal meaningful, diverse behavior patterns of connectome-scale network interactions. In particular, those networks' behavior patterns are distinct across HCP tasks such as motor, working memory, language and social tasks, and their dynamics well correspond to the temporal changes of specific task designs. In general, our framework offers a new approach to characterizing human brain function by quantitative description for the temporal evolution of spatial overlaps/interactions of connectome-scale brain networks in a standard reference space. Copyright © 2017 Elsevier Inc. All rights reserved.

  1. scMRI reveals large-scale brain network abnormalities in autism.

    Directory of Open Access Journals (Sweden)

    Brandon A Zielinski

    Full Text Available Autism is a complex neurological condition characterized by childhood onset of dysfunction in multiple cognitive domains including socio-emotional function, speech and language, and processing of internally versus externally directed stimuli. Although gross brain anatomic differences in autism are well established, recent studies investigating regional differences in brain structure and function have yielded divergent and seemingly contradictory results. How regional abnormalities relate to the autistic phenotype remains unclear. We hypothesized that autism exhibits distinct perturbations in network-level brain architecture, and that cognitive dysfunction may be reflected by abnormal network structure. Network-level anatomic abnormalities in autism have not been previously described. We used structural covariance MRI to investigate network-level differences in gray matter structure within two large-scale networks strongly implicated in autism, the salience network and the default mode network, in autistic subjects and age-, gender-, and IQ-matched controls. We report specific perturbations in brain network architecture in the salience and default-mode networks consistent with clinical manifestations of autism. Extent and distribution of the salience network, involved in social-emotional regulation of environmental stimuli, is restricted in autism. In contrast, posterior elements of the default mode network have increased spatial distribution, suggesting a 'posteriorization' of this network. These findings are consistent with a network-based model of autism, and suggest a unifying interpretation of previous work. Moreover, we provide evidence of specific abnormalities in brain network architecture underlying autism that are quantifiable using standard clinical MRI.

  2. Spatial dependencies between large-scale brain networks.

    Directory of Open Access Journals (Sweden)

    Robert Leech

    Full Text Available Functional neuroimaging reveals both increases (task-positive and decreases (task-negative in neural activation with many tasks. Many studies show a temporal relationship between task positive and task negative networks that is important for efficient cognitive functioning. Here we provide evidence for a spatial relationship between task positive and negative networks. There are strong spatial similarities between many reported task negative brain networks, termed the default mode network, which is typically assumed to be a spatially fixed network. However, this is not the case. The spatial structure of the DMN varies depending on what specific task is being performed. We test whether there is a fundamental spatial relationship between task positive and negative networks. Specifically, we hypothesize that the distance between task positive and negative voxels is consistent despite different spatial patterns of activation and deactivation evoked by different cognitive tasks. We show significantly reduced variability in the distance between within-condition task positive and task negative voxels than across-condition distances for four different sensory, motor and cognitive tasks--implying that deactivation patterns are spatially dependent on activation patterns (and vice versa, and that both are modulated by specific task demands. We also show a similar relationship between positively and negatively correlated networks from a third 'rest' dataset, in the absence of a specific task. We propose that this spatial relationship may be the macroscopic analogue of microscopic neuronal organization reported in sensory cortical systems, and that this organization may reflect homeostatic plasticity necessary for efficient brain function.

  3. How scale-free networks and large-scale collective cooperation emerge in complex homogeneous social systems.

    Science.gov (United States)

    Li, Wei; Zhang, Xiaoming; Hu, Gang

    2007-10-01

    We study how heterogeneous degree distributions and large-scale collective cooperation in social networks emerge in complex homogeneous systems by a simple local rule: learning from the best in both strategy selections and linking choices. The prisoner's dilemma game is used as the local dynamics. We show that the social structure may evolve into single-scale, broad-scale, and scale-free (SF) degree distributions for different control parameters. In particular, in a relatively strong-selfish parameter region the SF property can be self-organized in social networks by dynamic evolutions and these SF structures help the whole node community to reach a high level of cooperation under the poor condition of a high selfish intention of individuals.

  4. Structural imaging in late-life depression: association with mood and cognitive responses to antidepressant treatment.

    Science.gov (United States)

    Marano, Christopher M; Workman, Clifford I; Lyman, Christopher H; Munro, Cynthia A; Kraut, Michael A; Smith, Gwenn S

    2015-01-01

    Recent positron emission tomography studies of cerebral glucose metabolism have identified the functional neural circuitry associated with mood and cognitive responses to antidepressant treatment in late life depression (LLD). The structural alterations in these networks are not well understood. The present study used magnetic resonance (MR) imaging and voxel-based morphometry to evaluate the association between gray matter volumes and changes in mood symptoms and cognitive function with treatment with the antidepressant citalopram. Open-label trial with baseline brain MR scan. Mood and cognitive assessments performed at baseline and during citalopram treatment. Outpatient clinics of an academic medical center. 17 previously unmedicated patients age 55 years or older with a major depressive episode and 17 non-depressed comparison subjects. 12-week trial of flexibly dosed citalopram. Gray matter volumes, Hamilton Depression Rating Scale, California Verbal Learning Test, Delis-Kaplan Executive Function System. In LLD, higher gray matter volumes in the cingulate gyrus, superior and middle frontal gyri, middle temporal gyrus, and precuneus was associated with greater mood improvement. Higher gray matter volumes in primarily frontal areas were associated with greater improvement in verbal memory and verbal fluency performance. Associations with antidepressant induced improvements in mood and cognition were observed in several brain regions previously correlated with normalization of glucose metabolism after citalopram treatment in LLD. Future studies will investigate molecular mechanisms underlying these associations (e.g., beta-amyloid, inflammation, glutamate). Copyright © 2015 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.

  5. Mental energy: Assessing the mood dimension.

    Science.gov (United States)

    O'Connor, Patrick J

    2006-07-01

    Conceptualizing mental energy as a mood is impor tant, because these feelings are important to people and can influence behavior in the real world. If a person feels a lack of energy, for example, he or she is more likely to avoid physical or mental work if it is possible to do so. Alternatively, this person may seek to improve feelings of energy by eating, drinking, taking dietary supplements or drugs, sleeping, or engaging in other behaviors. Thus, the measurement of the mood of energy has importance in numerous ways, including public health, work productivity, and ultimately economic growth and productivity. Mood data have limitations, for example, self aware ness and literacy are necessary and faking is possible. The problem of faking is most salient in situations in which there is a strong motivation to fake, such as when psychological testing is used as part of an employment application. Despite these limitations, overwhelming evidence supports the validity for certain measures of the mood of energy such as the POMS vigor scale. This is not to say that mood measures are error free in all situations. Despite some error, however, validity evidence for mood measures is published in the scientific literature weekly. Future research aimed at determining the biological bases for the mood of energy, and its relationships to overlapping phenomena such as cognitive fatigue, should yield results that ultimately help us to understand how to optimize our feelings of energy.

  6. 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.

  7. Analysis of Community Detection Algorithms for Large Scale Cyber Networks

    Energy Technology Data Exchange (ETDEWEB)

    Mane, Prachita; Shanbhag, Sunanda; Kamath, Tanmayee; Mackey, Patrick S.; Springer, John

    2016-09-30

    The aim of this project is to use existing community detection algorithms on an IP network dataset to create supernodes within the network. This study compares the performance of different algorithms on the network in terms of running time. The paper begins with an introduction to the concept of clustering and community detection followed by the research question that the team aimed to address. Further the paper describes the graph metrics that were considered in order to shortlist algorithms followed by a brief explanation of each algorithm with respect to the graph metric on which it is based. The next section in the paper describes the methodology used by the team in order to run the algorithms and determine which algorithm is most efficient with respect to running time. Finally, the last section of the paper includes the results obtained by the team and a conclusion based on those results as well as future work.

  8. Locating the Source of Diffusion in Large-Scale Networks

    Science.gov (United States)

    Pinto, Pedro C.; Thiran, Patrick; Vetterli, Martin

    2012-08-01

    How can we localize the source of diffusion in a complex network? Because of the tremendous size of many real networks—such as the internet or the human social graph—it is usually unfeasible to observe the state of all nodes in a network. We show that it is fundamentally possible to estimate the location of the source from measurements collected by sparsely placed observers. We present a strategy that is optimal for arbitrary trees, achieving maximum probability of correct localization. We describe efficient implementations with complexity O(Nα), where α=1 for arbitrary trees and α=3 for arbitrary graphs. In the context of several case studies, we determine how localization accuracy is affected by various system parameters, including the structure of the network, the density of observers, and the number of observed cascades.

  9. Maritime Semantic Labeling of Optical Remote Sensing Images with Multi-Scale Fully Convolutional Network

    Directory of Open Access Journals (Sweden)

    Haoning Lin

    2017-05-01

    Full Text Available In current remote sensing literature, the problems of sea-land segmentation and ship detection (including in-dock ships are investigated separately despite the high correlation between them. This inhibits joint optimization and makes the implementation of the methods highly complicated. In this paper, we propose a novel fully convolutional network to accomplish the two tasks simultaneously, in a semantic labeling fashion, i.e., to label every pixel of the image into 3 classes, sea, land and ships. A multi-scale structure for the network is proposed to address the huge scale gap between different classes of targets, i.e., sea/land and ships. Conventional multi-scale structure utilizes shortcuts to connect low level, fine scale feature maps to high level ones to increase the network’s ability to produce finer results. In contrast, our proposed multi-scale structure focuses on increasing the receptive field of the network while maintaining the ability towards fine scale details. The multi-scale convolution network accommodates the huge scale difference between sea-land and ships and provides comprehensive features, and is able to accomplish the tasks in an end-to-end manner that is easy for implementation and feasible for joint optimization. In the network, the input forks into fine-scale and coarse-scale paths, which share the same convolution layers to minimize network parameter increase, and then are joined together to produce the final result. The experiments show that the network tackles the semantic labeling problem with improved performance.

  10. Large-Scale Functional Brain Network Reorganization During Taoist Meditation.

    Science.gov (United States)

    Jao, Tun; Li, Chia-Wei; Vértes, Petra E; Wu, Changwei Wesley; Achard, Sophie; Hsieh, Chao-Hsien; Liou, Chien-Hui; Chen, Jyh-Horng; Bullmore, Edward T

    2016-02-01

    Meditation induces a distinct and reversible mental state that provides insights into brain correlates of consciousness. We explored brain network changes related to meditation by graph theoretical analysis of resting-state functional magnetic resonance imaging data. Eighteen Taoist meditators with varying levels of expertise were scanned using a within-subjects counterbalanced design during resting and meditation states. State-related differences in network topology were measured globally and at the level of individual nodes and edges. Although measures of global network topology, such as small-worldness, were unchanged, meditation was characterized by an extensive and expertise-dependent reorganization of the hubs (highly connected nodes) and edges (functional connections). Areas of sensory cortex, especially the bilateral primary visual and auditory cortices, and the bilateral temporopolar areas, which had the highest degree (or connectivity) during the resting state, showed the biggest decrease during meditation. Conversely, bilateral thalamus and components of the default mode network, mainly the bilateral precuneus and posterior cingulate cortex, had low degree in the resting state but increased degree during meditation. Additionally, these changes in nodal degree were accompanied by reorganization of anatomical orientation of the edges. During meditation, long-distance longitudinal (antero-posterior) edges increased proportionally, whereas orthogonal long-distance transverse (right-left) edges connecting bilaterally homologous cortices decreased. Our findings suggest that transient changes in consciousness associated with meditation introduce convergent changes in the topological and spatial properties of brain functional networks, and the anatomical pattern of integration might be as important as the global level of integration when considering the network basis for human consciousness.

  11. An adaptive routing scheme in scale-free networks

    Science.gov (United States)

    Ben Haddou, Nora; Ez-Zahraouy, Hamid; Benyoussef, Abdelilah

    2015-05-01

    We suggest an optimal form of traffic awareness already introduced as a routing protocol which combines structural and local dynamic properties of the network to determine the followed path between source and destination of the packet. Instead of using the shortest path, we incorporate the "efficient path" in the protocol and we propose a new parameter α that controls the contribution of the queue in the routing process. Compared to the original model, the capacity of the network can be improved more than twice when using the optimal conditions of our model. Moreover, the adjustment of the proposed parameter allows the minimization of the travel time.

  12. Clustering and Visualizing Functionally Similar Regions in Large-Scale Spatial Networks

    National Research Council Canada - National Science Library

    Fushimi, Takayasu; Saito, Kazumi; Ikeda, Tetsuo; Kazama, Kazuhiro

    2017-01-01

    .... For this purpose, based on our previous algorithm called the FCE method that extracted functional clusters for each network, we propose a new method that efficiently deals with several large-scale...

  13. Stress-induced alterations in large-scale functional networks of the rodent brain

    NARCIS (Netherlands)

    Henckens, Marloes J A G; van der Marel, Kajo; van der Toorn, A|info:eu-repo/dai/nl/138484821; Pillai, Anup G.; Fernández, Guillén; Dijkhuizen, Rick M.|info:eu-repo/dai/nl/174680058; Joëls, Marianne

    2015-01-01

    Stress-related psychopathology is associated with altered functioning of large-scale brain networks. Animal research into chronic stress, one of the most prominent environmental risk factors for development of psychopathology, has revealed molecular and cellular mechanisms potentially contributing

  14. Stroke Social Network Scale: development and psychometric evaluation of a new patient-reported measure

    OpenAIRE

    Northcott, S.; Hilari, K.

    2013-01-01

    Objective: To describe the development and psychometric evaluation of a new patient-reported measure which assesses a person’s social network in the first six months post stroke. Although it is known that the social networks of those with stroke and aphasia are vulnerable to change, there is currently no social network scale that has been validated for this population.\\ud \\ud Design and Setting: Repeated measures psychometric study, evaluating internal consistency, construct validity, and res...

  15. Introducing Mood Swings

    NARCIS (Netherlands)

    Bialoskorski, Leticia S.S.; Westerink, Joyce H.D.M.; van den Broek, Egon; Markopoulos, P.; Hoonhout, J.; Soute, I.; Read, J.

    2008-01-01

    Mood Swings is introduced: an affective interactive art installation that interprets and visualizes affect expressed by a person. Founded on the integration of a color model and a framework for affective movements, Mood Swings recognizes affective movement characteristics, processes these, and

  16. Mean field analysis of algorithms for scale-free networks in molecular biology.

    Science.gov (United States)

    Konini, S; Janse van Rensburg, E J

    2017-01-01

    The sampling of scale-free networks in Molecular Biology is usually achieved by growing networks from a seed using recursive algorithms with elementary moves which include the addition and deletion of nodes and bonds. These algorithms include the Barabási-Albert algorithm. Later algorithms, such as the Duplication-Divergence algorithm, the Solé algorithm and the iSite algorithm, were inspired by biological processes underlying the evolution of protein networks, and the networks they produce differ essentially from networks grown by the Barabási-Albert algorithm. In this paper the mean field analysis of these algorithms is reconsidered, and extended to variant and modified implementations of the algorithms. The degree sequences of scale-free networks decay according to a powerlaw distribution, namely P(k) ∼ k-γ, where γ is a scaling exponent. We derive mean field expressions for γ, and test these by numerical simulations. Generally, good agreement is obtained. We also found that some algorithms do not produce scale-free networks (for example some variant Barabási-Albert and Solé networks).

  17. Students' Attitudes towards Edmodo, a Social Learning Network: A Scale Development Study

    Science.gov (United States)

    Yunkul, Eyup; Cankaya, Serkan

    2017-01-01

    Social Learning Networks (SLNs) are the developed forms of Social Network Sites (SNSs) adapted to educational environments, and they are used by quite a large population throughout the world. In addition, in related literature, there is no scale for the measurement of students' attitudes towards such sites. The purpose of this study was to develop…

  18. 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.

  19. 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...

  20. 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

  1. Energy Saving: Scaling Network Energy Efficiency Faster than Traffic Growth

    NARCIS (Netherlands)

    Chen, Y.; Blume, O.; Gati, A.; Capone, A.; Wu, C.E.; Barth, U.; Marzetta, T.; Zhang, H.; Xu, S.

    2013-01-01

    As the mobile traffic is expected to continue its exponential growth in the near future, energy efficiency has gradually become a must criterion for wireless network design. Three fundamental questions need to be answered before the detailed design could be carried out, namely what energy efficiency

  2. Multiple dynamical time-scales in networks with hierarchically ...

    Indian Academy of Sciences (India)

    2015-11-27

    Nov 27, 2015 ... Many natural and engineered complex networks have intricate mesoscopic organization, e.g., the clustering of the constituent nodes into several communities or modules. Often, such modularity is manifested at several different hierarchical levels, where the clusters defined at one level appear as ...

  3. Multiple dynamical time-scales in networks with hierarchically ...

    Indian Academy of Sciences (India)

    tion of the key players can be used to develop drugs targeted specifically towards these molecules [4]. On a much larger ... cial markets [15] and brain functional networks [16,17]. Figures 1a–b shows empirical ... nections between cortical regions in the cat [18] and macaque [19] brains obtained from anatomical studies.

  4. Output Regulation of Large-Scale Hydraulic Networks

    DEFF Research Database (Denmark)

    De Persis, Claudio; Jensen, Tom Nørgaard; Ortega, Romeo

    2014-01-01

    . The fact that the result is global and independent of the number of end users has the consequence that structural changes such as end-user addition and removal can be made in the network while maintaining the stability properties of the system. Furthermore, the decentralized nature of the control...

  5. The Multi-Scale Network Landscape of Collaboration: e0151784

    National Research Council Canada - National Science Library

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

    2016-01-01

    ... to cultural data sets including recipes, music, paintings, etc. to gain new insights and understanding, further expanding the application in their fields [3-7]. Of many new data modeling frameworks, networks in particular have gained popularity for analyzing systems whose structure and function depend critically on the connections or correlations be...

  6. The Multi-Scale Network Landscape of Collaboration

    National Research Council Canada - National Science Library

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

    2016-01-01

    ... to cultural data sets including recipes, music, paintings, etc. to gain new insights and understanding, further expanding the application in their fields [3-7]. Of many new data modeling frameworks, networks in particular have gained popularity for analyzing systems whose structure and function depend critically on the connections or correlations be...

  7. Software Defined Optics and Networking for Large Scale Data Centers

    DEFF Research Database (Denmark)

    Mehmeri, Victor; Andrus, Bogdan-Mihai; Tafur Monroy, Idelfonso

    Big data imposes correlations of large amounts of information between numerous systems and databases. This leads to large dynamically changing flows and traffic patterns between clusters and server racks that result in a decrease of the quality of transmission and degraded application performance....... Highly interconnected topologies combined with flexible, on demand network configuration can become a solution to the ever-increasing dynamic traffic...

  8. MAC Scheduling in Large-scale Underwater Acoustic Networks

    NARCIS (Netherlands)

    van Kleunen, W.A.P.; Meratnia, Nirvana; Havinga, Paul J.M.

    2011-01-01

    The acoustic propagation speed under water poses significant challenges to the design of underwater sensor networks and their medium access control protocols. Scheduling allows reducing the effects of long propagation delay of the acoustic signal and has significant impacts on throughput, energy

  9. Developmental changes in large-scale network connectivity in autism

    Directory of Open Access Journals (Sweden)

    Jason S. Nomi

    2015-01-01

    Conclusions: Characterizing within- and between-network functional connectivity in age-stratified cohorts of individuals with ASD and TD individuals demonstrates that functional connectivity atypicalities in the disorder are not uniform across the lifespan. These results demonstrate how explicitly characterizing participant age and adopting a developmental perspective can lead to a more nuanced understanding of atypicalities of functional brain connectivity in autism.

  10. Disease Modeling via Large-Scale Network Analysis

    Science.gov (United States)

    2015-05-20

    plant (Arabidopsis thaliana), worm (Caenorhabditis elegans), fruit fly (Drosophila melanogaster), mouse (Mus musculus), yeast ( Saccharomyces ... cerevisiae ), Escherichia coli, zebrafish (Danio rerio), and chicken (Gallus gallus). We use two kinds of human gene interactions: (a) Human Net, a large... characteristics of the heterogeneous network suggest that the kernel can be computed more efficiently than what the state-of- the-art methods imply

  11. The Design of a Large Scale Airline Network

    NARCIS (Netherlands)

    Carmona Benitez, R.B.

    2012-01-01

    Airlines invest a lot of money before opening new pax transportation services, for this reason, airlines have to analyze if their profits will overcome the amount of money they have to invest to open new services. The design and analysis of the feasibility of airlines networks can be done by using

  12. Output Regulation of Large-Scale Hydraulic Networks

    NARCIS (Netherlands)

    De Persis, C.; Jensen, T.N.; Ortega, R.; Wisniewski, R.

    The problem of output regulation for a class of hydraulic networks found in district heating systems is addressed in this brief. The results show that global asymptotic and semiglobal exponential output regulation is achievable using a set of decentralized proportional-integral controllers. The fact

  13. 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

  14. Modified Penna bit-string network evolution model for scale-free networks with assortative mixing

    Science.gov (United States)

    Kim, Yup; Choi, Woosik; Yook, Soon-Hyung

    2012-02-01

    Motivated by biological aging dynamics, we introduce a network evolution model for social interaction networks. In order to study the effect of social interactions originating from biological and sociological reasons on the topological properties of networks, we introduce the activitydependent rewiring process. From the numerical simulations, we show that the degree distribution of the obtained networks follows a power-law distribution with an exponentially decaying tail, P( k) ˜ ( k + c)- γ exp(- k/k 0). The obtained value of γ is in the range 2 networks. Moreover, we also show that the degree-degree correlation of the network is positive, which is a characteristic of social interaction networks. The possible applications of our model to real systems are also discussed.

  15. 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.

  16. The effect of grounding the human body on mood.

    Science.gov (United States)

    Chevalier, Gaétan

    2015-04-01

    Earthing (grounding) refers to bringing the body in contact with the Earth. Health benefits were previously reported, but no study exists about mood. This study was conducted to assess if Earthing improves mood. 40 adult participants were either grounded or sham-grounded (no grounding) for 1 hr. while relaxing in a comfortable recliner chair equipped with a conductive pillow, mat, and patches connecting them to the ground. This pilot project was double-blinded and the Brief Mood Introspection Scale (comprising 4 mood scales) was used. Pleasant and positive moods statistically significantly improved among grounded-but not sham-grounded-participants. It is concluded that the 1-hr. contact with the Earth improved mood more than expected by relaxation alone. More extensive studies are, therefore, warranted.

  17. Effects of maximum node degree on computer virus spreading in scale-free networks

    Science.gov (United States)

    Bamaarouf, O.; Ould Baba, A.; Lamzabi, S.; Rachadi, A.; Ez-Zahraouy, H.

    2017-10-01

    The increase of the use of the Internet networks favors the spread of viruses. In this paper, we studied the spread of viruses in the scale-free network with different topologies based on the Susceptible-Infected-External (SIE) model. It is found that the network structure influences the virus spreading. We have shown also that the nodes of high degree are more susceptible to infection than others. Furthermore, we have determined a critical maximum value of node degree (Kc), below which the network is more resistible and the computer virus cannot expand into the whole network. The influence of network size is also studied. We found that the network with low size is more effective to reduce the proportion of infected nodes.

  18. 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

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

    Directory of Open Access Journals (Sweden)

    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.

  20. Double and multiple knockout simulations for genome-scale metabolic network reconstructions.

    Science.gov (United States)

    Goldstein, Yaron Ab; Bockmayr, Alexander

    2015-01-01

    Constraint-based modeling of genome-scale metabolic network reconstructions has become a widely used approach in computational biology. Flux coupling analysis is a constraint-based method that analyses the impact of single reaction knockouts on other reactions in the network. We present an extension of flux coupling analysis for double and multiple gene or reaction knockouts, and develop corresponding algorithms for an in silico simulation. To evaluate our method, we perform a full single and double knockout analysis on a selection of genome-scale metabolic network reconstructions and compare the results. A prototype implementation of double knockout simulation is available at http://hoverboard.io/L4FC.

  1. Almost periodic solutions of impulsive BAM neural networks with variable delays on time scales

    Science.gov (United States)

    Wang, Chao

    2014-08-01

    This paper is concerned with a class of impulsive BAM neural networks with variable delays on time scales. Some sufficient conditions are established to ensure the existence and exponential stability of almost periodic solutions for such class of impulsive BAM neural networks. The results are essentially new when T=R or T=Z. It is the first time that the existence and exponential stability of almost periodic solutions for impulsive BAM neural networks are obtained on time scales. Furthermore, an example and numerical simulations are given to illustrate our effectiveness of the obtained results.

  2. Learning about memory from (very) large scale hippocampal networks

    Science.gov (United States)

    Meshulam, Leenoy; Gauthier, Jeffrey; Brody, Carlos; Tank, David; Bialek, William

    Recent technological progress has dramatically increased our access to the neural activity underlying memory-related tasks. These complex high-dimensional data call for theories that allow us to identify signatures of collective activity in the networks that are crucial for the emergence of cognitive functions. As an example, we study the neural activity in dorsal hippocampus as a mouse runs along a virtual linear track. One of the dominant features of this data is the activity of place cells, which fire when the animal visits particular locations. During the first stage of our work we used a maximum entropy framework to characterize the probability distribution of the joint activity patterns observed across ensembles of up to 100 cells. These models, which are equivalent to Ising models with competing interactions, make surprisingly accurate predictions for the activity of individual neurons given the state of the rest of the network, and this is true both for place cells and for non-place cells. Additionally, the model captures the high-order structure in the data, which cannot be explained by place-related activity alone. For the second stage of our work we study networks of 2000 neurons. To address this much larger system, we are exploring different methods of coarse graining, in the spirit of the renormalization group, searching for simplified models.

  3. Modelling expected train passenger delays on large scale railway networks

    DEFF Research Database (Denmark)

    Landex, Alex; Nielsen, Otto Anker

    2006-01-01

    Forecasts of regularity for railway systems have traditionally – if at all – been computed for trains, not for passengers. Relatively recently it has become possible to model and evaluate the actual passenger delays by a passenger regularity model for the operation already carried out. First the ...... and compare future scenarios. In this way it is possible to estimate the network effects of the passengers and to identify critical stations or sections in the railway network for further investigation or optimization.......Forecasts of regularity for railway systems have traditionally – if at all – been computed for trains, not for passengers. Relatively recently it has become possible to model and evaluate the actual passenger delays by a passenger regularity model for the operation already carried out. First...... the paper describes the passenger regularity model used to calculate passenger delays of the Copenhagen suburban rail network the previous day. Secondly, the paper describes how it is possible to estimate future passenger delays by combining the passenger regularity model with railway simulation software...

  4. Deep Convolutional Neural Networks for large-scale speech tasks.

    Science.gov (United States)

    Sainath, Tara N; Kingsbury, Brian; Saon, George; Soltau, Hagen; Mohamed, Abdel-rahman; Dahl, George; Ramabhadran, Bhuvana

    2015-04-01

    Convolutional Neural Networks (CNNs) are an alternative type of neural network that can be used to reduce spectral variations and model spectral correlations which exist in signals. Since speech signals exhibit both of these properties, we hypothesize that CNNs are a more effective model for speech compared to Deep Neural Networks (DNNs). In this paper, we explore applying CNNs to large vocabulary continuous speech recognition (LVCSR) tasks. First, we determine the appropriate architecture to make CNNs effective compared to DNNs for LVCSR tasks. Specifically, we focus on how many convolutional layers are needed, what is an appropriate number of hidden units, what is the best pooling strategy. Second, investigate how to incorporate speaker-adapted features, which cannot directly be modeled by CNNs as they do not obey locality in frequency, into the CNN framework. Third, given the importance of sequence training for speech tasks, we introduce a strategy to use ReLU+dropout during Hessian-free sequence training of CNNs. Experiments on 3 LVCSR tasks indicate that a CNN with the proposed speaker-adapted and ReLU+dropout ideas allow for a 12%-14% relative improvement in WER over a strong DNN system, achieving state-of-the art results in these 3 tasks. Copyright © 2014 Elsevier Ltd. All rights reserved.

  5. Stroke Social Network Scale: development and psychometric evaluation of a new patient-reported measure.

    Science.gov (United States)

    Northcott, Sarah; Hilari, Katerina

    2013-09-01

    To describe the development and psychometric evaluation of a new patient-reported measure that assesses a person's social network in the first six months post stroke. Although it is known that the social networks of those with stroke and aphasia are vulnerable to change, there is currently no social network scale that has been validated for this population. Repeated measures psychometric study, evaluating internal consistency, construct validity, and responsiveness to change of the Stroke Social Network Scale. Participants were interviewed two weeks, three months and six months following a first stroke. Stroke Social Network Scale; Medical Outcomes Studies (MOS) Social Support Survey; National Institute of Health Stroke Scale; Stroke and Aphasia Quality of Life Scale-39g; Frenchay Aphasia Screening Test. 87 participants were recruited, of whom 71 were followed up at six months. Factor analysis was used with the Stroke Social Network Scale to derive five subdomains: satisfaction; children; relatives; friends; and groups, which explained 63% of variance. There was good evidence for the scale's internal consistency (α = 0.85); acceptability; and convergent (r = 0.34; r = 0.53) and discriminant validity (r = -0.10; r = -0.19). It differentiated between those with high versus low perceived social support (p = 0.01). Moderate changes from two weeks to six months supported responsiveness (d = 0.32; standardised response mean (SRM) = 0.46), with the friends factor, as expected, showing more change than the children's factor (friends factor: d = 0.46; SRM = 0.50; children's factor: d = 0.06; SRM = 0.19). The Stroke Social Network Scale is a new measure that demonstrates good internal consistency, validity and responsiveness to change.

  6. Emotional intelligence and mood states associated with optimal performance

    OpenAIRE

    Lane, A; Thelwell, Richard; Devonport, T.

    2009-01-01

    This study utilized a within-subject design to investigate relationships between emotional intelligence and memories of mood states associated with optimal and dysfunctional performance in competitive sport and academic situations. Sport students (N = 436) completed a self-report Emotional Intelligence Scale (EIS), whilst retrospective accounts of mood states associated with optimal and dysfunctional sporting competition and academic examination performance were recorded using the Brunel Mood...

  7. Analysis of Average Shortest-Path Length of Scale-Free Network

    Directory of Open Access Journals (Sweden)

    Guoyong Mao

    2013-01-01

    Full Text Available Computing the average shortest-path length of a large scale-free network needs much memory space and computation time. Hence, parallel computing must be applied. In order to solve the load-balancing problem for coarse-grained parallelization, the relationship between the computing time of a single-source shortest-path length of node and the features of node is studied. We present a dynamic programming model using the average outdegree of neighboring nodes of different levels as the variable and the minimum time difference as the target. The coefficients are determined on time measurable networks. A native array and multimap representation of network are presented to reduce the memory consumption of the network such that large networks can still be loaded into the memory of each computing core. The simplified load-balancing model is applied on a network of tens of millions of nodes. Our experiment shows that this model can solve the load-imbalance problem of large scale-free network very well. Also, the characteristic of this model can meet the requirements of networks with ever-increasing complexity and scale.

  8. The spread of computer viruses over a reduced scale-free network

    Science.gov (United States)

    Yang, Lu-Xing; Yang, Xiaofan

    2014-02-01

    Due to the high dimensionality of an epidemic model of computer viruses over a general scale-free network, it is difficult to make a close study of its dynamics. In particular, it is extremely difficult, if not impossible, to prove the global stability of its viral equilibrium, if any. To overcome this difficulty, we suggest to simplify a general scale-free network by partitioning all of its nodes into two classes: higher-degree nodes and lower-degree nodes, and then equating the degrees of all higher-degree nodes and all lower-degree nodes, respectively, yielding a reduced scale-free network. We then propose an epidemic model of computer viruses over a reduced scale-free network. A theoretical analysis reveals that the proposed model is bound to have a globally stable viral equilibrium, implying that any attempt to eradicate network viruses would prove unavailing. As a result, the next best thing we can do is to restrain virus prevalence. Based on an analysis of the impact of different model parameters on virus prevalence, some practicable measures are recommended to contain virus spreading. The work in this paper adequately justifies the idea of reduced scale-free networks.

  9. General scaling of maximum degree of synchronization in noisy complex networks

    Science.gov (United States)

    Traxl, Dominik; Boers, Niklas; Kurths, Jürgen

    2014-11-01

    The effects of white noise and global coupling strength on the maximum degree of synchronization in complex networks are explored. We perform numerical simulations of generic oscillator models with both linear and non-linear coupling functions on a broad spectrum of network topologies. The oscillator models include the Fitzhugh-Nagumo model, the Izhikevich model and the Kuramoto phase oscillator model. The network topologies range from regular, random and highly modular networks to scale-free and small-world networks, with both directed and undirected edges. We then study the dependency of the maximum degree of synchronization on the global coupling strength and the noise intensity. We find a general scaling of the synchronizability, and quantify its validity by fitting a regression model to the numerical data.

  10. Efficient model predictive control for large-scale urban traffic networks

    NARCIS (Netherlands)

    Lin, S.

    2011-01-01

    Model Predictive Control is applied to control and coordinate large-scale urban traffic networks. However, due to the large scale or the nonlinear, non-convex nature of the on-line optimization problems solved, the MPC controllers become real-time infeasible in practice, even though the problem is

  11. Scaling behaviours in the growth of networked systems and their geometric origins.

    Science.gov (United States)

    Zhang, Jiang; Li, Xintong; Wang, Xinran; Wang, Wen-Xu; Wu, Lingfei

    2015-04-29

    Two classes of scaling behaviours, namely the super-linear scaling of links or activities, and the sub-linear scaling of area, diversity, or time elapsed with respect to size have been found to prevail in the growth of complex networked systems. Despite some pioneering modelling approaches proposed for specific systems, whether there exists some general mechanisms that account for the origins of such scaling behaviours in different contexts, especially in socioeconomic systems, remains an open question. We address this problem by introducing a geometric network model without free parameter, finding that both super-linear and sub-linear scaling behaviours can be simultaneously reproduced and that the scaling exponents are exclusively determined by the dimension of the Euclidean space in which the network is embedded. We implement some realistic extensions to the basic model to offer more accurate predictions for cities of various scaling behaviours and the Zipf distribution reported in the literature and observed in our empirical studies. All of the empirical results can be precisely recovered by our model with analytical predictions of all major properties. By virtue of these general findings concerning scaling behaviour, our models with simple mechanisms gain new insights into the evolution and development of complex networked systems.

  12. Integrating Temporal and Spatial Scales: Human Structural Network Motifs Across Age and Region of Interest Size

    Science.gov (United States)

    Echtermeyer, Christoph; Han, Cheol E.; Rotarska-Jagiela, Anna; Mohr, Harald; Uhlhaas, Peter J.; Kaiser, Marcus

    2011-01-01

    Human brain networks can be characterized at different temporal or spatial scales given by the age of the subject or the spatial resolution of the neuroimaging method. Integration of data across scales can only be successful if the combined networks show a similar architecture. One way to compare networks is to look at spatial features, based on fiber length, and topological features of individual nodes where outlier nodes form single node motifs whose frequency yields a fingerprint of the network. Here, we observe how characteristic single node motifs change over age (12–23 years) and network size (414, 813, and 1615 nodes) for diffusion tensor imaging structural connectivity in healthy human subjects. First, we find the number and diversity of motifs in a network to be strongly correlated. Second, comparing different scales, the number and diversity of motifs varied across the temporal (subject age) and spatial (network resolution) scale: certain motifs might only occur at one spatial scale or for a certain age range. Third, regions of interest which show one motif at a lower resolution may show a range of motifs at a higher resolution which may or may not include the original motif at the lower resolution. Therefore, both the type and localization of motifs differ for different spatial resolutions. Our results also indicate that spatial resolution has a higher effect on topological measures whereas spatial measures, based on fiber lengths, remain more comparable between resolutions. Therefore, spatial resolution is crucial when comparing characteristic node fingerprints given by topological and spatial network features. As node motifs are based on topological and spatial properties of brain connectivity networks, these conclusions are also relevant to other studies using connectome analysis. PMID:21811454

  13. Natural language acquisition in large scale neural semantic networks

    Science.gov (United States)

    Ealey, Douglas

    This thesis puts forward the view that a purely signal- based approach to natural language processing is both plausible and desirable. By questioning the veracity of symbolic representations of meaning, it argues for a unified, non-symbolic model of knowledge representation that is both biologically plausible and, potentially, highly efficient. Processes to generate a grounded, neural form of this model-dubbed the semantic filter-are discussed. The combined effects of local neural organisation, coincident with perceptual maturation, are used to hypothesise its nature. This theoretical model is then validated in light of a number of fundamental neurological constraints and milestones. The mechanisms of semantic and episodic development that the model predicts are then used to explain linguistic properties, such as propositions and verbs, syntax and scripting. To mimic the growth of locally densely connected structures upon an unbounded neural substrate, a system is developed that can grow arbitrarily large, data- dependant structures composed of individual self- organising neural networks. The maturational nature of the data used results in a structure in which the perception of concepts is refined by the networks, but demarcated by subsequent structure. As a consequence, the overall structure shows significant memory and computational benefits, as predicted by the cognitive and neural models. Furthermore, the localised nature of the neural architecture also avoids the increasing error sensitivity and redundancy of traditional systems as the training domain grows. The semantic and episodic filters have been demonstrated to perform as well, or better, than more specialist networks, whilst using significantly larger vocabularies, more complex sentence forms and more natural corpora.

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

    Directory of Open Access Journals (Sweden)

    Fei Yu

    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.

  15. Measuring Large-Scale Social Networks with High Resolution

    DEFF Research Database (Denmark)

    Stopczynski, Arkadiusz; Sekara, Vedran; Sapiezynski, Piotr

    2014-01-01

    , telecommunication, social networks, location, and background information (personality, demographics, health, politics) for a densely connected population of 1 000 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....

  16. Full-Duplex Communications in Large-Scale Cellular Networks

    KAUST Repository

    AlAmmouri, Ahmad

    2016-04-01

    In-band full-duplex (FD) communications have been optimistically promoted to improve the spectrum utilization and efficiency. However, the penetration of FD communications to the cellular networks domain is challenging due to the imposed uplink/downlink interference. This thesis presents a tractable framework, based on stochastic geometry, to study FD communications in multi-tier cellular networks. Particularly, we assess the FD communications effect on the network performance and quantify the associated gains. The study proves the vulnerability of the uplink to the downlink interference and shows that the improved FD rate gains harvested in the downlink (up to 97%) comes at the expense of a significant degradation in the uplink rate (up to 94%). Therefore, we propose a novel fine-grained duplexing scheme, denoted as α-duplex scheme, which allows a partial overlap between the uplink and the downlink frequency bands. We derive the required conditions to harvest rate gains from the α-duplex scheme and show its superiority to both the FD and half-duplex (HD) schemes. In particular, we show that the α-duplex scheme provides a simultaneous improvement of 28% for the downlink rate and 56% for the uplink rate. We also show that the amount of the overlap can be optimized based on the network design objective. Moreover, backward compatibility is an essential ingredient for the success of new technologies. In the context of in-band FD communication, FD base stations (BSs) should support HD users\\' equipment (UEs) without sacrificing the foreseen FD gains. The results show that FD-UEs are not necessarily required to harvest rate gains from FD-BSs. In particular, the results show that adding FD-UEs to FD-BSs offers a maximum of 5% rate gain over FD-BSs and HD-UEs case, which is a marginal gain compared to the burden required to implement FD transceivers at the UEs\\' side. To this end, we shed light on practical scenarios where HD-UEs operation with FD-BSs outperforms the

  17. 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.

  18. 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.

  19. 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, Jeremy D; 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.

  20. Automated large-scale control of gene regulatory networks.

    Science.gov (United States)

    Tan, Mehmet; Alhajj, Reda; Polat, Faruk

    2010-04-01

    Controlling gene regulatory networks (GRNs) is an important and hard problem. As it is the case in all control problems, the curse of dimensionality is the main issue in real applications. It is possible that hundreds of genes may regulate one biological activity in an organism; this implies a huge state space, even in the case of Boolean models. This is also evident in the literature that shows that only models of small portions of the genome could be used in control applications. In this paper, we empower our framework for controlling GRNs by eliminating the need for expert knowledge to specify some crucial threshold that is necessary for producing effective results. Our framework is characterized by applying the factored Markov decision problem (FMDP) method to the control problem of GRNs. The FMDP is a suitable framework for large state spaces as it represents the probability distribution of state transitions using compact models so that more space and time efficient algorithms could be devised for solving control problems. We successfully mapped the GRN control problem to an FMDP and propose a model reduction algorithm that helps find approximate solutions for large networks by using existing FMDP solvers. The test results reported in this paper demonstrate the efficiency and effectiveness of the proposed approach.

  1. Scaling up: human genetics as a Cold War network.

    Science.gov (United States)

    Lindee, Susan

    2014-09-01

    In this commentary I explore how the papers here illuminate the processes of collection that have been so central to the history of human genetics since 1945. The development of human population genetics in the Cold War period produced databases and biobanks that have endured into the present, and that continue to be used and debated. In the decades after the bomb, scientists collected and transferred human biological materials and information from populations of interest, and as they moved these biological resources or biosocial resources acquired new meanings and uses. The papers here collate these practices and map their desires and ironies. They explore how a large international network of geneticists, biological anthropologists, virologists and other physicians and scientists interacted with local informants, research subjects and public officials. They also track the networks and standards that mobilized the transfer of information, genealogies, tissue and blood samples. As Joanna Radin suggests here, the massive collections of human biological materials and data were often understood to be resources for an "as-yet-unknown" future. The stories told here contain elements of surveillance, extraction, salvage and eschatology. Copyright © 2014 Elsevier Ltd. All rights reserved.

  2. Large Scale Experiments of Multihop Networks in Mobile Scenarios

    Directory of Open Access Journals (Sweden)

    Yacine Benchaïb

    2016-03-01

    Full Text Available This paper presents the latest advances in our research work focused on VIRMANEL and SILUMOD, a couple of tools developed for research in wireless mobile multihop networks. SILUMOD is a domain specific language dedicated to the definition of mobility models. This language contains key- words and special operators that make it easy to define a mobility model and calculate the positions of a trajectory. These positions are sent to VIRMANEL, a tool that man- ages virtual machines corresponding to mobile nodes, emu- lates their movements and the resulting connections and dis- connections, and displays the network evolution to the user, thanks to its graphical user interface. The virtualization ap- proach we take here allows to run real code and to test real protocol implementations without deploying an important experimental platform. For the experimentation of a large number of virtual mobile nodes, we defined and implemented a new algorithm for the nearest neighbor search to find the nodes that are within communication range. We then car- ried out a considerable measurement campaign in order to evaluate the performance of this algorithm. The results show that even with an experiment using a large number of mobile nodes, our algorithm make it possible to evaluate the state of connectivity between mobile nodes within a reasonable time and number of operations.

  3. Robustness of cooperation on scale-free networks under continuous topological change

    CERN Document Server

    Ichinose, Genki; Tanizawa, Toshihiro

    2013-01-01

    In this paper, we numerically investigate the robustness of cooperation clusters in prisoner's dilemma played on scale-free networks, where their network topologies change by continuous removal and addition of nodes. Each of these removal and addition can be either random or intentional. We therefore have four different strategies in changing network topology: random removal and random addition (RR), random removal and preferential addition (RP), targeted removal and random addition (TR), and targeted removal and preferential addition (TP). We find that cooperation clusters are the most fragile against TR, while they are the most robust against RP even in high temptation coefficients for defect. The effect of the degree mixing pattern of the network is not the primary factor for the robustness of cooperation under continuous change in network topology due to consequential removal and addition of nodes, which is quite different from the cases observed in static networks. Cooperation clusters become more robust...

  4. An improved local immunization strategy for scale-free networks with a high degree of clustering

    Science.gov (United States)

    Xia, Lingling; Jiang, Guoping; Song, Yurong; Song, Bo

    2017-01-01

    The design of immunization strategies is an extremely important issue for disease or computer virus control and prevention. In this paper, we propose an improved local immunization strategy based on node's clustering which was seldom considered in the existing immunization strategies. The main aim of the proposed strategy is to iteratively immunize the node which has a high connectivity and a low clustering coefficient. To validate the effectiveness of our strategy, we compare it with two typical local immunization strategies on both real and artificial networks with a high degree of clustering. Simulations on these networks demonstrate that the performance of our strategy is superior to that of two typical strategies. The proposed strategy can be regarded as a compromise between computational complexity and immune effect, which can be widely applied in scale-free networks of high clustering, such as social network, technological networks and so on. In addition, this study provides useful hints for designing optimal immunization strategy for specific network.

  5. 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

  6. Effects of component-subscription network topology on large-scale data centre performance scaling

    OpenAIRE

    Sriram, Ilango; Cliff, Dave

    2010-01-01

    Modern large-scale date centres, such as those used for cloud computing service provision, are becoming ever-larger as the operators of those data centres seek to maximise the benefits from economies of scale. With these increases in size comes a growth in system complexity, which is usually problematic. There is an increased desire for automated "self-star" configuration, management, and failure-recovery of the data-centre infrastructure, but many traditional techniques scale much worse than...

  7. Enabling large-scale viscoelastic calculations via neural network acceleration

    Science.gov (United States)

    DeVries, Phoebe M. R.; Thompson, T. Ben; Meade, Brendan J.

    2017-03-01

    One of the most significant challenges involved in efforts to understand the effects of repeated earthquake cycle activity is the computational costs of large-scale viscoelastic earthquake cycle models. Computationally intensive viscoelastic codes must be evaluated at thousands of times and locations, and as a result, studies tend to adopt a few fixed rheological structures and model geometries and examine the predicted time-dependent deformation over short (learn a computationally efficient representation of viscoelastic solutions, at any time, location, and for a large range of rheological structures, allows these calculations to be done quickly and reliably, with high spatial and temporal resolutions. We demonstrate that this machine learning approach accelerates viscoelastic calculations by more than 50,000%. This magnitude of acceleration will enable the modeling of geometrically complex faults over thousands of earthquake cycles across wider ranges of model parameters and at larger spatial and temporal scales than have been previously possible.

  8. Group Interaction Sustains Positive Moods and Diminishes Negative Moods

    OpenAIRE

    Park, Ernest S.; Verlin B. Hinsz

    2015-01-01

    The social interactions of task groups were investigated for their influences on member moods. Initially, participants’ received an induction of positive, negative, or neutral moods via listening to music that continued throughout the experimental session. Moods were measured after the induction. Students then made decisions on four choice dilemmas alone or as members of a four-person group. Subsequently, positive and negative moods were again measured. Positive moods of participants who work...

  9. On the topologic structure of economic complex networks: Empirical evidence from large scale payment network of Estonia

    Science.gov (United States)

    Rendón de la Torre, Stephanie; Kalda, Jaan; Kitt, Robert; Engelbrecht, Jüri

    2016-09-01

    This paper presents the first topological analysis of the economic structure of an entire country based on payments data obtained from Swedbank. This data set is exclusive in its kind because around 80% of Estonia's bank transactions are done through Swedbank, hence, the economic structure of the country can be reconstructed. Scale-free networks are commonly observed in a wide array of different contexts such as nature and society. In this paper, the nodes are comprised by customers of the bank (legal entities) and the links are established by payments between these nodes. We study the scaling-free and structural properties of this network. We also describe its topology, components and behaviors. We show that this network shares typical structural characteristics known in other complex networks: degree distributions follow a power law, low clustering coefficient and low average shortest path length. We identify the key nodes of the network and perform simulations of resiliency against random and targeted attacks of the nodes with two different approaches. With this, we find that by identifying and studying the links between the nodes is possible to perform vulnerability analysis of the Estonian economy with respect to economic shocks.

  10. Mood, media experiences and advertising

    NARCIS (Netherlands)

    Bronner, F.; van Velthoven, S.; Costa Pereira, F.; Veríssimo, J.; Neijens, P.C.

    2008-01-01

    Studying moods and the effects that a mood has is an important topic in research into advertising. But nearly all data on mood effects are gathered in a forced exposure and lab context. In a real-life study we relate in this contribution mood to moments of media consumption. So we analyze at the

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

    Science.gov (United States)

    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, pnetworking, 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

  12. FCAAIS: Anomaly based network intrusion detection through feature correlation analysis and association impact scale

    Directory of Open Access Journals (Sweden)

    V. Jyothsna

    2016-09-01

    Full Text Available Due to the sensitivity of the information required to detect network intrusions efficiently, collecting huge amounts of network transactions is inevitable and the volume and details of network transactions available in recent years have been high. The meta-heuristic anomaly based assessment is vital in an exploratory analysis of intrusion related network transaction data. In order to forecast and deliver predictions about intrusion possibility from the available details of the attributes involved in network transaction. In this regard, a meta-heuristic assessment model called the feature correlation analysis and association impact scale is explored to estimate the degree of intrusion scope threshold from the optimal features of network transaction data available for training. With the motivation gained from the model called “network intrusion detection by feature association impact scale” that was explored in our earlier work, a novel and improved meta-heuristic assessment strategy for intrusion prediction is derived. In this strategy, linear canonical correlation for feature optimization is used and feature association impact scale is explored from the selected optimal features. The experimental result indicates that the feature correlation has a significant impact towards minimizing the computational and time complexity of measuring the feature association impact scale.

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

    Science.gov (United States)

    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, pnetworking, 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.

  14. Visual analog rating of mood by people with aphasia.

    Science.gov (United States)

    Haley, Katarina L; Womack, Jennifer L; Harmon, Tyson G; Williams, Sharon W

    2015-08-01

    Considerable attention has been given to the identification of depression in stroke survivors with aphasia, but there is more limited information about other mood states. Visual analog scales are often used to collect subjective information from people with aphasia. However, the validity of these methods for communicating about mood has not been established in people with moderately to severely impaired language. The dual purposes of this study were to characterize the relative endorsement of negative and positive mood states in people with chronic aphasia after stroke and to examine congruent validity for visual analog rating methods for people with a range of aphasia severity. Twenty-three left-hemisphere stroke survivors with aphasia were asked to indicate their present mood by using two published visual analog rating methods. The congruence between the methods was estimated through correlation analysis, and scores for different moods were compared. Endorsement was significantly stronger for "happy" than for mood states with negative valence. At the same time, several participants displayed pronounced negative mood compared to previously published norms for neurologically healthy adults. Results from the two rating methods were moderately and positively correlated. Positive mood is prominent in people with aphasia who are in the chronic stage of recovery after stroke, but negative moods can also be salient and individual presentations are diverse. Visual analog rating methods are valid methods for discussing mood with people with aphasia; however, design optimization should be explored.

  15. Inference and Evolutionary Analysis of Genome-Scale Regulatory Networks in Large Phylogenies.

    Science.gov (United States)

    Koch, Christopher; Konieczka, Jay; Delorey, Toni; Lyons, Ana; Socha, Amanda; Davis, Kathleen; Knaack, Sara A; Thompson, Dawn; O'Shea, Erin K; Regev, Aviv; Roy, Sushmita

    2017-05-24

    Changes in transcriptional regulatory networks can significantly contribute to species evolution and adaptation. However, identification of genome-scale regulatory networks is an open challenge, especially in non-model organisms. Here, we introduce multi-species regulatory network learning (MRTLE), a computational approach that uses phylogenetic structure, sequence-specific motifs, and transcriptomic data, to infer the regulatory networks in different species. Using simulated data from known networks and transcriptomic data from six divergent yeasts, we demonstrate that MRTLE predicts networks with greater accuracy than existing methods because it incorporates phylogenetic information. We used MRTLE to infer the structure of the transcriptional networks that control the osmotic stress responses of divergent, non-model yeast species and then validated our predictions experimentally. Interrogating these networks reveals that gene duplication promotes network divergence across evolution. Taken together, our approach facilitates study of regulatory network evolutionary dynamics across multiple poorly studied species. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

  16. Modeling multiple time scale firing rate adaptation in a neural network of local field potentials.

    Science.gov (United States)

    Lundstrom, Brian Nils

    2015-02-01

    In response to stimulus changes, the firing rates of many neurons adapt, such that stimulus change is emphasized. Previous work has emphasized that rate adaptation can span a wide range of time scales and produce time scale invariant power law adaptation. However, neuronal rate adaptation is typically modeled using single time scale dynamics, and constructing a conductance-based model with arbitrary adaptation dynamics is nontrivial. Here, a modeling approach is developed in which firing rate adaptation, or spike frequency adaptation, can be understood as a filtering of slow stimulus statistics. Adaptation dynamics are modeled by a stimulus filter, and quantified by measuring the phase leads of the firing rate in response to varying input frequencies. Arbitrary adaptation dynamics are approximated by a set of weighted exponentials with parameters obtained by fitting to a desired filter. With this approach it is straightforward to assess the effect of multiple time scale adaptation dynamics on neural networks. To demonstrate this, single time scale and power law adaptation were added to a network model of local field potentials. Rate adaptation enhanced the slow oscillations of the network and flattened the output power spectrum, dampening intrinsic network frequencies. Thus, rate adaptation may play an important role in network dynamics.

  17. Mood-dependent memory.

    Science.gov (United States)

    Lewis, Penelope A; Critchley, Hugo D

    2003-10-01

    Have you ever noticed that when you are in a bad mood the whole world seems to be against you? More negative things seem to happen, and you even remember past episodes of your life in a more negative way than usual. Most of us have experienced this phenomenon, but few will have thought about how this mood might interact with our ability to remember. In a recent paper, Susanne Erk et al. shed light on a possible neural basis for this phenomena.

  18. Chronobiology and mood disorders

    OpenAIRE

    Wirz-Justice, Anna

    2003-01-01

    The clinical observations of diurnal variation of mood and early morning awakening in depression have been incorporated into established diagnostic systems, as has the seasonal modifier defining winter depression (seasonal affective disorder, SAD). Many circadian rhythms measured in depressive patients are abnormal: earlier in timing, diminished in amplitude, or of greater variability. Whether these disturbances are of etiological significance for the role of circadian rhythms in mood disorde...

  19. Comfort Foods and Mood

    Science.gov (United States)

    2008-07-01

    Comfort Foods and Mood Tracy Sbrocco, Ph.D. Assoc. Prof. Dept Medical & Clinical Psychology Uniformed Services University QuickTime™ and a...ORGANIZATION NAME(S) AND ADDRESS(ES) Dept Medical & Clinical Psychology Uniformed Services University Bethesda, MD 8. PERFORMING ORGANIZATION REPORT NUMBER...Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 Overview • Stress & eating • Does food improve mood? • Emotional eating • Comfort Foods

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

    Directory of Open Access Journals (Sweden)

    Seungseob Lee

    Full Text Available 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

  1. 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

  2. Music feels like moods feel

    Directory of Open Access Journals (Sweden)

    Kris eGoffin

    2014-04-01

    Full Text Available While it is widely accepted that music evokes moods, there is disagreement over whether music-induced moods are relevant to the aesthetic appreciation of music as such. The arguments against the aesthetic relevance of music-induced moods are: (1 moods cannot be intentionally directed at the music and (2 music-induced moods are highly subjective experiences and are therefore a kind of mind-wandering. This paper presents a novel account of musical moods that avoids these objections. It is correct to say that a listener's entire mood is not relevant to the aesthetic appreciation of music. However, the experience of mood consists of having different feelings. Music induces feelings that are intentionally directed at the music and clusters of these feelings can be recognized as typical of a specific mood. Therefore, mood-feelings are relevant to the aesthetic appreciation of music.

  3. 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.

  4. Enhanced storage capacity with errors in scale-free Hopfield neural networks: An analytical study.

    Science.gov (United States)

    Kim, Do-Hyun; Park, Jinha; Kahng, Byungnam

    2017-01-01

    The Hopfield model is a pioneering neural network model with associative memory retrieval. The analytical solution of the model in mean field limit revealed that memories can be retrieved without any error up to a finite storage capacity of O(N), where N is the system size. Beyond the threshold, they are completely lost. Since the introduction of the Hopfield model, the theory of neural networks has been further developed toward realistic neural networks using analog neurons, spiking neurons, etc. Nevertheless, those advances are based on fully connected networks, which are inconsistent with recent experimental discovery that the number of connections of each neuron seems to be heterogeneous, following a heavy-tailed distribution. Motivated by this observation, we consider the Hopfield model on scale-free networks and obtain a different pattern of associative memory retrieval from that obtained on the fully connected network: the storage capacity becomes tremendously enhanced but with some error in the memory retrieval, which appears as the heterogeneity of the connections is increased. Moreover, the error rates are also obtained on several real neural networks and are indeed similar to that on scale-free model networks.

  5. In the winning mood

    Directory of Open Access Journals (Sweden)

    Marieke de Vries

    2008-01-01

    Full Text Available The present research aimed to test the role of mood in the Iowa Gambling Task (IGT; Bechara et al., 1994. In the IGT, participants can win or lose money by picking cards from four different decks. They have to learn by experience that two decks are overall advantageous and two decks are overall disadvantageous. Previous studies have shown that at an early stage in this card-game, players begin to display a tendency towards the advantageous decks. Subsequent research suggested that at this stage, people base their decisions on conscious gut feelings (Wagar and Dixon, 2006. Based on empirical evidence for the relation between mood and cognitive processing-styles, we expected and consistently found that, compared to a negative mood state, reported and induced positive mood states increased this early tendency towards advantageous decks. Our results provide support for the idea that a positive mood causes stronger reliance on affective signals in decision-making than a negative mood.

  6. Propriétés psychométriques de la version française d'une échelle de mesure de l'intelligence émotionnelle perçue : la Trait Meta-Mood Scale (TMMS)/Psychometric properties of the French version of a scale measuring perceived emotional intelligence : the Trait Meta-Mood Scale (TMMS)

    National Research Council Canada - National Science Library

    Anne-Solène Maria; Léna Bourdier; Jeanne Duclos; Damien Ringuenet; Sylvie Berthoz

    2016-01-01

    ...). Besides TMMS, participants completed self-assessment questionnaires for affectivity (Shortened Beck Depression Inventory, State and Trait Anxiety Inventory, Positive and Negative emotion scale), alexithymia...

  7. STUDENTS’ ATTITUDES TOWARDS EDMODO, A SOCIAL LEARNING NETWORK: A SCALE DEVELOPMENT STUDY

    Directory of Open Access Journals (Sweden)

    Eyup YUNKUL

    2017-04-01

    Full Text Available Social Learning Networks (SLNs are the developed forms of Social Network Sites (SNSs adapted to educational environments, and they are used by quite a large population throughout the world. In addition, in related literature, there is no scale for the measurement of students’ attitudes towards such sites. The purpose of this study was to develop a scale to determine students’ attitudes towards Edmodo, a Social Learning Network (Edmodo Attitude Scale, EAS. The scale development process included reviewing the related literature, developing an item pool, asking for expert’s views, developing a draft form, carrying out two different applications for exploratory and confirmatory factor analyses and conducting validity and reliability analyses. The scale was developed in Turkish and applied online. The participants of the study were selected among undergraduate students who experienced Edmodo in a university in Turkey. At the end of the research process, a scale made up of 18 items and 4 factors was developed. The factors were found to be collaboration, usefulness, instructor support and self-confidence. Consequently, the scale could be said to be a valid and reliable attitude scale that could be used in learning environments which involves the use of a SLN.

  8. Screening for depressed mood in an adolescent psychiatric context by brief self-assessment scales -- testing psychometric validity of WHO-5 and BDI-6 indices by latent trait analyses

    DEFF Research Database (Denmark)

    Blom, Eva Henje; Bech, Per; Högberg, Göran

    2012-01-01

    ABSTRACT: BACKGROUND: Major depressive disorder is prevalent in the adolescent psychiatric clinical setting and often comorbid with other primary psychiatric diagnoses such as ADHD or social anxiety disorder. Systematic manual-based diagnostic procedures are recommended to identify such comorbidity...... of two such scales, which may be used in a two-step screening procedure, the WHO-Five Well-being Index (WHO-5) and the six-item version of Beck's Depression Inventory (BDI-6). METHOD: 66 adolescent psychiatric patients with a clinical diagnosis of major depressive disorder (MDD), 60 girls and 6 boys....... The BDI-6 may be recommended as a second step in the screening procedure, since it is statistically valid and has the ability to unidimensionally capture the severity of depressed mood....

  9. Robustness of scale-free networks to cascading failures induced by fluctuating loads.

    Science.gov (United States)

    Mizutaka, Shogo; Yakubo, Kousuke

    2015-07-01

    Taking into account the fact that overload failures in real-world functional networks are usually caused by extreme values of temporally fluctuating loads that exceed the allowable range, we study the robustness of scale-free networks against cascading overload failures induced by fluctuating loads. In our model, loads are described by random walkers moving on a network and a node fails when the number of walkers on the node is beyond the node capacity. Our results obtained by using the generating function method show that scale-free networks are more robust against cascading overload failures than Erdős-Rényi random graphs with homogeneous degree distributions. This conclusion is contrary to that predicted by previous works, which neglect the effect of fluctuations of loads.

  10. 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.

  11. Scaling Dissolved Nutrient Removal in River Networks: A Comparative Modeling Investigation

    Science.gov (United States)

    Ye, Sheng; Reisinger, Alexander J.; Tank, Jennifer L.; Baker, Michelle A.; Hall, Robert O.; Rosi, Emma J.; Sivapalan, Murugesu

    2017-11-01

    Along the river network, water, sediment, and nutrients are transported, cycled, and altered by coupled hydrological and biogeochemical processes. Our current understanding of the rates and processes controlling the cycling and removal of dissolved inorganic nutrients in river networks is limited due to a lack of empirical measurements in large, (nonwadeable), rivers. The goal of this paper was to develop a coupled hydrological and biogeochemical process model to simulate nutrient uptake at the network scale during summer base flow conditions. The model was parameterized with literature values from headwater streams, and empirical measurements made in 15 rivers with varying hydrological, biological, and topographic characteristics, to simulate nutrient uptake at the network scale. We applied the coupled model to 15 catchments describing patterns in uptake for three different solutes to determine the role of rivers in network-scale nutrient cycling. Model simulation results, constrained by empirical data, suggested that rivers contributed proportionally more to nutrient removal than headwater streams given the fraction of their length represented in a network. In addition, variability of nutrient removal patterns among catchments was varied among solutes, and as expected, was influenced by nutrient concentration and discharge. Net ammonium uptake was not significantly correlated with any environmental descriptor. In contrast, net daily nitrate removal was linked to suspended chlorophyll a (an indicator of primary producers) and land use characteristics. Finally, suspended sediment characteristics and agricultural land use were correlated with net daily removal of soluble reactive phosphorus, likely reflecting abiotic sorption dynamics. Rivers are understudied relative to streams, and our model suggests that rivers can contribute more to network-scale nutrient removal than would be expected based upon their representative fraction of network channel length.

  12. Using the reconstructed genome-scale human metabolic network to study physiology and pathology

    OpenAIRE

    Bordbar, Aarash; Palsson, Bernhard O.

    2012-01-01

    Metabolism plays a key role in many major human diseases. Generation of high-throughput omics data has ushered in a new era of systems biology. Genome-scale metabolic network reconstructions provide a platform to interpret omics data in a biochemically meaningful manner. The release of the global human metabolic network, Recon 1, in 2007 has enabled new systems biology approaches to study human physiology, pathology, and pharmacology. There are currently over 20 publications that utilize Reco...

  13. Tie Strength, Embeddedness, and Social Influence: A Large-Scale Networked Experiment

    OpenAIRE

    Walker, Dylan; Aral, Sinan Kayhan

    2012-01-01

    We leverage the newly emerging business analytical capability to rapidly deploy and iterate large-scale, microlevel, in vivo randomized experiments to understand how social influence in networks impacts consumer demand. Understanding peer influence is critical to estimating product demand and diffusion, creating effective viral marketing, and designing “network interventions” to promote positive social change. But several statistical challenges make it difficult to econometrically identify pe...

  14. Large-scale Multi-label Text Classification - Revisiting Neural Networks

    OpenAIRE

    Nam, Jinseok; Kim, Jungi; Mencía, Eneldo Loza; Gurevych, Iryna; Fürnkranz, Johannes

    2013-01-01

    Neural networks have recently been proposed for multi-label classification because they are able to capture and model label dependencies in the output layer. In this work, we investigate limitations of BP-MLL, a neural network (NN) architecture that aims at minimizing pairwise ranking error. Instead, we propose to use a comparably simple NN approach with recently proposed learning techniques for large-scale multi-label text classification tasks. In particular, we show that BP-MLL's ranking lo...

  15. Fast-scale network dynamics in human cortex have specific spectral covariance patterns

    OpenAIRE

    Freudenburg, Zachary V.; Gaona, Charles M.; Sharma, Mohit; Bundy, David T.; Breshears, Jonathan D.; Robert B Pless; Leuthardt, Eric C.

    2014-01-01

    How different cortical regions are coordinated during a cognitive task is fundamentally important to understanding brain function. At rest, the brain is subdivided into different functional networks that are bound together at very slow oscillating time scales. Less is understood about how this networked behavior operates during the brief moments of a cognitive operation. By recording brain signals directly from the surface of the human brain, we find that, when performing a simple speech task...

  16. Double and multiple knockout simulations for genome-scale metabolic network reconstructions

    OpenAIRE

    Goldstein, Yaron AB; Bockmayr, Alexander

    2015-01-01

    Background Constraint-based modeling of genome-scale metabolic network reconstructions has become a widely used approach in computational biology. Flux coupling analysis is a constraint-based method that analyses the impact of single reaction knockouts on other reactions in the network. Results We present an extension of flux coupling analysis for double and multiple gene or reaction knockouts, and develop corresponding algorithms for an in silico simulation. To evaluate our method, we perfor...

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

    Directory of Open Access Journals (Sweden)

    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

  18. How Did the Information Flow in the #AlphaGo Hashtag Network? A Social Network Analysis of the Large-Scale Information Network on Twitter.

    Science.gov (United States)

    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.

  19. Scale-Crossing Brokers and Network Governance of Urban Ecosystem Services: The Case of Stockholm

    Directory of Open Access Journals (Sweden)

    Henrik Ernstson

    2010-12-01

    Full Text Available Urban ecosystem services are crucial for human well-being and the livability of cities. A central challenge for sustaining ecosystem services lies in addressing scale mismatches between ecological processes on one hand, and social processes of governance on the other. This article synthesizes a set of case studies from urban green areas in Stockholm, Sweden - allotment gardens, urban parks, cemeteries and protected areas - and discusses how governmental agencies and civil society groups engaged in urban green area management can be linked through social networks so as to better match spatial scales of ecosystem processes. The article develops a framework that combines ecological scales with social network structure, with the latter being taken as the patterns of interaction between actor groups. Based on this framework, the article (1 assesses current ecosystem governance, and (2 develops a theoretical understanding of how social network structure influences ecosystem governance and how certain actors can work as agents to promote beneficial network structures. The main results show that the mesoscale of what is conceptualized as city scale green networks (i.e., functionally interconnected local green areas is not addressed by any actor in Stockholm, and that the management practices of civil society groups engaged in local ecosystem management play a crucial but neglected role in upholding ecosystem services. The article proposes an alternative network structure and discusses the role of midscale managers (for improving ecological functioning and scale-crossing brokers (engaged in practices to connect actors across ecological scales. Dilemmas, strategies, and practices for establishing this governance system are discussed.

  20. Dependence of River Network Scaling and Geomorphic Properties on Initial Conditions in Landscape Evolution Models

    Science.gov (United States)

    Poore, G. M.; Kieffer, S. W.

    2008-12-01

    Initial conditions affect river network scaling and geomorphic properties, but the effect has not been systematically studied. Previous numerical and experimental studies have found that initial conditions affect river network drainage patterns, determining whether patterns are more parallel or more dendritic. They have also found that some network properties depend on initial conditions. We investigated the effect of initial conditions in the context of numerical models, using simulations of a stream power law. A common initial condition consists of a flat or sloping surface combined with random fluctuations in elevation. We used these initial conditions and focused on the effect of the magnitude of initial slope and the magnitude of initial randomness on standard network scaling and geomorphic properties, such as the Hack exponent, sinuosity, and hypsometry. Preliminary results indicate that some of the scaling and geomorphic properties show a strong dependence on initial conditions, while others exhibit little or no dependence. The strength of dependence can be sensitive to the statistical methods employed. Our results are relevant to numerical and analog modeling methodologies. The results suggest that initial conditions deserve greater consideration in attempts to understand the emergence of scaling in river networks.

  1. Research on Large-Scale Road Network Partition and Route Search Method Combined with Traveler Preferences

    Directory of Open Access Journals (Sweden)

    De-Xin Yu

    2013-01-01

    Full Text Available Combined with improved Pallottino parallel algorithm, this paper proposes a large-scale route search method, which considers travelers’ route choice preferences. And urban road network is decomposed into multilayers effectively. Utilizing generalized travel time as road impedance function, the method builds a new multilayer and multitasking road network data storage structure with object-oriented class definition. Then, the proposed path search algorithm is verified by using the real road network of Guangzhou city as an example. By the sensitive experiments, we make a comparative analysis of the proposed path search method with the current advanced optimal path algorithms. The results demonstrate that the proposed method can increase the road network search efficiency by more than 16% under different search proportion requests, node numbers, and computing process numbers, respectively. Therefore, this method is a great breakthrough in the guidance field of urban road network.

  2. Scalable and Fully Distributed Localization in Large-Scale Sensor Networks

    Directory of Open Access Journals (Sweden)

    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.

  3. 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

  4. Explosive synchronization in clustered scale-free networks: Revealing the existence of chimera state

    Science.gov (United States)

    Berec, V.

    2016-02-01

    The collective dynamics of Kuramoto oscillators with a positive correlation between the incoherent and fully coherent domains in clustered scale-free networks is studied. Emergence of chimera states for the onsets of explosive synchronization transition is observed during an intermediate coupling regime when degree-frequency correlation is established for the hubs with the highest degrees. Diagnostic of the abrupt synchronization is revealed by the intrinsic spectral properties of the network graph Laplacian encoded in the heterogeneous phase space manifold, through extensive analytical investigation, presenting realistic MC simulations of nonlocal interactions in discrete time dynamics evolving on the network.

  5. Output Regulation of Large-Scale Hydraulic Networks with Minimal Steady State Power Consumption

    DEFF Research Database (Denmark)

    Jensen, Tom Nørgaard; Wisniewski, Rafal; De Persis, Claudio

    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 system is overactuated is exploited for minimizing the steady state electrical power consumption of the pumps in the system, while output regulation is maintained. The proposed control actions are decentralized in order to make changes in the structure of the hydraulic network easy to implement....

  6. 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

  7. 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

  8. Multi-scale structure and topological anomaly detection via a new network statistic: The onion decomposition

    Science.gov (United States)

    Hébert-Dufresne, Laurent; Grochow, Joshua A.; Allard, Antoine

    2016-08-01

    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.

  9. Intensive Working Memory Training Produces Functional Changes in Large-scale Frontoparietal Networks.

    Science.gov (United States)

    Thompson, Todd W; Waskom, Michael L; Gabrieli, John D E

    2016-04-01

    Working memory is central to human cognition, and intensive cognitive training has been shown to expand working memory capacity in a given domain. It remains unknown, however, how the neural systems that support working memory are altered through intensive training to enable the expansion of working memory capacity. We used fMRI to measure plasticity in activations associated with complex working memory before and after 20 days of training. Healthy young adults were randomly assigned to train on either a dual n-back working memory task or a demanding visuospatial attention task. Training resulted in substantial and task-specific expansion of dual n-back abilities accompanied by changes in the relationship between working memory load and activation. Training differentially affected activations in two large-scale frontoparietal networks thought to underlie working memory: the executive control network and the dorsal attention network. Activations in both networks linearly scaled with working memory load before training, but training dissociated the role of the two networks and eliminated this relationship in the executive control network. Load-dependent functional connectivity both within and between these two networks increased following training, and the magnitudes of increased connectivity were positively correlated with improvements in task performance. These results provide insight into the adaptive neural systems that underlie large gains in working memory capacity through training.

  10. Multi-scale structure and topological anomaly detection via a new network statistic: The onion decomposition.

    Science.gov (United States)

    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.

  11. 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

  12. 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

  13. 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.

  14. Scene Classification of Remote Sensing Image Based on Multi-scale Feature and Deep Neural Network

    Directory of Open Access Journals (Sweden)

    XU Suhui

    2016-07-01

    Full Text Available Aiming at low precision of remote sensing image scene classification owing to small sample sizes, a new classification approach is proposed based on multi-scale deep convolutional neural network (MS-DCNN, which is composed of nonsubsampled Contourlet transform (NSCT, deep convolutional neural network (DCNN, and multiple-kernel support vector machine (MKSVM. Firstly, remote sensing image multi-scale decomposition is conducted via NSCT. Secondly, the decomposing high frequency and low frequency subbands are trained by DCNN to obtain image features in different scales. Finally, MKSVM is adopted to integrate multi-scale image features and implement remote sensing image scene classification. The experiment results in the standard image classification data sets indicate that the proposed approach obtains great classification effect due to combining the recognition superiority to different scenes of low frequency and high frequency subbands.

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

    Directory of Open Access Journals (Sweden)

    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.

  16. The Path to Presence in Performance through Movement, Physiological Response, and Mood

    Science.gov (United States)

    Preeshl, Artemis; George, Gwen; Hicks, Wendy

    2015-01-01

    Presence may occur when actors are alert and relaxed in performance. A positive mood is associated with physical activity, but little is known about how movement qualities affect mood and vital signs of actors. This study examined the effects of vibratory, pendular, abrupt, and sustained movement qualities on the Brief Mood Introspection Scale,…

  17. Multirelational organization of large-scale social networks in an online world.

    Science.gov (United States)

    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.

  18. Alternative Path Communication in Wide-Scale Cluster-Tree Wireless Sensor Networks Using Inactive Periods.

    Science.gov (United States)

    Leão, Erico; Montez, Carlos; Moraes, Ricardo; Portugal, Paulo; Vasques, Francisco

    2017-05-06

    The IEEE 802.15.4/ZigBee cluster-tree topology is a suitable technology to deploy wide-scale Wireless Sensor Networks (WSNs). These networks are usually designed to support convergecast traffic, where all communication paths go through the PAN (Personal Area Network) coordinator. Nevertheless, peer-to-peer communication relationships may be also required for different types of WSN applications. That is the typical case of sensor and actuator networks, where local control loops must be closed using a reduced number of communication hops. The use of communication schemes optimised just for the support of convergecast traffic may result in higher network congestion and in a potentially higher number of communication hops. Within this context, this paper proposes an Alternative-Route Definition (ARounD) communication scheme for WSNs. The underlying idea of ARounD is to setup alternative communication paths between specific source and destination nodes, avoiding congested cluster-tree paths. These alternative paths consider shorter inter-cluster paths, using a set of intermediate nodes to relay messages during their inactive periods in the cluster-tree network. Simulation results show that the ARounD communication scheme can significantly decrease the end-to-end communication delay, when compared to the use of standard cluster-tree communication schemes. Moreover, the ARounD communication scheme is able to reduce the network congestion around the PAN coordinator, enabling the reduction of the number of message drops due to queue overflows in the cluster-tree network.

  19. Computational solution to automatically map metabolite libraries in the context of genome scale metabolic networks

    Directory of Open Access Journals (Sweden)

    Benjamin eMerlet

    2016-02-01

    Full Text Available This article describes a generic programmatic method for mapping chemical compound libraries on organism-specific metabolic networks from various databases (KEGG, BioCyc and flat file formats (SBML and Matlab files. We show how this pipeline was successfully applied to decipher the coverage of chemical libraries set up by two metabolomics facilities MetaboHub (French National infrastructure for metabolomics and fluxomics and Glasgow Polyomics on the metabolic networks available in the MetExplore web server. The present generic protocol is designed to formalize and reduce the volume of information transfer between the library and the network database. Matching of metabolites between libraries and metabolic networks is based on InChIs or InChIKeys and therefore requires that these identifiers are specified in both libraries and networks.In addition to providing covering statistics, this pipeline also allows the visualization of mapping results in the context of metabolic networks.In order to achieve this goal we tackled issues on programmatic interaction between two servers, improvement of metabolite annotation in metabolic networks and automatic loading of a mapping in genome scale metabolic network analysis tool MetExplore. It is important to note that this mapping can also be performed on a single or a selection of organisms of interest and is thus not limited to large facilities.

  20. Sleep Disturbances in Mood Disorders.

    Science.gov (United States)

    Rumble, Meredith E; White, Kaitlin Hanley; Benca, Ruth M

    2015-12-01

    The article provides an overview of common and differentiating self-reported and objective sleep disturbances seen in mood-disordered populations. The importance of considering sleep disturbances in the context of mood disorders is emphasized, because a large body of evidence supports the notion that sleep disturbances are a risk factor for onset, exacerbation, and relapse of mood disorders. In addition, potential mechanisms for sleep disturbance in depression, other primary sleep disorders that often occur with mood disorders, effects of antidepressant and mood-stabilizing drugs on sleep, and the adjunctive effect of treating sleep in patients with mood disorders are discussed. Copyright © 2015 Elsevier Inc. All rights reserved.

  1. Music feels like moods feel

    OpenAIRE

    Kris eGoffin

    2014-01-01

    While it is widely accepted that music evokes moods, there is disagreement over whether music-induced moods are relevant to the aesthetic appreciation of music as such. The arguments against the aesthetic relevance of music-induced moods are: (1) moods cannot be intentionally directed at the music and (2) music-induced moods are highly subjective experiences and are therefore a kind of mind-wandering. This paper presents a novel account of musical moods that avoids these objections. It is cor...

  2. BicNET: Flexible module discovery in large-scale biological networks using biclustering.

    Science.gov (United States)

    Henriques, Rui; Madeira, Sara C

    2016-01-01

    Despite the recognized importance of module discovery in biological networks to enhance our understanding of complex biological systems, existing methods generally suffer from two major drawbacks. First, there is a focus on modules where biological entities are strongly connected, leading to the discovery of trivial/well-known modules and to the inaccurate exclusion of biological entities with subtler yet relevant roles. Second, there is a generalized intolerance towards different forms of noise, including uncertainty associated with less-studied biological entities (in the context of literature-driven networks) and experimental noise (in the context of data-driven networks). Although state-of-the-art biclustering algorithms are able to discover modules with varying coherency and robustness to noise, their application for the discovery of non-dense modules in biological networks has been poorly explored and it is further challenged by efficiency bottlenecks. This work proposes Biclustering NETworks (BicNET), a biclustering algorithm to discover non-trivial yet coherent modules in weighted biological networks with heightened efficiency. Three major contributions are provided. First, we motivate the relevance of discovering network modules given by constant, symmetric, plaid and order-preserving biclustering models. Second, we propose an algorithm to discover these modules and to robustly handle noisy and missing interactions. Finally, we provide new searches to tackle time and memory bottlenecks by effectively exploring the inherent structural sparsity of network data. Results in synthetic network data confirm the soundness, efficiency and superiority of BicNET. The application of BicNET on protein interaction and gene interaction networks from yeast, E. coli and Human reveals new modules with heightened biological significance. BicNET is, to our knowledge, the first method enabling the efficient unsupervised analysis of large-scale network data for the discovery of

  3. Yin and Yang of disease genes and death genes between reciprocally scale-free biological networks.

    Science.gov (United States)

    Han, Hyun Wook; Ohn, Jung Hun; Moon, Jisook; Kim, Ju Han

    2013-11-01

    Biological networks often show a scale-free topology with node degree following a power-law distribution. Lethal genes tend to form functional hubs, whereas non-lethal disease genes are located at the periphery. Uni-dimensional analyses, however, are flawed. We created and investigated two distinct scale-free networks; a protein-protein interaction (PPI) and a perturbation sensitivity network (PSN). The hubs of both networks exhibit a low molecular evolutionary rate (P genes but not with disease genes, whereas PSN hubs are highly enriched with disease genes and drug targets but not with lethal genes. PPI hub genes are enriched with essential cellular processes, but PSN hub genes are enriched with environmental interaction processes, having more TATA boxes and transcription factor binding sites. It is concluded that biological systems may balance internal growth signaling and external stress signaling by unifying the two opposite scale-free networks that are seemingly opposite to each other but work in concert between death and disease.

  4. Sparse cliques trump scale-free networks in coordination and competition

    Science.gov (United States)

    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.

  5. 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.

  6. Light scattering optimization of chitin random network in ultrawhite beetle scales

    Science.gov (United States)

    Utel, Francesco; Cortese, Lorenzo; Pattelli, Lorenzo; Burresi, Matteo; Vignolini, Silvia; Wiersma, Diederik

    2017-09-01

    Among the natural white colored photonics structures, a bio-system has become of great interest in the field of disordered optical media: the scale of the white beetle Chyphochilus. Despite its low thickness, on average 7 μm, and low refractive index, this beetle exhibits extreme high brightness and unique whiteness. These properties arise from the interaction of light with a complex network of chitin nano filaments embedded in the interior of the scales. As it's been recently claimed, this could be a consequence of the peculiar morphology of the filaments network that, by means of high filling fraction (0.61) and structural anisotropy, optimizes the multiple scattering of light. We therefore performed a numerical analysis on the structural properties of the chitin network in order to understand their role in the enhancement of the scale scattering intensity. Modeling the filaments as interconnected rod shaped scattering centers, we numerically generated the spatial coordinates of the network components. Controlling the quantities that are claimed to play a fundamental role in the brightness and whiteness properties of the investigated system (filling fraction and average rods orientation, i.e. the anisotropy of the ensemble of scattering centers), we obtained a set of customized random networks. FDTD simulations of light transport have been performed on these systems, observing high reflectance for all the visible frequencies and proving the implemented algorithm to numerically generate the structures is suitable to investigate the dependence of reflectance by anisotropy.

  7. Identification and Description of Novel Mood Profile Clusters

    Science.gov (United States)

    Parsons-Smith, Renée L.; Terry, Peter C.; Machin, M. Anthony

    2017-01-01

    Mood profiling has been a popular assessment strategy since the 1970s, although little evidence exists of distinct mood profiles beyond the realm of sport and exercise. In the present study, we investigated clusters of mood profiles derived from the six subscales of the Brunel Mood Scale using the In The Mood website. Mood responses in three samples (n = 2,364, n = 2,303, n = 1,865) were analyzed using agglomerative, hierarchical cluster analysis, which distinguished six distinct and theoretically meaningful profiles. K-means clustering further refined the final parameter solution. Mood profiles identified were termed the iceberg, inverse iceberg, inverse Everest, shark fin, surface, and submerged profiles. Simultaneous multiple discriminant function analysis showed that cluster membership was correctly classified with a high degree of accuracy. Chi-squared tests indicated that the six mood profiles were unequally distributed according to the gender, age, and education of participants. Future research should investigate the antecedents, correlates and consequences of these six mood profile clusters.

  8. Identification and Description of Novel Mood Profile Clusters

    Directory of Open Access Journals (Sweden)

    Renée L. Parsons-Smith

    2017-11-01

    Full Text Available Mood profiling has been a popular assessment strategy since the 1970s, although little evidence exists of distinct mood profiles beyond the realm of sport and exercise. In the present study, we investigated clusters of mood profiles derived from the six subscales of the Brunel Mood Scale using the In The Mood website. Mood responses in three samples (n = 2,364, n = 2,303, n = 1,865 were analyzed using agglomerative, hierarchical cluster analysis, which distinguished six distinct and theoretically meaningful profiles. K-means clustering further refined the final parameter solution. Mood profiles identified were termed the iceberg, inverse iceberg, inverse Everest, shark fin, surface, and submerged profiles. Simultaneous multiple discriminant function analysis showed that cluster membership was correctly classified with a high degree of accuracy. Chi-squared tests indicated that the six mood profiles were unequally distributed according to the gender, age, and education of participants. Future research should investigate the antecedents, correlates and consequences of these six mood profile clusters.

  9. Dimensions of mood in mood-dependent memory.

    Science.gov (United States)

    Balch, W R; Myers, D M; Papotto, C

    1999-01-01

    In this investigation of the roles of 2 different dimensions of mood (pleasantness and arousal) in mood-dependent memory (MDM), participants generated words while listening to a selection of independently rated mood music (normative study and Experiment 5). Then they recalled the words while listening to another mood-music selection (Experiments 1-3) or to a verbal-mood scenario (Experiment 4). Changing only the dimension of mood pleasantness from generation to recall decreased memory whether the intended moods were explicitly defined or not. However, changing only arousal decreased memory only when moods were defined. Thus, pleasantness-dependent memory, but not arousal-dependent memory, occurred consistently. Although MDM also occurred with simultaneous changes in both dimensions, the effect was not significantly greater than that of pleasantness-dependent memory. The results are discussed in terms of 2-dimensional theories of emotion as applied to memory.

  10. 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.

  11. 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.

  12. Streaming Parallel GPU Acceleration of Large-Scale filter-based Spiking Neural Networks

    NARCIS (Netherlands)

    L.P. Slazynski (Leszek); S.M. Bohte (Sander)

    2012-01-01

    htmlabstractThe arrival of graphics processing (GPU) cards suitable for massively parallel computing promises a↵ordable large-scale neural network simulation previously only available at supercomputing facil- ities. While the raw numbers suggest that GPUs may outperform CPUs by at least an order of

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

    Directory of Open Access Journals (Sweden)

    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.

  14. Scaling laws for file dissemination in P2P networks with random contacts

    NARCIS (Netherlands)

    Núñez-Queija, R.; Prabhu, B.

    2008-01-01

    In this paper we obtain the scaling law for the mean broadcast time of a file in a P2P network with an initial population of N nodes. In the model, at Poisson rate lambda a node initiates a contact with another node chosen uniformly at random. This contact is said to be successful if the contacted

  15. Effect of morphology on water sorption in cellular solid foods. Part I: Pore scale network model

    NARCIS (Netherlands)

    Esveld, D.C.; Sman, van der R.G.M.; Dalen, van G.; Duynhoven, van J.P.M.; Meinders, M.B.J.

    2012-01-01

    A pore scale network model is developed to predict the dynamics of moisture diffusion into complex cellular solid foods like bread, crackers, and cereals. The morphological characteristics of the sample, including the characteristics of each cellular void and the open pore connections between them

  16. Emergence of Super Cooperation of Prisoner’s Dilemma Games on Scale-Free Networks

    Science.gov (United States)

    Li, Angsheng; Yong, Xi

    2015-01-01

    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. PMID:25643279

  17. 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

  18. A Java-Based Distributed Approach for Generating Large-Scale Social Network Graphs

    NARCIS (Netherlands)

    V.N. Serbanescu (Vlad); F.S. de Boer (Frank)

    2016-01-01

    textabstractBig Data management is an important topic of research not only in Computer Science, but also in several other domains. A challenging use of Big Data is the generation of large-scale graphs used to model social networks. In this paper, we present an actor-based Java library that eases the

  19. NETMORPH: a framework for the stochastic generation of large scale neuronal networks with realistic neuron morphologies

    NARCIS (Netherlands)

    Koene, R.A.; Tijms, B.; van Hees, P.; Postma, F.; de Ridder, A.; Ramakers, G.J.A.; van Pelt, J.; van Ooyen, A.

    2009-01-01

    We present a simulation framework, called NETMORPH, for the developmental generation of 3D large-scale neuronal networks with realistic neuron morphologies. In NETMORPH, neuronal morphogenesis is simulated from the perspective of the individual growth cone. For each growth cone in a growing axonal

  20. Scale-dependent effects of habitat area on species interaction networks: invasive species alter relationships

    Directory of Open Access Journals (Sweden)

    Sugiura Shinji

    2012-07-01

    Full Text Available Abstract Background The positive relationship between habitat area and species number is considered a fundamental rule in ecology. This relationship predicts that the link number of species interactions increases with habitat area, and structure is related to habitat area. Biological invasions can affect species interactions and area relationships. However, how these relationships change at different spatial scales has remained unexplored. We analysed understory plant–pollinator networks in seven temperate forest sites at 20 spatial scales (radius 120–2020 m to clarify scale-associated relationships between forest area and plant–pollinator networks. Results The pooled data described interactions between 18 plant (including an exotic and 89 pollinator (including an exotic species. The total number of species and the number of interaction links between plant and pollinator species were negatively correlated with forest area, with the highest correlation coefficient at radii of 1520 and 1620 m, respectively. These results are not concordant with the pattern predicted by species–area relationships. However, when associations with exotic species were excluded, the total number of species and the number of interaction links were positively correlated with forest area (the highest correlation coefficient at a radius of 820 m. The network structure, i.e., connectance and nestedness, was also related to forest area (the highest correlation coefficients at radii of 720–820 m, when associations with exotics were excluded. In the study area, the exotic plant species Alliaria petiolata, which has invaded relatively small forest patches surrounded by agricultural fields, may have supported more native pollinator species than initially expected. Therefore, this invasive plant may have altered the original relationships between forest area and plant–pollinator networks. Conclusions Our results demonstrate scale-dependent effects of forest

  1. Scale-dependent effects of habitat area on species interaction networks: invasive species alter relationships.

    Science.gov (United States)

    Sugiura, Shinji; Taki, Hisatomo

    2012-07-20

    The positive relationship between habitat area and species number is considered a fundamental rule in ecology. This relationship predicts that the link number of species interactions increases with habitat area, and structure is related to habitat area. Biological invasions can affect species interactions and area relationships. However, how these relationships change at different spatial scales has remained unexplored. We analysed understory plant-pollinator networks in seven temperate forest sites at 20 spatial scales (radius 120-2020 m) to clarify scale-associated relationships between forest area and plant-pollinator networks. The pooled data described interactions between 18 plant (including an exotic) and 89 pollinator (including an exotic) species. The total number of species and the number of interaction links between plant and pollinator species were negatively correlated with forest area, with the highest correlation coefficient at radii of 1520 and 1620 m, respectively. These results are not concordant with the pattern predicted by species-area relationships. However, when associations with exotic species were excluded, the total number of species and the number of interaction links were positively correlated with forest area (the highest correlation coefficient at a radius of 820 m). The network structure, i.e., connectance and nestedness, was also related to forest area (the highest correlation coefficients at radii of 720-820 m), when associations with exotics were excluded. In the study area, the exotic plant species Alliaria petiolata, which has invaded relatively small forest patches surrounded by agricultural fields, may have supported more native pollinator species than initially expected. Therefore, this invasive plant may have altered the original relationships between forest area and plant-pollinator networks. Our results demonstrate scale-dependent effects of forest area on the size and structure of plant-pollinator networks. We also suggest

  2. 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.

  3. Stress-related clinical pain and mood in women with chronic pain: moderating effects of depression and positive mood induction.

    Science.gov (United States)

    Davis, Mary C; Thummala, Kirti; Zautra, Alex J

    2014-08-01

    Chronic pain with comorbid depression is characterized by poor mood regulation and stress-related pain. This study aims to compare depressed and non-depressed pain patients in mood and pain stress reactivity and recovery, and test whether a post-stress positive mood induction moderates pain recovery. Women with fibromyalgia and/or osteoarthritis (N = 110) underwent interpersonal stress and were then randomly assigned by pain condition and depression status, assessed via the Center for Epidemiological Studies-Depression scale, to positive versus neutral mood induction. Depression did not predict stress-related reactivity in despondency, joviality, or clinical pain. However, depression × mood condition predicted recovery in joviality and clinical pain; depressed women recovered only in the positive mood condition, whereas non-depressed women recovered in both mood conditions. Depression does not alter pain and mood stress reactivity, but does impair recovery. Boosting post-stress jovial mood ameliorates pain recovery deficits in depressed patients, a finding relevant to chronic pain interventions.

  4. Detecting and mitigating abnormal events in large scale networks: budget constrained placement on smart grids

    Energy Technology Data Exchange (ETDEWEB)

    Santhi, Nandakishore [Los Alamos National Laboratory; Pan, Feng [Los Alamos National Laboratory

    2010-10-19

    Several scenarios exist in the modern interconnected world which call for an efficient network interdiction algorithm. Applications are varied, including various monitoring and load shedding applications on large smart energy grids, computer network security, preventing the spread of Internet worms and malware, policing international smuggling networks, and controlling the spread of diseases. In this paper we consider some natural network optimization questions related to the budget constrained interdiction problem over general graphs, specifically focusing on the sensor/switch placement problem for large-scale energy grids. Many of these questions turn out to be computationally hard to tackle. We present a particular form of the interdiction question which is practically relevant and which we show as computationally tractable. A polynomial-time algorithm will be presented for solving this problem.

  5. Decreased hedonic responsiveness during a brief depressive mood swing.

    Science.gov (United States)

    Willner, P; Healy, S

    1994-09-01

    Female volunteers completed the Fawcett-Clark Pleasure Capacity Scale (FCPCS) and rated the pleasantness and desirability of a taste stimulus (cheese), before and during a depressive mood swing. Mood change was induced by reading negative self-referent statements, with additional 'booster' periods of mood induction to prolong the duration of the mood swing. The mood induction procedure (MIP) caused a decrease in contentment and alertness, as derived from a set of visual analogue mood scales, and also decreased hedonic capacity, as measured by responses to the taste stimulus and by the FCPCS. No changes on any measure were shown by a control group who read an equivalent set of positive self-referent statements. Prior to the MIP, there were no significant correlations between mood measures and hedonic measures, or between taste responses and the FCPCS. However, there were significant correlations between the size of the changes in these various measures following the depressive MIP. The results suggest that hedonic capacity is responsive to acute depressive mood swings.

  6. The Association Between Insomnia Symptoms and Mood Changes During Exercise Among Patients Enrolled in Cardiac Rehabilitation.

    Science.gov (United States)

    Rouleau, Codie R; Horsley, Kristin J; Morse, Erin; Aggarwal, Sandeep; Bacon, Simon L; Campbell, Tavis S

    2015-01-01

    Insomnia symptoms (ie, difficulty falling asleep, difficulty staying asleep, and early awakenings) are common among patients with cardiovascular disease and may interfere with the beneficial impact of exercise on mood state. This study investigated the association of insomnia symptom severity with mood disturbance and with changes in mood state during exercise in a cardiac rehabilitation (CR) population. Insomnia symptom severity was measured using the Insomnia Severity Index upon admission to a 12-week CR program (n = 57). The Physical Activity Affect Scale was administered before and during a single bout of moderate intensity exercise to measure changes in mood state. Indices of mood disturbance included depressive symptoms (Hospital Anxiety and Depression Scale) and pre-exercise mood state (Physical Activity Affect Scale). Greater severity of insomnia symptoms was associated with less pleasant mood overall (r = -0.45, P positive affect (r = -0.39, P = .003), and worse fatigue (r = 0.36, P = .005); greater insomnia symptom severity also predicted greater improvements during exercise in both overall mood state (b = 0.26, standard error = 0.10, P = .009) and tranquility (b = 0.09, standard error = 0.04, P = .04), following statistical adjustment for demographic variables and pre-exercise mood state. Although CR patients reporting insomnia symptoms tend to experience daytime mood disturbance, they may benefit from mood-elevating properties of exercise. Future research is needed to help optimize mood during exercise, which may have implications for improving psychological distress and CR adherence.

  7. Large-Scale Hypoconnectivity Between Resting-State Functional Networks in Unmedicated Adolescent Major Depressive Disorder.

    Science.gov (United States)

    Sacchet, Matthew D; Ho, Tiffany C; Connolly, Colm G; Tymofiyeva, Olga; Lewinn, Kaja Z; Han, Laura Km; Blom, Eva H; Tapert, Susan F; Max, Jeffrey E; Frank, Guido Kw; Paulus, Martin P; Simmons, Alan N; Gotlib, Ian H; Yang, Tony T

    2016-11-01

    Major depressive disorder (MDD) often emerges during adolescence, a critical period of brain development. Recent resting-state fMRI studies of adults suggest that MDD is associated with abnormalities within and between resting-state networks (RSNs). Here we tested whether adolescent MDD is characterized by abnormalities in interactions among RSNs. Participants were 55 unmedicated adolescents diagnosed with MDD and 56 matched healthy controls. Functional connectivity was mapped using resting-state fMRI. We used the network-based statistic (NBS) to compare large-scale connectivity between groups and also compared the groups on graph metrics. We further assessed whether group differences identified using nodes defined from functionally defined RSNs were also evident when using anatomically defined nodes. In addition, we examined relations between network abnormalities and depression severity and duration. Finally, we compared intranetwork connectivity between groups and assessed the replication of previously reported MDD-related abnormalities in connectivity. The NBS indicated that, compared with controls, depressed adolescents exhibited reduced connectivity (pdepression was significantly correlated with reduced connectivity in this set of network interactions (p=0.020, corrected), specifically with reduced connectivity between components of the dorsal attention network. The dorsal attention network was also characterized by reduced intranetwork connectivity in the MDD group. Finally, we replicated previously reported abnormal connectivity in individuals with MDD. In summary, adolescents with MDD show hypoconnectivity between large-scale brain networks compared with healthy controls. Given that connectivity among these networks typically increases during adolescent neurodevelopment, these results suggest that adolescent depression is associated with abnormalities in neural systems that are still developing during this critical period.

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

    Science.gov (United States)

    2016-11-01

    Under this model, the amount of wireless traffic will skyrocket, and a single WiFi access point may This work is sponsored by the Defense Advanced...nonzero TTL it will rebroadcast that message. The protocol uses duplicate detection to try to limit the number of packets transmitted, which works ...Android phones are Samsung Galaxy S4 running Cyanogenmod 10.2. For the network, we use 802.11ac WiFi running in the 5GHz band and using a transmit power

  9. Congenital blindness is associated with large-scale reorganization of anatomical networks.

    Science.gov (United States)

    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.

  10. Weather change and Mood Disorder

    National Research Council Canada - National Science Library

    Jun Sato; Hiroyuki Mizoguchi; Kanoko Fukaya

    2011-01-01

      Mood disorder such as depression is serious problem in today's society. Weather change has been known to influence the condition of patients with mood disorder, and the seasonality in the evolvement of depressive symptoms...

  11. Integration of expression data in genome-scale metabolic network reconstructions

    Directory of Open Access Journals (Sweden)

    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.

  12. Modeling Peer-to-Peer Botnet on Scale-Free Network

    Directory of Open Access Journals (Sweden)

    Liping Feng

    2014-01-01

    Full Text Available Peer-to-peer (P2P botnets have emerged as one of the serious threats to Internet security. To prevent effectively P2P botnet, in this paper, a mathematical model which combines the scale-free trait of Internet with the formation of P2P botnet is presented. Explicit mathematical analysis demonstrates that the model has a globally stable endemic equilibrium when infection rate is greater than a critical value. Meanwhile, we find that, in scale-free network, the critical value is very little. Hence, it is unrealistic to completely dispel the P2P botnet. Numerical simulations show that one can take effective countermeasures to reduce the scale of P2P botnet or delay its outbreak. Our findings can provide meaningful instruction to network security management.

  13. A survey on routing protocols for large-scale wireless sensor networks.

    Science.gov (United States)

    Li, Changle; Zhang, Hanxiao; Hao, Binbin; Li, Jiandong

    2011-01-01

    With the advances in micro-electronics, wireless sensor devices have been made much smaller and more integrated, and large-scale wireless sensor networks (WSNs) based the cooperation among the significant amount of nodes have become a hot topic. "Large-scale" means mainly large area or high density of a network. Accordingly the routing protocols must scale well to the network scope extension and node density increases. A sensor node is normally energy-limited and cannot be recharged, and thus its energy consumption has a quite significant effect on the scalability of the protocol. To the best of our knowledge, currently the mainstream methods to solve the energy problem in large-scale WSNs are the hierarchical routing protocols. In a hierarchical routing protocol, all the nodes are divided into several groups with different assignment levels. The nodes within the high level are responsible for data aggregation and management work, and the low level nodes for sensing their surroundings and collecting information. The hierarchical routing protocols are proved to be more energy-efficient than flat ones in which all the nodes play the same role, especially in terms of the data aggregation and the flooding of the control packets. With focus on the hierarchical structure, in this paper we provide an insight into routing protocols designed specifically for large-scale WSNs. According to the different objectives, the protocols are generally classified based on different criteria such as control overhead reduction, energy consumption mitigation and energy balance. In order to gain a comprehensive understanding of each protocol, we highlight their innovative ideas, describe the underlying principles in detail and analyze their advantages and disadvantages. Moreover a comparison of each routing protocol is conducted to demonstrate the differences between the protocols in terms of message complexity, memory requirements, localization, data aggregation, clustering manner and

  14. Mood, food, and obesity

    Directory of Open Access Journals (Sweden)

    Minati eSingh

    2014-09-01

    Full Text Available Food is a potent natural reward and food intake is a complex process. Reward and gratification associated with food consumption leads to dopamine (DA production, which in turn activates reward and pleasure centers in the brain. An individual will repeatedly eat a particular food to experience this positive feeling of gratification. This type of repetitive behavior of food intake leads to the activation of brain reward pathways that eventually overrides other signals of satiety and hunger. Thus, a gratification habit through a favorable food leads to overeating and morbid obesity. Overeating and obesity stems from many biological factors engaging both central and peripheral systems in a bi-directional manner involving mood and emotions. Emotional eating and altered mood can also lead to altered food choice and intake leading to overeating and obesity. Research findings from human and animal studies support a two-way link between three concepts, mood, food, and obesity. The focus of this article is to provide an overview of complex nature of food intake where various biological factors link mood, food intake, and brain signaling that engages both peripheral and central nervous system signaling pathways in a bi-directional manner in obesity.

  15. Vitamins, Minerals, and Mood

    Science.gov (United States)

    Kaplan, Bonnie J.; Crawford, Susan G.; Field, Catherine J.; Simpson, J. Steven A.

    2007-01-01

    In this article, the authors explore the breadth and depth of published research linking dietary vitamins and minerals (micronutrients) to mood. Since the 1920s, there have been many studies on individual vitamins (especially B vitamins and Vitamins C, D, and E), minerals (calcium, chromium, iron, magnesium, zinc, and selenium), and vitamin-like…

  16. Mood, food, and obesity.

    Science.gov (United States)

    Singh, Minati

    2014-01-01

    Food is a potent natural reward and food intake is a complex process. Reward and gratification associated with food consumption leads to dopamine (DA) production, which in turn activates reward and pleasure centers in the brain. An individual will repeatedly eat a particular food to experience this positive feeling of gratification. This type of repetitive behavior of food intake leads to the activation of brain reward pathways that eventually overrides other signals of satiety and hunger. Thus, a gratification habit through a favorable food leads to overeating and morbid obesity. Overeating and obesity stems from many biological factors engaging both central and peripheral systems in a bi-directional manner involving mood and emotions. Emotional eating and altered mood can also lead to altered food choice and intake leading to overeating and obesity. Research findings from human and animal studies support a two-way link between three concepts, mood, food, and obesity. The focus of this article is to provide an overview of complex nature of food intake where various biological factors link mood, food intake, and brain signaling that engages both peripheral and central nervous system signaling pathways in a bi-directional manner in obesity.

  17. Epilepsy and Mood Disorders

    Directory of Open Access Journals (Sweden)

    Sermin Kesebir

    2012-03-01

    Full Text Available Mood disorders are the most common psychiatric comorbid disorder that affects quality of life and prognosis in epilepsy. The relation between depression and epilepsy is bidirectional. Not only the risk of having a depression among epilepsy cases is more than the healthy control cases, but also the risk of having epilepsy among depressive cases is more than the healthy control cases. People diagnosed with epilepsy are five times more likely than their peers to commit suicide. Moreover it seems that some epilepsy types like temporal lobe epilepsy have a much higher risk (25 times for suicide. Risk of suicide in epilepsy, which is independent from depression, increases more with the presence of depression. The common pathway between epilepsy, depression and suicide is hypofrontality and irregularity of serotonin metabolism. Contrary to depression, data on relationship between bipolar disorder and epilepsy is limited. However, mood disorder, mixed episodes with irritable character and mania are more frequent than assumed. As a matter of fact, both disorders share some common features. Both are episodic and can become chronic. Kindling phenomenon, irregularities in neurotransmitters, irregularities in voltage gate ion channels and irregularities in secondary messenger systems are variables that are presented in the etiologies of both disorders. Anticonvulsant drugs with mood regulatory effects are the common points of treatment. Understanding their mechanisms of action will clarify the pathophysiological processes. In this article, the relationhip between epilepsy and mood disorders, comorbidity, secondary states and treatment options in both cases have been discussed.

  18. Mood, food, and obesity

    Science.gov (United States)

    Singh, Minati

    2014-01-01

    Food is a potent natural reward and food intake is a complex process. Reward and gratification associated with food consumption leads to dopamine (DA) production, which in turn activates reward and pleasure centers in the brain. An individual will repeatedly eat a particular food to experience this positive feeling of gratification. This type of repetitive behavior of food intake leads to the activation of brain reward pathways that eventually overrides other signals of satiety and hunger. Thus, a gratification habit through a favorable food leads to overeating and morbid obesity. Overeating and obesity stems from many biological factors engaging both central and peripheral systems in a bi-directional manner involving mood and emotions. Emotional eating and altered mood can also lead to altered food choice and intake leading to overeating and obesity. Research findings from human and animal studies support a two-way link between three concepts, mood, food, and obesity. The focus of this article is to provide an overview of complex nature of food intake where various biological factors link mood, food intake, and brain signaling that engages both peripheral and central nervous system signaling pathways in a bi-directional manner in obesity. PMID:25225489

  19. MOOD AND PERFORMANCE IN YOUNG MALAYSIAN KARATEKA

    Directory of Open Access Journals (Sweden)

    Rebecca S. K. Wong

    2006-07-01

    Full Text Available In an attempt to test the conceptual model by Lane and Terry, the purposes of this study were 1 to assess mood states in non-depressed and depressed young karate athletes; 2 to assess mood states in relation to performance in young karate athletes. The participants were recruited from the 2004 Malaysian Games (72 males, 19.20 ± 1.16 years; 37 females, 18.78 ± 0.88 years. The athletes were divided into winners (medalists and losers. The Brunel Mood Scale (BRUMS was administered prior to the start of competition. MANOVA was employed to treat the data, while Pearson correlations were calculated for mood states in each depressed mood group and by gender. In terms of non-depressed and depressed mood, tension in the females was higher in the depressed group (5.61 ± 3.02 vs. 3.11 ± 1.90, p = 0.026, eta2 = 0.133, as was fatigue (3.64 ± 2.61 vs. 0.89 ± 1.69, p = 0.006, eta2 = 0.199. Tension in the males was higher in the depressed group (4.41 ± 2.52 vs. 1.50 ± 1.55, p < 0.001, eta2 = 0.215, as was anger (1.43 ± 1.88 vs. 0.25 ± 1.00, p = 0.019, eta2 = 0.076. The highest associations among mood subscales were between anger and depression (r = 0.57, and between depression and fatigue ( r = 0.55 in depressed males. The female winning karateka scored higher on anger (3.08 ± 2.96 vs. 1.29 ± 2.24, p = 0.046, eta2 = 0.109. The highest correlations between mood dimensions in depressed females were between depression and anger (r = 0.85 and between depression and confusion (r = 0.85. Contrary to previous research on the influence of depression on anger, only the female winners scored higher on anger. Several negative mood dimensions were higher in both male and female depressed groups, lending some support to the conceptual model advanced by Lane and Terry

  20. Diurnal Mood Fluctuation and Age.

    Science.gov (United States)

    Templer, Donald I.; And Others

    1981-01-01

    Studied the nature of diurnal mood variations in 173 persons aged 13 to 82. Results indicated adolescents and young adults tended to report better moods toward evening while middle-aged and elderly persons reported better moods in the morning. Limited findings suggest the opposite trends for depressed psychiatric patients (Author/JA)

  1. Mood disorders in Parkinson's disease.

    Science.gov (United States)

    Tan, Louis C S

    2012-01-01

    An increasing emphasis has been placed on the identification and management of non-motor features of Parkinson's disease (PD). Of the behavioural disorders in PD, mood disorders are amongst the most common and can occur in both early and late stages of PD. In some cases, these problems may even precede the development of motor symptoms of PD. These disorders have a major impact in the quality of life and affect daily function. This review will focus on depression, anxiety and apathy, and will discuss the epidemiological, clinical features, diagnosis and management of these disorders. The diagnosis and evaluation of these problems remain a challenge in view of the overlapping symptoms between these disorders and also with the clinical features of PD. The development and validation of diagnostic criteria and rating scales for these disorders remain a priority particularly in relation to anxiety and apathy. Another gap in the management of these disorders is the limited empirical evidence for the treatment of these problems. There is therefore an urgent need for systematic studies in the pharmacological and non-pharmacological management of these disorders to enable a holistic and evidence-based approach to the management of mood disorders in PD. Copyright © 2011 Elsevier Ltd. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    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.

  3. The feasibility of genome-scale biological network inference using Graphics Processing Units.

    Science.gov (United States)

    Thiagarajan, Raghuram; Alavi, Amir; Podichetty, Jagdeep T; Bazil, Jason N; Beard, Daniel A

    2017-01-01

    Systems research spanning fields from biology to finance involves the identification of models to represent the underpinnings of complex systems. Formal approaches for data-driven identification of network interactions include statistical inference-based approaches and methods to identify dynamical systems models that are capable of fitting multivariate data. Availability of large data sets and so-called 'big data' applications in biology present great opportunities as well as major challenges for systems identification/reverse engineering applications. For example, both inverse identification and forward simulations of genome-scale gene regulatory network models pose compute-intensive problems. This issue is addressed here by combining the processing power of Graphics Processing Units (GPUs) and a parallel reverse engineering algorithm for inference of regulatory networks. It is shown that, given an appropriate data set, information on genome-scale networks (systems of 1000 or more state variables) can be inferred using a reverse-engineering algorithm in a matter of days on a small-scale modern GPU cluster.

  4. High Fidelity Simulations of Large-Scale Wireless Networks (Plus-Up)

    Energy Technology Data Exchange (ETDEWEB)

    Onunkwo, Uzoma [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2015-11-01

    Sandia has built a strong reputation in scalable network simulation and emulation for cyber security studies to protect our nation’s critical information infrastructures. Georgia Tech has preeminent reputation in academia for excellence in scalable discrete event simulations, with strong emphasis on simulating cyber networks. Many of the experts in this field, such as Dr. Richard Fujimoto, Dr. George Riley, and Dr. Chris Carothers, have strong affiliations with Georgia Tech. The collaborative relationship that we intend to immediately pursue is in high fidelity simulations of practical large-scale wireless networks using ns-3 simulator via Dr. George Riley. This project will have mutual benefits in bolstering both institutions’ expertise and reputation in the field of scalable simulation for cyber-security studies. This project promises to address high fidelity simulations of large-scale wireless networks. This proposed collaboration is directly in line with Georgia Tech’s goals for developing and expanding the Communications Systems Center, the Georgia Tech Broadband Institute, and Georgia Tech Information Security Center along with its yearly Emerging Cyber Threats Report. At Sandia, this work benefits the defense systems and assessment area with promise for large-scale assessment of cyber security needs and vulnerabilities of our nation’s critical cyber infrastructures exposed to wireless communications.

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

    Directory of Open Access Journals (Sweden)

    German Villalba

    2017-03-01

    Full Text Available 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.

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

    Directory of Open Access Journals (Sweden)

    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.

  7. 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.

  8. 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

  9. Sign: large-scale gene network estimation environment for high performance computing.

    Science.gov (United States)

    Tamada, Yoshinori; Shimamura, Teppei; Yamaguchi, Rui; Imoto, Seiya; Nagasaki, Masao; Miyano, Satoru

    2011-01-01

    Our research group is currently developing software for estimating large-scale gene networks from gene expression data. The software, called SiGN, is specifically designed for the Japanese flagship supercomputer "K computer" which is planned to achieve 10 petaflops in 2012, and other high performance computing environments including Human Genome Center (HGC) supercomputer system. SiGN is a collection of gene network estimation software with three different sub-programs: SiGN-BN, SiGN-SSM and SiGN-L1. In these three programs, five different models are available: static and dynamic nonparametric Bayesian networks, state space models, graphical Gaussian models, and vector autoregressive models. All these models require a huge amount of computational resources for estimating large-scale gene networks and therefore are designed to be able to exploit the speed of 10 petaflops. The software will be available freely for "K computer" and HGC supercomputer system users. The estimated networks can be viewed and analyzed by Cell Illustrator Online and SBiP (Systems Biology integrative Pipeline). The software project web site is available at http://sign.hgc.jp/ .

  10. Chronobiology and mood disorders.

    Science.gov (United States)

    Wirz-Justice, Anna

    2003-12-01

    The clinical observations of diurnal variation of mood and early morning awakening in depression have been incorporated into established diagnostic systems, as has the seasonal modifier defining winter depression (seasonal affective disorder, SAD). Many circadian rhythms measured in depressive patients are abnormal: earlier in timing, diminished in amplitude, or of greater variability. Whether these disturbances are of etiological significance for the role of circadian rhythms in mood disorders, or a consequence of altered behavior can only be dissected out with stringent protocols (eg, constant routine or forced desynchrony). These protocols quantify contributions of the circadian pacemaker and a homeostatic sleep process impacting on mood, energy, appetite, and sleep. Future studies will elucidate any allelic mutations in "circadian clock" -related or "sleep"-related genes in depression. With respect to treatment, antidepressants and mood stabilizers have no consistent effect on circadian rhythmicity. The most rapid antidepressant modality known so far is nonpharmacological: total or partial sleep deprivation in the second half of the night. The disadvantage of sleep deprivation, that most patients relapse after recovery sleep, can be prevented by coadministration of lithium, pindolol, serotonin (5-HT) reuptake inhibitors, bright light, or a subsequent phase-advance procedure. Phase advance of the sleep-wake cycle alone also has rapid effects on depressed mood, which lasts longer than sleep deprivation. Light is the treatment of choice for SAD and may prove to be useful for nonseasonal depression, alone or as an adjunct to medication. Chronobiological concepts emphasize the important role of zeitgebers to stabilize phase, light being the most important, but dark (and rest) periods, regularity of social schedules and meal times, and use of melatonin or its analogues should also be considered. Advances in chronobiology continue to contribute novel treatments for

  11. 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.

  12. TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks

    Directory of Open Access Journals (Sweden)

    Jensen Paul A

    2011-09-01

    Full Text Available Abstract Background Several methods have been developed for analyzing genome-scale models of metabolism and transcriptional regulation. Many of these methods, such as Flux Balance Analysis, use constrained optimization to predict relationships between metabolic flux and the genes that encode and regulate enzyme activity. Recently, mixed integer programming has been used to encode these gene-protein-reaction (GPR relationships into a single optimization problem, but these techniques are often of limited generality and lack a tool for automating the conversion of rules to a coupled regulatory/metabolic model. Results We present TIGER, a Toolbox for Integrating Genome-scale Metabolism, Expression, and Regulation. TIGER converts a series of generalized, Boolean or multilevel rules into a set of mixed integer inequalities. The package also includes implementations of existing algorithms to integrate high-throughput expression data with genome-scale models of metabolism and transcriptional regulation. We demonstrate how TIGER automates the coupling of a genome-scale metabolic model with GPR logic and models of transcriptional regulation, thereby serving as a platform for algorithm development and large-scale metabolic analysis. Additionally, we demonstrate how TIGER's algorithms can be used to identify inconsistencies and improve existing models of transcriptional regulation with examples from the reconstructed transcriptional regulatory network of Saccharomyces cerevisiae. Conclusion The TIGER package provides a consistent platform for algorithm development and extending existing genome-scale metabolic models with regulatory networks and high-throughput data.

  13. 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...... of propulsion power. The model was optimized using a double cross validation procedure. The network was able to predict the propulsion power with accuracy between 0.8-1.7% using onboard measurement system data and 7% from manually acquired noon reports....

  14. 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.

  15. 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.

  16. Large-scale brain networks are distinctly affected in right and left mesial temporal lobe epilepsy.

    Science.gov (United States)

    de Campos, Brunno Machado; Coan, Ana Carolina; Lin Yasuda, Clarissa; Casseb, Raphael Fernandes; Cendes, Fernando

    2016-09-01

    Mesial temporal lobe epilepsy (MTLE) with hippocampus sclerosis (HS) is associated with functional and structural alterations extending beyond the temporal regions and abnormal pattern of brain resting state networks (RSNs) connectivity. We hypothesized that the interaction of large-scale RSNs is differently affected in patients with right- and left-MTLE with HS compared to controls. We aimed to determine and characterize these alterations through the analysis of 12 RSNs, functionally parceled in 70 regions of interest (ROIs), from resting-state functional-MRIs of 99 subjects (52 controls, 26 right- and 21 left-MTLE patients with HS). Image preprocessing and statistical analysis were performed using UF(2) C-toolbox, which provided ROI-wise results for intranetwork and internetwork connectivity. Intranetwork abnormalities were observed in the dorsal default mode network (DMN) in both groups of patients and in the posterior salience network in right-MTLE. Both groups showed abnormal correlation between the dorsal-DMN and the posterior salience, as well as between the dorsal-DMN and the executive-control network. Patients with left-MTLE also showed reduced correlation between the dorsal-DMN and visuospatial network and increased correlation between bilateral thalamus and the posterior salience network. The ipsilateral hippocampus stood out as a central area of abnormalities. Alterations on left-MTLE expressed a low cluster coefficient, whereas the altered connections on right-MTLE showed low cluster coefficient in the DMN but high in the posterior salience regions. Both right- and left-MTLE patients with HS have widespread abnormal interactions of large-scale brain networks; however, all parameters evaluated indicate that left-MTLE has a more intricate bihemispheric dysfunction compared to right-MTLE. Hum Brain Mapp 37:3137-3152, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. © 2016 The Authors Human Brain Mapping Published by

  17. 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

  18. 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.

  19. Extraversion is encoded by scale-free dynamics of default mode network.

    Science.gov (United States)

    Lei, Xu; Zhao, Zhiying; Chen, Hong

    2013-07-01

    Resting-state functional Magnetic Resonance Imaging (rsfMRI) is a powerful tool to investigate neurological and psychiatric diseases. Recently, the evidences linking the scaling properties of resting-state activity and the personality have been accumulated. However, it remains unknown whether the personality is associated with the scale-free dynamics of default mode network (DMN) - the most widely studied network in the rsfMRI literatures. To investigate this question, we estimated the Hurst exponent, quantifying long memory of a time-series, in DMN of rsfMRI in 20 healthy individuals. The Hurst exponent in DMN, whether extracted by independent component analysis (ICA) or region of interest (ROI), was significantly associated with the extraversion score of the revised Eysenck Personality Questionnaire. Specifically, longer memory in DMN corresponded to lower extraversion. We provide evidences for an association between individual differences in personality and scaling dynamics in DMN, whose alteration has been previously linked with introspective cognition. This association might arise from the efficiency in online information processing. Our results suggest that personality trait may be reflected by the scaling property of resting-state networks. Copyright © 2012 Elsevier Inc. All rights reserved.

  20. Accelerating a Network Model of Care: Taking a Social Innovation to Scale

    Directory of Open Access Journals (Sweden)

    Kerry Byrne

    2012-07-01

    Full Text Available Government-funded systems of health and social care are facing enormous fiscal and human-resource challenges. The space for innovation in care is wide open and new disruptive patterns are emerging. These include self-management and personal budgets, participatory and integrated care, supported decision making and a renewed focus on prevention. Taking these disruptive patterns to scale can be accelerated by a technologically enabled shift to a network model of care to co-create the best outcomes for individuals, family caregivers, and health and social care organizations. The connections, relationships, and activities within an individual’s personal network lay the foundation for care that health and social care systems/policy must simultaneously support and draw on for positive outcomes. Practical tools, adequate information, and tangible resources are required to coordinate and sustain care. Tyze Personal Networks is a social venture that uses technology to engage and inform the individual, their personal networks, and their care providers to co-create the best outcomes. In this article, we demonstrate how Tyze contributes to a shift to a network model of care by strengthening our networks and enhancing partnerships between care providers, individuals, and family and friends.

  1. 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.

  2. Harnessing diversity towards the reconstructing of large scale gene regulatory networks.

    Directory of Open Access Journals (Sweden)

    Takeshi Hase

    Full Text Available Elucidating gene regulatory network (GRN from large scale experimental data remains a central challenge in systems biology. Recently, numerous techniques, particularly consensus driven approaches combining different algorithms, have become a potentially promising strategy to infer accurate GRNs. Here, we develop a novel consensus inference algorithm, TopkNet that can integrate multiple algorithms to infer GRNs. Comprehensive performance benchmarking on a cloud computing framework demonstrated that (i a simple strategy to combine many algorithms does not always lead to performance improvement compared to the cost of consensus and (ii TopkNet integrating only high-performance algorithms provide significant performance improvement compared to the best individual algorithms and community prediction. These results suggest that a priori determination of high-performance algorithms is a key to reconstruct an unknown regulatory network. Similarity among gene-expression datasets can be useful to determine potential optimal algorithms for reconstruction of unknown regulatory networks, i.e., if expression-data associated with known regulatory network is similar to that with unknown regulatory network, optimal algorithms determined for the known regulatory network can be repurposed to infer the unknown regulatory network. Based on this observation, we developed a quantitative measure of similarity among gene-expression datasets and demonstrated that, if similarity between the two expression datasets is high, TopkNet integrating algorithms that are optimal for known dataset perform well on the unknown dataset. The consensus framework, TopkNet, together with the similarity measure proposed in this study provides a powerful strategy towards harnessing the wisdom of the crowds in reconstruction of unknown regulatory networks.

  3. Harnessing diversity towards the reconstructing of large scale gene regulatory networks.

    Science.gov (United States)

    Hase, Takeshi; Ghosh, Samik; Yamanaka, Ryota; Kitano, Hiroaki

    2013-01-01

    Elucidating gene regulatory network (GRN) from large scale experimental data remains a central challenge in systems biology. Recently, numerous techniques, particularly consensus driven approaches combining different algorithms, have become a potentially promising strategy to infer accurate GRNs. Here, we develop a novel consensus inference algorithm, TopkNet that can integrate multiple algorithms to infer GRNs. Comprehensive performance benchmarking on a cloud computing framework demonstrated that (i) a simple strategy to combine many algorithms does not always lead to performance improvement compared to the cost of consensus and (ii) TopkNet integrating only high-performance algorithms provide significant performance improvement compared to the best individual algorithms and community prediction. These results suggest that a priori determination of high-performance algorithms is a key to reconstruct an unknown regulatory network. Similarity among gene-expression datasets can be useful to determine potential optimal algorithms for reconstruction of unknown regulatory networks, i.e., if expression-data associated with known regulatory network is similar to that with unknown regulatory network, optimal algorithms determined for the known regulatory network can be repurposed to infer the unknown regulatory network. Based on this observation, we developed a quantitative measure of similarity among gene-expression datasets and demonstrated that, if similarity between the two expression datasets is high, TopkNet integrating algorithms that are optimal for known dataset perform well on the unknown dataset. The consensus framework, TopkNet, together with the similarity measure proposed in this study provides a powerful strategy towards harnessing the wisdom of the crowds in reconstruction of unknown regulatory networks.

  4. Scaling from gauge and scalar radiation in Abelian-Higgs string networks

    Science.gov (United States)

    Hindmarsh, Mark; Lizarraga, Joanes; Urrestilla, Jon; Daverio, David; Kunz, Martin

    2017-07-01

    We investigate cosmic string networks in the Abelian Higgs model using data from a campaign of large-scale numerical simulations on lattices of up to 409 63 grid points. We observe scaling or self-similarity of the networks over a wide range of scales and estimate the asymptotic values of the mean string separation in horizon length units ξ ˙ and of the mean square string velocity v¯2 in the continuum and large time limits. The scaling occurs because the strings lose energy into classical radiation of the scalar and gauge fields of the Abelian Higgs model. We quantify the energy loss with a dimensionless radiative efficiency parameter and show that it does not vary significantly with lattice spacing or string separation. This implies that the radiative energy loss underlying the scaling behavior is not a lattice artifact, and justifies the extrapolation of measured network properties to large times for computations of cosmological perturbations. We also show that the core growth method, which increases the defect core width with time to extend the dynamic range of simulations, does not introduce significant systematic error. We compare ξ ˙ and v¯2 to values measured in simulations using the Nambu-Goto approximation, finding that the latter underestimate the mean string separation by about 25%, and overestimate v¯2 by about 10%. The scaling of the string separation implies that string loops decay by the emission of massive radiation within a Hubble time in field theory simulations, in contrast to the Nambu-Goto scenario which neglects this energy loss mechanism. String loops surviving for only one Hubble time emit much less gravitational radiation than in the Nambu-Goto scenario and are consequently subject to much weaker gravitational wave constraints on their tension.

  5. Personality Does not Influence Exercise-Induced Mood Enhancement Among Female Exercisers.

    Science.gov (United States)

    Lane, Andrew M; Milton, Karen E; Terry, Peter C

    2005-09-01

    The present study investigated the influence of personality on exercise-induced mood changes. It was hypothesised that (a) exercise would be associated with significant mood enhancement across all personality types, (b) extroversion would be associated with positive mood and neuroticism with negative mood both pre- and post-exercise, and (c) personality measures would interact with exercise-induced mood changes. Participants were 90 female exercisers (M = 25.8 yr, SD = 9.0 yr) who completed the Eysenck Personality Inventory (EPI) once and the Brunel Mood Scale (BRUMS) before and after a 60-minute exercise session. Median splits were used to group participants into four personality types: stable introverts (n = 25), stable extroverts (n = 20), neurotic introverts (n = 26), and neurotic extroverts (n = 19). Repeated measures MANOVA showed significant mood enhancement following exercise across all personality types. Neuroticism was associated with negative mood scores pre- and post-exercise but the effect of extroversion on reported mood was relatively weak. There was no significant interaction effect between exercise-induced mood enhancement and personality. In conclusion, findings lend support to the notion that exercise is associated with improved mood. However, findings show that personality did not influence this effect, although neuroticism was associated with negative mood. Key PointsResearch in general psychology has found that stable personality trait are associated changes in mood states. Ninety females exercisers completed a personality test and mood scales before and after exercise. Results indicated mood changes were not associated with personality, although neuroticism was associated with negative mood.

  6. A Japanese version of Mother-to-Infant Bonding Scale: factor structure, longitudinal changes and links with maternal mood during the early postnatal period in Japanese mothers

    OpenAIRE

    Yoshida, Keiko; Yamashita, Hiroshi; Conroy, Susan; Marks, Maureen; Kumar, Chianni

    2012-01-01

    The objectives of this study were (1) to develop a Japanese version of Mother-to- Infant Bonding Scale Japanese version (MIBS-J) based on Kumar’s Mother Infant Bonding Questionnaire that could be used to screen the general population for problems in the mother’s feelings towards her new baby and to validate it for clinical use and (2) to examine the factor structure of the items and create subscales of the questionnaire for the Japanese version. The MIBS-J is a simple self-report questionnair...

  7. Spreading dynamics of an e-commerce preferential information model on scale-free networks

    Science.gov (United States)

    Wan, Chen; Li, Tao; Guan, Zhi-Hong; Wang, Yuanmei; Liu, Xiongding

    2017-02-01

    In order to study the influence of the preferential degree and the heterogeneity of underlying networks on the spread of preferential e-commerce information, we propose a novel susceptible-infected-beneficial model based on scale-free networks. The spreading dynamics of the preferential information are analyzed in detail using the mean-field theory. We determine the basic reproductive number and equilibria. The theoretical analysis indicates that the basic reproductive number depends mainly on the preferential degree and the topology of the underlying networks. We prove the global stability of the information-elimination equilibrium. The permanence of preferential information and the global attractivity of the information-prevailing equilibrium are also studied in detail. Some numerical simulations are presented to verify the theoretical results.

  8. Computing global structural balance in large-scale signed social networks.

    Science.gov (United States)

    Facchetti, Giuseppe; Iacono, Giovanni; Altafini, Claudio

    2011-12-27

    Structural balance theory affirms that signed social networks (i.e., graphs whose signed edges represent friendly/hostile interactions among individuals) tend to be organized so as to avoid conflictual situations, corresponding to cycles of negative parity. Using an algorithm for ground-state calculation in large-scale Ising spin glasses, in this paper we compute the global level of balance of very large online social networks and verify that currently available networks are indeed extremely balanced. This property is explainable in terms of the high degree of skewness of the sign distributions on the nodes of the graph. In particular, individuals linked by a large majority of negative edges create mostly "apparent disorder," rather than true "frustration."

  9. Optimal control strategy for a novel computer virus propagation model on scale-free networks

    Science.gov (United States)

    Zhang, Chunming; Huang, Haitao

    2016-06-01

    This paper aims to study the combined impact of reinstalling system and network topology on the spread of computer viruses over the Internet. Based on scale-free network, this paper proposes a novel computer viruses propagation model-SLBOSmodel. A systematic analysis of this new model shows that the virus-free equilibrium is globally asymptotically stable when its spreading threshold is less than one; nevertheless, it is proved that the viral equilibrium is permanent if the spreading threshold is greater than one. Then, the impacts of different model parameters on spreading threshold are analyzed. Next, an optimally controlled SLBOS epidemic model on complex networks is also studied. We prove that there is an optimal control existing for the control problem. Some numerical simulations are finally given to illustrate the main results.

  10. Micro-connectomics: probing the organization of neuronal networks at the cellular scale.

    Science.gov (United States)

    Schröter, Manuel; Paulsen, Ole; Bullmore, Edward T

    2017-03-01

    Defining the organizational principles of neuronal networks at the cellular scale, or micro-connectomics, is a key challenge of modern neuroscience. In this Review, we focus on graph theoretical parameters of micro-connectome topology, often informed by economical principles that conceptually originated with Ramón y Cajal's conservation laws. First, we summarize results from studies in intact small organisms and in samples from larger nervous systems. We then evaluate the evidence for an economical trade-off between biological cost and functional value in the organization of neuronal networks. Various results suggest that many aspects of neuronal network organization are indeed the outcome of competition between these two fundamental selection pressures.

  11. A Congestion Control Strategy for Power Scale-Free Communication Network

    Directory of Open Access Journals (Sweden)

    Min Xiang

    2017-10-01

    Full Text Available The scale-free topology of power communication network leads to more data flow in less hub nodes, which can cause local congestion. Considering the differences of the nodes’ delivery capacity and cache capacity, an integrated routing based on the communication service classification is proposed to reduce network congestion. In the power communication network, packets can be classified as key operational services (I-level and affairs management services (II-level. The shortest routing, which selects the path of the least hops, is adopted to transmit I-level packets. The load-balanced global dynamic routing, which uses the node’s queue length and delivery capacity to establish the cost function and chooses the path with minimal cost, is adopted to transmit II-level packets. The simulation results show that the integrated routing has a larger critical packet generation rate and can effectively reduce congestion.

  12. 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

    Co-clustering is a problem of both theoretical and practical importance, e.g., market basket analysis and collaborative filtering, and in web scale text processing. We state the co-clustering problem in terms of non-parametric generative models which can address the issue of estimating the number...... 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...... memory consumption on the GPU we exploit the structure of the posterior likelihood to obtain a decomposition that easily allows model estimation of the co-clustering problem on arbitrary large networks as well as distributed estimation on multiple GPUs. Finally we evaluate the implementation on real...

  13. Effects of structured heart failure disease management on mortality and morbidity depend on patients' mood: results from the Interdisciplinary Network for Heart Failure Study.

    Science.gov (United States)

    Gelbrich, Götz; Störk, Stefan; Kreißl-Kemmer, Sonja; Faller, Hermann; Prettin, Christiane; Heuschmann, Peter U; Ertl, Georg; Angermann, Christiane E

    2014-10-01

    Depression is common in heart failure (HF) and associated with adverse outcomes. Randomized comparisons of the effectiveness of HF care strategies by patients' mood are scarce. We therefore investigated in a randomized trial a structured collaborative disease management programme (HeartNetCare-HF™; HNC) recording mortality, morbidity, and symptoms in patients enrolled after hospitalization for decompensated systolic HF according to their responses to the 9-item Patient Health Questionnaire (PHQ-9) during an observation period of 180 days. Subjects scoring <12/≥12 were categorized as non-depressed/depressed, and those ignoring the questionnaire as PHQ-deniers. Amongst 715 participants (69 ± 12 years, 29% female), 141 (20%) were depressed, 466 (65%) non-depressed, and 108 (15%) PHQ-deniers. The composite endpoint of mortality and re-hospitalization was neutral overall and in all subgroups. However, HNC reduced mortality risk in both depressed and non-depressed patients [adjusted hazard ratios (HRs) 0.12, 95% confidence interval (CI) 0.03-0.56, P = 0.006, and 0.49, 95% CI 0.25-0.93, P = 0.03, respectively], but not in PHQ-deniers (HR 1.74, 95% CI 0.77-3.96, P = 0.19; P = 0.006 for homogeneity of HRs). Average frequencies of patient contacts in the HNC arm were 12.8 ± 7.9 in non-depressed patients, 12.4 ± 7.1 in depressed patients, and 5.5 ± 7.2 in PHQ-deniers (P < 0.001). Early after decompensation, HNC reduced mortality risk in non-depressed and even more in depressed subjects, but not in PHQ-deniers. This suggests that differential acceptability and chance of success of care strategies such as HNC might be predicted by appropriate assessment of patients' baseline characteristics including psychological disposition. These post-hoc results should be reassessed by prospective evaluation of HNC in larger HF populations. © 2014 The Authors. European Journal of Heart Failure © 2014 European Society of Cardiology.

  14. A robust biomarker of large-scale network failure in Alzheimer's disease.

    Science.gov (United States)

    Wiepert, Daniela A; Lowe, Val J; Knopman, David S; Boeve, Bradley F; Graff-Radford, Jonathan; Petersen, Ronald C; Jack, Clifford R; Jones, David T

    2017-01-01

    Biomarkers for Alzheimer's disease (AD) pathophysiology have been developed that focus on various levels of brain organization. However, no robust biomarker of large-scale network failure has been developed. Using the recently introduced cascading network failure model of AD, we developed the network failure quotient (NFQ) as a biomarker of this process. We developed and optimized the NFQ using our recently published analyses of task-free functional magnetic resonance imaging data in clinically normal (n = 43) and AD dementia participants (n = 28) from the Alzheimer's Disease Neuroimaging Initiative. The optimized NFQ (oNFQ) was then validated in a cohort spanning the AD spectrum from the Mayo Clinic (n = 218). The oNFQ (d = 1.25, 95% confidence interval [1.25, 1.26]) had the highest effect size for differentiating persons with AD dementia from clinically normal participants. The oNFQ measure performed similarly well on the validation Mayo Clinic sample (d = 1.44, 95% confidence interval [1.43, 1.44]). The oNFQ was also associated with other available key biomarkers in the Mayo cohort. This study demonstrates a measure of functional connectivity, based on a cascading network failure model of AD, and was highly successful in identifying AD dementia. A robust biomarker of the large-scale effects of AD pathophysiology will allow for richer descriptions of the disease process and its modifiers, but is not currently suitable for discriminating clinical diagnostic categories. The large-scale network level may be one of the earliest manifestations of AD, making this an attractive target for continued biomarker development to be used in prevention trials.

  15. Large-Scale Simulations of Plastic Neural Networks on Neuromorphic Hardware

    Science.gov (United States)

    Knight, James C.; Tully, Philip J.; Kaplan, Bernhard A.; Lansner, Anders; Furber, Steve B.

    2016-01-01

    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 2.0 × 104 neurons and 5.1 × 107 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 45× 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. PMID:27092061

  16. 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.

  17. Neuronal dynamics and neuropsychiatric disorders: toward a translational paradigm for dysfunctional large-scale networks.

    Science.gov (United States)

    Uhlhaas, Peter J; Singer, Wolf

    2012-09-20

    In recent years, numerous studies have tested the relevance of neural oscillations in neuropsychiatric conditions, highlighting the potential role of changes in temporal coordination as a pathophysiological mechanism in brain disorders. In the current review, we provide an update on this hypothesis because of the growing evidence that temporal coordination is essential for the context and goal-dependent, dynamic formation of large-scale cortical networks. We shall focus on issues that we consider particularly promising for a translational research program aimed at furthering our understanding of the origins of neuropsychiatric disorders and the development of effective therapies. We will focus on schizophrenia and autism spectrum disorders (ASDs) to highlight important issues and challenges for the implementation of such an approach. Specifically, we will argue that deficits in temporal coordination lead to a disruption of functional large-scale networks, which in turn can account for several specific dysfunctions associated with these disorders. Copyright © 2012 Elsevier Inc. All rights reserved.

  18. A Japanese version of Mother-to-Infant Bonding Scale: factor structure, longitudinal changes and links with maternal mood during the early postnatal period in Japanese mothers.

    Science.gov (United States)

    Yoshida, Keiko; Yamashita, Hiroshi; Conroy, Susan; Marks, Maureen; Kumar, Chianni

    2012-10-01

    The objectives of this study were (1) to develop a Japanese version of Mother-to- Infant Bonding Scale Japanese version (MIBS-J) based on Kumar's Mother Infant Bonding Questionnaire that could be used to screen the general population for problems in the mother's feelings towards her new baby and to validate it for clinical use and (2) to examine the factor structure of the items and create subscales of the questionnaire for the Japanese version. The MIBS-J is a simple self-report questionnaire designed to detect the problems in a mother's feelings towards her newborn baby. Participants (n = 554) were recruited at an outpatient clinic of a maternity hospital in a community after 30-weeks gestation. MIBS-J and the Edinburgh Postnatal Depression Scale (EPDS) were administered on the fifth day at the maternity ward and mailed at 1 and 4 months postnatally. Exploratory factor analysis and confirmatory factor analysis demonstrated a two-factor structure out of eight items: lack of affection (LA) and anger/rejection (AR). Chronbach's α coefficients were 0.71 and 0.57, respectively. The LA and AR scores had strong correlations across postnatal times. The mothers with higher (worse) AR scores on the MIBS-J at any of the three periods had higher scores on the EPDS. MIBS-J demonstrated acceptable reliability and reasonable construct validity in this Japanese sample.

  19. Linking Supply Chain Network Complexity to Interdependence and Risk-Assessment: Scale Development and Empirical Investigation

    Directory of Open Access Journals (Sweden)

    Samyadip Chakraborty

    2015-12-01

    Full Text Available Concepts like supply chain network complexity, interdependence and risk assessment have been prominently discussed directly and indirectly in management literature over past decades and plenty of frameworks and conceptual prescriptive research works have been published contributing towards building the body of knowledge. However previous studies often lacked quantification of the findings. Consequently, the need for suitable scales becomes prominent for measuring those constructs to empirically support the conceptualized relationships. This paper expands the understanding of supply chain network complexity (SCNC and also highlights its implications on interdependence (ID between the actors and risk assessment (RAS in transaction relationships. In doing so, SCNC and RAS are operationalized to understand how SCNC affects interdependence and risk assessment between the actors in the supply chain network. The contribution of this study lies in developing and validating multi-item scales for these constructs and empirically establishing the hypothesized relationships in the Indian context based on firm data collected using survey–based questionnaire. The methodology followed included structural equation modeling. The study findings indicate that SCNC had significant relationship with interdependence, which in turn significantly affected risk assessment. This study carries both academic and managerial implications and provides an empirically supported framework linking network complexity with the two key variables (ID and RAS, playing crucial roles in managerial decision making. This study contributes to the body of knowledge and aims at guiding managers in better understanding transaction relationships.

  20. Scalable Node-Centric Route Mutation for Defense of Large-Scale Software-Defined Networks

    Directory of Open Access Journals (Sweden)

    Yang Zhou

    2017-01-01

    Full Text Available Exploiting software-defined networking techniques, randomly and instantly mutating routes can disguise strategically important infrastructure and protect the integrity of data networks. Route mutation has been to date formulated as NP-complete constraint satisfaction problem where feasible sets of routes need to be generated with exponential computational complexities, limiting algorithmic scalability to large-scale networks. In this paper, we propose a novel node-centric route mutation method which interprets route mutation as a signature matching problem. We formulate the route mutation problem as a three-dimensional earth mover’s distance (EMD model and solve it by using a binary branch and bound method. Considering the scalability, we further propose that a heuristic method yields significantly lower computational complexities with marginal loss of robustness against eavesdropping. Simulation results show that our proposed methods can effectively disguise key infrastructure by reducing the difference of historically accumulative traffic among different switches. With significantly reduced complexities, our algorithms are of particular interest to safeguard large-scale networks.

  1. Optimization of a large-scale microseismic monitoring network in northern Switzerland

    Science.gov (United States)

    Kraft, Toni; Mignan, Arnaud; Giardini, Domenico

    2013-10-01

    We have developed a network optimization method for regional-scale microseismic monitoring networks and applied it to optimize the densification of the existing seismic network in northeastern Switzerland. The new network will build the backbone of a 10-yr study on the neotectonic activity of this area that will help to better constrain the seismic hazard imposed on nuclear power plants and waste repository sites. This task defined the requirements regarding location precision (0.5 km in epicentre and 2 km in source depth) and detection capability [magnitude of completeness Mc = 1.0 (ML)]. The goal of the optimization was to find the geometry and size of the network that met these requirements. Existing stations in Switzerland, Germany and Austria were considered in the optimization procedure. We based the optimization on the simulated annealing approach proposed by Hardt & Scherbaum, which aims to minimize the volume of the error ellipsoid of the linearized earthquake location problem (D-criterion). We have extended their algorithm to: calculate traveltimes of seismic body waves using a finite difference ray tracer and the 3-D velocity model of Switzerland, calculate seismic body-wave amplitudes at arbitrary stations assuming the Brune source model and using scaling and attenuation relations recently derived for Switzerland, and estimate the noise level at arbitrary locations within Switzerland using a first-order ambient seismic noise model based on 14 land-use classes defined by the EU-project CORINE and open GIS data. We calculated optimized geometries for networks with 10-35 added stations and tested the stability of the optimization result by repeated runs with changing initial conditions. Further, we estimated the attainable magnitude of completeness (Mc) for the different sized optimal networks using the Bayesian Magnitude of Completeness (BMC) method introduced by Mignan et al. The algorithm developed in this study is also applicable to smaller

  2. Psychotherapy of Mood Disorders

    OpenAIRE

    Picardi, Angelo; Gaetano, Paola

    2014-01-01

    In the last decades, psychotherapy has gained increasing acceptance as a major treatment option for mood disorders. Empirically supported treatments for major depression include cognitive behavioural therapy (CBT), interpersonal psychotherapy (IPT), behavioural therapy and, to a lesser extent, short-term psychodynamic psychotherapy. Meta-analytic evidence suggests that psychotherapy has a significant and clinically relevant, though not large, effect on chronic forms of depression. Psychothera...

  3. Uniform sampling of steady states in metabolic networks: heterogeneous scales and rounding.

    Directory of Open Access Journals (Sweden)

    Daniele De Martino

    Full Text Available The uniform sampling of convex polytopes is an interesting computational problem with many applications in inference from linear constraints, but the performances of sampling algorithms can be affected by ill-conditioning. This is the case of inferring the feasible steady states in models of metabolic networks, since they can show heterogeneous time scales. In this work we focus on rounding procedures based on building an ellipsoid that closely matches the sampling space, that can be used to define an efficient hit-and-run (HR Markov Chain Monte Carlo. In this way the uniformity of the sampling of the convex space of interest is rigorously guaranteed, at odds with non markovian methods. We analyze and compare three rounding methods in order to sample the feasible steady states of metabolic networks of three models of growing size up to genomic scale. The first is based on principal component analysis (PCA, the second on linear programming (LP and finally we employ the Lovazs ellipsoid method (LEM. Our results show that a rounding procedure dramatically improves the performances of the HR in these inference problems and suggest that a combination of LEM or LP with a subsequent PCA perform the best. We finally compare the distributions of the HR with that of two heuristics based on the Artificially Centered hit-and-run (ACHR, gpSampler and optGpSampler. They show a good agreement with the results of the HR for the small network, while on genome scale models present inconsistencies.

  4. Synchronization in Scale Free networks: The role of finite size effects

    CERN Document Server

    Torres, Débora; La Rocca, Cristian E; Braunstein, Lidia A

    2015-01-01

    Synchronization problems in complex networks are very often studied by researchers due to its many applications to various fields such as neurobiology, e-commerce and completion of tasks. In particular, Scale Free networks with degree distribution $P(k)\\sim k^{-\\lambda}$, 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 $2.5< \\lambda <3$, 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 $\\lambda$: 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 $\\lambda$, which means that the synchroniza...

  5. Fine-scale mapping of a locus for severe bipolar mood disorder on chromosome 18p11.3 in the Costa Rican population

    Science.gov (United States)

    McInnes, L. Alison; Service, Susan K.; Reus, Victor I.; Barnes, Glenn; Charlat, Olga; Jawahar, Satya; Lewitzky, Steve; Yang, Qing; Duong, Quyen; Spesny, Mitzi; Araya, Carmen; Araya, Xinia; Gallegos, Alvaro; Meza, Luis; Molina, Julio; Ramirez, Rolando; Mendez, Roxana; Silva, Sandra; Fournier, Eduardo; Batki, Steven L.; Mathews, Carol A.; Neylan, Thomas; Glatt, Charles E.; Escamilla, Michael A.; Luo, David; Gajiwala, Paresh; Song, Terry; Crook, Stephen; Nguyen, Jasmine B.; Roche, Erin; Meyer, Joanne M.; Leon, Pedro; Sandkuijl, Lodewijk A.; Freimer, Nelson B.; Chen, Hong

    2001-01-01

    We have searched for genes predisposing to bipolar disorder (BP) by studying individuals with the most extreme form of the affected phenotype, BP-I, ascertained from the genetically isolated population of the Central Valley of Costa Rica (CVCR). The results of a previous linkage analysis on two extended CVCR BP-I pedigrees, CR001 and CR004, and of linkage disequilibrium (LD) analyses of a CVCR population sample of BP-I patients implicated a candidate region on 18p11.3. We further investigated this region by creating a physical map and developing 4 new microsatellite and 26 single-nucleotide polymorphism markers for typing in the pedigree and population samples. We report the results of fine-scale association analyses in the population sample, as well as evaluation of haplotypes in pedigree CR001. Our results suggest a candidate region containing six genes but also highlight the complexities of LD mapping of common disorders. PMID:11572994

  6. Exploring the low-energy landscape of large-scale signed social networks

    Science.gov (United States)

    Facchetti, G.; Iacono, G.; Altafini, C.

    2012-09-01

    Analogously to a spin glass, a large-scale signed social network is characterized by the presence of disorder, expressed in this context (and in the social network literature) by the concept of structural balance. If, as we have recently shown, the signed social networks currently available have a limited amount of true disorder (or frustration), it is also interesting to investigate how this frustration is organized, by exploring the landscape of near-optimal structural balance. What we obtain in this paper is that while one of the networks analyzed shows a unique valley of minima, and a funneled landscape that gradually and smoothly worsens as we move away from the optimum, another network shows instead several distinct valleys of optimal or near-optimal structural balance, separated by energy barriers determined by internally balanced subcommunities of users, a phenomenon similar to the replica-symmetry breaking of spin glasses. Multiple, essentially isoenergetic, arrangements of these communities are possible. Passing from one valley to another requires one to destroy the internal arrangement of these balanced subcommunities and then to reform it again. It is essentially this process of breaking the internal balance of the subcommunities which gives rise to the energy barriers.

  7. 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.

  8. Neural Schematics as a unified formal graphical representation of large-scale Neural Network Structures

    Directory of Open Access Journals (Sweden)

    Matthias eEhrlich

    2013-10-01

    Full Text Available One of the major outcomes of neuroscientific research are models of Neural Network Structures. Descriptions of these models usually consist of a non-standardized mixture of text, figures, and other means of visual information communication in print media. However, as neuroscience is an interdisciplinary domain by nature, a standardized way of consistently representing models of Neural Network Structures is required. While generic descriptions of such models in textual form have recently been developed, a formalized way of schematically expressing them does not exist to date. Hence, in this paper we present Neural Schematics as a concept inspired by similar approaches from other disciplines for a generic two dimensional representation of said structures. After introducing Neural Network Structures in general, a set of current visualizations of models of Neural Network Structures is reviewed and analyzed for what information they convey and how their elements are rendered. This analysis then allows for the definition of general items and symbols to consistently represent these models as Neural Schematics on a two dimensional plane. We will illustrate the possibilities an agreed upon standard can yield on sampled diagrams transformed into Neural Schematics and an example application for the design and modeling of large-scale Neural Network Structures.

  9. Spontaneous Neuronal Activity in Developing Neocortical Networks: From Single Cells to Large-Scale Interactions.

    Science.gov (United States)

    Luhmann, Heiko J; Sinning, Anne; Yang, Jenq-Wei; Reyes-Puerta, Vicente; Stüttgen, Maik C; Kirischuk, Sergei; Kilb, Werner

    2016-01-01

    Neuronal activity has been shown to be essential for the proper formation of neuronal circuits, affecting developmental processes like neurogenesis, migration, programmed cell death, cellular differentiation, formation of local and long-range axonal connections, synaptic plasticity or myelination. Accordingly, neocortical areas reveal distinct spontaneous and sensory-driven neuronal activity patterns already at early phases of development. At embryonic stages, when immature neurons start to develop voltage-dependent channels, spontaneous activity is highly synchronized within small neuronal networks and governed by electrical synaptic transmission. Subsequently, spontaneous activity patterns become more complex, involve larger networks and propagate over several neocortical areas. The developmental shift from local to large-scale network activity is accompanied by a gradual shift from electrical to chemical synaptic transmission with an initial excitatory action of chloride-gated channels activated by GABA, glycine and taurine. Transient neuronal populations in the subplate (SP) support temporary circuits that play an important role in tuning early neocortical activity and the formation of mature neuronal networks. Thus, early spontaneous activity patterns control the formation of developing networks in sensory cortices, and disturbances of these activity patterns may lead to long-lasting neuronal deficits.

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

    Directory of Open Access Journals (Sweden)

    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.

  11. A multi-scale convolutional neural network for phenotyping high-content cellular images.

    Science.gov (United States)

    Godinez, William J; Hossain, Imtiaz; Lazic, Stanley E; Davies, John W; Zhang, Xian

    2017-07-01

    Identifying phenotypes based on high-content cellular images is challenging. Conventional image analysis pipelines for phenotype identification comprise multiple independent steps, with each step requiring method customization and adjustment of multiple parameters. Here, we present an approach based on a multi-scale convolutional neural network (M-CNN) that classifies, in a single cohesive step, cellular images into phenotypes by using directly and solely the images' pixel intensity values. The only parameters in the approach are the weights of the neural network, which are automatically optimized based on training images. The approach requires no a priori knowledge or manual customization, and is applicable to single- or multi-channel images displaying single or multiple cells. We evaluated the classification performance of the approach on eight diverse benchmark datasets. The approach yielded overall a higher classification accuracy compared with state-of-the-art results, including those of other deep CNN architectures. In addition to using the network to simply obtain a yes-or-no prediction for a given phenotype, we use the probability outputs calculated by the network to quantitatively describe the phenotypes. This study shows that these probability values correlate with chemical treatment concentrations. This finding validates further our approach and enables chemical treatment potency estimation via CNNs. The network specifications and solver definitions are provided in Supplementary Software 1. william_jose.godinez_navarro@novartis.com or xian-1.zhang@novartis.com. Supplementary data are available at Bioinformatics online.

  12. 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.

  13. Relationships between Exercise as a Mood Regulation Strategy and Trait Emotional Intelligence.

    Science.gov (United States)

    Solanki, Dharmendra; Lane, Andrew M

    2010-12-01

    The aim of this study was to investigate the relationship between perception of emotional intelligence and beliefs in the extent to which exercising leads to mood-enhancement. Volunteer participants (N=315) completed a 33-item self-report measure of trait emotional intelligence and an exercise-mood regulation scale. Emotional intelligence significantly correlated with beliefs that exercise could be used to regulate mood (r =0.45, Pexercise to regulate mood relates significantly to emotional intelligence and suggest that individuals who use exercise to enhance mood report higher scores of emotional intelligence.

  14. 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

  15. 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 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

  16. [The impact of mood alterations on creativity].

    Science.gov (United States)

    Janka, Zoltán

    2006-07-20

    Basic elements of artistic (and other) creativity are the technical-professional skill and knowledge, the special talent and ability and the willingness or motivation; one of which being absent results in partially realised creativity like juvenile, frustrated or abandoned types, respectively. Psychometric scales have been developed to measure everyday and eminent creativity, which show that creativity correlates with higher psychoticism, impulsivity and venturesomeness scores and with lower neuroticism and conformity scores of the personality test employed in a general population. Among the psychological components of creativity are the cognitive processes, mood, motivation, and personality traits. Regarding mood, a theory of "inverted U" has been proposed as elevation of mood facilitates creativity to a certain point after what extreme increase has an adverse effect on achievement. Analysing psychopathology and creativity among various professions, higher rates of psychopathology, especially affective symptoms, have been found in art-related professions. Examples of immortal poets, writers, painters, sculptors and composers, having created invaluable cultural treasures for mankind, illustrate that many of them showed signs of mood alterations (unipolar or bipolar affective disorder spectrum) which were expressed in their artistic products.

  17. EOP and scale from continuous VLBI observing: CONT campaigns to future VGOS networks

    Science.gov (United States)

    MacMillan, D. S.

    2017-07-01

    Continuous (CONT) VLBI campaigns have been carried out about every 3 years since 2002. The basic idea of these campaigns is to acquire state-of-the-art VLBI data over a continuous time period of about 2 weeks to demonstrate the highest accuracy of which the current VLBI system is capable. In addition, these campaigns support scientific studies such as investigations of high-resolution Earth rotation, reference frame stability, and daily to sub-daily site motions. The size of the CONT networks and the observing data rate have increased steadily since 1994. Performance of these networks based on reference frame scale precision and polar motion/LOD comparison with global navigation satellite system (GNSS) earth orientation parameters (EOP) has been substantially better than the weekly operational R1 and R4 series. The precisions of CONT EOP and scale have improved by more than a factor of two since 2002. Polar motion precision based on the WRMS difference between VLBI and GNSS for the most recent CONT campaigns is at the 30 μas level, which is comparable to that of GNSS. The CONT campaigns are a natural precursor to the planned future VLBI observing networks, which are expected to observe continuously. We compare the performance of the most recent CONT campaigns in 2011 and 2014 with the expected performance of the future VLBI global observing system network using simulations. These simulations indicate that the expected future precision of scale and EOP will be at least 3 times better than the current CONT precision.

  18. Flexible sampling large-scale social networks by self-adjustable random walk

    Science.gov (United States)

    Xu, Xiao-Ke; Zhu, Jonathan J. H.

    2016-12-01

    Online social networks (OSNs) have become an increasingly attractive gold mine for academic and commercial researchers. However, research on OSNs faces a number of difficult challenges. One bottleneck lies in the massive quantity and often unavailability of OSN population data. Sampling perhaps becomes the only feasible solution to the problems. How to draw samples that can represent the underlying OSNs has remained a formidable task because of a number of conceptual and methodological reasons. Especially, most of the empirically-driven studies on network sampling are confined to simulated data or sub-graph data, which are fundamentally different from real and complete-graph OSNs. In the current study, we propose a flexible sampling method, called Self-Adjustable Random Walk (SARW), and test it against with the population data of a real large-scale OSN. We evaluate the strengths of the sampling method in comparison with four prevailing methods, including uniform, breadth-first search (BFS), random walk (RW), and revised RW (i.e., MHRW) sampling. We try to mix both induced-edge and external-edge information of sampled nodes together in the same sampling process. Our results show that the SARW sampling method has been able to generate unbiased samples of OSNs with maximal precision and minimal cost. The study is helpful for the practice of OSN research by providing a highly needed sampling tools, for the methodological development of large-scale network sampling by comparative evaluations of existing sampling methods, and for the theoretical understanding of human networks by highlighting discrepancies and contradictions between existing knowledge/assumptions of large-scale real OSN data.

  19. Genome-scale cold stress response regulatory networks in ten Arabidopsis thaliana ecotypes

    Science.gov (United States)

    2013-01-01

    Background Low temperature leads to major crop losses every year. Although several studies have been conducted focusing on diversity of cold tolerance level in multiple phenotypically divergent Arabidopsis thaliana (A. thaliana) ecotypes, genome-scale molecular understanding is still lacking. Results In this study, we report genome-scale transcript response diversity of 10 A. thaliana ecotypes originating from different geographical locations to non-freezing cold stress (10°C). To analyze the transcriptional response diversity, we initially compared transcriptome changes in all 10 ecotypes using Arabidopsis NimbleGen ATH6 microarrays. In total 6061 transcripts were significantly cold regulated (p cold stress regulon genes. Significant numbers of non-synonymous amino acid changes were observed in the coding region of the CBF regulon genes. Considering the limited knowledge about regulatory interactions between transcription factors and their target genes in the model plant A. thaliana, we have adopted a powerful systems genetics approach- Network Component Analysis (NCA) to construct an in-silico transcriptional regulatory network model during response to cold stress. The resulting regulatory network contained 1,275 nodes and 7,720 connections, with 178 transcription factors and 1,331 target genes. Conclusions A. thaliana ecotypes exhibit considerable variation in transcriptome level responses to non-freezing cold stress treatment. Ecotype specific transcripts and related gene ontology (GO) categories were identified to delineate natural variation of cold stress regulated differential gene expression in the model plant A. thaliana. The predicted regulatory network model was able to identify new ecotype specific transcription factors and their regulatory interactions, which might be crucial for their local geographic adaptation to cold temperature. Additionally, since the approach presented here is general, it could be adapted to study networks regulating

  20. Complex networks, community structure, and catchment classification in a large-scale river basin

    Science.gov (United States)

    Fang, Koren; Sivakumar, Bellie; Woldemeskel, Fitsum M.

    2017-02-01

    This study introduces the concepts of complex networks, especially community structure, to classify catchments in large-scale river basins. The Mississippi River basin (MRB) is considered as a representative large-scale basin, and daily streamflow from a network of 1663 stations are analyzed. Six community structure methods are employed: edge betweenness, greedy algorithm, multilevel modularity optimization, leading eigenvector, label propagation, and walktrap. The influence of correlation threshold (i.e. spatial correlation in flow between stations) on classification (i.e. community formation) is examined. The consistency among the methods in classifying catchments is assessed, using a normalized mutual information (NMI) index. An attempt is also made to explain the community formation in terms of river network/branching and some important catchment/flow properties. The results indicate that the correlation threshold has a notable influence on the number and size of communities identified and that there is a high level of consistency in the performance among the methods (except for the leading eigenvector method at lower thresholds). The results also reveal that only a few communities combine to represent a majority of the catchments, with the 10 largest communities (roughly 4% of the total number of communities) representing almost two-thirds of the catchments. Community formation is found to be influenced not only by geographic proximity but also, more importantly, by the organization of the river network (i.e. main stem and subsequent branching). Some communities are found to exhibit a greater variability in catchment/flow properties within themselves when compared to that of the whole network, thus indicating that such characteristics are unlikely to be a significant influence on community grouping.

  1. A scalable moment-closure approximation for large-scale biochemical reaction networks.

    Science.gov (United States)

    Kazeroonian, Atefeh; Theis, Fabian J; Hasenauer, Jan

    2017-07-15

    Stochastic molecular processes are a leading cause of cell-to-cell variability. Their dynamics are often described by continuous-time discrete-state Markov chains and simulated using stochastic simulation algorithms. As these stochastic simulations are computationally demanding, ordinary differential equation models for the dynamics of the statistical moments have been developed. The number of state variables of these approximating models, however, grows at least quadratically with the number of biochemical species. This limits their application to small- and medium-sized processes. In this article, we present a scalable moment-closure approximation (sMA) for the simulation of statistical moments of large-scale stochastic processes. The sMA exploits the structure of the biochemical reaction network to reduce the covariance matrix. We prove that sMA yields approximating models whose number of state variables depends predominantly on local properties, i.e. the average node degree of the reaction network, instead of the overall network size. The resulting complexity reduction is assessed by studying a range of medium- and large-scale biochemical reaction networks. To evaluate the approximation accuracy and the improvement in computational efficiency, we study models for JAK2/STAT5 signalling and NF κ B signalling. Our method is applicable to generic biochemical reaction networks and we provide an implementation, including an SBML interface, which renders the sMA easily accessible. The sMA is implemented in the open-source MATLAB toolbox CERENA and is available from https://github.com/CERENADevelopers/CERENA . jan.hasenauer@helmholtz-muenchen.de or atefeh.kazeroonian@tum.de. Supplementary data are available at Bioinformatics online.

  2. Negative and positive beliefs related to mood and health.

    Science.gov (United States)

    Ownby, Raymond L; Acevedo, Amarilis; Jacobs, Robin J; Caballero, Joshua; Waldrop-Valverde, Drenna

    2014-07-01

    To observe whether elderly patients' positive and negative beliefs about efforts improving or maintaining health are related to health and mood. We developed a brief scale to assess these beliefs. Factor analysis was used to evaluate its dimensions; the extent to which the scale's dimensions mediate the relationship between mood and self-reported health was explored. Analyses show that the scale reflects a general factor as well as 2 subscales that evaluate distinct but related positive and negative dimensions. The scale was not related to race, sex, or education, but showed modest relations to age. Scales were significantly related to mood, health status, and health-related quality of life. Both negative and positive beliefs mediated the relation between depression and self-reported health.

  3. Nonlinear dynamics of mood regulation in bipolar disorder.

    Science.gov (United States)

    Ortiz, Abigail; Bradler, Kamil; Garnham, Julie; Slaney, Claire; Alda, Martin

    2015-03-01

    We sought to study the underlying dynamic processes involved in mood regulation in subjects with bipolar disorder and healthy control subjects using time-series analysis and to then analyze the relation between anxiety and mood using cross-correlation techniques. We recruited 30 healthy controls and 30 euthymic patients with bipolar disorder. Participants rated their mood, anxiety, and energy levels using a paper-based visual analog scale; and they also recorded their sleep and any life events. Information on these variables was provided over a three-month period on a daily basis, twice per day. We analyzed the data using Box-Jenkins time series analysis to obtain information on the autocorrelation of the series (for mood) and cross-correlation (mood and anxiety series). Throughout the study, we analyzed 10,170 data points. Self-ratings for mood, anxiety, and energy were normally distributed in both groups. Autocorrelation functions for mood in both groups were governed by the autoregressive integrated moving average (ARIMA) (1,1,0) model, which means that current values in the series were related to one previous point only. We also found a negative cross-correlation between mood and anxiety. Mood can be considered a memory stochastic process; it is a flexible, dynamic process that has a 'short memory' both in healthy controls and euthymic patients with bipolar disorder. This process may be quite different in untreated patients or in those acutely ill. Our results suggest that nonlinear measures can be applied to the study of mood disorders. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  4. Responding to the challenge of artisanal and small-scale mining. How can knowledge networks help?

    Energy Technology Data Exchange (ETDEWEB)

    Buxton, Abbi

    2013-02-15

    This paper reviews what is known about the problems and structural challenges facing the 20-30 million artisanal and small-scale miners and their communities worldwide. Better understanding of these structural challenges is needed to improve policies and policy implementation to further sustainable development opportunities for the sector. The paper explores the current gaps in knowledge to achieve policy change from researchers, practitioners and artisanal and small-scale miners themselves. It explores how a 'knowledge intermediary', which acts to link knowledge with policy, could address these gaps and includes case studies of IIED’s work on knowledge networks and programmes. The paper concludes by proposing a way forward for designing a knowledge programme to meet the particular needs of the artisanal and small-scale mining (ASM) sector, and by inviting ASM sector stakeholders to share their views on the options outlined.

  5. Technology and Reflection: Mood and Memory Mechanisms for Well-Being.

    Science.gov (United States)

    Konrad, Artie; Tucker, Simon; Crane, John; Whittaker, Steve

    We report a psychologically motivated intervention to explore Technology Mediated Reflection (TMR), the process of systematically reviewing rich digital records of past personal experiences. Although TMR benefits well-being, and is increasingly being deployed, we know little about how one's mood when using TMR influences these benefits. We use theories of memory and emotion-regulation to motivate hypotheses about the relationship between reflection, mood, and well-being when using technology. We test these hypotheses in a large-scale month long real world deployment using a web-based application, MoodAdaptor. MoodAdaptor prompted participants to reflect on positive or negative memories depending on current mood. We evaluated how mood and memory interact during written reflection and measured effects on well-being. We analyzed qualitative and quantitative data from 128 participants who generated 11157 mood evaluations, 5051 logfiles, 256 surveys, and 20 interviews. TMR regulated emotion; when participants reflected on memories with valences opposite to their current mood, their mood became more neutral. However this did not impact overall well-being. Our findings also clarify underlying TMR mechanisms. Moods and memories competed with each other; when positive moods prevailed over negative memories, people demonstrated classic mechanisms shown in prior work to influence well-being. When negative moods prevailed over positive memories, memories became negatively tainted. Our results have implications for new well-being interventions and technologies that capitalize on the interconnectedness of memory and emotion.

  6. PERSONALITY DOES NOT INFLUENCE EXERCISE-INDUCED MOOD ENHANCEMENT AMONG FEMALE EXERCISERS

    Directory of Open Access Journals (Sweden)

    Andrew M. Lane

    2005-09-01

    Full Text Available The present study investigated the influence of personality on exercise-induced mood changes. It was hypothesised that (a exercise would be associated with significant mood enhancement across all personality types, (b extroversion would be associated with positive mood and neuroticism with negative mood both pre- and post-exercise, and (c personality measures would interact with exercise-induced mood changes. Participants were 90 female exercisers (M = 25.8 yr, SD = 9.0 yr who completed the Eysenck Personality Inventory (EPI once and the Brunel Mood Scale (BRUMS before and after a 60-minute exercise session. Median splits were used to group participants into four personality types: stable introverts (n = 25, stable extroverts (n = 20, neurotic introverts (n = 26, and neurotic extroverts (n = 19. Repeated measures MANOVA showed significant mood enhancement following exercise across all personality types. Neuroticism was associated with negative mood scores pre- and post-exercise but the effect of extroversion on reported mood was relatively weak. There was no significant interaction effect between exercise-induced mood enhancement and personality. In conclusion, findings lend support to the notion that exercise is associated with improved mood. However, findings show that personality did not influence this effect, although neuroticism was associated with negative mood

  7. A new asynchronous parallel algorithm for inferring large-scale gene regulatory networks.

    Directory of Open Access Journals (Sweden)

    Xiangyun Xiao

    Full Text Available The reconstruction of gene regulatory networks (GRNs from high-throughput experimental data has been considered one of the most important issues in systems biology research. With the development of high-throughput technology and the complexity of biological problems, we need to reconstruct GRNs that contain thousands of genes. However, when many existing algorithms are used to handle these large-scale problems, they will encounter two important issues: low accuracy and high computational cost. To overcome these difficulties, the main goal of this study is to design an effective parallel algorithm to infer large-scale GRNs based on high-performance parallel computing environments. In this study, we proposed a novel asynchronous parallel framework to improve the accuracy and lower the time complexity of large-scale GRN inference by combining splitting technology and ordinary differential equation (ODE-based optimization. The presented algorithm uses the sparsity and modularity of GRNs to split whole large-scale GRNs into many small-scale modular subnetworks. Through the ODE-based optimization of all subnetworks in parallel and their asynchronous communications, we can easily obtain the parameters of the whole network. To test the performance of the proposed approach, we used well-known benchmark datasets from Dialogue for Reverse Engineering Assessments and Methods challenge (DREAM, experimentally determined GRN of Escherichia coli and one published dataset that contains more than 10 thousand genes to compare the proposed approach with several popular algorithms on the same high-performance computing environments in terms of both accuracy and time complexity. The numerical results demonstrate that our parallel algorithm exhibits obvious superiority in inferring large-scale GRNs.

  8. Abnormal Development of Monoaminergic Neurons Is Implicated in Mood Fluctuations and Bipolar Disorder

    OpenAIRE

    Marin M Jukic; Carrillo-Roa, Tania; Bar, Michal; Becker, Gal; Jovanovic, Vukasin M; Zega, Ksenija; Binder, Elisabeth B.; Brodski, Claude

    2014-01-01

    Subtle mood fluctuations are normal emotional experiences, whereas drastic mood swings can be a manifestation of bipolar disorder (BPD). Despite their importance for normal and pathological behavior, the mechanisms underlying endogenous mood instability are largely unknown. During embryogenesis, the transcription factor Otx2 orchestrates the genetic networks directing the specification of dopaminergic (DA) and serotonergic (5-HT) neurons. Here we behaviorally phenotyped mouse mutants overexpr...

  9. Pore morphologies of root induced biopores from single pore to network scale investigated by XRCT

    Science.gov (United States)

    Peth, Stephan; Wittig, Marlen C.; Uteau Puschmann, Daniel; Pagenkemper, Sebastian; Haas, Christoph; Holthusen, Dörthe; Horn, Rainer

    2015-04-01

    Biopores are assumed to be an important factor for nutrient acquisition by providing biologically highly active soil-root interfaces to re-colonizing roots and controlling oxygen and water flows at the pedon scale and within the rhizosphere through the formation of branching channel networks which potentially enhance microbial turnover processes. Characteristic differences in pore morphologies are to be expected depending on the genesis of biopores which, for example, can be earthworm-induced or root-induced or subsequently modified by one of the two. Our understanding of biophysical interactions between plants and soil can be significantly improved by quantifying 3D biopore architectures across scales ranging from single biopores to pedon scale pore networks and linking pore morphologies to microscale measurements of transport processes (e.g. oxygen diffusion). While a few studies in the past have investigated biopore networks on a larger scale yet little is known on the micro-morphology of root-induces biopores and their associated rhizosphere. Also little data is available on lateral transport of oxygen through the rhizosphere which will strongly influence microbial turnover processes and consequently control the release and uptake of nutrients. This paper highlights results gathered within a research unit on nutrient acquisition from the subsoil. Here we focus on X-ray microtomography (XRCT) studies ranging from large soil columns (70 cm length and 20 cm diameter) to individual biopores and its surrounding rhizosphere. Samples were collected from sites with different preceding crops (fescue, chicory, alfalfa) and various cropping durations (1-3 years). We will present an approach for quantitative image analysis combined with micro-sensor measurements of oxygen diffusion and spatial gradients of O2 partial pressures to relate pore structure with transport functions. Implications of various biopore architectures for the accessibility of nutrient resources in

  10. 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

  11. Approach and Avoidance of Emotional Faces in Happy and Sad Mood.

    Science.gov (United States)

    Vrijsen, Janna N; van Oostrom, Iris; Speckens, Anne; Becker, Eni S; Rinck, Mike

    2013-02-01

    Since the introduction of the associative network theory, mood-congruent biases in emotional information processing have been established in individuals in a sad and happy mood. Research has concentrated on memory and attentional biases. According to the network theory, mood-congruent behavioral tendencies would also be predicted. Alternatively, a general avoidance pattern would also be in line with the theory. Since cognitive biases have been assumed to operate strongly in case of social stimuli, mood-induced biases in approach and avoidance behavior towards emotional facial expressions were studied. 306 females were subjected to a highly emotional fragment of a sad or a happy movie, to induce either a sad mood or a happy mood. An Approach-Avoidance Task was implemented, in which single pictures of faces (with angry, sad, happy, or neutral expression) and non-social control pictures were presented. In contrast to our expectations, mood states did not produce differential behavioral biases. Mood-congruent and mood-incongruent behavioral tendencies were, however, present in a subgroup of participants with highest depressive symptomatology scores. This suggests that behavioral approach-avoidance biases are not sensitive to mood state, but more related to depressive characteristics.

  12. Social-ecological network analysis of scale mismatches in estuary watershed restoration.

    Science.gov (United States)

    Sayles, Jesse S; Baggio, Jacopo A

    2017-03-07

    Resource management boundaries seldom align with environmental systems, which can lead to social and ecological problems. Mapping and analyzing how resource management organizations in different areas collaborate can provide vital information to help overcome such misalignment. Few quantitative approaches exist, however, to analyze social collaborations alongside environmental patterns, especially among local and regional organizations (i.e., in multilevel governance settings). This paper develops and applies such an approach using social-ecological network analysis (SENA), which considers relationships among and between social and ecological units. The framework and methods are shown using an estuary restoration case from Puget Sound, United States. Collaboration patterns and quality are analyzed among local and regional organizations working in hydrologically connected areas. These patterns are correlated with restoration practitioners' assessments of the productivity of their collaborations to inform network theories for natural resource governance. The SENA is also combined with existing ecological data to jointly consider social and ecological restoration concerns. Results show potentially problematic areas in nearshore environments, where collaboration networks measured by density (percentage of possible network connections) and productivity are weakest. Many areas also have high centralization (a few nodes hold the network together), making network cohesion dependent on key organizations. Although centralization and productivity are inversely related, no clear relationship between density and productivity is observed. This research can help practitioners to identify where governance capacity needs strengthening and jointly consider social and ecological concerns. It advances SENA by developing a multilevel approach to assess social-ecological (or social-environmental) misalignments, also known as scale mismatches.

  13. Contextualizing Wetlands within a River-Network Perspective for Assessing Nitrate Removal at the Watershed Scale

    Science.gov (United States)

    Czuba, J. A.; T Hansen, A.; Foufoula-Georgiou, E.; Finlay, J. C.

    2016-12-01

    Ensuring reliable food supply without compromising the environmental integrity of receiving waters is a challenge faced in many agricultural landscapes. In the Midwestern U.S., the draining of natural wetlands to facilitate and expand agriculture has reduced the water-retention and nitrate-removal capacity of the landscape. Individual wetlands have been shown to be effective at reducing nitrate concentrations, but there has not been sufficient research into understanding how the arrangement of wetlands in the landscape, either existing or proposed for restoration, collectively reduces nitrate concentrations at the watershed scale. In this work, we propose a network-based model to quantify nitrate removal and resulting nitrate concentrations through a collection of remnant wetlands within a river network. The model relies on: (1) establishing the set of wetlands (including lakes) that are directly connected to the river network including their attributes relevant to nitrate transport and transformation, (2) determining the flow-dependent travel time of nitrate through a wetland or channel, (3) embedding the denitrification process of concentration-dependent removal of nitrate within each wetland or channel, (4) introducing elevated nitrate concentrations from the agricultural landscape into the network of channels and wetlands, and (5) tracking nitrate concentrations as they are routed through the network and reduced through denitrification. We validate the model in the Le Sueur River basin, a 2,800 km2 agricultural landscape in southeastern Minnesota, using synoptic field measurements. We then quantify the relative importance of hydrology (transport) versus biogeochemistry (removal) throughout the wetland/channel network as a function of flow. Ultimately, the goal is to provide a framework for assessing where and with what specifications to restore wetlands for optimal environmental benefits in a watershed.

  14. Does mood affect trading behavior?

    OpenAIRE

    Kaustia, Markku; Rantapuska, Elias

    2012-01-01

    We test whether investor mood affects trading with data on all stock market transactions in Finland, utilizing variation in daylight and local weather. We find some evidence that environmental mood variables (local weather, length of day, daylight saving and lunar phase) affect investors’ direction of trade and volume. The effect magnitudes are roughly comparable to those of classical seasonals, such as the Monday effect. The statistical significance of the mood variables is weak in many case...

  15. Mood dynamics in bipolar disorder

    OpenAIRE

    Moore, Paul J; Little, Max A; McSharry, Patrick E; Goodwin, Guy M; Geddes, John R

    2014-01-01

    The nature of mood variation in bipolar disorder has been the subject of relatively little research because detailed time series data has been difficult to obtain until recently. However some papers have addressed the subject and claimed the presence of deterministic chaos and of stochastic nonlinear dynamics. This study uses mood data collected from eight outpatients using a telemonitoring system. The nature of mood dynamics in bipolar disorder is investigated using surrogate data techniques...

  16. Comprehensive Mapping of Pluripotent Stem Cell Metabolism Using Dynamic Genome-Scale Network Modeling

    Directory of Open Access Journals (Sweden)

    Sriram Chandrasekaran

    2017-12-01

    Full Text Available Summary: Metabolism is an emerging stem cell hallmark tied to cell fate, pluripotency, and self-renewal, yet systems-level understanding of stem cell metabolism has been limited by the lack of genome-scale network models. Here, we develop a systems approach to integrate time-course metabolomics data with a computational model of metabolism to analyze the metabolic state of naive and primed murine pluripotent stem cells. Using this approach, we find that one-carbon metabolism involving phosphoglycerate dehydrogenase, folate synthesis, and nucleotide synthesis is a key pathway that differs between the two states, resulting in differential sensitivity to anti-folates. The model also predicts that the pluripotency factor Lin28 regulates this one-carbon metabolic pathway, which we validate using metabolomics data from Lin28-deficient cells. Moreover, we identify and validate metabolic reactions related to S-adenosyl-methionine production that can differentially impact histone methylation in naive and primed cells. Our network-based approach provides a framework for characterizing metabolic changes influencing pluripotency and cell fate. : Chandrasekaran et al. use computational modeling, metabolomics, and metabolic inhibitors to discover metabolic differences between various pluripotent stem cell states and infer their impact on stem cell fate decisions. Keywords: systems biology, stem cell biology, metabolism, genome-scale modeling, pluripotency, histone methylation, naive (ground state, primed state, cell fate, metabolic network

  17. Overlapping communities reveal rich structure in large-scale brain networks during rest and task conditions.

    Science.gov (United States)

    Najafi, Mahshid; McMenamin, Brenton W; Simon, Jonathan Z; Pessoa, Luiz

    2016-07-15

    Large-scale analysis of functional MRI data has revealed that brain regions can be grouped into stable "networks" or communities. In many instances, the communities are characterized as relatively disjoint. Although recent work indicates that brain regions may participate in multiple communities (for example, hub regions), the extent of community overlap is poorly understood. To address these issues, here we investigated large-scale brain networks based on "rest" and task human functional MRI data by employing a mixed-membership Bayesian model that allows each brain region to belong to all communities simultaneously with varying membership strengths. The approach allowed us to 1) compare the structure of disjoint and overlapping communities; 2) determine the relationship between functional diversity (how diverse is a region's functional activation repertoire) and membership diversity (how diverse is a region's affiliation to communities); 3) characterize overlapping community structure; 4) characterize the degree of non-modularity in brain networks; 5) study the distribution of "bridges", including bottleneck and hub bridges. Our findings revealed the existence of dense community overlap that was not limited to "special" hubs. Furthermore, the findings revealed important differences between community organization during rest and during specific task states. Overall, we suggest that dense overlapping communities are well suited to capture the flexible and task dependent mapping between brain regions and their functions. Copyright © 2016 Elsevier Inc. All rights reserved.

  18. Functional models for large-scale gene regulation networks: realism and fiction.

    Science.gov (United States)

    Lagomarsino, Marco Cosentino; Bassetti, Bruno; Castellani, Gastone; Remondini, Daniel

    2009-04-01

    High-throughput experiments are shedding light on the topology of large regulatory networks and at the same time their functional states, namely the states of activation of the nodes (for example transcript or protein levels) in different conditions, times, environments. We now possess a certain amount of information about these two levels of description, stored in libraries, databases and ontologies. A current challenge is to bridge the gap between topology and function, i.e. developing quantitative models aimed at characterizing the expression patterns of large sets of genes. However, approaches that work well for small networks become impossible to master at large scales, mainly because parameters proliferate. In this review we discuss the state of the art of large-scale functional network models, addressing the issue of what can be considered as "realistic" and what the main limitations may be. We also show some directions for future work, trying to set the goals that future models should try to achieve. Finally, we will emphasize the possible benefits in the understanding of biological mechanisms underlying complex multifactorial diseases, and in the development of novel strategies for the description and the treatment of such pathologies.

  19. Large-scale transportation network congestion evolution prediction using deep learning theory.

    Directory of Open Access Journals (Sweden)

    Xiaolei Ma

    Full Text Available 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.

  20. Large-scale transportation network congestion evolution prediction using deep learning theory.

    Science.gov (United States)

    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.

  1. Rhythms of consciousness: binocular rivalry reveals large-scale oscillatory network dynamics mediating visual perception.

    Science.gov (United States)

    Doesburg, Sam M; Green, Jessica J; McDonald, John J; Ward, Lawrence M

    2009-07-03

    Consciousness has been proposed to emerge from functionally integrated large-scale ensembles of gamma-synchronous neural populations that form and dissolve at a frequency in the theta band. We propose that discrete moments of perceptual experience are implemented by transient gamma-band synchronization of relevant cortical regions, and that disintegration and reintegration of these assemblies is time-locked to ongoing theta oscillations. In support of this hypothesis we provide evidence that (1) perceptual switching during binocular rivalry is time-locked to gamma-band synchronizations which recur at a theta rate, indicating that the onset of new conscious percepts coincides with the emergence of a new gamma-synchronous assembly that is locked to an ongoing theta rhythm; (2) localization of the generators of these gamma rhythms reveals recurrent prefrontal and parietal sources; (3) theta modulation of gamma-band synchronization is observed between and within the activated brain regions. These results suggest that ongoing theta-modulated-gamma mechanisms periodically reintegrate a large-scale prefrontal-parietal network critical for perceptual experience. Moreover, activation and network inclusion of inferior temporal cortex and motor cortex uniquely occurs on the cycle immediately preceding responses signaling perceptual switching. This suggests that the essential prefrontal-parietal oscillatory network is expanded to include additional cortical regions relevant to tasks and perceptions furnishing consciousness at that moment, in this case image processing and response initiation, and that these activations occur within a time frame consistent with the notion that conscious processes directly affect behaviour.

  2. Network Thermodynamic Curation of Human and Yeast Genome-Scale Metabolic Models

    Science.gov (United States)

    Martínez, Verónica S.; Quek, Lake-Ee; Nielsen, Lars K.

    2014-01-01

    Genome-scale models are used for an ever-widening range of applications. Although there has been much focus on specifying the stoichiometric matrix, the predictive power of genome-scale models equally depends on reaction directions. Two-thirds of reactions in the two eukaryotic reconstructions Homo sapiens Recon 1 and Yeast 5 are specified as irreversible. However, these specifications are mainly based on biochemical textbooks or on their similarity to other organisms and are rarely underpinned by detailed thermodynamic analysis. In this study, a to our knowledge new workflow combining network-embedded thermodynamic and flux variability analysis was used to evaluate existing irreversibility constraints in Recon 1 and Yeast 5 and to identify new ones. A total of 27 and 16 new irreversible reactions were identified in Recon 1 and Yeast 5, respectively, whereas only four reactions were found with directions incorrectly specified against thermodynamics (three in Yeast 5 and one in Recon 1). The workflow further identified for both models several isolated internal loops that require further curation. The framework also highlighted the need for substrate channeling (in human) and ATP hydrolysis (in yeast) for the essential reaction catalyzed by phosphoribosylaminoimidazole carboxylase in purine metabolism. Finally, the framework highlighted differences in proline metabolism between yeast (cytosolic anabolism and mitochondrial catabolism) and humans (exclusively mitochondrial metabolism). We conclude that network-embedded thermodynamics facilitates the specification and validation of irreversibility constraints in compartmentalized metabolic models, at the same time providing further insight into network properties. PMID:25028891

  3. Theoretical model for mesoscopic-level scale-free self-organization of functional brain networks.

    Science.gov (United States)

    Piersa, Jaroslaw; Piekniewski, Filip; Schreiber, Tomasz

    2010-11-01

    In this paper, we provide theoretical and numerical analysis of a geometric activity flow network model which is aimed at explaining mathematically the scale-free functional graph self-organization phenomena emerging in complex nervous systems at a mesoscale level. In our model, each unit corresponds to a large number of neurons and may be roughly seen as abstracting the functional behavior exhibited by a single voxel under functional magnetic resonance imaging (fMRI). In the course of the dynamics, the units exchange portions of formal charge, which correspond to waves of activity in the underlying microscale neuronal circuit. The geometric model abstracts away the neuronal complexity and is mathematically tractable, which allows us to establish explicit results on its ground states and the resulting charge transfer graph modeling functional graph of the network. We show that, for a wide choice of parameters and geometrical setups, our model yields a scale-free functional connectivity with the exponent approaching 2, which is in agreement with previous empirical studies based on fMRI. The level of universality of the presented theory allows us to claim that the model does shed light on mesoscale functional self-organization phenomena of the nervous system, even without resorting to closer details of brain connectivity geometry which often remain unknown. The material presented here significantly extends our previous work where a simplified mean-field model in a similar spirit was constructed, ignoring the underlying network geometry.

  4. Neuroplasticity in mood disorders

    Science.gov (United States)

    Drevets, Wayne C.

    2004-01-01

    Neuroimaging and neuropathological studies of major depressive disorder (MDD) and bipolar disorder (BD) have identified abnormalities of brain structure in areas of the prefrontal cortex, amygdala, striatum, hippocampus, parahippocampal gyrus, and raphe nucleus. These structural imaging abnormalities persist across illness episodes, and preliminary evidence suggests they may in some cases arise prior to the onset of depressive episodes in subjects at high familial risk for MDD. In other cases, the magnitude of abnormality is reportedly correlated with time spent depressed. Postmortem histopathological studies of these regions have shown abnormal reductions of synaptic markers and glial cells, and, in rare cases, reductions in neurons in MDD and BD. Many of the regions affected by these structural abnormalities show increased glucose metabolism during depressive episodes. Because the glucose metabolic signal is dominated by glutamatergic transmission, these data support other evidence that excitatory amino acid transmission is elevated in limbic-cortical-striatal-pallidal-thalamic circuits during depression. Some of the subject samples in which these metabolic abnormalities have been demonstrated were also shown to manifest abnormally elevated stressed plasma cortisol levels. The co-occurrence of increased glutamatergic transmission and Cortisol hypersecretion raises the possibility that the gray matter volumetric reductions in these depressed subjects are partly accounted for by processes homologous to the dendritic atrophy induced by chronic stress in adult rodents, which depends upon interactions between elevated glucocorticoid secretion and N-meihyl-D-aspartate (NMDA)-glutamate receptor stimulation. Some mood-stabilizing and antidepressant drugs that exert neurotrophic effects in rodents appear to reverse or attenuate the gray matter volume abnormalities in humans with mood disorders. These neurotrophic effects may be integrally related to the therapeutic

  5. Sleep Deprivation in Mood Disorders

    National Research Council Canada - National Science Library

    Benedetti, Francesco; Colombo, Cristina

    2011-01-01

    ...: total versus partial, single versus repeated, alone or combined with antidepressant drugs, mood stabilizers, or other chronotherapeutic techniques, such as light therapy and sleep phase advance...

  6. Biology of mood & anxiety disorders

    National Research Council Canada - National Science Library

    2011-01-01

    An open access peer-reviewed journal that publishes highly innovative basic, translational, and clinical research that advances our understanding of the pathophysiology of mood and anxiety disorders...

  7. BFL: a node and edge betweenness based fast layout algorithm for large scale networks

    Science.gov (United States)

    Hashimoto, Tatsunori B; Nagasaki, Masao; Kojima, Kaname; Miyano, Satoru

    2009-01-01

    Background Network visualization would serve as a useful first step for analysis. However, current graph layout algorithms for biological pathways are insensitive to biologically important information, e.g. subcellular localization, biological node and graph attributes, or/and not available for large scale networks, e.g. more than 10000 elements. Results To overcome these problems, we propose the use of a biologically important graph metric, betweenness, a measure of network flow. This metric is highly correlated with many biological phenomena such as lethality and clusters. We devise a new fast parallel algorithm calculating betweenness to minimize the preprocessing cost. Using this metric, we also invent a node and edge betweenness based fast layout algorithm (BFL). BFL places the high-betweenness nodes to optimal positions and allows the low-betweenness nodes to reach suboptimal positions. Furthermore, BFL reduces the runtime by combining a sequential insertion algorim with betweenness. For a graph with n nodes, this approach reduces the expected runtime of the algorithm to O(n2) when considering edge crossings, and to O(n log n) when considering only density and edge lengths. Conclusion Our BFL algorithm is compared against fast graph layout algorithms and approaches requiring intensive optimizations. For gene networks, we show that our algorithm is faster than all layout algorithms tested while providing readability on par with intensive optimization algorithms. We achieve a 1.4 second runtime for a graph with 4000 nodes and 12000 edges on a standard desktop computer. PMID:19146673

  8. Secure Data Aggregation with Fully Homomorphic Encryption in Large-Scale Wireless Sensor Networks.

    Science.gov (United States)

    Li, Xing; Chen, Dexin; Li, Chunyan; Wang, Liangmin

    2015-07-03

    With the rapid development of wireless communication technology, sensor technology, information acquisition and processing technology, sensor networks will finally have a deep influence on all aspects of people's lives. The battery resources of sensor nodes should be managed efficiently in order to prolong network lifetime in large-scale wireless sensor networks (LWSNs). Data aggregation represents an important method to remove redundancy as well as unnecessary data transmission and hence cut down the energy used in communication. As sensor nodes are deployed in hostile environments, the security of the sensitive information such as confidentiality and integrity should be considered. This paper proposes Fully homomorphic Encryption based Secure data Aggregation (FESA) in LWSNs which can protect end-to-end data confidentiality and support arbitrary aggregation operations over encrypted data. In addition, by utilizing message authentication codes (MACs), this scheme can also verify data integrity during data aggregation and forwarding processes so that false data can be detected as early as possible. Although the FHE increase the computation overhead due to its large public key size, simulation results show that it is implementable in LWSNs and performs well. Compared with other protocols, the transmitted data and network overhead are reduced in our scheme.

  9. A scalable algorithm to explore the Gibbs energy landscape of genome-scale metabolic networks.

    Directory of Open Access Journals (Sweden)

    Daniele De Martino

    Full Text Available The integration of various types of genomic data into predictive models of biological networks is one of the main challenges currently faced by computational biology. Constraint-based models in particular play a key role in the attempt to obtain a quantitative understanding of cellular metabolism at genome scale. In essence, their goal is to frame the metabolic capabilities of an organism based on minimal assumptions that describe the steady states of the underlying reaction network via suitable stoichiometric constraints, specifically mass balance and energy balance (i.e. thermodynamic feasibility. The implementation of these requirements to generate viable configurations of reaction fluxes and/or to test given flux profiles for thermodynamic feasibility can however prove to be computationally intensive. We propose here a fast and scalable stoichiometry-based method to explore the Gibbs energy landscape of a biochemical network at steady state. The method is applied to the problem of reconstructing the Gibbs energy landscape underlying metabolic activity in the human red blood cell, and to that of identifying and removing thermodynamically infeasible reaction cycles in the Escherichia coli metabolic network (iAF1260. In the former case, we produce consistent predictions for chemical potentials (or log-concentrations of intracellular metabolites; in the latter, we identify a restricted set of loops (23 in total in the periplasmic and cytoplasmic core as the origin of thermodynamic infeasibility in a large sample (10(6 of flux configurations generated randomly and compatibly with the prior information available on reaction reversibility.

  10. A Large Scale Code Resolution Service Network in the Internet of Things

    Directory of Open Access Journals (Sweden)

    Xiangzhan Yu

    2012-11-01

    Full Text Available In the Internet of Things a code resolution service provides a discovery mechanism for a requester to obtain the information resources associated with a particular product code immediately. In large scale application scenarios a code resolution service faces some serious issues involving heterogeneity, big data and data ownership. A code resolution service network is required to address these issues. Firstly, a list of requirements for the network architecture and code resolution services is proposed. Secondly, in order to eliminate code resolution conflicts and code resolution overloads, a code structure is presented to create a uniform namespace for code resolution records. Thirdly, we propose a loosely coupled distributed network consisting of heterogeneous, independent; collaborating code resolution services and a SkipNet based code resolution service named SkipNet-OCRS, which not only inherits DHT’s advantages, but also supports administrative control and autonomy. For the external behaviors of SkipNet-OCRS, a novel external behavior mode named QRRA mode is proposed to enhance security and reduce requester complexity. For the internal behaviors of SkipNet-OCRS, an improved query algorithm is proposed to increase query efficiency. It is analyzed that integrating SkipNet-OCRS into our resolution service network can meet our proposed requirements. Finally, simulation experiments verify the excellent performance of SkipNet-OCRS.

  11. Dynamics of an epidemic model with quarantine on scale-free networks

    Science.gov (United States)

    Kang, Huiyan; Liu, Kaihui; Fu, Xinchu

    2017-12-01

    Quarantine strategies are frequently used to control or reduce the transmission risks of epidemic diseases such as SARS, tuberculosis and cholera. In this paper, we formulate a susceptible-exposed-infected-quarantined-recovered model on a scale-free network incorporating the births and deaths of individuals. Considering that the infectivity is related to the degrees of infectious nodes, we introduce quarantined rate as a function of degree into the model, and quantify the basic reproduction number, which is shown to be dependent on some parameters, such as quarantined rate, infectivity and network structures. A theoretical result further indicates the heterogeneity of networks and higher infectivity will raise the disease transmission risk while quarantine measure will contribute to the prevention of epidemic spreading. Meanwhile, the contact assumption between susceptibles and infectives may impact the disease transmission. Furthermore, we prove that the basic reproduction number serves as a threshold value for the global stability of the disease-free and endemic equilibria and the uniform persistence of the disease on the network by constructing appropriate Lyapunov functions. Finally, some numerical simulations are illustrated to perform and complement our analytical results.

  12. Collective behavior of large-scale neural networks with GPU acceleration.

    Science.gov (United States)

    Qu, Jingyi; Wang, Rubin

    2017-12-01

    In this paper, the collective behaviors of a small-world neuronal network motivated by the anatomy of a mammalian cortex based on both Izhikevich model and Rulkov model are studied. The Izhikevich model can not only reproduce the rich behaviors of biological neurons but also has only two equations and one nonlinear term. Rulkov model is in the form of difference equations that generate a sequence of membrane potential samples in discrete moments of time to improve computational efficiency. These two models are suitable for the construction of large scale neural networks. By varying some key parameters, such as the connection probability and the number of nearest neighbor of each node, the coupled neurons will exhibit types of temporal and spatial characteristics. It is demonstrated that the implementation of GPU can achieve more and more acceleration than CPU with the increasing of neuron number and iterations. These two small-world network models and GPU acceleration give us a new opportunity to reproduce the real biological network containing a large number of neurons.

  13. 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.

  14. Large-scale directional connections among multi resting-state neural networks in human brain: a functional MRI and Bayesian network modeling study.

    Science.gov (United States)

    Li, Rui; Chen, Kewei; Fleisher, Adam S; Reiman, Eric M; Yao, Li; Wu, Xia

    2011-06-01

    This study examined the large-scale connectivity among multiple resting-state networks (RSNs) in the human brain. Independent component analysis was first applied to the resting-state functional MRI (fMRI) data acquired from 12 healthy young subjects for the separation of RSNs. Four sensory (lateral and medial visual, auditory, and sensory-motor) RSNs and four cognitive (default-mode, self-referential, dorsal and ventral attention) RSNs were identified. Gaussian Bayesian network (BN) learning approach was then used for the examination of the conditional dependencies among these RSNs and the construction of the network-to-network directional connectivity patterns. The BN based results demonstrated that sensory networks and cognitive networks were hierarchically organized. Specially, we found the sensory networks were highly intra-dependent and the cognitive networks were strongly intra-influenced. In addition, the results depicted dominant bottom-up connectivity from sensory networks to cognitive networks in which the self-referential and the default-mode networks might play respectively important roles in the process of resting-state information transfer and integration. The present study characterized the global connectivity relations among RSNs and delineated more characteristics of spontaneous activity dynamics. Copyright © 2011 Elsevier Inc. All rights reserved.

  15. Boundary Detection Method for Large-Scale Coverage Holes in Wireless Sensor Network Based on Minimum Critical Threshold Constraint

    Directory of Open Access Journals (Sweden)

    Rong Jing

    2014-01-01

    Full Text Available The existing coverage hole boundary detection methods cannot detect large-scale coverage hole boundary in wireless sensor network quickly and efficiently. Aiming at this problem, a boundary detection method for large-scale coverage holes in wireless sensor network based on minimum critical threshold constraint is proposed. Firstly, the optimization problem of minimum critical threshold is highlighted, and its formulaic description is constructed according to probabilistic sensing model. On the basis of this, the distributed gradient information is used to approximately solve the optimization problem. After that, local-scale rough boundary detection algorithm incorporating the minimum critical threshold and its iterative thinning algorithm are proposed according to blocking flow theory. The experimental results show that the proposed method has low computational complexity and network overhead when detecting large-scale coverage hole boundary in wireless sensor network.

  16. Genome-scale reconstruction of the Streptococcus pyogenes M49 metabolic network reveals growth requirements and indicates potential drug targets

    NARCIS (Netherlands)

    Levering, J.; Fiedler, T.; Sieg, A.; van Grinsven, K.W.A.; Hering, S.; Veith, N.; Olivier, B.G.; Klett, L.; Hugenholtz, J.; Teusink, B.; Kreikemeyer, B.; Kummer, U.

    2016-01-01

    Genome-scale metabolic models comprise stoichiometric relations between metabolites, as well as associations between genes and metabolic reactions and facilitate the analysis of metabolism. We computationally reconstructed the metabolic network of the lactic acid bacterium Streptococcus pyogenes

  17. Next-Generation Global Biomonitoring: Large-scale, Automated Reconstruction of Ecological Networks.

    Science.gov (United States)

    Bohan, David A; Vacher, Corinne; Tamaddoni-Nezhad, Alireza; Raybould, Alan; Dumbrell, Alex J; Woodward, Guy

    2017-07-01

    We foresee a new global-scale, ecological approach to biomonitoring emerging within the next decade that can detect ecosystem change accurately, cheaply, and generically. Next-generation sequencing of DNA sampled from the Earth's environments would provide data for the relative abundance of operational taxonomic units or ecological functions. Machine-learning methods would then be used to reconstruct the ecological networks of interactions implicit in the raw NGS data. Ultimately, we envision the development of autonomous samplers that would sample nucleic acids and upload NGS sequence data to the cloud for network reconstruction. Large numbers of these samplers, in a global array, would allow sensitive automated biomonitoring of the Earth's major ecosystems at high spatial and temporal resolution, revolutionising our understanding of ecosystem change. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  18. Dynamics of epidemic spreading model with drug-resistant variation on scale-free networks

    Science.gov (United States)

    Wan, Chen; Li, Tao; Zhang, Wu; Dong, Jing

    2018-03-01

    Considering the influence of the virus' drug-resistant variation, a novel SIVRS (susceptible-infected-variant-recovered-susceptible) epidemic spreading model with variation characteristic on scale-free networks is proposed in this paper. By using the mean-field theory, the spreading dynamics of the model is analyzed in detail. Then, the basic reproductive number R0 and equilibriums are derived. Studies show that the existence of disease-free equilibrium is determined by the basic reproductive number R0. The relationships between the basic reproductive number R0, the variation characteristic and the topology of the underlying networks are studied in detail. Furthermore, our studies prove the global stability of the disease-free equilibrium, the permanence of epidemic and the global attractivity of endemic equilibrium. Numerical simulations are performed to confirm the analytical results.

  19. On the visualization of social and other scale-free networks.

    Science.gov (United States)

    Jia, Yuntao; Hoberock, Jared; Garland, Michael; Hart, John C

    2008-01-01

    This paper proposes novel methods for visualizing specifically the large power-law graphs that arise in sociology and the sciences. In such cases a large portion of edges can be shown to be less important and removed while preserving component connectedness and other features (e.g. cliques) to more clearly reveal the network's underlying connection pathways. This simplification approach deterministically filters (instead of clustering) the graph to retain important node and edge semantics, and works both automatically and interactively. The improved graph filtering and layout is combined with a novel computer graphics anisotropic shading of the dense crisscrossing array of edges to yield a full social network and scale-free graph visualization system. Both quantitative analysis and visual results demonstrate the effectiveness of this approach.

  20. The Effects of Topology on Throughput Capacity of Large Scale Wireless Networks

    Directory of Open Access Journals (Sweden)

    Qiuming Liu

    2017-03-01

    Full Text Available In this paper, we jointly consider the inhomogeneity and spatial dimension in large scale wireless networks. We study the effects of topology on the throughput capacity. This problem is inherently difficult since it is complex to handle the interference caused by simultaneous transmission. To solve this problem, we, according to the inhomogeneity of topology, divide the transmission into intra-cluster transmission and inter-cluster transmission. For the intra-cluster transmission, a spheroidal percolation model is constructed. The spheroidal percolation model guarantees a constant rate when a power control strategy is adopted. We also propose a cube percolation mode for the inter-cluster transmission. Different from the spheroidal percolation model, a constant transmission rate can be achieved without power control. For both transmissions, we propose a routing scheme with five phases. By comparing the achievable rate of each phase, we get the rate bottleneck, which is the throughput capacity of the network.

  1. A Feature Selection Method for Large-Scale Network Traffic Classification Based on Spark

    Directory of Open Access Journals (Sweden)

    Yong Wang

    2016-02-01

    Full Text Available Currently, with the rapid increasing of data scales in network traffic classifications, how to select traffic features efficiently is becoming a big challenge. Although a number of traditional feature selection methods using the Hadoop-MapReduce framework have been proposed, the execution time was still unsatisfactory with numeral iterative computations during the processing. To address this issue, an efficient feature selection method for network traffic based on a new parallel computing framework called Spark is proposed in this paper. In our approach, the complete feature set is firstly preprocessed based on Fisher score, and a sequential forward search strategy is employed for subsets. The optimal feature subset is then selected using the continuous iterations of the Spark computing framework. The implementation demonstrates that, on the precondition of keeping the classification accuracy, our method reduces the time cost of modeling and classification, and improves the execution efficiency of feature selection significantly.

  2. Search for large-scale coincidences in network observation of cosmic ray air showers

    CERN Document Server

    Ochi, N; Kimura, H; Konishi, T; Nakamura, T; Nakatsuka, T; Ohara, S; Ohmori, N; Okei, K; Saitoh, K; Takahashi, N; Tsuji, S; Wada, T; Yamamoto, I; Yamashita, Y; Yanagimoto, Y

    2003-01-01

    The Large Area Air Shower (LAAS) group has been performing a network observation of extensive air showers (EAS) since 1996 in Japan. Eight compact EAS arrays (ten in the near future) are operating simultaneously and independently at distant stations (up to approx 1000 km), constituting a gigantic detector system as a whole. Using five stations' datasets, large-scale coincidences of EAS have been searched for with the aim of detecting signals from extremely short bursts in the universe. By comparing arrival times and arrival directions of all registered EAS, three coincident and parallel EAS pairs were extracted out of a sea of background cosmic rays. One of them was observed almost from the direction of the Crab Nebula, a previously reported ultra-high-energy gamma-ray source. The first application reported here allows the analysis techniques to be tested and demonstrates the potential of observations with the full operation of the network detector system.

  3. Aespoe Hard Rock Laboratory. Analysis of fracture networks based on the integration of structural and hydrogeological observations on different scales

    Energy Technology Data Exchange (ETDEWEB)

    Bossart, P. [Geotechnical Inst. Ltd., Bern (Switzerland); Hermanson, Jan [Golder Associates, Stockholm (Sweden); Mazurek, M. [Univ. of Bern (Switzerland)

    2001-05-01

    Fracture networks at Aespoe have been studied for several rock types exhibiting different degrees of ductile and brittle deformation, as well as on different scales. Mesoscopic fault systems have been characterised and classified in an earlier report, this report focuses mainly on fracture networks derived on smaller scales, but also includes mesoscopic and larger scales. The TRUE-1 block has been selected for detailed structural analysis on a small scale due to the high density of relevant information. In addition to the data obtained from core materials, structural maps, BIP data and the results of hydro tests were synthesised to derive a conceptual structural model. The approach used to derive this conceptual model is based on the integration of deterministic structural evidence, probabilistic information and both upscaling and downscaling of observations and concepts derived on different scales. Twelve fracture networks mapped at different sites and scales and exhibiting various styles of tectonic deformation were analysed for fractal properties and structural and hydraulic interconnectedness. It was shown that these analysed fracture networks are not self-similar. An important result is the structural and hydraulic interconnectedness of fracture networks on all scales in the Aespoe rocks, which is further corroborated by geochemical evidence. Due to the structural and hydraulic interconnectedness of fracture systems on all scales at Aespoe, contaminants from waste canisters placed in tectonically low deformation environments would be transported - after having passed through the engineered barriers -from low-permeability fractures towards higher permeability fractures and may thus eventually reach high-permeability features.

  4. Environmental networks for large-scale monitoring of Earth and atmosphere

    Science.gov (United States)

    Maurodimou, Olga; Kolios, Stavros; Konstantaras, Antonios; Georgoulas, George; Stylios, Chrysostomos

    2013-04-01

    Installation and operation of instrument/sensor networks are proven fundamental in the monitoring of the physical environment from local to global scale. The advances in electronics, wireless communications and informatics has led to the development of a huge number of networks at different spatial scales that measure, collect and store a wide range of environmental parameters. These networks have been gradually evolved into integrated information systems that provide real time monitoring, forecasts and different products from the initial collected datasets. Instrument/sensor networks have nowadays become important solutions for environmental monitoring, comprising a basic component of fully automated systems developing worldwide that contribute in the efforts for a sustainable Earth's environment (e.g. Hart et al., 2006, Othman et al., 2012). They are also used as a source of data for models parameterization and as verification tools for accuracy assessment techniques of the satellite imagery. Environmental networks can be incorporated into decision support systems (e.g Rizzi et al., 2012) providing informational background along with data from satellites for decision making, manage problems, suggest solutions and best practices for a sustainable management of the environment. This is a comparative study aiming to examine and highlight the significant role of existing instrument/sensor networks for large-scale monitoring of environmental issues, especially atmospheric and marine environment as well as weather and climate. We provide characteristic examples of integrated systems based on large scale instrument/sensor networks along with other sources of data (like satellite datasets) as informational background to measure, identify, monitor, analyze and forecast a vast series of atmospheric parameters (like CO2, O3, particle matter and solar irradiance), weather, climate and their impacts (e.g., cloud systems, lightnings, rainfall, air and surface temperature

  5. The role of social networks in development of small-scale enterprises in the Chimanimani district of Zimbabwe

    OpenAIRE

    Zuwarimwe, J.; Kirsten, Johann F.

    2010-01-01

    The past decade has witnessed an increased interest in the concept of social networks after the seminal theses of Coleman (1988) and Putnam (1993). An area that has attracted a great deal of interest is the value of social networks in small-scale enterprise development. This paper interrogates the role of social networks in the establishment and expansion of rural non-farm enterprises in the Chimanimani district of Zimbabwe and established that rural non-farm entrepreneurs resort to their soc...

  6. A Prioritized Multi-Channel Multi-Time slot MAC Protocol For Large-Scale Wireless Sensor Networks

    OpenAIRE

    Ben Sliman, Jamila; Song, Ye-Qiong; Koubâa, Anis

    2009-01-01

    International audience; This paper addresses a new prioritized multichannel multi-time slot MAC protocol (PMCMTP) for large-scale WSNs especially for Ultra-Wide Band (UWB) based networks. To reduce the complexity of resource sharing, the global network is composed of a set of Personal Area Networks (PANs) or cells. According to available resource and PANs duty cycle, PMCMTP can dynamically assign several data channels per PAN and efficiently allocate time slots to each PAN's members. This sig...

  7. Ecological Momentary Assessment of Mood Disorders and Mood Dysregulation

    Science.gov (United States)

    Ebner-Priemer, Ulrich W.; Trull, Timothy J.

    2009-01-01

    In this review, we discuss ecological momentary assessment (EMA) studies on mood disorders and mood dysregulation, illustrating 6 major benefits of the EMA approach to clinical assessment: (a) Real-time assessments increase accuracy and minimize retrospective bias; (b) repeated assessments can reveal dynamic processes; (c) multimodal assessments…

  8. Group Interaction Sustains Positive Moods and Diminishes Negative Moods.

    Science.gov (United States)

    Park, Ernest S; Hinsz, Verlin B

    2015-12-01

    The social interactions of task groups were investigated for their influences on member moods. Initially, participants' received an induction of positive, negative, or neutral moods via listening to music that continued throughout the experimental session. Moods were measured after the induction. Students then made decisions on four choice dilemmas alone or as members of a four-person group. Subsequently, positive and negative moods were again measured. Positive moods of participants who worked with other group members on the task were sustained, but diminished for those working alone. Negative moods of participants working in groups diminished over time, but were sustained for those working individually. These results were interpreted in the context of motivational systems theory of group involvement (Park & Hinsz, 2006). Additionally, although there was a tendency for member moods to homogenize over assessments, this did not reach significance. Results document the affective benefits that often accompany task group interaction suggesting that group interaction has features of positive mood induction. This report highlights the need to consider social influences on affect in task settings so that group dynamics, processes, and behaviors can be better understood.

  9. Multi-scale needle-network model of complex dendritic microstructure formation

    Science.gov (United States)

    Tourret, Damien; Karma, Alain

    2012-07-01

    We present a novel multi-scale Dendritic Needle Network (DNN) approach in order to model well-developed highly-ramified dendritic microstructures on the coarser scale of several crystal grains while retaining a faithful quantitative description of the transient dynamics of individual dendritic branches. This approach is intended to bridge the scale gap between phase-field and cellular automaton methods. The dynamics of each needle-like branch, characterized by its tip velocity V and radius ρ, is fixed by two conditions: (i) on the inner tip scale, a standard microscopic solvability condition relates ρ2V to the strength of surface tension anisotropy, and (ii) on the outer diffusion length scale, a flux balance condition relates the product ρV2 to a flux intensity factor extracted from a contour integral analogous to the J-integral of fracture mechanics. The method is tested for low supersaturation and reproduces the analytical solutions for both early stage and steady state growth dynamics. The results are directly compared with a quantitative phase-field simulation for an experimentally relevant supersaturation. We present as well an illustrative simulation for highly branched polycrystalline growth. This model should permit to investigate the macroscale grain evolution through the dynamics of individual primary dendrites and higher-order branches, controlled by both the intragrain history-dependent selection and the intergrain dendrite interactions.

  10. Mood and affect disorders.

    Science.gov (United States)

    Tang, Michael H; Pinsky, Elizabeth G

    2015-02-01

    Depressive disorders are common in children and adolescents, with estimates for depressive episodes as high as 18.2% for girls and 7.7% for boys by age 17 years, and are a major cause of morbidity and even mortality. The primary care pediatrician should be able to (1) diagnose depressive disorders and use standardized instruments; (2) ask about suicide, self-harm, homicide, substance use, mania, and psychosis; (3) triage the severity of illness; (4) be aware of the differential diagnosis, including normal development, other depressive disorders, bipolar disorders, and comorbid disorders, such as anxiety and substance use; (5) refer to evidenced-based psychotherapies; (6) prescribe first-line medications; and (7) provide ongoing coordination in a medical home. Pediatric bipolar disorders and the new disruptive mood dysregulation disorder (DMDD) diagnoses are controversial but not uncommon, with prevalence estimates ranging from 0.8% to 4.3% in children at various ages. Although the pediatrician is not likely to be prescribing medications for children with bipolar disorder and DMDD diagnoses, all clinicians should be familiar with common neuroleptics and other mood stabilizers, including important potential adverse effects. Basic management of depressive and bipolar disorders is an important skill for primary care pediatricians. © American Academy of Pediatrics, 2015. All rights reserved.

  11. In Silico Genome-Scale Reconstruction and Validation of the Corynebacterium glutamicum Metabolic Network

    DEFF Research Database (Denmark)

    Kjeldsen, Kjeld Raunkjær; Nielsen, J.

    2009-01-01

    A genome-scale metabolic model of the Gram-positive bacteria Corynebacterium glutamicum ATCC 13032 was constructed comprising 446 reactions and 411 metabolite, based on the annotated genome and available biochemical information. The network was analyzed using constraint based methods. The model...... was extensively validated against published flux data, and flux distribution values were found to correlate well between simulations and experiments. The split pathway of the lysine synthesis pathway of C. glutamicum was investigated, and it was found that the direct dehydrogenase variant gave a higher lysine...

  12. The future of genome-scale modeling of yeast through integration of a transcriptional regulatory network

    DEFF Research Database (Denmark)

    Liu, Guodong; Marras, Antonio; Nielsen, Jens

    2014-01-01

    regulatory information is necessary to improve the accuracy and predictive ability of metabolic models. Here we review the strategies for the reconstruction of a transcriptional regulatory network (TRN) for yeast and the integration of such a reconstruction into a flux balance analysis-based metabolic model....... While many large-scale TRN reconstructions have been reported for yeast, these reconstructions still need to be improved regarding the functionality and dynamic property of the regulatory interactions. In addition, mathematical modeling approaches need to be further developed to efficiently integrate...

  13. Electricity network limitations on large-scale deployment of wind energy

    Energy Technology Data Exchange (ETDEWEB)

    Fairbairn, R.J.

    1999-07-01

    This report sought to identify limitation on large scale deployment of wind energy in the UK. A description of the existing electricity supply system in England, Scotland and Wales is given, and operational aspects of the integrated electricity networks, licence conditions, types of wind turbine generators, and the scope for deployment of wind energy in the UK are addressed. A review of technical limitations and technical criteria stipulated by the Distribution and Grid Codes, the effects of system losses, and commercial issues are examined. Potential solutions to technical limitations are proposed, and recommendations are outlined.

  14. Spatial heterogeneity regulates plant-pollinator networks across multiple landscape scales.

    Directory of Open Access Journals (Sweden)

    Eduardo Freitas Moreira

    Full Text Available Mutualistic plant-pollinator interactions play a key role in biodiversity conservation and ecosystem functioning. In a community, the combination of these interactions can generate emergent properties, e.g., robustness and resilience to disturbances such as fluctuations in populations and extinctions. Given that these systems are hierarchical and complex, environmental changes must have multiple levels of influence. In addition, changes in habitat quality and in the landscape structure are important threats to plants, pollinators and their interactions. However, despite the importance of these phenomena for the understanding of biological systems, as well as for conservation and management strategies, few studies have empirically evaluated these effects at the network level. Therefore, the objective of this study was to investigate the influence of local conditions and landscape structure at multiple scales on the characteristics of plant-pollinator networks. This study was conducted in agri-natural lands in Chapada Diamantina, Bahia, Brazil. Pollinators were collected in 27 sampling units distributed orthogonally along a gradient of proportion of agriculture and landscape diversity. The Akaike information criterion was used to select models that best fit the metrics for network characteristics, comparing four hypotheses represented by a set of a priori candidate models with specific combinations of the proportion of agriculture, the average shape of the landscape elements, the diversity of the landscape and the structure of local vegetation. The results indicate that a reduction of habitat quality and landscape heterogeneity can cause species loss and decrease of networks nestedness. These structural changes can reduce robustness and resilience of plant-pollinator networks what compromises the reproductive success of plants, the maintenance of biodiversity and the pollination service stability. We also discuss the possible explanations for

  15. Spatial heterogeneity regulates plant-pollinator networks across multiple landscape scales.

    Science.gov (United States)

    Moreira, Eduardo Freitas; Boscolo, Danilo; Viana, Blandina Felipe

    2015-01-01

    Mutualistic plant-pollinator interactions play a key role in biodiversity conservation and ecosystem functioning. In a community, the combination of these interactions can generate emergent properties, e.g., robustness and resilience to disturbances such as fluctuations in populations and extinctions. Given that these systems are hierarchical and complex, environmental changes must have multiple levels of influence. In addition, changes in habitat quality and in the landscape structure are important threats to plants, pollinators and their interactions. However, despite the importance of these phenomena for the understanding of biological systems, as well as for conservation and management strategies, few studies have empirically evaluated these effects at the network level. Therefore, the objective of this study was to investigate the influence of local conditions and landscape structure at multiple scales on the characteristics of plant-pollinator networks. This study was conducted in agri-natural lands in Chapada Diamantina, Bahia, Brazil. Pollinators were collected in 27 sampling units distributed orthogonally along a gradient of proportion of agriculture and landscape diversity. The Akaike information criterion was used to select models that best fit the metrics for network characteristics, comparing four hypotheses represented by a set of a priori candidate models with specific combinations of the proportion of agriculture, the average shape of the landscape elements, the diversity of the landscape and the structure of local vegetation. The results indicate that a reduction of habitat quality and landscape heterogeneity can cause species loss and decrease of networks nestedness. These structural changes can reduce robustness and resilience of plant-pollinator networks what compromises the reproductive success of plants, the maintenance of biodiversity and the pollination service stability. We also discuss the possible explanations for these relationships and

  16. Predicting protein functions from redundancies in large-scale protein interaction networks

    Science.gov (United States)

    Samanta, Manoj Pratim; Liang, Shoudan

    2003-01-01

    Interpreting data from large-scale protein interaction experiments has been a challenging task because of the widespread presence of random false positives. Here, we present a network-based statistical algorithm that overcomes this difficulty and allows us to derive functions of unannotated proteins from large-scale interaction data. Our algorithm uses the insight that if two proteins share significantly larger number of common interaction partners than random, they have close functional associations. Analysis of publicly available data from Saccharomyces cerevisiae reveals >2,800 reliable functional associations, 29% of which involve at least one unannotated protein. By further analyzing these associations, we derive tentative functions for 81 unannotated proteins with high certainty. Our method is not overly sensitive to the false positives present in the data. Even after adding 50% randomly generated interactions to the measured data set, we are able to recover almost all (approximately 89%) of the original associations.

  17. Restoring large-scale brain networks in PTSD and related disorders: a proposal for neuroscientifically-informed treatment interventions

    Directory of Open Access Journals (Sweden)

    Ruth A. Lanius

    2015-03-01

    Full Text Available Background: Three intrinsic connectivity networks in the brain, namely the central executive, salience, and default mode networks, have been identified as crucial to the understanding of higher cognitive functioning, and the functioning of these networks has been suggested to be impaired in psychopathology, including posttraumatic stress disorder (PTSD. Objective: 1 To describe three main large-scale networks of the human brain; 2 to discuss the functioning of these neural networks in PTSD and related symptoms; and 3 to offer hypotheses for neuroscientifically-informed interventions based on treating the abnormalities observed in these neural networks in PTSD and related disorders. Method: Literature relevant to this commentary was reviewed. Results: Increasing evidence for altered functioning of the central executive, salience, and default mode networks in PTSD has been demonstrated. We suggest that each network is associated with specific clinical symptoms observed in PTSD, including cognitive dysfunction (central executive network, increased and decreased arousal/interoception (salience network, and an altered sense of self (default mode network. Specific testable neuroscientifically-informed treatments aimed to restore each of these neural networks and related clinical dysfunction are proposed. Conclusions: Neuroscientifically-informed treatment interventions will be essential to future research agendas aimed at targeting specific PTSD and related symptoms.

  18. A triple network connectivity study of large-scale brain systems in cognitively normal APOE4 carriers

    Directory of Open Access Journals (Sweden)

    Xia Wu

    2016-09-01

    Full Text Available The triple network model, consisting of the central executive network, salience network and default mode network, has been recently employed to understand dysfunction in core networks across various disorders. Here we used the triple network model to investigate the large-scale brain networks in cognitively normal APOE4 carriers who are at risk of Alzheimer’s disease (AD. To explore the functional connectivity for each of the three networks and the effective connectivity among them, we evaluated 17 cognitively normal individuals with a family history of AD and at least one copy of the apolipoprotein e4 (APOE4 allele and compared the findings to those of 12 individuals who did not carry the APOE4 gene or have a family history of AD, using independent component analysis and Bayesian network approach. Our findings indicated altered within-network connectivity that suggests future cognitive decline risk, and preserved between-network connectivity that may support their current preserved cognition in the cognitively normal APOE4 allele carries. The study provides novel sights into our understanding of the risk factors for AD and their influence on the triple network model of major psychopathology.

  19. Dimensions in Expressed Music Mood

    NARCIS (Netherlands)

    Brinker, A.C. den; Van Dinther, C.H.B.A.; Skowronek, J.

    2013-01-01

    Mood is an important aspect of music and knowledge on mood can be used as a basic ingredient in music recommender and retrieval systems.A music experiment was carried out establishing ratings for variousmoods and a number of attributes like valence and arousal. The analysis of these data is

  20. Mood disorders and season ofpresentation

    African Journals Online (AJOL)

    admission were found in the spring and winter periods; these peaks were statistically significant. The results for admissions for mood disorders were also compared with figures for total admissions for the year 1989 (R Thom - personal communication). (Table 1). TABLEt. Mood disorder admissions versus total admissions by.

<