Modeling the Emergence of Modular Leadership Hierarchy During the Collective Motion of Herds Made of Harems

NASA Astrophysics Data System (ADS)

Ozogány, Katalin; Vicsek, Tamás

2015-02-01

Gregarious animals need to make collective decisions in order to keep their cohesiveness. Several species of them live in multilevel societies, and form herds composed of smaller communities. We present a model for the development of a leadership hierarchy in a herd consisting of loosely connected sub-groups (e.g. harems) by combining self organization and social dynamics. It starts from unfamiliar individuals without relationships and reproduces the emergence of a hierarchical and modular leadership network that promotes an effective spreading of the decisions from more capable individuals to the others, and thus gives rise to a beneficial collective decision. Our results stemming from the model are in a good agreement with our observations of a Przewalski horse herd (Hortobágy, Hungary). We find that the harem-leader to harem-member ratio observed in Przewalski horses corresponds to an optimal network in this approach regarding common success, and that the observed and modeled harem size distributions are close to a lognormal.

Human Impacts and Climate Change Influence Nestedness and Modularity in Food-Web and Mutualistic Networks.

PubMed

Takemoto, Kazuhiro; Kajihara, Kosuke

2016-01-01

Theoretical studies have indicated that nestedness and modularity-non-random structural patterns of ecological networks-influence the stability of ecosystems against perturbations; as such, climate change and human activity, as well as other sources of environmental perturbations, affect the nestedness and modularity of ecological networks. However, the effects of climate change and human activities on ecological networks are poorly understood. Here, we used a spatial analysis approach to examine the effects of climate change and human activities on the structural patterns of food webs and mutualistic networks, and found that ecological network structure is globally affected by climate change and human impacts, in addition to current climate. In pollination networks, for instance, nestedness increased and modularity decreased in response to increased human impacts. Modularity in seed-dispersal networks decreased with temperature change (i.e., warming), whereas food web nestedness increased and modularity declined in response to global warming. Although our findings are preliminary owing to data-analysis limitations, they enhance our understanding of the effects of environmental change on ecological communities.

Hippocampal Network Modularity Is Associated With Relational Memory Dysfunction in Schizophrenia.

PubMed

Avery, Suzanne N; Rogers, Baxter P; Heckers, Stephan

2018-05-01

Functional dysconnectivity has been proposed as a major pathophysiological mechanism for cognitive dysfunction in schizophrenia. The hippocampus is a focal point of dysconnectivity in schizophrenia, with decreased hippocampal functional connectivity contributing to the marked memory deficits observed in patients. Normal memory function relies on the interaction of complex corticohippocampal networks. However, only recent technological advances have enabled the large-scale exploration of functional networks with accuracy and precision. We investigated the modularity of hippocampal resting-state functional networks in a sample of 45 patients with schizophrenia spectrum disorders and 38 healthy control subjects. Modularity was calculated for two distinct functional networks: a core hippocampal-medial temporal lobe cortex network and an extended hippocampal-cortical network. As hippocampal function differs along its longitudinal axis, follow-up analyses examined anterior and posterior networks separately. To explore effects of resting network function on behavior, we tested associations between modularity and relational memory ability. Age, sex, handedness, and parental education were similar between groups. Network modularity was lower in schizophrenia patients, especially in the posterior hippocampal network. Schizophrenia patients also showed markedly lower relational memory ability compared with control subjects. We found a distinct brain-behavior relationship in schizophrenia that differed from control subjects by network and anterior/posterior division-while relational memory in control subjects was associated with anterior hippocampal-cortical modularity, schizophrenia patients showed an association with posterior hippocampal-medial temporal lobe cortex network modularity. Our findings support a model of abnormal resting-state corticohippocampal network coherence in schizophrenia, which may contribute to relational memory deficits. Copyright © 2018 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Dynamic information routing in complex networks

PubMed Central

Kirst, Christoph; Timme, Marc; Battaglia, Demian

2016-01-01

Flexible information routing fundamentally underlies the function of many biological and artificial networks. Yet, how such systems may specifically communicate and dynamically route information is not well understood. Here we identify a generic mechanism to route information on top of collective dynamical reference states in complex networks. Switching between collective dynamics induces flexible reorganization of information sharing and routing patterns, as quantified by delayed mutual information and transfer entropy measures between activities of a network's units. We demonstrate the power of this mechanism specifically for oscillatory dynamics and analyse how individual unit properties, the network topology and external inputs co-act to systematically organize information routing. For multi-scale, modular architectures, we resolve routing patterns at all levels. Interestingly, local interventions within one sub-network may remotely determine nonlocal network-wide communication. These results help understanding and designing information routing patterns across systems where collective dynamics co-occurs with a communication function. PMID:27067257

Modularity Induced Gating and Delays in Neuronal Networks

PubMed Central

Shein-Idelson, Mark; Cohen, Gilad; Hanein, Yael

2016-01-01

Neural networks, despite their highly interconnected nature, exhibit distinctly localized and gated activation. Modularity, a distinctive feature of neural networks, has been recently proposed as an important parameter determining the manner by which networks support activity propagation. Here we use an engineered biological model, consisting of engineered rat cortical neurons, to study the role of modular topology in gating the activity between cell populations. We show that pairs of connected modules support conditional propagation (transmitting stronger bursts with higher probability), long delays and propagation asymmetry. Moreover, large modular networks manifest diverse patterns of both local and global activation. Blocking inhibition decreased activity diversity and replaced it with highly consistent transmission patterns. By independently controlling modularity and disinhibition, experimentally and in a model, we pose that modular topology is an important parameter affecting activation localization and is instrumental for population-level gating by disinhibition. PMID:27104350

Brain modularity controls the critical behavior of spontaneous activity.

PubMed

Russo, R; Herrmann, H J; de Arcangelis, L

2014-03-13

The human brain exhibits a complex structure made of scale-free highly connected modules loosely interconnected by weaker links to form a small-world network. These features appear in healthy patients whereas neurological diseases often modify this structure. An important open question concerns the role of brain modularity in sustaining the critical behaviour of spontaneous activity. Here we analyse the neuronal activity of a model, successful in reproducing on non-modular networks the scaling behaviour observed in experimental data, on a modular network implementing the main statistical features measured in human brain. We show that on a modular network, regardless the strength of the synaptic connections or the modular size and number, activity is never fully scale-free. Neuronal avalanches can invade different modules which results in an activity depression, hindering further avalanche propagation. Critical behaviour is solely recovered if inter-module connections are added, modifying the modular into a more random structure.

Multi-scale modularity and motif distributional effect in metabolic networks.

PubMed

Gao, Shang; Chen, Alan; Rahmani, Ali; Zeng, Jia; Tan, Mehmet; Alhajj, Reda; Rokne, Jon; Demetrick, Douglas; Wei, Xiaohui

2016-01-01

Metabolism is a set of fundamental processes that play important roles in a plethora of biological and medical contexts. It is understood that the topological information of reconstructed metabolic networks, such as modular organization, has crucial implications on biological functions. Recent interpretations of modularity in network settings provide a view of multiple network partitions induced by different resolution parameters. Here we ask the question: How do multiple network partitions affect the organization of metabolic networks? Since network motifs are often interpreted as the super families of evolved units, we further investigate their impact under multiple network partitions and investigate how the distribution of network motifs influences the organization of metabolic networks. We studied Homo sapiens, Saccharomyces cerevisiae and Escherichia coli metabolic networks; we analyzed the relationship between different community structures and motif distribution patterns. Further, we quantified the degree to which motifs participate in the modular organization of metabolic networks.

Hierarchical organization of brain functional networks during visual tasks.

PubMed

Zhuo, Zhao; Cai, Shi-Min; Fu, Zhong-Qian; Zhang, Jie

2011-09-01

The functional network of the brain is known to demonstrate modular structure over different hierarchical scales. In this paper, we systematically investigated the hierarchical modular organizations of the brain functional networks that are derived from the extent of phase synchronization among high-resolution EEG time series during a visual task. In particular, we compare the modular structure of the functional network from EEG channels with that of the anatomical parcellation of the brain cortex. Our results show that the modular architectures of brain functional networks correspond well to those from the anatomical structures over different levels of hierarchy. Most importantly, we find that the consistency between the modular structures of the functional network and the anatomical network becomes more pronounced in terms of vision, sensory, vision-temporal, motor cortices during the visual task, which implies that the strong modularity in these areas forms the functional basis for the visual task. The structure-function relationship further reveals that the phase synchronization of EEG time series in the same anatomical group is much stronger than that of EEG time series from different anatomical groups during the task and that the hierarchical organization of functional brain network may be a consequence of functional segmentation of the brain cortex.

Investigation on changes of modularity and robustness by edge-removal mutations in signaling networks.

PubMed

Truong, Cong-Doan; Kwon, Yung-Keun

2017-12-21

Biological networks consisting of molecular components and interactions are represented by a graph model. There have been some studies based on that model to analyze a relationship between structural characteristics and dynamical behaviors in signaling network. However, little attention has been paid to changes of modularity and robustness in mutant networks. In this paper, we investigated the changes of modularity and robustness by edge-removal mutations in three signaling networks. We first observed that both the modularity and robustness increased on average in the mutant network by the edge-removal mutations. However, the modularity change was negatively correlated with the robustness change. This implies that it is unlikely that both the modularity and the robustness values simultaneously increase by the edge-removal mutations. Another interesting finding is that the modularity change was positively correlated with the degree, the number of feedback loops, and the edge betweenness of the removed edges whereas the robustness change was negatively correlated with them. We note that these results were consistently observed in randomly structure networks. Additionally, we identified two groups of genes which are incident to the highly-modularity-increasing and the highly-robustness-decreasing edges with respect to the edge-removal mutations, respectively, and observed that they are likely to be central by forming a connected component of a considerably large size. The gene-ontology enrichment of each of these gene groups was significantly different from the rest of genes. Finally, we showed that the highly-robustness-decreasing edges can be promising edgetic drug-targets, which validates the usefulness of our analysis. Taken together, the analysis of changes of robustness and modularity against edge-removal mutations can be useful to unravel novel dynamical characteristics underlying in signaling networks.

Environmental versatility promotes modularity in genome-scale metabolic networks.

PubMed

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 environments. This organizational principle is insensitive to the environments we consider and to the number of reactions in a metabolic network. Because we observe this principle not just in one or few biological networks, but in large random samples of networks, we propose that it may be a generic principle of metabolic network organization.

Modular thought in the circuit analysis

NASA Astrophysics Data System (ADS)

Wang, Feng

2018-04-01

Applied to solve the problem of modular thought, provides a whole for simplification's method, the complex problems have become of, and the study of circuit is similar to the above problems: the complex connection between components, make the whole circuit topic solution seems to be more complex, and actually components the connection between the have rules to follow, this article mainly tells the story of study on the application of the circuit modular thought. First of all, this paper introduces the definition of two-terminal network and the concept of two-terminal network equivalent conversion, then summarizes the common source resistance hybrid network modular approach, containing controlled source network modular processing method, lists the common module, typical examples analysis.

Small worlds in space: Synchronization, spatial and relational modularity

NASA Astrophysics Data System (ADS)

Brede, M.

2010-06-01

In this letter we investigate networks that have been optimized to realize a trade-off between enhanced synchronization and cost of wire to connect the nodes in space. Analyzing the evolved arrangement of nodes in space and their corresponding network topology, a class of small-world networks characterized by spatial and network modularity is found. More precisely, for low cost of wire optimal configurations are characterized by a division of nodes into two spatial groups with maximum distance from each other, whereas network modularity is low. For high cost of wire, the nodes organize into several distinct groups in space that correspond to network modules connected on a ring. In between, spatially and relationally modular small-world networks are found.

Episodic Memory Retrieval Benefits from a Less Modular Brain Network Organization.

PubMed

Westphal, Andrew J; Wang, Siliang; Rissman, Jesse

2017-03-29

Most complex cognitive tasks require the coordinated interplay of multiple brain networks, but the act of retrieving an episodic memory may place especially heavy demands for communication between the frontoparietal control network (FPCN) and the default mode network (DMN), two networks that do not strongly interact with one another in many task contexts. We applied graph theoretical analysis to task-related fMRI functional connectivity data from 20 human participants and found that global brain modularity-a measure of network segregation-is markedly reduced during episodic memory retrieval relative to closely matched analogical reasoning and visuospatial perception tasks. Individual differences in modularity were correlated with memory task performance, such that lower modularity levels were associated with a lower false alarm rate. Moreover, the FPCN and DMN showed significantly elevated coupling with each other during the memory task, which correlated with the global reduction in brain modularity. Both networks also strengthened their functional connectivity with the hippocampus during the memory task. Together, these results provide a novel demonstration that reduced modularity is conducive to effective episodic retrieval, which requires close collaboration between goal-directed control processes supported by the FPCN and internally oriented self-referential processing supported by the DMN. SIGNIFICANCE STATEMENT Modularity, an index of the degree to which nodes of a complex system are organized into discrete communities, has emerged as an important construct in the characterization of brain connectivity dynamics. We provide novel evidence that the modularity of the human brain is reduced when individuals engage in episodic memory retrieval, relative to other cognitive tasks, and that this state of lower modularity is associated with improved memory performance. We propose a neural systems mechanism for this finding where the nodes of the frontoparietal control network and default mode network strengthen their interaction with one another during episodic retrieval. Such across-network communication likely facilitates effective access to internally generated representations of past event knowledge. Copyright © 2017 the authors 0270-6474/17/373523-09$15.00/0.

On the role of sparseness in the evolution of modularity in gene regulatory networks

PubMed Central

2018-01-01

Modularity is a widespread property in biological systems. It implies that interactions occur mainly within groups of system elements. A modular arrangement facilitates adjustment of one module without perturbing the rest of the system. Therefore, modularity of developmental mechanisms is a major factor for evolvability, the potential to produce beneficial variation from random genetic change. Understanding how modularity evolves in gene regulatory networks, that create the distinct gene activity patterns that characterize different parts of an organism, is key to developmental and evolutionary biology. One hypothesis for the evolution of modules suggests that interactions between some sets of genes become maladaptive when selection favours additional gene activity patterns. The removal of such interactions by selection would result in the formation of modules. A second hypothesis suggests that modularity evolves in response to sparseness, the scarcity of interactions within a system. Here I simulate the evolution of gene regulatory networks and analyse diverse experimentally sustained networks to study the relationship between sparseness and modularity. My results suggest that sparseness alone is neither sufficient nor necessary to explain modularity in gene regulatory networks. However, sparseness amplifies the effects of forms of selection that, like selection for additional gene activity patterns, already produce an increase in modularity. That evolution of new gene activity patterns is frequent across evolution also supports that it is a major factor in the evolution of modularity. That sparseness is widespread across gene regulatory networks indicates that it may have facilitated the evolution of modules in a wide variety of cases. PMID:29775459

On the role of sparseness in the evolution of modularity in gene regulatory networks.

PubMed

Espinosa-Soto, Carlos

2018-05-01

Modularity is a widespread property in biological systems. It implies that interactions occur mainly within groups of system elements. A modular arrangement facilitates adjustment of one module without perturbing the rest of the system. Therefore, modularity of developmental mechanisms is a major factor for evolvability, the potential to produce beneficial variation from random genetic change. Understanding how modularity evolves in gene regulatory networks, that create the distinct gene activity patterns that characterize different parts of an organism, is key to developmental and evolutionary biology. One hypothesis for the evolution of modules suggests that interactions between some sets of genes become maladaptive when selection favours additional gene activity patterns. The removal of such interactions by selection would result in the formation of modules. A second hypothesis suggests that modularity evolves in response to sparseness, the scarcity of interactions within a system. Here I simulate the evolution of gene regulatory networks and analyse diverse experimentally sustained networks to study the relationship between sparseness and modularity. My results suggest that sparseness alone is neither sufficient nor necessary to explain modularity in gene regulatory networks. However, sparseness amplifies the effects of forms of selection that, like selection for additional gene activity patterns, already produce an increase in modularity. That evolution of new gene activity patterns is frequent across evolution also supports that it is a major factor in the evolution of modularity. That sparseness is widespread across gene regulatory networks indicates that it may have facilitated the evolution of modules in a wide variety of cases.

Revisiting the variation of clustering coefficient of biological networks suggests new modular structure.

PubMed

Hao, Dapeng; Ren, Cong; Li, Chuanxing

2012-05-01

A central idea in biology is the hierarchical organization of cellular processes. A commonly used method to identify the hierarchical modular organization of network relies on detecting a global signature known as variation of clustering coefficient (so-called modularity scaling). Although several studies have suggested other possible origins of this signature, it is still widely used nowadays to identify hierarchical modularity, especially in the analysis of biological networks. Therefore, a further and systematical investigation of this signature for different types of biological networks is necessary. We analyzed a variety of biological networks and found that the commonly used signature of hierarchical modularity is actually the reflection of spoke-like topology, suggesting a different view of network architecture. We proved that the existence of super-hubs is the origin that the clustering coefficient of a node follows a particular scaling law with degree k in metabolic networks. To study the modularity of biological networks, we systematically investigated the relationship between repulsion of hubs and variation of clustering coefficient. We provided direct evidences for repulsion between hubs being the underlying origin of the variation of clustering coefficient, and found that for biological networks having no anti-correlation between hubs, such as gene co-expression network, the clustering coefficient doesn't show dependence of degree. Here we have shown that the variation of clustering coefficient is neither sufficient nor exclusive for a network to be hierarchical. Our results suggest the existence of spoke-like modules as opposed to "deterministic model" of hierarchical modularity, and suggest the need to reconsider the organizational principle of biological hierarchy.

Classification of functional interactions from multi-electrodes data using conditional modularity analysis

NASA Astrophysics Data System (ADS)

Makhtar, Siti Noormiza; Senik, Mohd Harizal

2018-02-01

The availability of massive amount of neuronal signals are attracting widespread interest in functional connectivity analysis. Functional interactions estimated by multivariate partial coherence analysis in the frequency domain represent the connectivity strength in this study. Modularity is a network measure for the detection of community structure in network analysis. The discovery of community structure for the functional neuronal network was implemented on multi-electrode array (MEA) signals recorded from hippocampal regions in isoflurane-anaesthetized Lister-hooded rats. The analysis is expected to show modularity changes before and after local unilateral kainic acid (KA)-induced epileptiform activity. The result is presented using color-coded graphic of conditional modularity measure for 19 MEA nodes. This network is separated into four sub-regions to show the community detection within each sub-region. The results show that classification of neuronal signals into the inter- and intra-modular nodes is feasible using conditional modularity analysis. Estimation of segregation properties using conditional modularity analysis may provide further information about functional connectivity from MEA data.

Spatial-Temporal Dynamics of High-Resolution Animal Networks: What Can We Learn from Domestic Animals?

PubMed

Chen, Shi; Ilany, Amiyaal; White, Brad J; Sanderson, Michael W; Lanzas, Cristina

2015-01-01

Animal social network is the key to understand many ecological and epidemiological processes. We used real-time location system (RTLS) to accurately track cattle position, analyze their proximity networks, and tested the hypothesis of temporal stationarity and spatial homogeneity in these networks during different daily time periods and in different areas of the pen. The network structure was analyzed using global network characteristics (network density), subgroup clustering (modularity), triadic property (transitivity), and dyadic interactions (correlation coefficient from a quadratic assignment procedure) at hourly level. We demonstrated substantial spatial-temporal heterogeneity in these networks and potential link between indirect animal-environment contact and direct animal-animal contact. But such heterogeneity diminished if data were collected at lower spatial (aggregated at entire pen level) or temporal (aggregated at daily level) resolution. The network structure (described by the characteristics such as density, modularity, transitivity, etc.) also changed substantially at different time and locations. There were certain time (feeding) and location (hay) that the proximity network structures were more consistent based on the dyadic interaction analysis. These results reveal new insights for animal network structure and spatial-temporal dynamics, provide more accurate descriptions of animal social networks, and allow more accurate modeling of multiple (both direct and indirect) disease transmission pathways.

Epidemic outbreaks in growing scale-free networks with local structure

NASA Astrophysics Data System (ADS)

Ni, Shunjiang; Weng, Wenguo; Shen, Shifei; Fan, Weicheng

2008-09-01

The class of generative models has already attracted considerable interest from researchers in recent years and much expanded the original ideas described in BA model. Most of these models assume that only one node per time step joins the network. In this paper, we grow the network by adding n interconnected nodes as a local structure into the network at each time step with each new node emanating m new edges linking the node to the preexisting network by preferential attachment. This successfully generates key features observed in social networks. These include power-law degree distribution pk∼k, where μ=(n-1)/m is a tuning parameter defined as the modularity strength of the network, nontrivial clustering, assortative mixing, and modular structure. Moreover, all these features are dependent in a similar way on the parameter μ. We then study the susceptible-infected epidemics on this network with identical infectivity, and find that the initial epidemic behavior is governed by both of the infection scheme and the network structure, especially the modularity strength. The modularity of the network makes the spreading velocity much lower than that of the BA model. On the other hand, increasing the modularity strength will accelerate the propagation velocity.

Episodic Memory Retrieval Benefits from a Less Modular Brain Network Organization

PubMed Central

2017-01-01

Most complex cognitive tasks require the coordinated interplay of multiple brain networks, but the act of retrieving an episodic memory may place especially heavy demands for communication between the frontoparietal control network (FPCN) and the default mode network (DMN), two networks that do not strongly interact with one another in many task contexts. We applied graph theoretical analysis to task-related fMRI functional connectivity data from 20 human participants and found that global brain modularity—a measure of network segregation—is markedly reduced during episodic memory retrieval relative to closely matched analogical reasoning and visuospatial perception tasks. Individual differences in modularity were correlated with memory task performance, such that lower modularity levels were associated with a lower false alarm rate. Moreover, the FPCN and DMN showed significantly elevated coupling with each other during the memory task, which correlated with the global reduction in brain modularity. Both networks also strengthened their functional connectivity with the hippocampus during the memory task. Together, these results provide a novel demonstration that reduced modularity is conducive to effective episodic retrieval, which requires close collaboration between goal-directed control processes supported by the FPCN and internally oriented self-referential processing supported by the DMN. SIGNIFICANCE STATEMENT Modularity, an index of the degree to which nodes of a complex system are organized into discrete communities, has emerged as an important construct in the characterization of brain connectivity dynamics. We provide novel evidence that the modularity of the human brain is reduced when individuals engage in episodic memory retrieval, relative to other cognitive tasks, and that this state of lower modularity is associated with improved memory performance. We propose a neural systems mechanism for this finding where the nodes of the frontoparietal control network and default mode network strengthen their interaction with one another during episodic retrieval. Such across-network communication likely facilitates effective access to internally generated representations of past event knowledge. PMID:28242796

Linear motif-mediated interactions have contributed to the evolution of modularity in complex protein interaction networks.

PubMed

Kim, Inhae; Lee, Heetak; Han, Seong Kyu; Kim, Sanguk

2014-10-01

The modular architecture of protein-protein interaction (PPI) networks is evident in diverse species with a wide range of complexity. However, the molecular components that lead to the evolution of modularity in PPI networks have not been clearly identified. Here, we show that weak domain-linear motif interactions (DLIs) are more likely to connect different biological modules than strong domain-domain interactions (DDIs). This molecular division of labor is essential for the evolution of modularity in the complex PPI networks of diverse eukaryotic species. In particular, DLIs may compensate for the reduction in module boundaries that originate from increased connections between different modules in complex PPI networks. In addition, we show that the identification of biological modules can be greatly improved by including molecular characteristics of protein interactions. Our findings suggest that transient interactions have played a unique role in shaping the architecture and modularity of biological networks over the course of evolution.

Convergent evolution of modularity in metabolic networks through different community structures.

PubMed

Zhou, Wanding; Nakhleh, Luay

2012-09-14

It has been reported that the modularity of metabolic networks of bacteria is closely related to the variability of their living habitats. However, given the dependency of the modularity score on the community structure, it remains unknown whether organisms achieve certain modularity via similar or different community structures. In this work, we studied the relationship between similarities in modularity scores and similarities in community structures of the metabolic networks of 1021 species. Both similarities are then compared against the genetic distances. We revisited the association between modularity and variability of the microbial living environments and extended the analysis to other aspects of their life style such as temperature and oxygen requirements. We also tested both topological and biological intuition of the community structures identified and investigated the extent of their conservation with respect to the taxonomy. We find that similar modularities are realized by different community structures. We find that such convergent evolution of modularity is closely associated with the number of (distinct) enzymes in the organism's metabolome, a consequence of different life styles of the species. We find that the order of modularity is the same as the order of the number of the enzymes under the classification based on the temperature preference but not on the oxygen requirement. Besides, inspection of modularity-based communities reveals that these communities are graph-theoretically meaningful yet not reflective of specific biological functions. From an evolutionary perspective, we find that the community structures are conserved only at the level of kingdoms. Our results call for more investigation into the interplay between evolution and modularity: how evolution shapes modularity, and how modularity affects evolution (mainly in terms of fitness and evolvability). Further, our results call for exploring new measures of modularity and network communities that better correspond to functional categorizations.

A flexible CAMAC based data system for Space Shuttle scientific instruments

NASA Technical Reports Server (NTRS)

Ehrmann, C. H.; Baker, R. G.; Smith, R. L.; Kaminski, T. J.

1979-01-01

An effort has been made within NASA to produce a low-cost modular system for implementation of Shuttle payloads based on the CAMAC standards for packaging and data transfer. A key element of such a modular system is a means for controlling the data system, collecting and processing the data for transmission to the ground, and issuing commands to the instrument either from the ground or based on the data collected. A description is presented of such a means based on a network of digital processors and CAMAC crate controllers, which allows for the implementation of instruments ranging from those requiring only a single CAMAC crate of functional modules and no data processing to ones requiring multiple crates and multiple data processors.

Emergence of consensus as a modular-to-nested transition in communication dynamics

NASA Astrophysics Data System (ADS)

Borge-Holthoefer, Javier; Baños, Raquel A.; Gracia-Lázaro, Carlos; Moreno, Yamir

2017-01-01

Online social networks have transformed the way in which humans communicate and interact, leading to a new information ecosystem where people send and receive information through multiple channels, including traditional communication media. Despite many attempts to characterize the structure and dynamics of these techno-social systems, little is known about fundamental aspects such as how collective attention arises and what determines the information life-cycle. Current approaches to these problems either focus on human temporal dynamics or on semiotic dynamics. In addition, as recently shown, information ecosystems are highly competitive, with humans and memes striving for scarce resources -visibility and attention, respectively. Inspired by similar problems in ecology, here we develop a methodology that allows to cast all the previous aspects into a compact framework and to characterize, using microblogging data, information-driven systems as mutualistic networks. Our results show that collective attention around a topic is reached when the user-meme network self-adapts from a modular to a nested structure, which ultimately allows minimizing competition and attaining consensus. Beyond a sociological interpretation, we explore such resemblance to natural mutualistic communities via well-known dynamics of ecological systems.

Emergence of consensus as a modular-to-nested transition in communication dynamics.

PubMed

Borge-Holthoefer, Javier; Baños, Raquel A; Gracia-Lázaro, Carlos; Moreno, Yamir

2017-01-30

Online social networks have transformed the way in which humans communicate and interact, leading to a new information ecosystem where people send and receive information through multiple channels, including traditional communication media. Despite many attempts to characterize the structure and dynamics of these techno-social systems, little is known about fundamental aspects such as how collective attention arises and what determines the information life-cycle. Current approaches to these problems either focus on human temporal dynamics or on semiotic dynamics. In addition, as recently shown, information ecosystems are highly competitive, with humans and memes striving for scarce resources -visibility and attention, respectively. Inspired by similar problems in ecology, here we develop a methodology that allows to cast all the previous aspects into a compact framework and to characterize, using microblogging data, information-driven systems as mutualistic networks. Our results show that collective attention around a topic is reached when the user-meme network self-adapts from a modular to a nested structure, which ultimately allows minimizing competition and attaining consensus. Beyond a sociological interpretation, we explore such resemblance to natural mutualistic communities via well-known dynamics of ecological systems.

Emergence of consensus as a modular-to-nested transition in communication dynamics

PubMed Central

Borge-Holthoefer, Javier; Baños, Raquel A.; Gracia-Lázaro, Carlos; Moreno, Yamir

2017-01-01

Online social networks have transformed the way in which humans communicate and interact, leading to a new information ecosystem where people send and receive information through multiple channels, including traditional communication media. Despite many attempts to characterize the structure and dynamics of these techno-social systems, little is known about fundamental aspects such as how collective attention arises and what determines the information life-cycle. Current approaches to these problems either focus on human temporal dynamics or on semiotic dynamics. In addition, as recently shown, information ecosystems are highly competitive, with humans and memes striving for scarce resources –visibility and attention, respectively. Inspired by similar problems in ecology, here we develop a methodology that allows to cast all the previous aspects into a compact framework and to characterize, using microblogging data, information-driven systems as mutualistic networks. Our results show that collective attention around a topic is reached when the user-meme network self-adapts from a modular to a nested structure, which ultimately allows minimizing competition and attaining consensus. Beyond a sociological interpretation, we explore such resemblance to natural mutualistic communities via well-known dynamics of ecological systems. PMID:28134358

To cut or not to cut? Assessing the modular structure of brain networks.

PubMed

Chang, Yu-Teng; Pantazis, Dimitrios; Leahy, Richard M

2014-05-01

A wealth of methods has been developed to identify natural divisions of brain networks into groups or modules, with one of the most prominent being modularity. Compared with the popularity of methods to detect community structure, only a few methods exist to statistically control for spurious modules, relying almost exclusively on resampling techniques. It is well known that even random networks can exhibit high modularity because of incidental concentration of edges, even though they have no underlying organizational structure. Consequently, interpretation of community structure is confounded by the lack of principled and computationally tractable approaches to statistically control for spurious modules. In this paper we show that the modularity of random networks follows a transformed version of the Tracy-Widom distribution, providing for the first time a link between module detection and random matrix theory. We compute parametric formulas for the distribution of modularity for random networks as a function of network size and edge variance, and show that we can efficiently control for false positives in brain and other real-world networks. Copyright © 2014 Elsevier Inc. All rights reserved.

Modularity and the spread of perturbations in complex dynamical systems

NASA Astrophysics Data System (ADS)

Kolchinsky, Artemy; Gates, Alexander J.; Rocha, Luis M.

2015-12-01

We propose a method to decompose dynamical systems based on the idea that modules constrain the spread of perturbations. We find partitions of system variables that maximize "perturbation modularity," defined as the autocovariance of coarse-grained perturbed trajectories. The measure effectively separates the fast intramodular from the slow intermodular dynamics of perturbation spreading (in this respect, it is a generalization of the "Markov stability" method of network community detection). Our approach captures variation of modular organization across different system states, time scales, and in response to different kinds of perturbations: aspects of modularity which are all relevant to real-world dynamical systems. It offers a principled alternative to detecting communities in networks of statistical dependencies between system variables (e.g., "relevance networks" or "functional networks"). Using coupled logistic maps, we demonstrate that the method uncovers hierarchical modular organization planted in a system's coupling matrix. Additionally, in homogeneously coupled map lattices, it identifies the presence of self-organized modularity that depends on the initial state, dynamical parameters, and type of perturbations. Our approach offers a powerful tool for exploring the modular organization of complex dynamical systems.

Modularity and the spread of perturbations in complex dynamical systems.

PubMed

Kolchinsky, Artemy; Gates, Alexander J; Rocha, Luis M

2015-12-01

We propose a method to decompose dynamical systems based on the idea that modules constrain the spread of perturbations. We find partitions of system variables that maximize "perturbation modularity," defined as the autocovariance of coarse-grained perturbed trajectories. The measure effectively separates the fast intramodular from the slow intermodular dynamics of perturbation spreading (in this respect, it is a generalization of the "Markov stability" method of network community detection). Our approach captures variation of modular organization across different system states, time scales, and in response to different kinds of perturbations: aspects of modularity which are all relevant to real-world dynamical systems. It offers a principled alternative to detecting communities in networks of statistical dependencies between system variables (e.g., "relevance networks" or "functional networks"). Using coupled logistic maps, we demonstrate that the method uncovers hierarchical modular organization planted in a system's coupling matrix. Additionally, in homogeneously coupled map lattices, it identifies the presence of self-organized modularity that depends on the initial state, dynamical parameters, and type of perturbations. Our approach offers a powerful tool for exploring the modular organization of complex dynamical systems.

A simplified computational memory model from information processing.

PubMed

Zhang, Lanhua; Zhang, Dongsheng; Deng, Yuqin; Ding, Xiaoqian; Wang, Yan; Tang, Yiyuan; Sun, Baoliang

2016-11-23

This paper is intended to propose a computational model for memory from the view of information processing. The model, called simplified memory information retrieval network (SMIRN), is a bi-modular hierarchical functional memory network by abstracting memory function and simulating memory information processing. At first meta-memory is defined to express the neuron or brain cortices based on the biology and graph theories, and we develop an intra-modular network with the modeling algorithm by mapping the node and edge, and then the bi-modular network is delineated with intra-modular and inter-modular. At last a polynomial retrieval algorithm is introduced. In this paper we simulate the memory phenomena and functions of memorization and strengthening by information processing algorithms. The theoretical analysis and the simulation results show that the model is in accordance with the memory phenomena from information processing view.

Scalable detection of statistically significant communities and hierarchies, using message passing for modularity

PubMed Central

Zhang, Pan; Moore, Cristopher

2014-01-01

Modularity is a popular measure of community structure. However, maximizing the modularity can lead to many competing partitions, with almost the same modularity, that are poorly correlated with each other. It can also produce illusory ‘‘communities’’ in random graphs where none exist. We address this problem by using the modularity as a Hamiltonian at finite temperature and using an efficient belief propagation algorithm to obtain the consensus of many partitions with high modularity, rather than looking for a single partition that maximizes it. We show analytically and numerically that the proposed algorithm works all of the way down to the detectability transition in networks generated by the stochastic block model. It also performs well on real-world networks, revealing large communities in some networks where previous work has claimed no communities exist. Finally we show that by applying our algorithm recursively, subdividing communities until no statistically significant subcommunities can be found, we can detect hierarchical structure in real-world networks more efficiently than previous methods. PMID:25489096

Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills

PubMed Central

Ellefsen, Kai Olav; Mouret, Jean-Baptiste; Clune, Jeff

2015-01-01

A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand). To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1) that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2) that one benefit of the modularity ubiquitous in the brains of natural animals might be to alleviate the problem of catastrophic forgetting. PMID:25837826

Neural modularity helps organisms evolve to learn new skills without forgetting old skills.

PubMed

Ellefsen, Kai Olav; Mouret, Jean-Baptiste; Clune, Jeff

2015-04-01

A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand). To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1) that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2) that one benefit of the modularity ubiquitous in the brains of natural animals might be to alleviate the problem of catastrophic forgetting.

Brain Network Modularity Predicts Exercise-Related Executive Function Gains in Older Adults

PubMed Central

Baniqued, Pauline L.; Gallen, Courtney L.; Voss, Michelle W.; Burzynska, Agnieszka Z.; Wong, Chelsea N.; Cooke, Gillian E.; Duffy, Kristin; Fanning, Jason; Ehlers, Diane K.; Salerno, Elizabeth A.; Aguiñaga, Susan; McAuley, Edward; Kramer, Arthur F.; D'Esposito, Mark

2018-01-01

Recent work suggests that the brain can be conceptualized as a network comprised of groups of sub-networks or modules. The extent of segregation between modules can be quantified with a modularity metric, where networks with high modularity have dense connections within modules and sparser connections between modules. Previous work has shown that higher modularity predicts greater improvements after cognitive training in patients with traumatic brain injury and in healthy older and young adults. It is not known, however, whether modularity can also predict cognitive gains after a physical exercise intervention. Here, we quantified modularity in older adults (N = 128, mean age = 64.74) who underwent one of the following interventions for 6 months (NCT01472744 on ClinicalTrials.gov): (1) aerobic exercise in the form of brisk walking (Walk), (2) aerobic exercise in the form of brisk walking plus nutritional supplement (Walk+), (3) stretching, strengthening and stability (SSS), or (4) dance instruction. After the intervention, the Walk, Walk+ and SSS groups showed gains in cardiorespiratory fitness (CRF), with larger effects in both walking groups compared to the SSS and Dance groups. The Walk, Walk+ and SSS groups also improved in executive function (EF) as measured by reasoning, working memory, and task-switching tests. In the Walk, Walk+, and SSS groups that improved in EF, higher baseline modularity was positively related to EF gains, even after controlling for age, in-scanner motion and baseline EF. No relationship between modularity and EF gains was observed in the Dance group, which did not show training-related gains in CRF or EF control. These results are consistent with previous studies demonstrating that individuals with a more modular brain network organization are more responsive to cognitive training. These findings suggest that the predictive power of modularity may be generalizable across interventions aimed to enhance aspects of cognition and that, especially in low-performing individuals, global network properties can capture individual differences in neuroplasticity. PMID:29354050

Anatomical Network Analysis Shows Decoupling of Modular Lability and Complexity in the Evolution of the Primate Skull

PubMed Central

Esteve-Altava, Borja; Boughner, Julia C.; Diogo, Rui; Villmoare, Brian A.; Rasskin-Gutman, Diego

2015-01-01

Modularity and complexity go hand in hand in the evolution of the skull of primates. Because analyses of these two parameters often use different approaches, we do not know yet how modularity evolves within, or as a consequence of, an also-evolving complex organization. Here we use a novel network theory-based approach (Anatomical Network Analysis) to assess how the organization of skull bones constrains the co-evolution of modularity and complexity among primates. We used the pattern of bone contacts modeled as networks to identify connectivity modules and quantify morphological complexity. We analyzed whether modularity and complexity evolved coordinately in the skull of primates. Specifically, we tested Herbert Simon’s general theory of near-decomposability, which states that modularity promotes the evolution of complexity. We found that the skulls of extant primates divide into one conserved cranial module and up to three labile facial modules, whose composition varies among primates. Despite changes in modularity, statistical analyses reject a positive feedback between modularity and complexity. Our results suggest a decoupling of complexity and modularity that translates to varying levels of constraint on the morphological evolvability of the primate skull. This study has methodological and conceptual implications for grasping the constraints that underlie the developmental and functional integration of the skull of humans and other primates. PMID:25992690

Mining the modular structure of protein interaction networks.

PubMed

Berenstein, Ariel José; Piñero, Janet; Furlong, Laura Inés; Chernomoretz, Ariel

2015-01-01

Cluster-based descriptions of biological networks have received much attention in recent years fostered by accumulated evidence of the existence of meaningful correlations between topological network clusters and biological functional modules. Several well-performing clustering algorithms exist to infer topological network partitions. However, due to respective technical idiosyncrasies they might produce dissimilar modular decompositions of a given network. In this contribution, we aimed to analyze how alternative modular descriptions could condition the outcome of follow-up network biology analysis. We considered a human protein interaction network and two paradigmatic cluster recognition algorithms, namely: the Clauset-Newman-Moore and the infomap procedures. We analyzed to what extent both methodologies yielded different results in terms of granularity and biological congruency. In addition, taking into account Guimera's cartographic role characterization of network nodes, we explored how the adoption of a given clustering methodology impinged on the ability to highlight relevant network meso-scale connectivity patterns. As a case study we considered a set of aging related proteins and showed that only the high-resolution modular description provided by infomap, could unveil statistically significant associations between them and inter/intra modular cartographic features. Besides reporting novel biological insights that could be gained from the discovered associations, our contribution warns against possible technical concerns that might affect the tools used to mine for interaction patterns in network biology studies. In particular our results suggested that sub-optimal partitions from the strict point of view of their modularity levels might still be worth being analyzed when meso-scale features were to be explored in connection with external source of biological knowledge.

Generalized epidemic process on modular networks.

PubMed

Chung, Kihong; Baek, Yongjoo; Kim, Daniel; Ha, Meesoon; Jeong, Hawoong

2014-05-01

Social reinforcement and modular structure are two salient features observed in the spreading of behavior through social contacts. In order to investigate the interplay between these two features, we study the generalized epidemic process on modular networks with equal-sized finite communities and adjustable modularity. Using the analytical approach originally applied to clique-based random networks, we show that the system exhibits a bond-percolation type continuous phase transition for weak social reinforcement, whereas a discontinuous phase transition occurs for sufficiently strong social reinforcement. Our findings are numerically verified using the finite-size scaling analysis and the crossings of the bimodality coefficient.

The Dichotomy in Degree Correlation of Biological Networks

PubMed Central

Hao, Dapeng; Li, Chuanxing

2011-01-01

Most complex networks from different areas such as biology, sociology or technology, show a correlation on node degree where the possibility of a link between two nodes depends on their connectivity. It is widely believed that complex networks are either disassortative (links between hubs are systematically suppressed) or assortative (links between hubs are enhanced). In this paper, we analyze a variety of biological networks and find that they generally show a dichotomous degree correlation. We find that many properties of biological networks can be explained by this dichotomy in degree correlation, including the neighborhood connectivity, the sickle-shaped clustering coefficient distribution and the modularity structure. This dichotomy distinguishes biological networks from real disassortative networks or assortative networks such as the Internet and social networks. We suggest that the modular structure of networks accounts for the dichotomy in degree correlation and vice versa, shedding light on the source of modularity in biological networks. We further show that a robust and well connected network necessitates the dichotomy of degree correlation, suggestive of an evolutionary motivation for its existence. Finally, we suggest that a dichotomous degree correlation favors a centrally connected modular network, by which the integrity of network and specificity of modules might be reconciled. PMID:22164269

A simplified computational memory model from information processing

PubMed Central

Zhang, Lanhua; Zhang, Dongsheng; Deng, Yuqin; Ding, Xiaoqian; Wang, Yan; Tang, Yiyuan; Sun, Baoliang

2016-01-01

This paper is intended to propose a computational model for memory from the view of information processing. The model, called simplified memory information retrieval network (SMIRN), is a bi-modular hierarchical functional memory network by abstracting memory function and simulating memory information processing. At first meta-memory is defined to express the neuron or brain cortices based on the biology and graph theories, and we develop an intra-modular network with the modeling algorithm by mapping the node and edge, and then the bi-modular network is delineated with intra-modular and inter-modular. At last a polynomial retrieval algorithm is introduced. In this paper we simulate the memory phenomena and functions of memorization and strengthening by information processing algorithms. The theoretical analysis and the simulation results show that the model is in accordance with the memory phenomena from information processing view. PMID:27876847

A new multi-scale method to reveal hierarchical modular structures in biological networks.

PubMed

Jiao, Qing-Ju; Huang, Yan; Shen, Hong-Bin

2016-11-15

Biological networks are effective tools for studying molecular interactions. Modular structure, in which genes or proteins may tend to be associated with functional modules or protein complexes, is a remarkable feature of biological networks. Mining modular structure from biological networks enables us to focus on a set of potentially important nodes, which provides a reliable guide to future biological experiments. The first fundamental challenge in mining modular structure from biological networks is that the quality of the observed network data is usually low owing to noise and incompleteness in the obtained networks. The second problem that poses a challenge to existing approaches to the mining of modular structure is that the organization of both functional modules and protein complexes in networks is far more complicated than was ever thought. For instance, the sizes of different modules vary considerably from each other and they often form multi-scale hierarchical structures. To solve these problems, we propose a new multi-scale protocol for mining modular structure (named ISIMB) driven by a node similarity metric, which works in an iteratively converged space to reduce the effects of the low data quality of the observed network data. The multi-scale node similarity metric couples both the local and the global topology of the network with a resolution regulator. By varying this resolution regulator to give different weightings to the local and global terms in the metric, the ISIMB method is able to fit the shape of modules and to detect them on different scales. Experiments on protein-protein interaction and genetic interaction networks show that our method can not only mine functional modules and protein complexes successfully, but can also predict functional modules from specific to general and reveal the hierarchical organization of protein complexes.

The patterns of organisation and structure of interactions in a fish-parasite network of a neotropical river.

PubMed

Bellay, Sybelle; Oliveira, Edson F de; Almeida-Neto, Mário; Abdallah, Vanessa D; Azevedo, Rodney K de; Takemoto, Ricardo M; Luque, José L

2015-07-01

The use of the complex network approach to study host-parasite interactions has helped to improve the understanding of the structure and dynamics of ecological communities. In this study, this network approach is applied to evaluate the patterns of organisation and structure of interactions in a fish-parasite network of a neotropical Atlantic Forest river. The network includes 20 fish species and 73 metazoan parasite species collected from the Guandu River, Rio de Janeiro State, Brazil. According to the usual measures in studies of networks, the organisation of the network was evaluated using measures of host susceptibility, parasite dependence, interaction asymmetry, species strength and complementary specialisation of each species as well as the network. The network structure was evaluated using connectance, nestedness and modularity measures. Host susceptibility typically presented low values, whereas parasite dependence was high. The asymmetry and species strength were correlated with host taxonomy but not with parasite taxonomy. Differences among parasite taxonomic groups in the complementary specialisation of each species on hosts were also observed. However, the complementary specialisation and species strength values were not correlated. The network had a high complementary specialisation, low connectance and nestedness, and high modularity, thus indicating variability in the roles of species in the network organisation and the expected presence of many specialist species. Copyright © 2015 Australian Society for Parasitology Inc. Published by Elsevier Ltd. All rights reserved.

Modularity-like objective function in annotated networks

NASA Astrophysics Data System (ADS)

Xie, Jia-Rong; Wang, Bing-Hong

2017-12-01

We ascertain the modularity-like objective function whose optimization is equivalent to the maximum likelihood in annotated networks. We demonstrate that the modularity-like objective function is a linear combination of modularity and conditional entropy. In contrast with statistical inference methods, in our method, the influence of the metadata is adjustable; when its influence is strong enough, the metadata can be recovered. Conversely, when it is weak, the detection may correspond to another partition. Between the two, there is a transition. This paper provides a concept for expanding the scope of modularity methods.

Convergent evolution of modularity in metabolic networks through different community structures

PubMed Central

2012-01-01

Background It has been reported that the modularity of metabolic networks of bacteria is closely related to the variability of their living habitats. However, given the dependency of the modularity score on the community structure, it remains unknown whether organisms achieve certain modularity via similar or different community structures. Results In this work, we studied the relationship between similarities in modularity scores and similarities in community structures of the metabolic networks of 1021 species. Both similarities are then compared against the genetic distances. We revisited the association between modularity and variability of the microbial living environments and extended the analysis to other aspects of their life style such as temperature and oxygen requirements. We also tested both topological and biological intuition of the community structures identified and investigated the extent of their conservation with respect to the taxomony. Conclusions We find that similar modularities are realized by different community structures. We find that such convergent evolution of modularity is closely associated with the number of (distinct) enzymes in the organism’s metabolome, a consequence of different life styles of the species. We find that the order of modularity is the same as the order of the number of the enzymes under the classification based on the temperature preference but not on the oxygen requirement. Besides, inspection of modularity-based communities reveals that these communities are graph-theoretically meaningful yet not reflective of specific biological functions. From an evolutionary perspective, we find that the community structures are conserved only at the level of kingdoms. Our results call for more investigation into the interplay between evolution and modularity: how evolution shapes modularity, and how modularity affects evolution (mainly in terms of fitness and evolvability). Further, our results call for exploring new measures of modularity and network communities that better correspond to functional categorizations. PMID:22974099

An iterative network partition algorithm for accurate identification of dense network modules

PubMed Central

Sun, Siqi; Dong, Xinran; Fu, Yao; Tian, Weidong

2012-01-01

A key step in network analysis is to partition a complex network into dense modules. Currently, modularity is one of the most popular benefit functions used to partition network modules. However, recent studies suggested that it has an inherent limitation in detecting dense network modules. In this study, we observed that despite the limitation, modularity has the advantage of preserving the primary network structure of the undetected modules. Thus, we have developed a simple iterative Network Partition (iNP) algorithm to partition a network. The iNP algorithm provides a general framework in which any modularity-based algorithm can be implemented in the network partition step. Here, we tested iNP with three modularity-based algorithms: multi-step greedy (MSG), spectral clustering and Qcut. Compared with the original three methods, iNP achieved a significant improvement in the quality of network partition in a benchmark study with simulated networks, identified more modules with significantly better enrichment of functionally related genes in both yeast protein complex network and breast cancer gene co-expression network, and discovered more cancer-specific modules in the cancer gene co-expression network. As such, iNP should have a broad application as a general method to assist in the analysis of biological networks. PMID:22121225

Network community-detection enhancement by proper weighting

NASA Astrophysics Data System (ADS)

Khadivi, Alireza; Ajdari Rad, Ali; Hasler, Martin

2011-04-01

In this paper, we show how proper assignment of weights to the edges of a complex network can enhance the detection of communities and how it can circumvent the resolution limit and the extreme degeneracy problems associated with modularity. Our general weighting scheme takes advantage of graph theoretic measures and it introduces two heuristics for tuning its parameters. We use this weighting as a preprocessing step for the greedy modularity optimization algorithm of Newman to improve its performance. The result of the experiments of our approach on computer-generated and real-world data networks confirm that the proposed approach not only mitigates the problems of modularity but also improves the modularity optimization.

Chimera states: Effects of different coupling topologies

NASA Astrophysics Data System (ADS)

Bera, Bidesh K.; Majhi, Soumen; Ghosh, Dibakar; Perc, Matjaž

2017-04-01

Collective behavior among coupled dynamical units can emerge in various forms as a result of different coupling topologies as well as different types of coupling functions. Chimera states have recently received ample attention as a fascinating manifestation of collective behavior, in particular describing a symmetry breaking spatiotemporal pattern where synchronized and desynchronized states coexist in a network of coupled oscillators. In this perspective, we review the emergence of different chimera states, focusing on the effects of different coupling topologies that describe the interaction network connecting the oscillators. We cover chimera states that emerge in local, nonlocal and global coupling topologies, as well as in modular, temporal and multilayer networks. We also provide an outline of challenges and directions for future research.

Analysis of the structure of complex networks at different resolution levels

NASA Astrophysics Data System (ADS)

Arenas, A.; Fernández, A.; Gómez, S.

2008-05-01

Modular structure is ubiquitous in real-world complex networks, and its detection is important because it gives insights into the structure-functionality relationship. The standard approach is based on the optimization of a quality function, modularity, which is a relative quality measure for the partition of a network into modules. Recently, some authors (Fortunato and Barthélemy 2007 Proc. Natl Acad. Sci. USA 104 36 and Kumpula et al 2007 Eur. Phys. J. B 56 41) have pointed out that the optimization of modularity has a fundamental drawback: the existence of a resolution limit beyond which no modular structure can be detected even though these modules might have their own entity. The reason is that several topological descriptions of the network coexist at different scales, which is, in general, a fingerprint of complex systems. Here, we propose a method that allows for multiple resolution screening of the modular structure. The method has been validated using synthetic networks, discovering the predefined structures at all scales. Its application to two real social networks allows us to find the exact splits reported in the literature, as well as the substructure beyond the actual split.

Investigating cellular network heterogeneity and modularity in cancer: a network entropy and unbalanced motif approach.

PubMed

Cheng, Feixiong; Liu, Chuang; Shen, Bairong; Zhao, Zhongming

2016-08-26

Cancer is increasingly recognized as a cellular system phenomenon that is attributed to the accumulation of genetic or epigenetic alterations leading to the perturbation of the molecular network architecture. Elucidation of network properties that can characterize tumor initiation and progression, or pinpoint the molecular targets related to the drug sensitivity or resistance, is therefore of critical importance for providing systems-level insights into tumorigenesis and clinical outcome in the molecularly targeted cancer therapy. In this study, we developed a network-based framework to quantitatively examine cellular network heterogeneity and modularity in cancer. Specifically, we constructed gene co-expressed protein interaction networks derived from large-scale RNA-Seq data across 8 cancer types generated in The Cancer Genome Atlas (TCGA) project. We performed gene network entropy and balanced versus unbalanced motif analysis to investigate cellular network heterogeneity and modularity in tumor versus normal tissues, different stages of progression, and drug resistant versus sensitive cancer cell lines. We found that tumorigenesis could be characterized by a significant increase of gene network entropy in all of the 8 cancer types. The ratio of the balanced motifs in normal tissues is higher than that of tumors, while the ratio of unbalanced motifs in tumors is higher than that of normal tissues in all of the 8 cancer types. Furthermore, we showed that network entropy could be used to characterize tumor progression and anticancer drug responses. For example, we found that kinase inhibitor resistant cancer cell lines had higher entropy compared to that of sensitive cell lines using the integrative analysis of microarray gene expression and drug pharmacological data collected from the Genomics of Drug Sensitivity in Cancer database. In addition, we provided potential network-level evidence that smoking might increase cancer cellular network heterogeneity and further contribute to tyrosine kinase inhibitor (e.g., gefitinib) resistance. In summary, we demonstrated that network properties such as network entropy and unbalanced motifs associated with tumor initiation, progression, and anticancer drug responses, suggesting new potential network-based prognostic and predictive measure in cancer.

Model validation of simple-graph representations of metabolism

PubMed Central

Holme, Petter

2009-01-01

The large-scale properties of chemical reaction systems, such as metabolism, can be studied with graph-based methods. To do this, one needs to reduce the information, lists of chemical reactions, available in databases. Even for the simplest type of graph representation, this reduction can be done in several ways. We investigate different simple network representations by testing how well they encode information about one biologically important network structure—network modularity (the propensity for edges to be clustered into dense groups that are sparsely connected between each other). To achieve this goal, we design a model of reaction systems where network modularity can be controlled and measure how well the reduction to simple graphs captures the modular structure of the model reaction system. We find that the network types that best capture the modular structure of the reaction system are substrate–product networks (where substrates are linked to products of a reaction) and substance networks (with edges between all substances participating in a reaction). Furthermore, we argue that the proposed model for reaction systems with tunable clustering is a general framework for studies of how reaction systems are affected by modularity. To this end, we investigate statistical properties of the model and find, among other things, that it recreates correlations between degree and mass of the molecules. PMID:19158012

Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network

PubMed Central

2011-01-01

Background Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology. Results We herein introduce a framework for network modularization and Bayesian network analysis (FMB) to investigate organism’s metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model. Conclusions After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis. PMID:22784571

Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network.

PubMed

Kim, Hyun Uk; Kim, Tae Yong; Lee, Sang Yup

2011-01-01

Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology. We herein introduce a framework for network modularization and Bayesian network analysis (FMB) to investigate organism's metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model. After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis.

Resilience of networks formed of interdependent modular networks

NASA Astrophysics Data System (ADS)

Shekhtman, Louis M.; Shai, Saray; Havlin, Shlomo

2015-12-01

Many infrastructure networks have a modular structure and are also interdependent with other infrastructures. While significant research has explored the resilience of interdependent networks, there has been no analysis of the effects of modularity. Here we develop a theoretical framework for attacks on interdependent modular networks and support our results through simulations. We focus, for simplicity, on the case where each network has the same number of communities and the dependency links are restricted to be between pairs of communities of different networks. This is particularly realistic for modeling infrastructure across cities. Each city has its own infrastructures and different infrastructures are dependent only within the city. However, each infrastructure is connected within and between cities. For example, a power grid will connect many cities as will a communication network, yet a power station and communication tower that are interdependent will likely be in the same city. It has previously been shown that single networks are very susceptible to the failure of the interconnected nodes (between communities) (Shai et al 2014 arXiv:1404.4748) and that attacks on these nodes are even more crippling than attacks based on betweenness (da Cunha et al 2015 arXiv:1502.00353). In our example of cities these nodes have long range links which are more likely to fail. For both treelike and looplike interdependent modular networks we find distinct regimes depending on the number of modules, m. (i) In the case where there are fewer modules with strong intraconnections, the system first separates into modules in an abrupt first-order transition and then each module undergoes a second percolation transition. (ii) When there are more modules with many interconnections between them, the system undergoes a single transition. Overall, we find that modular structure can significantly influence the type of transitions observed in interdependent networks and should be considered in attempts to make interdependent networks more resilient.

An effective immunization strategy for airborne epidemics in modular and hierarchical social contact network

NASA Astrophysics Data System (ADS)

Song, Zhichao; Ge, Yuanzheng; Luo, Lei; Duan, Hong; Qiu, Xiaogang

2015-12-01

Social contact between individuals is the chief factor for airborne epidemic transmission among the crowd. Social contact networks, which describe the contact relationships among individuals, always exhibit overlapping qualities of communities, hierarchical structure and spatial-correlated. We find that traditional global targeted immunization strategy would lose its superiority in controlling the epidemic propagation in the social contact networks with modular and hierarchical structure. Therefore, we propose a hierarchical targeted immunization strategy to settle this problem. In this novel strategy, importance of the hierarchical structure is considered. Transmission control experiments of influenza H1N1 are carried out based on a modular and hierarchical network model. Results obtained indicate that hierarchical structure of the network is more critical than the degrees of the immunized targets and the modular network layer is the most important for the epidemic propagation control. Finally, the efficacy and stability of this novel immunization strategy have been validated as well.

Re-emergence of modular brain networks in stroke recovery.

PubMed

Siegel, Joshua S; Seitzman, Benjamin A; Ramsey, Lenny E; Ortega, Mario; Gordon, Evan M; Dosenbach, Nico U F; Petersen, Steven E; Shulman, Gordon L; Corbetta, Maurizio

2018-04-01

Studies of stroke have identified local reorganization in perilesional tissue. However, because the brain is highly networked, strokes also broadly alter the brain's global network organization. Here, we assess brain network structure longitudinally in adult stroke patients using resting state fMRI. The topology and boundaries of cortical regions remain grossly unchanged across recovery. In contrast, the modularity of brain systems i.e. the degree of integration within and segregation between networks, was significantly reduced sub-acutely (n = 107), but partially recovered by 3 months (n = 85), and 1 year (n = 67). Importantly, network recovery correlated with recovery from language, spatial memory, and attention deficits, but not motor or visual deficits. Finally, in-depth single subject analyses were conducted using tools for visualization of changes in brain networks over time. This exploration indicated that changes in modularity during successful recovery reflect specific alterations in the relationships between different networks. For example, in a patient with left temporo-parietal stroke and severe aphasia, sub-acute loss of modularity reflected loss of association between frontal and temporo-parietal regions bi-hemispherically across multiple modules. These long-distance connections then returned over time, paralleling aphasia recovery. This work establishes the potential importance of normalization of large-scale modular brain systems in stroke recovery. Copyright © 2017. Published by Elsevier Ltd.

Asynchronous networks: modularization of dynamics theorem

NASA Astrophysics Data System (ADS)

Bick, Christian; Field, Michael

2017-02-01

Building on the first part of this paper, we develop the theory of functional asynchronous networks. We show that a large class of functional asynchronous networks can be (uniquely) represented as feedforward networks connecting events or dynamical modules. For these networks we can give a complete description of the network function in terms of the function of the events comprising the network: the modularization of dynamics theorem. We give examples to illustrate the main results.

Evolutionary method for finding communities in bipartite networks.

PubMed

Zhan, Weihua; Zhang, Zhongzhi; Guan, Jihong; Zhou, Shuigeng

2011-06-01

An important step in unveiling the relation between network structure and dynamics defined on networks is to detect communities, and numerous methods have been developed separately to identify community structure in different classes of networks, such as unipartite networks, bipartite networks, and directed networks. Here, we show that the finding of communities in such networks can be unified in a general framework-detection of community structure in bipartite networks. Moreover, we propose an evolutionary method for efficiently identifying communities in bipartite networks. To this end, we show that both unipartite and directed networks can be represented as bipartite networks, and their modularity is completely consistent with that for bipartite networks, the detection of modular structure on which can be reformulated as modularity maximization. To optimize the bipartite modularity, we develop a modified adaptive genetic algorithm (MAGA), which is shown to be especially efficient for community structure detection. The high efficiency of the MAGA is based on the following three improvements we make. First, we introduce a different measure for the informativeness of a locus instead of the standard deviation, which can exactly determine which loci mutate. This measure is the bias between the distribution of a locus over the current population and the uniform distribution of the locus, i.e., the Kullback-Leibler divergence between them. Second, we develop a reassignment technique for differentiating the informative state a locus has attained from the random state in the initial phase. Third, we present a modified mutation rule which by incorporating related operations can guarantee the convergence of the MAGA to the global optimum and can speed up the convergence process. Experimental results show that the MAGA outperforms existing methods in terms of modularity for both bipartite and unipartite networks.

The Human Thalamus Is an Integrative Hub for Functional Brain Networks

PubMed Central

Bertolero, Maxwell A.

2017-01-01

The thalamus is globally connected with distributed cortical regions, yet the functional significance of this extensive thalamocortical connectivity remains largely unknown. By performing graph-theoretic analyses on thalamocortical functional connectivity data collected from human participants, we found that most thalamic subdivisions display network properties that are capable of integrating multimodal information across diverse cortical functional networks. From a meta-analysis of a large dataset of functional brain-imaging experiments, we further found that the thalamus is involved in multiple cognitive functions. Finally, we found that focal thalamic lesions in humans have widespread distal effects, disrupting the modular organization of cortical functional networks. This converging evidence suggests that the human thalamus is a critical hub region that could integrate diverse information being processed throughout the cerebral cortex as well as maintain the modular structure of cortical functional networks. SIGNIFICANCE STATEMENT The thalamus is traditionally viewed as a passive relay station of information from sensory organs or subcortical structures to the cortex. However, the thalamus has extensive connections with the entire cerebral cortex, which can also serve to integrate information processing between cortical regions. In this study, we demonstrate that multiple thalamic subdivisions display network properties that are capable of integrating information across multiple functional brain networks. Moreover, the thalamus is engaged by tasks requiring multiple cognitive functions. These findings support the idea that the thalamus is involved in integrating information across cortical networks. PMID:28450543

Modular synchronization in complex networks.

PubMed

Oh, E; Rho, K; Hong, H; Kahng, B

2005-10-01

We study the synchronization transition (ST) of a modified Kuramoto model on two different types of modular complex networks. It is found that the ST depends on the type of intermodular connections. For the network with decentralized (centralized) intermodular connections, the ST occurs at finite coupling constant (behaves abnormally). Such distinct features are found in the yeast protein interaction network and the Internet, respectively. Moreover, by applying the finite-size scaling analysis to an artificial network with decentralized intermodular connections, we obtain the exponent associated with the order parameter of the ST to be beta approximately 1 different from beta(MF) approximately 1/2 obtained from the scale-free network with the same degree distribution but the absence of modular structure, corresponding to the mean field value.

RedeR: R/Bioconductor package for representing modular structures, nested networks and multiple levels of hierarchical associations

PubMed Central

2012-01-01

Visualization and analysis of molecular networks are both central to systems biology. However, there still exists a large technological gap between them, especially when assessing multiple network levels or hierarchies. Here we present RedeR, an R/Bioconductor package combined with a Java core engine for representing modular networks. The functionality of RedeR is demonstrated in two different scenarios: hierarchical and modular organization in gene co-expression networks and nested structures in time-course gene expression subnetworks. Our results demonstrate RedeR as a new framework to deal with the multiple network levels that are inherent to complex biological systems. RedeR is available from http://bioconductor.org/packages/release/bioc/html/RedeR.html. PMID:22531049

The influence of floral traits on specialization and modularity of plant–pollinator networks in a biodiversity hotspot in the Peruvian Andes

PubMed Central

Watts, Stella; Dormann, Carsten F.; Martín González, Ana M.; Ollerton, Jeff

2016-01-01

Background and Aims Modularity is a ubiquitous and important structural property of ecological networks which describes the relative strengths of sets of interacting species and gives insights into the dynamics of ecological communities. However, this has rarely been studied in species-rich, tropical plant–pollinator networks. Working in a biodiversity hotspot in the Peruvian Andes we assessed the structure of quantitative plant–pollinator networks in nine valleys, quantifying modularity among networks, defining the topological roles of species and the influence of floral traits on specialization. Methods A total of 90 transects were surveyed for plants and pollinators at different altitudes and across different life zones. Quantitative modularity (QuanBiMo) was used to detect modularity and six indices were used to quantify specialization. Key Results All networks were highly structured, moderately specialized and significantly modular regardless of size. The strongest hubs were Baccharis plants, Apis mellifera, Bombus funebris and Diptera spp., which were the most ubiquitous and abundant species with the longest phenologies. Species strength showed a strong association with the modular structure of plant–pollinator networks. Hubs and connectors were the most centralized participants in the networks and were ranked highest (high generalization) when quantifying specialization with most indices. However, complementary specialization d' quantified hubs and connectors as moderately specialized. Specialization and topological roles of species were remarkably constant across some sites, but highly variable in others. Networks were dominated by ecologically and functionally generalist plant species with open access flowers which are closely related taxonomically with similar morphology and rewards. Plants associated with hummingbirds had the highest level of complementary specialization and exclusivity in modules (functional specialists) and the longest corollas. Conclusions We have demonstrated that the topology of networks in this tropical montane environment was non-random and highly organized. Our findings underline that specialization indices convey different concepts of specialization and hence quantify different aspects, and that measuring specialization requires careful consideration of what defines a specialist. PMID:27562649

The influence of floral traits on specialization and modularity of plant-pollinator networks in a biodiversity hotspot in the Peruvian Andes.

PubMed

Watts, Stella; Dormann, Carsten F; Martín González, Ana M; Ollerton, Jeff

2016-09-01

Modularity is a ubiquitous and important structural property of ecological networks which describes the relative strengths of sets of interacting species and gives insights into the dynamics of ecological communities. However, this has rarely been studied in species-rich, tropical plant-pollinator networks. Working in a biodiversity hotspot in the Peruvian Andes we assessed the structure of quantitative plant-pollinator networks in nine valleys, quantifying modularity among networks, defining the topological roles of species and the influence of floral traits on specialization. A total of 90 transects were surveyed for plants and pollinators at different altitudes and across different life zones. Quantitative modularity (QuanBiMo) was used to detect modularity and six indices were used to quantify specialization. All networks were highly structured, moderately specialized and significantly modular regardless of size. The strongest hubs were Baccharis plants, Apis mellifera, Bombus funebris and Diptera spp., which were the most ubiquitous and abundant species with the longest phenologies. Species strength showed a strong association with the modular structure of plant-pollinator networks. Hubs and connectors were the most centralized participants in the networks and were ranked highest (high generalization) when quantifying specialization with most indices. However, complementary specialization d' quantified hubs and connectors as moderately specialized. Specialization and topological roles of species were remarkably constant across some sites, but highly variable in others. Networks were dominated by ecologically and functionally generalist plant species with open access flowers which are closely related taxonomically with similar morphology and rewards. Plants associated with hummingbirds had the highest level of complementary specialization and exclusivity in modules (functional specialists) and the longest corollas. We have demonstrated that the topology of networks in this tropical montane environment was non-random and highly organized. Our findings underline that specialization indices convey different concepts of specialization and hence quantify different aspects, and that measuring specialization requires careful consideration of what defines a specialist. © The Author 2016. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

An adaptive neural swarm approach for intrusion defense in ad hoc networks

NASA Astrophysics Data System (ADS)

Cannady, James

2011-06-01

Wireless sensor networks (WSN) and mobile ad hoc networks (MANET) are being increasingly deployed in critical applications due to the flexibility and extensibility of the technology. While these networks possess numerous advantages over traditional wireless systems in dynamic environments they are still vulnerable to many of the same types of host-based and distributed attacks common to those systems. Unfortunately, the limited power and bandwidth available in WSNs and MANETs, combined with the dynamic connectivity that is a defining characteristic of the technology, makes it extremely difficult to utilize traditional intrusion detection techniques. This paper describes an approach to accurately and efficiently detect potentially damaging activity in WSNs and MANETs. It enables the network as a whole to recognize attacks, anomalies, and potential vulnerabilities in a distributive manner that reflects the autonomic processes of biological systems. Each component of the network recognizes activity in its local environment and then contributes to the overall situational awareness of the entire system. The approach utilizes agent-based swarm intelligence to adaptively identify potential data sources on each node and on adjacent nodes throughout the network. The swarm agents then self-organize into modular neural networks that utilize a reinforcement learning algorithm to identify relevant behavior patterns in the data without supervision. Once the modular neural networks have established interconnectivity both locally and with neighboring nodes the analysis of events within the network can be conducted collectively in real-time. The approach has been shown to be extremely effective in identifying distributed network attacks.

A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition

PubMed Central

Sánchez, Daniela; Melin, Patricia

2017-01-01

A grey wolf optimizer for modular neural network (MNN) with a granular approach is proposed. The proposed method performs optimal granulation of data and design of modular neural networks architectures to perform human recognition, and to prove its effectiveness benchmark databases of ear, iris, and face biometric measures are used to perform tests and comparisons against other works. The design of a modular granular neural network (MGNN) consists in finding optimal parameters of its architecture; these parameters are the number of subgranules, percentage of data for the training phase, learning algorithm, goal error, number of hidden layers, and their number of neurons. Nowadays, there is a great variety of approaches and new techniques within the evolutionary computing area, and these approaches and techniques have emerged to help find optimal solutions to problems or models and bioinspired algorithms are part of this area. In this work a grey wolf optimizer is proposed for the design of modular granular neural networks, and the results are compared against a genetic algorithm and a firefly algorithm in order to know which of these techniques provides better results when applied to human recognition. PMID:28894461

A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition.

PubMed

Sánchez, Daniela; Melin, Patricia; Castillo, Oscar

2017-01-01

A grey wolf optimizer for modular neural network (MNN) with a granular approach is proposed. The proposed method performs optimal granulation of data and design of modular neural networks architectures to perform human recognition, and to prove its effectiveness benchmark databases of ear, iris, and face biometric measures are used to perform tests and comparisons against other works. The design of a modular granular neural network (MGNN) consists in finding optimal parameters of its architecture; these parameters are the number of subgranules, percentage of data for the training phase, learning algorithm, goal error, number of hidden layers, and their number of neurons. Nowadays, there is a great variety of approaches and new techniques within the evolutionary computing area, and these approaches and techniques have emerged to help find optimal solutions to problems or models and bioinspired algorithms are part of this area. In this work a grey wolf optimizer is proposed for the design of modular granular neural networks, and the results are compared against a genetic algorithm and a firefly algorithm in order to know which of these techniques provides better results when applied to human recognition.

Sustained Activity in Hierarchical Modular Neural Networks: Self-Organized Criticality and Oscillations

PubMed Central

Wang, Sheng-Jun; Hilgetag, Claus C.; Zhou, Changsong

2010-01-01

Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. In particular, they are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and finally circuits made up of individual neurons. Second, the networks display self-organized sustained activity, which is persistent in the absence of external stimuli. At the systems level, such activity is characterized by complex rhythmical oscillations over a broadband background, while at the cellular level, neuronal discharges have been observed to display avalanches, indicating that cortical networks are at the state of self-organized criticality (SOC). We explored the relationship between hierarchical neural network organization and sustained dynamics using large-scale network modeling. Previously, it was shown that sparse random networks with balanced excitation and inhibition can sustain neural activity without external stimulation. We found that a hierarchical modular architecture can generate sustained activity better than random networks. Moreover, the system can simultaneously support rhythmical oscillations and SOC, which are not present in the respective random networks. The mechanism underlying the sustained activity is that each dense module cannot sustain activity on its own, but displays SOC in the presence of weak perturbations. Therefore, the hierarchical modular networks provide the coupling among subsystems with SOC. These results imply that the hierarchical modular architecture of cortical networks plays an important role in shaping the ongoing spontaneous activity of the brain, potentially allowing the system to take advantage of both the sensitivity of critical states and the predictability and timing of oscillations for efficient information processing. PMID:21852971

Network modularity reveals critical scales for connectivity in ecology and evolution

USGS Publications Warehouse

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.

MOBS - A modular on-board switching system

NASA Astrophysics Data System (ADS)

Berner, W.; Grassmann, W.; Piontek, M.

The authors describe a multibeam satellite system that is designed for business services and for communications at a high bit rate. The repeater is regenerative with a modular onboard switching system. It acts not only as baseband switch but also as the central node of the network, performing network control and protocol evaluation. The hardware is based on a modular bus/memory architecture with associated processors.

Deinterlacing using modular neural network

NASA Astrophysics Data System (ADS)

Woo, Dong H.; Eom, Il K.; Kim, Yoo S.

2004-05-01

Deinterlacing is the conversion process from the interlaced scan to progressive one. While many previous algorithms that are based on weighted-sum cause blurring in edge region, deinterlacing using neural network can reduce the blurring through recovering of high frequency component by learning process, and is found robust to noise. In proposed algorithm, input image is divided into edge and smooth region, and then, to each region, one neural network is assigned. Through this process, each neural network learns only patterns that are similar, therefore it makes learning more effective and estimation more accurate. But even within each region, there are various patterns such as long edge and texture in edge region. To solve this problem, modular neural network is proposed. In proposed modular neural network, two modules are combined in output node. One is for low frequency feature of local area of input image, and the other is for high frequency feature. With this structure, each modular neural network can learn different patterns with compensating for drawback of counterpart. Therefore it can adapt to various patterns within each region effectively. In simulation, the proposed algorithm shows better performance compared with conventional deinterlacing methods and single neural network method.

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

PubMed

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

2018-07-01

Inter-subject networks are used to model correlations between brain regions and are particularly useful for metabolic imaging techniques, like 18F-2-deoxy-2-(18F)fluoro-D-glucose (FDG) positron emission tomography (PET). Since FDG PET typically produces a single image, correlations cannot be calculated over time. Little focus has been placed on the basic properties of inter-subject networks and if they are affected by group size and image normalization. FDG PET images were acquired from rats (n = 18), normalized by whole brain, visual cortex, or cerebellar FDG uptake, and used to construct correlation matrices. Group size effects on network stability were investigated by systematically adding rats and evaluating local network connectivity (node strength and clustering coefficient). Modularity and community structure were also evaluated in the differently normalized networks to assess meso-scale network relationships. Local network properties are stable regardless of normalization region for groups of at least 10. Whole brain-normalized networks are more modular than visual cortex- or cerebellum-normalized network (p < 0.00001); however, community structure is similar at network resolutions where modularity differs most between brain and randomized networks. Hierarchical analysis reveals consistent modules at different scales and clustering of spatially-proximate brain regions. Findings suggest inter-subject FDG PET networks are stable for reasonable group sizes and exhibit multi-scale modularity.

Modular Toolkit for Data Processing (MDP): A Python Data Processing Framework.

PubMed

Zito, Tiziano; Wilbert, Niko; Wiskott, Laurenz; Berkes, Pietro

2008-01-01

Modular toolkit for Data Processing (MDP) is a data processing framework written in Python. From the user's perspective, MDP is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures. Computations are performed efficiently in terms of speed and memory requirements. From the scientific developer's perspective, MDP is a modular framework, which can easily be expanded. The implementation of new algorithms is easy and intuitive. The new implemented units are then automatically integrated with the rest of the library. MDP has been written in the context of theoretical research in neuroscience, but it has been designed to be helpful in any context where trainable data processing algorithms are used. Its simplicity on the user's side, the variety of readily available algorithms, and the reusability of the implemented units make it also a useful educational tool.

Online Community Detection for Large Complex Networks

PubMed Central

Pan, Gang; Zhang, Wangsheng; Wu, Zhaohui; Li, Shijian

2014-01-01

Complex networks describe a wide range of systems in nature and society. To understand complex networks, it is crucial to investigate their community structure. In this paper, we develop an online community detection algorithm with linear time complexity for large complex networks. Our algorithm processes a network edge by edge in the order that the network is fed to the algorithm. If a new edge is added, it just updates the existing community structure in constant time, and does not need to re-compute the whole network. Therefore, it can efficiently process large networks in real time. Our algorithm optimizes expected modularity instead of modularity at each step to avoid poor performance. The experiments are carried out using 11 public data sets, and are measured by two criteria, modularity and NMI (Normalized Mutual Information). The results show that our algorithm's running time is less than the commonly used Louvain algorithm while it gives competitive performance. PMID:25061683

Impact of delays on the synchronization transitions of modular neuronal networks with hybrid synapses

NASA Astrophysics Data System (ADS)

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

2013-09-01

The combined effects of the information transmission delay and the ratio of the electrical and chemical synapses on the synchronization transitions in the hybrid modular neuronal network are investigated in this paper. Numerical results show that the synchronization of neuron activities can be either promoted or destroyed as the information transmission delay increases, irrespective of the probability of electrical synapses in the hybrid-synaptic network. Interestingly, when the number of the electrical synapses exceeds a certain level, further increasing its proportion can obviously enhance the spatiotemporal synchronization transitions. Moreover, the coupling strength has a significant effect on the synchronization transition. The dominated type of the synapse always has a more profound effect on the emergency of the synchronous behaviors. Furthermore, the results of the modular neuronal network structures demonstrate that excessive partitioning of the modular network may result in the dramatic detriment of neuronal synchronization. Considering that information transmission delays are inevitable in intra- and inter-neuronal networks communication, the obtained results may have important implications for the exploration of the synchronization mechanism underlying several neural system diseases such as Parkinson's Disease.

Optimal Network Modularity for Information Diffusion

NASA Astrophysics Data System (ADS)

Nematzadeh, Azadeh; Ferrara, Emilio; Flammini, Alessandro; Ahn, Yong-Yeol

2014-08-01

We investigate the impact of community structure on information diffusion with the linear threshold model. Our results demonstrate that modular structure may have counterintuitive effects on information diffusion when social reinforcement is present. We show that strong communities can facilitate global diffusion by enhancing local, intracommunity spreading. Using both analytic approaches and numerical simulations, we demonstrate the existence of an optimal network modularity, where global diffusion requires the minimal number of early adopters.

System level mechanisms of adaptation, learning, memory formation and evolvability: the role of chaperone and other networks.

PubMed

Gyurko, David M; Soti, Csaba; Stetak, Attila; Csermely, Peter

2014-05-01

During the last decade, network approaches became a powerful tool to describe protein structure and dynamics. Here, we describe first the protein structure networks of molecular chaperones, then characterize chaperone containing sub-networks of interactomes called as chaperone-networks or chaperomes. We review the role of molecular chaperones in short-term adaptation of cellular networks in response to stress, and in long-term adaptation discussing their putative functions in the regulation of evolvability. We provide a general overview of possible network mechanisms of adaptation, learning and memory formation. We propose that changes of network rigidity play a key role in learning and memory formation processes. Flexible network topology provides ' learning-competent' state. Here, networks may have much less modular boundaries than locally rigid, highly modular networks, where the learnt information has already been consolidated in a memory formation process. Since modular boundaries are efficient filters of information, in the 'learning-competent' state information filtering may be much smaller, than after memory formation. This mechanism restricts high information transfer to the 'learning competent' state. After memory formation, modular boundary-induced segregation and information filtering protect the stored information. The flexible networks of young organisms are generally in a 'learning competent' state. On the contrary, locally rigid networks of old organisms have lost their 'learning competent' state, but store and protect their learnt information efficiently. We anticipate that the above mechanism may operate at the level of both protein-protein interaction and neuronal networks.

A proposal of optimal sampling design using a modularity strategy

NASA Astrophysics Data System (ADS)

Simone, A.; Giustolisi, O.; Laucelli, D. B.

2016-08-01

In real water distribution networks (WDNs) are present thousands nodes and optimal placement of pressure and flow observations is a relevant issue for different management tasks. The planning of pressure observations in terms of spatial distribution and number is named sampling design and it was faced considering model calibration. Nowadays, the design of system monitoring is a relevant issue for water utilities e.g., in order to manage background leakages, to detect anomalies and bursts, to guarantee service quality, etc. In recent years, the optimal location of flow observations related to design of optimal district metering areas (DMAs) and leakage management purposes has been faced considering optimal network segmentation and the modularity index using a multiobjective strategy. Optimal network segmentation is the basis to identify network modules by means of optimal conceptual cuts, which are the candidate locations of closed gates or flow meters creating the DMAs. Starting from the WDN-oriented modularity index, as a metric for WDN segmentation, this paper proposes a new way to perform the sampling design, i.e., the optimal location of pressure meters, using newly developed sampling-oriented modularity index. The strategy optimizes the pressure monitoring system mainly based on network topology and weights assigned to pipes according to the specific technical tasks. A multiobjective optimization minimizes the cost of pressure meters while maximizing the sampling-oriented modularity index. The methodology is presented and discussed using the Apulian and Exnet networks.

Changes in functional organization and white matter integrity in the connectome in Parkinson's disease.

PubMed

Tinaz, Sule; Lauro, Peter M; Ghosh, Pritha; Lungu, Codrin; Horovitz, Silvina G

2017-01-01

Parkinson's disease (PD) leads to dysfunction in multiple cortico-striatal circuits. The neurodegeneration has also been associated with impaired white matter integrity. This structural and functional "disconnection" in PD needs further characterization. We investigated the structural and functional organization of the PD whole brain connectome consisting of 200 nodes using diffusion tensor imaging and resting-state functional MRI, respectively. Data from 20 non-demented PD patients on dopaminergic medication and 20 matched controls were analyzed using graph theory-based methods. We focused on node strength, clustering coefficient, and local efficiency as measures of local network properties; and network modularity as a measure of information flow. PD patients showed reduced white matter connectivity in frontoparietal-striatal nodes compared to controls, but no change in modular organization of the white matter tracts. PD group also showed reduction in functional local network metrics in many nodes distributed across the connectome. There was also decreased functional modularity in the core cognitive networks including the default mode and dorsal attention networks, and sensorimotor network, as well as a lack of modular distinction in the orbitofrontal and basal ganglia nodes in the PD group compared to controls. Our results suggest that despite subtle white matter connectivity changes, the overall structural organization of the PD connectome remains robust at relatively early disease stages. However, there is a breakdown in the functional modular organization of the PD connectome.

Phage-bacteria infection networks: From nestedness to modularity

NASA Astrophysics Data System (ADS)

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

2013-03-01

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

Network Disruption in the Preclinical Stages of Alzheimer's Disease: From Subjective Cognitive Decline to Mild Cognitive Impairment.

PubMed

López-Sanz, David; Garcés, Pilar; Álvarez, Blanca; Delgado-Losada, María Luisa; López-Higes, Ramón; Maestú, Fernando

2017-12-01

Subjective Cognitive Decline (SCD) is a largely unknown state thought to represent a preclinical stage of Alzheimer's Disease (AD) previous to mild cognitive impairment (MCI). However, the course of network disruption in these stages is scarcely characterized. We employed resting state magnetoencephalography in the source space to calculate network smallworldness, clustering, modularity and transitivity. Nodal measures (clustering and node degree) as well as modular partitions were compared between groups. The MCI group exhibited decreased smallworldness, clustering and transitivity and increased modularity in theta and beta bands. SCD showed similar but smaller changes in clustering and transitivity, while exhibiting alterations in the alpha band in opposite direction to those showed by MCI for modularity and transitivity. At the node level, MCI disrupted both clustering and nodal degree while SCD showed minor changes in the latter. Additionally, we observed an increase in modular partition variability in both SCD and MCI in theta and beta bands. SCD elders exhibit a significant network disruption, showing intermediate values between HC and MCI groups in multiple parameters. These results highlight the relevance of cognitive concerns in the clinical setting and suggest that network disorganization in AD could start in the preclinical stages before the onset of cognitive symptoms.

Finding modules and hierarchy in weighted financial network using transfer entropy

NASA Astrophysics Data System (ADS)

Yook, Soon-Hyung; Chae, Huiseung; Kim, Jinho; Kim, Yup

2016-04-01

We study the modular structure of financial network based on the transfer entropy (TE). From the comparison with the obtained modular structure using the cross-correlation (CC), we find that TE and CC both provide well organized modular structure and the hierarchical relationship between each industrial group when the time scale of the measurement is less than one month. However, when the time scale of the measurement becomes larger than one month, we find that the modular structure from CC cannot correctly reflect the known industrial classification and their hierarchy. In addition the measured maximum modularity, Qmax, for TE is always larger than that for CC, which indicates that TE is a better weight measure than CC for the system with asymmetric relationship.

Drawing Inspiration from Human Brain Networks: Construction of Interconnected Virtual Networks

PubMed Central

Kominami, Daichi; Leibnitz, Kenji; Murata, Masayuki

2018-01-01

Virtualization of wireless sensor networks (WSN) is widely considered as a foundational block of edge/fog computing, which is a key technology that can help realize next-generation Internet of things (IoT) networks. In such scenarios, multiple IoT devices and service modules will be virtually deployed and interconnected over the Internet. Moreover, application services are expected to be more sophisticated and complex, thereby increasing the number of modifications required for the construction of network topologies. Therefore, it is imperative to establish a method for constructing a virtualized WSN (VWSN) topology that achieves low latency on information transmission and high resilience against network failures, while keeping the topological construction cost low. In this study, we draw inspiration from inter-modular connectivity in human brain networks, which achieves high performance when dealing with large-scale networks composed of a large number of modules (i.e., regions) and nodes (i.e., neurons). We propose a method for assigning inter-modular links based on a connectivity model observed in the cerebral cortex of the brain, known as the exponential distance rule (EDR) model. We then choose endpoint nodes of these links by controlling inter-modular assortativity, which characterizes the topological connectivity of brain networks. We test our proposed methods using simulation experiments. The results show that the proposed method based on the EDR model can construct a VWSN topology with an optimal combination of communication efficiency, robustness, and construction cost. Regarding the selection of endpoint nodes for the inter-modular links, the results also show that high assortativity enhances the robustness and communication efficiency because of the existence of inter-modular links of two high-degree nodes. PMID:29642483

Drawing Inspiration from Human Brain Networks: Construction of Interconnected Virtual Networks.

PubMed

Murakami, Masaya; Kominami, Daichi; Leibnitz, Kenji; Murata, Masayuki

2018-04-08

Virtualization of wireless sensor networks (WSN) is widely considered as a foundational block of edge/fog computing, which is a key technology that can help realize next-generation Internet of things (IoT) networks. In such scenarios, multiple IoT devices and service modules will be virtually deployed and interconnected over the Internet. Moreover, application services are expected to be more sophisticated and complex, thereby increasing the number of modifications required for the construction of network topologies. Therefore, it is imperative to establish a method for constructing a virtualized WSN (VWSN) topology that achieves low latency on information transmission and high resilience against network failures, while keeping the topological construction cost low. In this study, we draw inspiration from inter-modular connectivity in human brain networks, which achieves high performance when dealing with large-scale networks composed of a large number of modules (i.e., regions) and nodes (i.e., neurons). We propose a method for assigning inter-modular links based on a connectivity model observed in the cerebral cortex of the brain, known as the exponential distance rule (EDR) model. We then choose endpoint nodes of these links by controlling inter-modular assortativity, which characterizes the topological connectivity of brain networks. We test our proposed methods using simulation experiments. The results show that the proposed method based on the EDR model can construct a VWSN topology with an optimal combination of communication efficiency, robustness, and construction cost. Regarding the selection of endpoint nodes for the inter-modular links, the results also show that high assortativity enhances the robustness and communication efficiency because of the existence of inter-modular links of two high-degree nodes.

A Modular IoT Platform for Real-Time Indoor Air Quality Monitoring.

PubMed

Benammar, Mohieddine; Abdaoui, Abderrazak; Ahmad, Sabbir H M; Touati, Farid; Kadri, Abdullah

2018-02-14

The impact of air quality on health and on life comfort is well established. In many societies, vulnerable elderly and young populations spend most of their time indoors. Therefore, indoor air quality monitoring (IAQM) is of great importance to human health. Engineers and researchers are increasingly focusing their efforts on the design of real-time IAQM systems using wireless sensor networks. This paper presents an end-to-end IAQM system enabling measurement of CO₂, CO, SO₂, NO₂, O₃, Cl₂, ambient temperature, and relative humidity. In IAQM systems, remote users usually use a local gateway to connect wireless sensor nodes in a given monitoring site to the external world for ubiquitous access of data. In this work, the role of the gateway in processing collected air quality data and its reliable dissemination to end-users through a web-server is emphasized. A mechanism for the backup and the restoration of the collected data in the case of Internet outage is presented. The system is adapted to an open-source Internet-of-Things (IoT) web-server platform, called Emoncms, for live monitoring and long-term storage of the collected IAQM data. A modular IAQM architecture is adopted, which results in a smart scalable system that allows seamless integration of various sensing technologies, wireless sensor networks (WSNs) and smart mobile standards. The paper gives full hardware and software details of the proposed solution. Sample IAQM results collected in various locations are also presented to demonstrate the abilities of the system.

Dynamic burstiness of word-occurrence and network modularity in textbook systems

NASA Astrophysics Data System (ADS)

Cui, Xue-Mei; Yoon, Chang No; Youn, Hyejin; Lee, Sang Hoon; Jung, Jean S.; Han, Seung Kee

2017-12-01

We show that the dynamic burstiness of word occurrence in textbook systems is attributed to the modularity of the word association networks. At first, a measure of dynamic burstiness is introduced to quantify burstiness of word occurrence in a textbook. The advantage of this measure is that the dynamic burstiness is decomposable into two contributions: one coming from the inter-event variance and the other from the memory effects. Comparing network structures of physics textbook systems with those of surrogate random textbooks without the memory or variance effects are absent, we show that the network modularity increases systematically with the dynamic burstiness. The intra-connectivity of individual word representing the strength of a tie with which a node is bound to a module accordingly increases with the dynamic burstiness, suggesting individual words with high burstiness are strongly bound to one module. Based on the frequency and dynamic burstiness, physics terminology is classified into four categories: fundamental words, topical words, special words, and common words. In addition, we test the correlation between the dynamic burstiness of word occurrence and network modularity using a two-state model of burst generation.

Modularity, pollination systems, and interaction turnover in plant-pollinator networks across space.

PubMed

Carstensen, Daniel W; Sabatino, Malena; Morellato, Leonor Patricia C

2016-05-01

Mutualistic interaction networks have been shown to be structurally conserved over space and time while pairwise interactions show high variability. In such networks, modularity is the division of species into compartments, or modules, where species within modules share more interactions with each other than they do with species from other modules. Such a modular structure is common in mutualistic networks and several evolutionary and ecological mechanisms have been proposed as underlying drivers. One prominent explanation is the existence of pollination syndromes where flowers tend to attract certain pollinators as determined by a set of traits. We investigate the modularity of seven community level plant-pollinator networks sampled in rupestrian grasslands, or campos rupestres, in SE Brazil. Defining pollination systems as corresponding groups of flower syndromes and pollinator functional groups, we test the two hypotheses that (1) interacting species from the same pollination system are more often assigned to the same module than interacting species from different pollination systems and; that (2) interactions between species from the same pollination system are more consistent across space than interactions between species from different pollination systems. Specifically we ask (1) whether networks are consistently modular across space; (2) whether interactions among species of the same pollination system occur more often inside modules, compared to interactions among species of different pollination systems, and finally; (3) whether the spatial variation in interaction identity, i.e., spatial interaction rewiring, is affected by trait complementarity among species as indicated by pollination systems. We confirm that networks are consistently modular across space and that interactions within pollination systems principally occur inside modules. Despite a strong tendency, we did not find a significant effect of pollination systems on the spatial consistency of pairwise interactions. These results indicate that the spatial rewiring of interactions could be constrained by pollination systems, resulting in conserved network structures in spite of high variation in pairwise interactions. Our findings suggest a relevant role of pollination systems in structuring plant-pollinator networks and we argue that structural patterns at the sub-network level can help us to fully understand how and why interactions vary across space and time.

Multiple Assembly Rules Drive the Co-occurrence of Orthopteran and Plant Species in Grasslands: Combining Network, Functional and Phylogenetic Approaches

PubMed Central

Fournier, Bertrand; Mouly, Arnaud; Gillet, François

2016-01-01

Understanding the factors underlying the co-occurrence of multiple species remains a challenge in ecology. Biotic interactions, environmental filtering and neutral processes are among the main mechanisms evoked to explain species co-occurrence. However, they are most often studied separately or even considered as mutually exclusive. This likely hampers a more global understanding of species assembly. Here, we investigate the general hypothesis that the structure of co-occurrence networks results from multiple assembly rules and its potential implications for grassland ecosystems. We surveyed orthopteran and plant communities in 48 permanent grasslands of the French Jura Mountains and gathered functional and phylogenetic data for all species. We constructed a network of plant and orthopteran species co-occurrences and verified whether its structure was modular or nested. We investigated the role of all species in the structure of the network (modularity and nestedness). We also investigated the assembly rules driving the structure of the plant-orthopteran co-occurrence network by using null models on species functional traits, phylogenetic relatedness and environmental conditions. We finally compared our results to abundance-based approaches. We found that the plant-orthopteran co-occurrence network had a modular organization. Community assembly rules differed among modules for plants while interactions with plants best explained the distribution of orthopterans into modules. Few species had a disproportionately high positive contribution to this modular organization and are likely to have a key importance to modulate future changes. The impact of agricultural practices was restricted to some modules (3 out of 5) suggesting that shifts in agricultural practices might not impact the entire plant-orthopteran co-occurrence network. These findings support our hypothesis that multiple assembly rules drive the modular structure of the plant-orthopteran network. This modular structure is likely to play a key role in the response of grassland ecosystems to future changes by limiting the impact of changes in agricultural practices such as intensification to some modules leaving species from other modules poorly impacted. The next step is to understand the importance of this modular structure for the long-term maintenance of grassland ecosystem structure and functions as well as to develop tools to integrate network structure into models to improve their capacity to predict future changes. PMID:27582754

Modular and hierarchical structure of social contact networks

NASA Astrophysics Data System (ADS)

Ge, Yuanzheng; Song, Zhichao; Qiu, Xiaogang; Song, Hongbin; Wang, Yong

2013-10-01

Social contact networks exhibit overlapping qualities of communities, hierarchical structure and spatial-correlated nature. We propose a mixing pattern of modular and growing hierarchical structures to reconstruct social contact networks by using an individual’s geospatial distribution information in the real world. The hierarchical structure of social contact networks is defined based on the spatial distance between individuals, and edges among individuals are added in turn from the modular layer to the highest layer. It is a gradual process to construct the hierarchical structure: from the basic modular model up to the global network. The proposed model not only shows hierarchically increasing degree distribution and large clustering coefficients in communities, but also exhibits spatial clustering features of individual distributions. As an evaluation of the method, we reconstruct a hierarchical contact network based on the investigation data of a university. Transmission experiments of influenza H1N1 are carried out on the generated social contact networks, and results show that the constructed network is efficient to reproduce the dynamic process of an outbreak and evaluate interventions. The reproduced spread process exhibits that the spatial clustering of infection is accordant with the clustering of network topology. Moreover, the effect of individual topological character on the spread of influenza is analyzed, and the experiment results indicate that the spread is limited by individual daily contact patterns and local clustering topology rather than individual degree.

Fitness landscape complexity and the emergence of modularity in neural networks

NASA Astrophysics Data System (ADS)

Lowell, Jessica

Previous research has shown that the shape of the fitness landscape can affect the evolution of modularity. We evolved neural networks to solve different tasks with different fitness landscapes, using NEAT, a popular neuroevolution algorithm that quantifies similarity between genomes in order to divide them into species. We used this speciation mechanism as a means to examine fitness landscape complexity, and to examine connections between fitness landscape complexity and the emergence of modularity.

Large-Scale Network Analysis of Whole-Brain Resting-State Functional Connectivity in Spinal Cord Injury: A Comparative Study.

PubMed

Kaushal, Mayank; Oni-Orisan, Akinwunmi; Chen, Gang; Li, Wenjun; Leschke, Jack; Ward, Doug; Kalinosky, Benjamin; Budde, Matthew; Schmit, Brian; Li, Shi-Jiang; Muqeet, Vaishnavi; Kurpad, Shekar

2017-09-01

Network analysis based on graph theory depicts the brain as a complex network that allows inspection of overall brain connectivity pattern and calculation of quantifiable network metrics. To date, large-scale network analysis has not been applied to resting-state functional networks in complete spinal cord injury (SCI) patients. To characterize modular reorganization of whole brain into constituent nodes and compare network metrics between SCI and control subjects, fifteen subjects with chronic complete cervical SCI and 15 neurologically intact controls were scanned. The data were preprocessed followed by parcellation of the brain into 116 regions of interest (ROI). Correlation analysis was performed between every ROI pair to construct connectivity matrices and ROIs were categorized into distinct modules. Subsequently, local efficiency (LE) and global efficiency (GE) network metrics were calculated at incremental cost thresholds. The application of a modularity algorithm organized the whole-brain resting-state functional network of the SCI and the control subjects into nine and seven modules, respectively. The individual modules differed across groups in terms of the number and the composition of constituent nodes. LE demonstrated statistically significant decrease at multiple cost levels in SCI subjects. GE did not differ significantly between the two groups. The demonstration of modular architecture in both groups highlights the applicability of large-scale network analysis in studying complex brain networks. Comparing modules across groups revealed differences in number and membership of constituent nodes, indicating modular reorganization due to neural plasticity.

Subcommunities and Their Mutual Relationships in a Transaction Network

NASA Astrophysics Data System (ADS)

Iino, T.; Iyetomi, H.

We investigate a Japanese transaction network consisting ofabout 800 thousand firms (nodes) and four million business relations (links) with focus on its modular structure. Communities detected by maximizing modularity often are dominated by firms with common features or behaviors in the network, such as characterized by regions or industry sectors. However, it is well known that the modularity optimization approach has a resolution limit problem, that is, it fails in identifying fine communities buried in large communities. To unfold such hidden structures, we apply the community detection to each of subnetworks formed by isolating those communities from the whole body. Subcommunities thus identified are composed of firms with finer regions, more specified sectors or business affiliations. Also we introduce a new idea of reduced modularity matrix to measure the strength of relations between (sub)communities.

Evolution of a modular software network

PubMed Central

Fortuna, Miguel A.; Bonachela, Juan A.; Levin, Simon A.

2011-01-01

“Evolution behaves like a tinkerer” (François Jacob, Science, 1977). Software systems provide a singular opportunity to understand biological processes using concepts from network theory. The Debian GNU/Linux operating system allows us to explore the evolution of a complex network in a unique way. The modular design detected during its growth is based on the reuse of existing code in order to minimize costs during programming. The increase of modularity experienced by the system over time has not counterbalanced the increase in incompatibilities between software packages within modules. This negative effect is far from being a failure of design. A random process of package installation shows that the higher the modularity, the larger the fraction of packages working properly in a local computer. The decrease in the relative number of conflicts between packages from different modules avoids a failure in the functionality of one package spreading throughout the entire system. Some potential analogies with the evolutionary and ecological processes determining the structure of ecological networks of interacting species are discussed. PMID:22106260

Ad hoc Laser networks component technology for modular spacecraft

NASA Astrophysics Data System (ADS)

Huang, Xiujun; Shi, Dele; Ma, Zongfeng; Shen, Jingshi

2016-03-01

Distributed reconfigurable satellite is a new kind of spacecraft system, which is based on a flexible platform of modularization and standardization. Based on the module data flow analysis of the spacecraft, this paper proposes a network component of ad hoc Laser networks architecture. Low speed control network with high speed load network of Microwave-Laser communication mode, no mesh network mode, to improve the flexibility of the network. Ad hoc Laser networks component technology was developed, and carried out the related performance testing and experiment. The results showed that ad hoc Laser networks components can meet the demand of future networking between the module of spacecraft.

Ad hoc laser networks component technology for modular spacecraft

NASA Astrophysics Data System (ADS)

Huang, Xiujun; Shi, Dele; Shen, Jingshi

2017-10-01

Distributed reconfigurable satellite is a new kind of spacecraft system, which is based on a flexible platform of modularization and standardization. Based on the module data flow analysis of the spacecraft, this paper proposes a network component of ad hoc Laser networks architecture. Low speed control network with high speed load network of Microwave-Laser communication mode, no mesh network mode, to improve the flexibility of the network. Ad hoc Laser networks component technology was developed, and carried out the related performance testing and experiment. The results showed that ad hoc Laser networks components can meet the demand of future networking between the module of spacecraft.

Disentangling the co-structure of multilayer interaction networks: degree distribution and module composition in two-layer bipartite networks.

PubMed

Astegiano, Julia; Altermatt, Florian; Massol, François

2017-11-13

Species establish different interactions (e.g. antagonistic, mutualistic) with multiple species, forming multilayer ecological networks. Disentangling network co-structure in multilayer networks is crucial to predict how biodiversity loss may affect the persistence of multispecies assemblages. Existing methods to analyse multilayer networks often fail to consider network co-structure. We present a new method to evaluate the modular co-structure of multilayer networks through the assessment of species degree co-distribution and network module composition. We focus on modular structure because of its high prevalence among ecological networks. We apply our method to two Lepidoptera-plant networks, one describing caterpillar-plant herbivory interactions and one representing adult Lepidoptera nectaring on flowers, thereby possibly pollinating them. More than 50% of the species established either herbivory or visitation interactions, but not both. These species were over-represented among plants and lepidopterans, and were present in most modules in both networks. Similarity in module composition between networks was high but not different from random expectations. Our method clearly delineates the importance of interpreting multilayer module composition similarity in the light of the constraints imposed by network structure to predict the potential indirect effects of species loss through interconnected modular networks.

Discovering Multimodal Behavior in Ms. Pac-Man through Evolution of Modular Neural Networks.

PubMed

Schrum, Jacob; Miikkulainen, Risto

2016-03-12

Ms. Pac-Man is a challenging video game in which multiple modes of behavior are required: Ms. Pac-Man must escape ghosts when they are threats and catch them when they are edible, in addition to eating all pills in each level. Past approaches to learning behavior in Ms. Pac-Man have treated the game as a single task to be learned using monolithic policy representations. In contrast, this paper uses a framework called Modular Multi-objective NEAT (MM-NEAT) to evolve modular neural networks. Each module defines a separate behavior. The modules are used at different times according to a policy that can be human-designed (i.e. Multitask) or discovered automatically by evolution. The appropriate number of modules can be fixed or discovered using a genetic operator called Module Mutation. Several versions of Module Mutation are evaluated in this paper. Both fixed modular networks and Module Mutation networks outperform monolithic networks and Multitask networks. Interestingly, the best networks dedicate modules to critical behaviors (such as escaping when surrounded after luring ghosts near a power pill) that do not follow the customary division of the game into chasing edible and escaping threat ghosts. The results demonstrate that MM-NEAT can discover interesting and effective behavior for agents in challenging games.

Discovering Multimodal Behavior in Ms. Pac-Man through Evolution of Modular Neural Networks

PubMed Central

Schrum, Jacob; Miikkulainen, Risto

2015-01-01

Ms. Pac-Man is a challenging video game in which multiple modes of behavior are required: Ms. Pac-Man must escape ghosts when they are threats and catch them when they are edible, in addition to eating all pills in each level. Past approaches to learning behavior in Ms. Pac-Man have treated the game as a single task to be learned using monolithic policy representations. In contrast, this paper uses a framework called Modular Multi-objective NEAT (MM-NEAT) to evolve modular neural networks. Each module defines a separate behavior. The modules are used at different times according to a policy that can be human-designed (i.e. Multitask) or discovered automatically by evolution. The appropriate number of modules can be fixed or discovered using a genetic operator called Module Mutation. Several versions of Module Mutation are evaluated in this paper. Both fixed modular networks and Module Mutation networks outperform monolithic networks and Multitask networks. Interestingly, the best networks dedicate modules to critical behaviors (such as escaping when surrounded after luring ghosts near a power pill) that do not follow the customary division of the game into chasing edible and escaping threat ghosts. The results demonstrate that MM-NEAT can discover interesting and effective behavior for agents in challenging games. PMID:27030803

Relative impacts of environmental variation and evolutionary history on the nestedness and modularity of tree–herbivore networks

PubMed Central

Robinson, Kathryn M; Hauzy, Céline; Loeuille, Nicolas; Albrectsen, Benedicte R

2015-01-01

Nestedness and modularity are measures of ecological networks whose causative effects are little understood. We analyzed antagonistic plant–herbivore bipartite networks using common gardens in two contrasting environments comprised of aspen trees with differing evolutionary histories of defence against herbivores. These networks were tightly connected owing to a high level of specialization of arthropod herbivores that spend a large proportion of the life cycle on aspen. The gardens were separated by ten degrees of latitude with resultant differences in abiotic conditions. We evaluated network metrics and reported similar connectance between gardens but greater numbers of links per species in the northern common garden. Interaction matrices revealed clear nestedness, indicating subsetting of the bipartite interactions into specialist divisions, in both the environmental and evolutionary aspen groups, although nestedness values were only significant in the northern garden. Variation in plant vulnerability, measured as the frequency of herbivore specialization in the aspen population, was significantly partitioned by environment (common garden) but not by evolutionary origin of the aspens. Significant values of modularity were observed in all network matrices. Trait-matching indicated that growth traits, leaf morphology, and phenolic metabolites affected modular structure in both the garden and evolutionary groups, whereas extra-floral nectaries had little influence. Further examination of module configuration revealed that plant vulnerability explained considerable variance in web structure. The contrasting conditions between the two gardens resulted in bottom-up effects of the environment, which most strongly influenced the overall network architecture, however, the aspen groups with dissimilar evolutionary history also showed contrasting degrees of nestedness and modularity. Our research therefore shows that, while evolution does affect the structure of aspen–herbivore bipartite networks, the role of environmental variations is a dominant constraint. PMID:26306175

Functional Brain Network Modularity Captures Inter- and Intra-Individual Variation in Working Memory Capacity

PubMed Central

Stevens, Alexander A.; Tappon, Sarah C.; Garg, Arun; Fair, Damien A.

2012-01-01

Background Cognitive abilities, such as working memory, differ among people; however, individuals also vary in their own day-to-day cognitive performance. One potential source of cognitive variability may be fluctuations in the functional organization of neural systems. The degree to which the organization of these functional networks is optimized may relate to the effective cognitive functioning of the individual. Here we specifically examine how changes in the organization of large-scale networks measured via resting state functional connectivity MRI and graph theory track changes in working memory capacity. Methodology/Principal Findings Twenty-two participants performed a test of working memory capacity and then underwent resting-state fMRI. Seventeen subjects repeated the protocol three weeks later. We applied graph theoretic techniques to measure network organization on 34 brain regions of interest (ROI). Network modularity, which measures the level of integration and segregation across sub-networks, and small-worldness, which measures global network connection efficiency, both predicted individual differences in memory capacity; however, only modularity predicted intra-individual variation across the two sessions. Partial correlations controlling for the component of working memory that was stable across sessions revealed that modularity was almost entirely associated with the variability of working memory at each session. Analyses of specific sub-networks and individual circuits were unable to consistently account for working memory capacity variability. Conclusions/Significance The results suggest that the intrinsic functional organization of an a priori defined cognitive control network measured at rest provides substantial information about actual cognitive performance. The association of network modularity to the variability in an individual's working memory capacity suggests that the organization of this network into high connectivity within modules and sparse connections between modules may reflect effective signaling across brain regions, perhaps through the modulation of signal or the suppression of the propagation of noise. PMID:22276205

Trapping in scale-free networks with hierarchical organization of modularity.

PubMed

Zhang, Zhongzhi; Lin, Yuan; Gao, Shuyang; Zhou, Shuigeng; Guan, Jihong; Li, Mo

2009-11-01

A wide variety of real-life networks share two remarkable generic topological properties: scale-free behavior and modular organization, and it is natural and important to study how these two features affect the dynamical processes taking place on such networks. In this paper, we investigate a simple stochastic process--trapping problem, a random walk with a perfect trap fixed at a given location, performed on a family of hierarchical networks that exhibit simultaneously striking scale-free and modular structure. We focus on a particular case with the immobile trap positioned at the hub node having the largest degree. Using a method based on generating functions, we determine explicitly the mean first-passage time (MFPT) for the trapping problem, which is the mean of the node-to-trap first-passage time over the entire network. The exact expression for the MFPT is calculated through the recurrence relations derived from the special construction of the hierarchical networks. The obtained rigorous formula corroborated by extensive direct numerical calculations exhibits that the MFPT grows algebraically with the network order. Concretely, the MFPT increases as a power-law function of the number of nodes with the exponent much less than 1. We demonstrate that the hierarchical networks under consideration have more efficient structure for transport by diffusion in contrast with other analytically soluble media including some previously studied scale-free networks. We argue that the scale-free and modular topologies are responsible for the high efficiency of the trapping process on the hierarchical networks.

Disrupted resting-state brain network properties in obesity: decreased global and putaminal cortico-striatal network efficiency.

PubMed

Baek, K; Morris, L S; Kundu, P; Voon, V

2017-03-01

The efficient organization and communication of brain networks underlie cognitive processing and their disruption can lead to pathological behaviours. Few studies have focused on whole-brain networks in obesity and binge eating disorder (BED). Here we used multi-echo resting-state functional magnetic resonance imaging (rsfMRI) along with a data-driven graph theory approach to assess brain network characteristics in obesity and BED. Multi-echo rsfMRI scans were collected from 40 obese subjects (including 20 BED patients) and 40 healthy controls and denoised using multi-echo independent component analysis (ME-ICA). We constructed a whole-brain functional connectivity matrix with normalized correlation coefficients between regional mean blood oxygenation level-dependent (BOLD) signals from 90 brain regions in the Automated Anatomical Labeling atlas. We computed global and regional network properties in the binarized connectivity matrices with an edge density of 5%-25%. We also verified our findings using a separate parcellation, the Harvard-Oxford atlas parcellated into 470 regions. Obese subjects exhibited significantly reduced global and local network efficiency as well as decreased modularity compared with healthy controls, showing disruption in small-world and modular network structures. In regional metrics, the putamen, pallidum and thalamus exhibited significantly decreased nodal degree and efficiency in obese subjects. Obese subjects also showed decreased connectivity of cortico-striatal/cortico-thalamic networks associated with putaminal and cortical motor regions. These findings were significant with ME-ICA with limited group differences observed with conventional denoising or single-echo analysis. Using this data-driven analysis of multi-echo rsfMRI data, we found disruption in global network properties and motor cortico-striatal networks in obesity consistent with habit formation theories. Our findings highlight the role of network properties in pathological food misuse as possible biomarkers and therapeutic targets.

The Evolutionary Origins of Hierarchy

PubMed Central

Huizinga, Joost; Clune, Jeff

2016-01-01

Hierarchical organization—the recursive composition of sub-modules—is ubiquitous in biological networks, including neural, metabolic, ecological, and genetic regulatory networks, and in human-made systems, such as large organizations and the Internet. To date, most research on hierarchy in networks has been limited to quantifying this property. However, an open, important question in evolutionary biology is why hierarchical organization evolves in the first place. It has recently been shown that modularity evolves because of the presence of a cost for network connections. Here we investigate whether such connection costs also tend to cause a hierarchical organization of such modules. In computational simulations, we find that networks without a connection cost do not evolve to be hierarchical, even when the task has a hierarchical structure. However, with a connection cost, networks evolve to be both modular and hierarchical, and these networks exhibit higher overall performance and evolvability (i.e. faster adaptation to new environments). Additional analyses confirm that hierarchy independently improves adaptability after controlling for modularity. Overall, our results suggest that the same force–the cost of connections–promotes the evolution of both hierarchy and modularity, and that these properties are important drivers of network performance and adaptability. In addition to shedding light on the emergence of hierarchy across the many domains in which it appears, these findings will also accelerate future research into evolving more complex, intelligent computational brains in the fields of artificial intelligence and robotics. PMID:27280881

The Evolutionary Origins of Hierarchy.

PubMed

Mengistu, Henok; Huizinga, Joost; Mouret, Jean-Baptiste; Clune, Jeff

2016-06-01

Hierarchical organization-the recursive composition of sub-modules-is ubiquitous in biological networks, including neural, metabolic, ecological, and genetic regulatory networks, and in human-made systems, such as large organizations and the Internet. To date, most research on hierarchy in networks has been limited to quantifying this property. However, an open, important question in evolutionary biology is why hierarchical organization evolves in the first place. It has recently been shown that modularity evolves because of the presence of a cost for network connections. Here we investigate whether such connection costs also tend to cause a hierarchical organization of such modules. In computational simulations, we find that networks without a connection cost do not evolve to be hierarchical, even when the task has a hierarchical structure. However, with a connection cost, networks evolve to be both modular and hierarchical, and these networks exhibit higher overall performance and evolvability (i.e. faster adaptation to new environments). Additional analyses confirm that hierarchy independently improves adaptability after controlling for modularity. Overall, our results suggest that the same force-the cost of connections-promotes the evolution of both hierarchy and modularity, and that these properties are important drivers of network performance and adaptability. In addition to shedding light on the emergence of hierarchy across the many domains in which it appears, these findings will also accelerate future research into evolving more complex, intelligent computational brains in the fields of artificial intelligence and robotics.

A network function-based definition of communities in complex networks.

PubMed

Chauhan, Sanjeev; Girvan, Michelle; Ott, Edward

2012-09-01

We consider an alternate definition of community structure that is functionally motivated. We define network community structure based on the function the network system is intended to perform. In particular, as a specific example of this approach, we consider communities whose function is enhanced by the ability to synchronize and/or by resilience to node failures. Previous work has shown that, in many cases, the largest eigenvalue of the network's adjacency matrix controls the onset of both synchronization and percolation processes. Thus, for networks whose functional performance is dependent on these processes, we propose a method that divides a given network into communities based on maximizing a function of the largest eigenvalues of the adjacency matrices of the resulting communities. We also explore the differences between the partitions obtained by our method and the modularity approach (which is based solely on consideration of network structure). We do this for several different classes of networks. We find that, in many cases, modularity-based partitions do almost as well as our function-based method in finding functional communities, even though modularity does not specifically incorporate consideration of function.

Individual T1-weighted/T2-weighted ratio brain networks: Small-worldness, hubs and modular organization

NASA Astrophysics Data System (ADS)

Wu, Huijun; Wang, Hao; Lü, Linyuan

Applying network science to investigate the complex systems has become a hot topic. In neuroscience, understanding the architectures of complex brain networks was a vital issue. An enormous amount of evidence had supported the brain was cost/efficiency trade-off with small-worldness, hubness and modular organization through the functional MRI and structural MRI investigations. However, the T1-weighted/T2-weighted (T1w/T2w) ratio brain networks were mostly unexplored. Here, we utilized a KL divergence-based method to construct large-scale individual T1w/T2w ratio brain networks and investigated the underlying topological attributes of these networks. Our results supported that the T1w/T2w ratio brain networks were comprised of small-worldness, an exponentially truncated power-law degree distribution, frontal-parietal hubs and modular organization. Besides, there were significant positive correlations between the network metrics and fluid intelligence. Thus, the T1w/T2w ratio brain networks open a new avenue to understand the human brain and are a necessary supplement for future MRI studies.

Clustering algorithm for determining community structure in large networks

NASA Astrophysics Data System (ADS)

Pujol, Josep M.; Béjar, Javier; Delgado, Jordi

2006-07-01

We propose an algorithm to find the community structure in complex networks based on the combination of spectral analysis and modularity optimization. The clustering produced by our algorithm is as accurate as the best algorithms on the literature of modularity optimization; however, the main asset of the algorithm is its efficiency. The best match for our algorithm is Newman’s fast algorithm, which is the reference algorithm for clustering in large networks due to its efficiency. When both algorithms are compared, our algorithm outperforms the fast algorithm both in efficiency and accuracy of the clustering, in terms of modularity. Thus, the results suggest that the proposed algorithm is a good choice to analyze the community structure of medium and large networks in the range of tens and hundreds of thousand vertices.

Emergence of system roles in normative neurodevelopment

PubMed Central

Gu, Shi; Satterthwaite, Theodore D.; Medaglia, John D.; Yang, Muzhi; Gur, Raquel E.; Gur, Ruben C.; Bassett, Danielle S.

2015-01-01

Adult human cognition is supported by systems of brain regions, or modules, that are functionally coherent at rest and collectively activated by distinct task requirements. However, an understanding of how the formation of these modules supports evolving cognitive capabilities has not been delineated. Here, we quantify the formation of network modules in a sample of 780 youth (aged 8–22 y) who were studied as part of the Philadelphia Neurodevelopmental Cohort. We demonstrate that the brain’s functional network organization changes in youth through a process of modular evolution that is governed by the specific cognitive roles of each system, as defined by the balance of within- vs. between-module connectivity. Moreover, individual variability in these roles is correlated with cognitive performance. Collectively, these results suggest that dynamic maturation of network modules in youth may be a critical driver for the development of cognition. PMID:26483477

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

Franović, Igor, E-mail: franovic@ipb.ac.rs; Todorović, Kristina; Burić, Nikola

We use the mean-field approach to analyze the collective dynamics in macroscopic networks of stochastic Fitzhugh-Nagumo units with delayed couplings. The conditions for validity of the two main approximations behind the model, called the Gaussian approximation and the Quasi-independence approximation, are examined. It is shown that the dynamics of the mean-field model may indicate in a self-consistent fashion the parameter domains where the Quasi-independence approximation fails. Apart from a network of globally coupled units, we also consider the paradigmatic setup of two interacting assemblies to demonstrate how our framework may be extended to hierarchical and modular networks. In both cases,more » the mean-field model can be used to qualitatively analyze the stability of the system, as well as the scenarios for the onset and the suppression of the collective mode. In quantitative terms, the mean-field model is capable of predicting the average oscillation frequency corresponding to the global variables of the exact system.« less

MINE: Module Identification in Networks

PubMed Central

2011-01-01

Background Graphical models of network associations are useful for both visualizing and integrating multiple types of association data. Identifying modules, or groups of functionally related gene products, is an important challenge in analyzing biological networks. However, existing tools to identify modules are insufficient when applied to dense networks of experimentally derived interaction data. To address this problem, we have developed an agglomerative clustering method that is able to identify highly modular sets of gene products within highly interconnected molecular interaction networks. Results MINE outperforms MCODE, CFinder, NEMO, SPICi, and MCL in identifying non-exclusive, high modularity clusters when applied to the C. elegans protein-protein interaction network. The algorithm generally achieves superior geometric accuracy and modularity for annotated functional categories. In comparison with the most closely related algorithm, MCODE, the top clusters identified by MINE are consistently of higher density and MINE is less likely to designate overlapping modules as a single unit. MINE offers a high level of granularity with a small number of adjustable parameters, enabling users to fine-tune cluster results for input networks with differing topological properties. Conclusions MINE was created in response to the challenge of discovering high quality modules of gene products within highly interconnected biological networks. The algorithm allows a high degree of flexibility and user-customisation of results with few adjustable parameters. MINE outperforms several popular clustering algorithms in identifying modules with high modularity and obtains good overall recall and precision of functional annotations in protein-protein interaction networks from both S. cerevisiae and C. elegans. PMID:21605434

Modular sensor network node

DOEpatents

Davis, Jesse Harper Zehring [Berkeley, CA; Stark, Jr., Douglas Paul; Kershaw, Christopher Patrick [Hayward, CA; Kyker, Ronald Dean [Livermore, CA

2008-06-10

A distributed wireless sensor network node is disclosed. The wireless sensor network node includes a plurality of sensor modules coupled to a system bus and configured to sense a parameter. The parameter may be an object, an event or any other parameter. The node collects data representative of the parameter. The node also includes a communication module coupled to the system bus and configured to allow the node to communicate with other nodes. The node also includes a processing module coupled to the system bus and adapted to receive the data from the sensor module and operable to analyze the data. The node also includes a power module connected to the system bus and operable to generate a regulated voltage.

Protein secondary structure prediction using modular reciprocal bidirectional recurrent neural networks.

PubMed

Babaei, Sepideh; Geranmayeh, Amir; Seyyedsalehi, Seyyed Ali

2010-12-01

The supervised learning of recurrent neural networks well-suited for prediction of protein secondary structures from the underlying amino acids sequence is studied. Modular reciprocal recurrent neural networks (MRR-NN) are proposed to model the strong correlations between adjacent secondary structure elements. Besides, a multilayer bidirectional recurrent neural network (MBR-NN) is introduced to capture the long-range intramolecular interactions between amino acids in formation of the secondary structure. The final modular prediction system is devised based on the interactive integration of the MRR-NN and the MBR-NN structures to arbitrarily engage the neighboring effects of the secondary structure types concurrent with memorizing the sequential dependencies of amino acids along the protein chain. The advanced combined network augments the percentage accuracy (Q₃) to 79.36% and boosts the segment overlap (SOV) up to 70.09% when tested on the PSIPRED dataset in three-fold cross-validation. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Traffic-aware energy saving scheme with modularization supporting in TWDM-PON

NASA Astrophysics Data System (ADS)

Xiong, Yu; Sun, Peng; Liu, Chuanbo; Guan, Jianjun

2017-01-01

Time and wavelength division multiplexed passive optical network (TWDM-PON) is considered to be a primary solution for next-generation passive optical network stage 2 (NG-PON2). Due to the feature of multi-wavelength transmission of TWDM-PON, some of the transmitters/receivers at the optical line terminal (OLT) could be shut down to reduce the energy consumption. Therefore, a novel scheme called traffic-aware energy saving scheme with modularization supporting is proposed. Through establishing the modular energy consumption model of OLT, the wavelength transmitters/receivers at OLT could be switched on or shut down adaptively depending on sensing the status of network traffic load, thus the energy consumption of OLT will be effectively reduced. Furthermore, exploring the technology of optical network unit (ONU) modularization, each module of ONU could be switched to sleep or active mode independently in order to reduce the energy consumption of ONU. Simultaneously, the polling sequence of ONU could be changed dynamically via sensing the packet arrival time. In order to guarantee the delay performance of network traffic, the sub-cycle division strategy is designed to transmit the real-time traffic preferentially. Finally, simulation results verify that the proposed scheme is able to reduce the energy consumption of the network while maintaining the traffic delay performance.

Automatic decomposition of kinetic models of signaling networks minimizing the retroactivity among modules.

PubMed

Saez-Rodriguez, Julio; Gayer, Stefan; Ginkel, Martin; Gilles, Ernst Dieter

2008-08-15

The modularity of biochemical networks in general, and signaling networks in particular, has been extensively studied over the past few years. It has been proposed to be a useful property to analyze signaling networks: by decomposing the network into subsystems, more manageable units are obtained that are easier to analyze. While many powerful algorithms are available to identify modules in protein interaction networks, less attention has been paid to signaling networks de.ned as chemical systems. Such a decomposition would be very useful as most quantitative models are de.ned using the latter, more detailed formalism. Here, we introduce a novel method to decompose biochemical networks into modules so that the bidirectional (retroactive) couplings among the modules are minimized. Our approach adapts a method to detect community structures, and applies it to the so-called retroactivity matrix that characterizes the couplings of the network. Only the structure of the network, e.g. in SBML format, is required. Furthermore, the modularized models can be loaded into ProMoT, a modeling tool which supports modular modeling. This allows visualization of the models, exploiting their modularity and easy generation of models of one or several modules for further analysis. The method is applied to several relevant cases, including an entangled model of the EGF-induced MAPK cascade and a comprehensive model of EGF signaling, demonstrating its ability to uncover meaningful modules. Our approach can thus help to analyze large networks, especially when little a priori knowledge on the structure of the network is available. The decomposition algorithms implemented in MATLAB (Mathworks, Inc.) are freely available upon request. ProMoT is freely available at http://www.mpi-magdeburg.mpg.de/projects/promot. Supplementary data are available at Bioinformatics online.

A Modular IoT Platform for Real-Time Indoor Air Quality Monitoring

PubMed Central

Abdaoui, Abderrazak; Ahmad, Sabbir H.M.; Touati, Farid; Kadri, Abdullah

2018-01-01

The impact of air quality on health and on life comfort is well established. In many societies, vulnerable elderly and young populations spend most of their time indoors. Therefore, indoor air quality monitoring (IAQM) is of great importance to human health. Engineers and researchers are increasingly focusing their efforts on the design of real-time IAQM systems using wireless sensor networks. This paper presents an end-to-end IAQM system enabling measurement of CO2, CO, SO2, NO2, O3, Cl2, ambient temperature, and relative humidity. In IAQM systems, remote users usually use a local gateway to connect wireless sensor nodes in a given monitoring site to the external world for ubiquitous access of data. In this work, the role of the gateway in processing collected air quality data and its reliable dissemination to end-users through a web-server is emphasized. A mechanism for the backup and the restoration of the collected data in the case of Internet outage is presented. The system is adapted to an open-source Internet-of-Things (IoT) web-server platform, called Emoncms, for live monitoring and long-term storage of the collected IAQM data. A modular IAQM architecture is adopted, which results in a smart scalable system that allows seamless integration of various sensing technologies, wireless sensor networks (WSNs) and smart mobile standards. The paper gives full hardware and software details of the proposed solution. Sample IAQM results collected in various locations are also presented to demonstrate the abilities of the system. PMID:29443893

Advanced I&C for Fault-Tolerant Supervisory Control of Small Modular Reactors

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

Cole, Daniel G.

In this research, we have developed a supervisory control approach to enable automated control of SMRs. By design the supervisory control system has an hierarchical, interconnected, adaptive control architecture. A considerable advantage to this architecture is that it allows subsystems to communicate at different/finer granularity, facilitates monitoring of process at the modular and plant levels, and enables supervisory control. We have investigated the deployment of automation, monitoring, and data collection technologies to enable operation of multiple SMRs. Each unit's controller collects and transfers information from local loops and optimize that unit’s parameters. Information is passed from the each SMR unitmore » controller to the supervisory controller, which supervises the actions of SMR units and manage plant processes. The information processed at the supervisory level will provide operators the necessary information needed for reactor, unit, and plant operation. In conjunction with the supervisory effort, we have investigated techniques for fault-tolerant networks, over which information is transmitted between local loops and the supervisory controller to maintain a safe level of operational normalcy in the presence of anomalies. The fault-tolerance of the supervisory control architecture, the network that supports it, and the impact of fault-tolerance on multi-unit SMR plant control has been a second focus of this research. To this end, we have investigated the deployment of advanced automation, monitoring, and data collection and communications technologies to enable operation of multiple SMRs. We have created a fault-tolerant multi-unit SMR supervisory controller that collects and transfers information from local loops, supervise their actions, and adaptively optimize the controller parameters. The goal of this research has been to develop the methodologies and procedures for fault-tolerant supervisory control of small modular reactors. To achieve this goal, we have identified the following objectives. These objective are an ordered approach to the research: I) Development of a supervisory digital I&C system II) Fault-tolerance of the supervisory control architecture III) Automated decision making and online monitoring.« less

Modular architecture for robotics and teleoperation

DOEpatents

Anderson, Robert J.

1996-12-03

Systems and methods for modularization and discretization of real-time robot, telerobot and teleoperation systems using passive, network based control laws. Modules consist of network one-ports and two-ports. Wave variables and position information are passed between modules. The behavior of each module is decomposed into uncoupled linear-time-invariant, and coupled, nonlinear memoryless elements and then are separately discretized.

Development of the Mathematics of Learning Curve Models for Evaluating Small Modular Reactor Economics

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

Harrison, Thomas J.

2014-03-01

This report documents the efforts to perform dynamic model validation on the Eastern Interconnection (EI) by modeling governor deadband. An on-peak EI dynamic model is modified to represent governor deadband characteristics. Simulation results are compared with synchrophasor measurements collected by the Frequency Monitoring Network (FNET/GridEye). The comparison shows that by modeling governor deadband the simulated frequency response can closely align with the actual system response.

Optimal multi-community network modularity for information diffusion

NASA Astrophysics Data System (ADS)

Wu, Jiaocan; Du, Ruping; Zheng, Yingying; Liu, Dong

2016-02-01

Studies demonstrate that community structure plays an important role in information spreading recently. In this paper, we investigate the impact of multi-community structure on information diffusion with linear threshold model. We utilize extended GN network that contains four communities and analyze dynamic behaviors of information that spreads on it. And we discover the optimal multi-community network modularity for information diffusion based on the social reinforcement. Results show that, within the appropriate range, multi-community structure will facilitate information diffusion instead of hindering it, which accords with the results derived from two-community network.

Does biological intimacy shape ecological network structure? A test using a brood pollination mutualism on continental and oceanic islands.

PubMed

Hembry, David H; Raimundo, Rafael L G; Newman, Erica A; Atkinson, Lesje; Guo, Chang; Guimarães, Paulo R; Gillespie, Rosemary G

2018-04-25

Biological intimacy-the degree of physical proximity or integration of partner taxa during their life cycles-is thought to promote the evolution of reciprocal specialization and modularity in the networks formed by co-occurring mutualistic species, but this hypothesis has rarely been tested. Here, we test this "biological intimacy hypothesis" by comparing the network architecture of brood pollination mutualisms, in which specialized insects are simultaneously parasites (as larvae) and pollinators (as adults) of their host plants to that of other mutualisms which vary in their biological intimacy (including ant-myrmecophyte, ant-extrafloral nectary, plant-pollinator and plant-seed disperser assemblages). We use a novel dataset sampled from leafflower trees (Phyllanthaceae: Phyllanthus s. l. [Glochidion]) and their pollinating leafflower moths (Lepidoptera: Epicephala) on three oceanic islands (French Polynesia) and compare it to equivalent published data from congeners on continental islands (Japan). We infer taxonomic diversity of leafflower moths using multilocus molecular phylogenetic analysis and examine several network structural properties: modularity (compartmentalization), reciprocality (symmetry) of specialization and algebraic connectivity. We find that most leafflower-moth networks are reciprocally specialized and modular, as hypothesized. However, we also find that two oceanic island networks differ in their modularity and reciprocal specialization from the others, as a result of a supergeneralist moth taxon which interacts with nine of 10 available hosts. Our results generally support the biological intimacy hypothesis, finding that leafflower-moth networks (usually) share a reciprocally specialized and modular structure with other intimate mutualisms such as ant-myrmecophyte symbioses, but unlike nonintimate mutualisms such as seed dispersal and nonintimate pollination. Additionally, we show that generalists-common in nonintimate mutualisms-can also evolve in intimate mutualisms, and that their effect is similar in both types of assemblages: once generalists emerge they reshape the network organization by connecting otherwise isolated modules. © 2018 The Authors. Journal of Animal Ecology © 2018 British Ecological Society.

Modes of Large-Scale Brain Network Organization during Threat Processing and Posttraumatic Stress Disorder Symptom Reduction during TF-CBT among Adolescent Girls.

PubMed

Cisler, Josh M; Sigel, Benjamin A; Kramer, Teresa L; Smitherman, Sonet; Vanderzee, Karin; Pemberton, Joy; Kilts, Clinton D

2016-01-01

Posttraumatic stress disorder (PTSD) is often chronic and disabling across the lifespan. The gold standard treatment for adolescent PTSD is Trauma-Focused Cognitive-Behavioral Therapy (TF-CBT), though treatment response is variable and mediating neural mechanisms are not well understood. Here, we test whether PTSD symptom reduction during TF-CBT is associated with individual differences in large-scale brain network organization during emotion processing. Twenty adolescent girls, aged 11-16, with PTSD related to assaultive violence completed a 12-session protocol of TF-CBT. Participants completed an emotion processing task, in which neutral and fearful facial expressions were presented either overtly or covertly during 3T fMRI, before and after treatment. Analyses focused on characterizing network properties of modularity, assortativity, and global efficiency within an 824 region-of-interest brain parcellation separately during each of the task blocks using weighted functional connectivity matrices. We similarly analyzed an existing dataset of healthy adolescent girls undergoing an identical emotion processing task to characterize normative network organization. Pre-treatment individual differences in modularity, assortativity, and global efficiency during covert fear vs neutral blocks predicted PTSD symptom reduction. Patients who responded better to treatment had greater network modularity and assortativity but lesser efficiency, a pattern that closely resembled the control participants. At a group level, greater symptom reduction was associated with greater pre-to-post-treatment increases in network assortativity and modularity, but this was more pronounced among participants with less symptom improvement. The results support the hypothesis that modularized and resilient brain organization during emotion processing operate as mechanisms enabling symptom reduction during TF-CBT.

Organization of excitable dynamics in hierarchical biological networks.

PubMed

Müller-Linow, Mark; Hilgetag, Claus C; Hütt, Marc-Thorsten

2008-09-26

This study investigates the contributions of network topology features to the dynamic behavior of hierarchically organized excitable networks. Representatives of different types of hierarchical networks as well as two biological neural networks are explored with a three-state model of node activation for systematically varying levels of random background network stimulation. The results demonstrate that two principal topological aspects of hierarchical networks, node centrality and network modularity, correlate with the network activity patterns at different levels of spontaneous network activation. The approach also shows that the dynamic behavior of the cerebral cortical systems network in the cat is dominated by the network's modular organization, while the activation behavior of the cellular neuronal network of Caenorhabditis elegans is strongly influenced by hub nodes. These findings indicate the interaction of multiple topological features and dynamic states in the function of complex biological networks.

Modular-extramodular organization in developing multisensory shell regions of the mouse inferior colliculus.

PubMed

Dillingham, Christopher H; Gay, Sean M; Behrooz, Roxana; Gabriele, Mark L

2017-12-01

The complex neuroanatomical connections of the inferior colliculus (IC) and its major subdivisions offer a juxtaposition of segregated processing streams with distinct organizational features. While the tonotopically layered central nucleus is well-documented, less is known about functional compartments in the neighboring lateral cortex (LCIC). In addition to a laminar framework, LCIC afferent-efferent patterns suggest a multimodal mosaic, consisting of a patchy modular network with surrounding extramodular domains. This study utilizes several neurochemical markers that reveal an emerging LCIC modular-extramodular microarchitecture. In newborn and post-hearing C57BL/6J and CBA/CaJ mice, histochemical and immunocytochemical stains were performed for acetylcholinesterase (AChE), nicotinamide adenine dinucleotide phosphate-diaphorase (NADPH-d), glutamic acid decarboxylase (GAD), cytochrome oxidase (CO), and calretinin (CR). Discontinuous layer 2 modules are positive for AChE, NADPH-d, GAD, and CO throughout the rostrocaudal LCIC. While not readily apparent at birth, discrete cell clusters emerge over the first postnatal week, yielding an identifiable modular network prior to hearing onset. Modular boundaries continue to become increasingly distinct with age, as surrounding extramodular fields remain largely negative for each marker. Alignment of modular markers in serial sections suggests each highlight the same periodic patchy network throughout the nascent LCIC. In contrast, CR patterns appear complementary, preferentially staining extramodular LCIC zones. Double-labeling experiments confirm that NADPH-d, the most consistent developmental modular marker, and CR label separate, nonoverlapping LCIC compartments. Determining how this emerging modularity may align with similar LCIC patch-matrix-like Eph/ephrin guidance patterns, and how each interface with, and potentially influence developing multimodal LCIC projection configurations is discussed. © 2017 Wiley Periodicals, Inc.

De novo design of protein homo-oligomers with modular hydrogen bond network-mediated specificity

PubMed Central

Boyken, Scott E.; Chen, Zibo; Groves, Benjamin; Langan, Robert A.; Oberdorfer, Gustav; Ford, Alex; Gilmore, Jason; Xu, Chunfu; DiMaio, Frank; Pereira, Jose Henrique; Sankaran, Banumathi; Seelig, Georg; Zwart, Peter H.; Baker, David

2017-01-01

In nature, structural specificity in DNA and proteins is encoded quite differently: in DNA, specificity arises from modular hydrogen bonds in the core of the double helix, whereas in proteins, specificity arises largely from buried hydrophobic packing complemented by irregular peripheral polar interactions. Here we describe a general approach for designing a wide range of protein homo-oligomers with specificity determined by modular arrays of central hydrogen bond networks. We use the approach to design dimers, trimers, and tetramers consisting of two concentric rings of helices, including previously not seen triangular, square, and supercoiled topologies. X-ray crystallography confirms that the structures overall, and the hydrogen bond networks in particular, are nearly identical to the design models, and the networks confer interaction specificity in vivo. The ability to design extensive hydrogen bond networks with atomic accuracy is a milestone for protein design and enables the programming of protein interaction specificity for a broad range of synthetic biology applications. PMID:27151862

Detecting network communities beyond assortativity-related attributes

NASA Astrophysics Data System (ADS)

Liu, Xin; Murata, Tsuyoshi; Wakita, Ken

2014-07-01

In network science, assortativity refers to the tendency of links to exist between nodes with similar attributes. In social networks, for example, links tend to exist between individuals of similar age, nationality, location, race, income, educational level, religious belief, and language. Thus, various attributes jointly affect the network topology. An interesting problem is to detect community structure beyond some specific assortativity-related attributes ρ, i.e., to take out the effect of ρ on network topology and reveal the hidden community structures which are due to other attributes. An approach to this problem is to redefine the null model of the modularity measure, so as to simulate the effect of ρ on network topology. However, a challenge is that we do not know to what extent the network topology is affected by ρ and by other attributes. In this paper, we propose a distance modularity, which allows us to freely choose any suitable function to simulate the effect of ρ. Such freedom can help us probe the effect of ρ and detect the hidden communities which are due to other attributes. We test the effectiveness of distance modularity on synthetic benchmarks and two real-world networks.

A novel community detection method in bipartite networks

NASA Astrophysics Data System (ADS)

Zhou, Cangqi; Feng, Liang; Zhao, Qianchuan

2018-02-01

Community structure is a common and important feature in many complex networks, including bipartite networks, which are used as a standard model for many empirical networks comprised of two types of nodes. In this paper, we propose a two-stage method for detecting community structure in bipartite networks. Firstly, we extend the widely-used Louvain algorithm to bipartite networks. The effectiveness and efficiency of the Louvain algorithm have been proved by many applications. However, there lacks a Louvain-like algorithm specially modified for bipartite networks. Based on bipartite modularity, a measure that extends unipartite modularity and that quantifies the strength of partitions in bipartite networks, we fill the gap by developing the Bi-Louvain algorithm that iteratively groups the nodes in each part by turns. This algorithm in bipartite networks often produces a balanced network structure with equal numbers of two types of nodes. Secondly, for the balanced network yielded by the first algorithm, we use an agglomerative clustering method to further cluster the network. We demonstrate that the calculation of the gain of modularity of each aggregation, and the operation of joining two communities can be compactly calculated by matrix operations for all pairs of communities simultaneously. At last, a complete hierarchical community structure is unfolded. We apply our method to two benchmark data sets and a large-scale data set from an e-commerce company, showing that it effectively identifies community structure in bipartite networks.

Multiway spectral community detection in networks

NASA Astrophysics Data System (ADS)

Zhang, Xiao; Newman, M. E. J.

2015-11-01

One of the most widely used methods for community detection in networks is the maximization of the quality function known as modularity. Of the many maximization techniques that have been used in this context, some of the most conceptually attractive are the spectral methods, which are based on the eigenvectors of the modularity matrix. Spectral algorithms have, however, been limited, by and large, to the division of networks into only two or three communities, with divisions into more than three being achieved by repeated two-way division. Here we present a spectral algorithm that can directly divide a network into any number of communities. The algorithm makes use of a mapping from modularity maximization to a vector partitioning problem, combined with a fast heuristic for vector partitioning. We compare the performance of this spectral algorithm with previous approaches and find it to give superior results, particularly in cases where community sizes are unbalanced. We also give demonstrative applications of the algorithm to two real-world networks and find that it produces results in good agreement with expectations for the networks studied.

Modular neural networks: a survey.

PubMed

Auda, G; Kamel, M

1999-04-01

Modular Neural Networks (MNNs) is a rapidly growing field in artificial Neural Networks (NNs) research. This paper surveys the different motivations for creating MNNs: biological, psychological, hardware, and computational. Then, the general stages of MNN design are outlined and surveyed as well, viz., task decomposition techniques, learning schemes and multi-module decision-making strategies. Advantages and disadvantages of the surveyed methods are pointed out, and an assessment with respect to practical potential is provided. Finally, some general recommendations for future designs are presented.

A spectral method to detect community structure based on distance modularity matrix

NASA Astrophysics Data System (ADS)

Yang, Jin-Xuan; Zhang, Xiao-Dong

2017-08-01

There are many community organizations in social and biological networks. How to identify these community structure in complex networks has become a hot issue. In this paper, an algorithm to detect community structure of networks is proposed by using spectra of distance modularity matrix. The proposed algorithm focuses on the distance of vertices within communities, rather than the most weakly connected vertex pairs or number of edges between communities. The experimental results show that our method achieves better effectiveness to identify community structure for a variety of real-world networks and computer generated networks with a little more time-consumption.

Automated analysis of information processing, kinetic independence and modular architecture in biochemical networks using MIDIA.

PubMed

Bowsher, Clive G

2011-02-15

Understanding the encoding and propagation of information by biochemical reaction networks and the relationship of such information processing properties to modular network structure is of fundamental importance in the study of cell signalling and regulation. However, a rigorous, automated approach for general biochemical networks has not been available, and high-throughput analysis has therefore been out of reach. Modularization Identification by Dynamic Independence Algorithms (MIDIA) is a user-friendly, extensible R package that performs automated analysis of how information is processed by biochemical networks. An important component is the algorithm's ability to identify exact network decompositions based on both the mass action kinetics and informational properties of the network. These modularizations are visualized using a tree structure from which important dynamic conditional independence properties can be directly read. Only partial stoichiometric information needs to be used as input to MIDIA, and neither simulations nor knowledge of rate parameters are required. When applied to a signalling network, for example, the method identifies the routes and species involved in the sequential propagation of information between its multiple inputs and outputs. These routes correspond to the relevant paths in the tree structure and may be further visualized using the Input-Output Path Matrix tool. MIDIA remains computationally feasible for the largest network reconstructions currently available and is straightforward to use with models written in Systems Biology Markup Language (SBML). The package is distributed under the GNU General Public License and is available, together with a link to browsable Supplementary Material, at http://code.google.com/p/midia. Further information is at www.maths.bris.ac.uk/~macgb/Software.html.

A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder.

PubMed

Boi, Fabio; Moraitis, Timoleon; De Feo, Vito; Diotalevi, Francesco; Bartolozzi, Chiara; Indiveri, Giacomo; Vato, Alessandro

2016-01-01

Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world. A decoder translates recorded neural activity into motor commands and an encoder delivers sensory information collected from the environment directly to the brain creating a closed-loop system. These two modules are typically integrated in bulky external devices. However, the clinical support of patients with severe motor and sensory deficits requires compact, low-power, and fully implantable systems that can decode neural signals to control external devices. As a first step toward this goal, we developed a modular bidirectional BMI setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits. On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn to decode neural signals recorded from the brain into motor outputs controlling the movements of an external device. The modularity of the BMI allowed us to tune the individual components of the setup without modifying the whole system. In this paper, we present the features of this modular BMI and describe how we configured the network of spiking neuron circuits to implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm that connects bidirectionally the brain of an anesthetized rat with an external object. We show that the chip learned the decoding task correctly, allowing the interfaced brain to control the object's trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is mature enough for the development of BMI modules that are sufficiently low-power and compact, while being highly computationally powerful and adaptive.

A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder

PubMed Central

Boi, Fabio; Moraitis, Timoleon; De Feo, Vito; Diotalevi, Francesco; Bartolozzi, Chiara; Indiveri, Giacomo; Vato, Alessandro

2016-01-01

Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world. A decoder translates recorded neural activity into motor commands and an encoder delivers sensory information collected from the environment directly to the brain creating a closed-loop system. These two modules are typically integrated in bulky external devices. However, the clinical support of patients with severe motor and sensory deficits requires compact, low-power, and fully implantable systems that can decode neural signals to control external devices. As a first step toward this goal, we developed a modular bidirectional BMI setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits. On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn to decode neural signals recorded from the brain into motor outputs controlling the movements of an external device. The modularity of the BMI allowed us to tune the individual components of the setup without modifying the whole system. In this paper, we present the features of this modular BMI and describe how we configured the network of spiking neuron circuits to implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm that connects bidirectionally the brain of an anesthetized rat with an external object. We show that the chip learned the decoding task correctly, allowing the interfaced brain to control the object's trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is mature enough for the development of BMI modules that are sufficiently low-power and compact, while being highly computationally powerful and adaptive. PMID:28018162

Deep Logic Networks: Inserting and Extracting Knowledge From Deep Belief Networks.

PubMed

Tran, Son N; d'Avila Garcez, Artur S

2018-02-01

Developments in deep learning have seen the use of layerwise unsupervised learning combined with supervised learning for fine-tuning. With this layerwise approach, a deep network can be seen as a more modular system that lends itself well to learning representations. In this paper, we investigate whether such modularity can be useful to the insertion of background knowledge into deep networks, whether it can improve learning performance when it is available, and to the extraction of knowledge from trained deep networks, and whether it can offer a better understanding of the representations learned by such networks. To this end, we use a simple symbolic language-a set of logical rules that we call confidence rules-and show that it is suitable for the representation of quantitative reasoning in deep networks. We show by knowledge extraction that confidence rules can offer a low-cost representation for layerwise networks (or restricted Boltzmann machines). We also show that layerwise extraction can produce an improvement in the accuracy of deep belief networks. Furthermore, the proposed symbolic characterization of deep networks provides a novel method for the insertion of prior knowledge and training of deep networks. With the use of this method, a deep neural-symbolic system is proposed and evaluated, with the experimental results indicating that modularity through the use of confidence rules and knowledge insertion can be beneficial to network performance.

Modular structure of functional networks in olfactory memory.

PubMed

Meunier, David; Fonlupt, Pierre; Saive, Anne-Lise; Plailly, Jane; Ravel, Nadine; Royet, Jean-Pierre

2014-07-15

Graph theory enables the study of systems by describing those systems as a set of nodes and edges. Graph theory has been widely applied to characterize the overall structure of data sets in the social, technological, and biological sciences, including neuroscience. Modular structure decomposition enables the definition of sub-networks whose components are gathered in the same module and work together closely, while working weakly with components from other modules. This processing is of interest for studying memory, a cognitive process that is widely distributed. We propose a new method to identify modular structure in task-related functional magnetic resonance imaging (fMRI) networks. The modular structure was obtained directly from correlation coefficients and thus retained information about both signs and weights. The method was applied to functional data acquired during a yes-no odor recognition memory task performed by young and elderly adults. Four response categories were explored: correct (Hit) and incorrect (False alarm, FA) recognition and correct and incorrect rejection. We extracted time series data for 36 areas as a function of response categories and age groups and calculated condition-based weighted correlation matrices. Overall, condition-based modular partitions were more homogeneous in young than elderly subjects. Using partition similarity-based statistics and a posteriori statistical analyses, we demonstrated that several areas, including the hippocampus, caudate nucleus, and anterior cingulate gyrus, belonged to the same module more frequently during Hit than during all other conditions. Modularity values were negatively correlated with memory scores in the Hit condition and positively correlated with bias scores (liberal/conservative attitude) in the Hit and FA conditions. We further demonstrated that the proportion of positive and negative links between areas of different modules (i.e., the proportion of correlated and anti-correlated areas) accounted for most of the observed differences in signed modularity. Taken together, our results provided some evidence that the neural networks involved in odor recognition memory are organized into modules and that these modular partitions are linked to behavioral performance and individual strategies. Copyright © 2014 Elsevier Inc. All rights reserved.

Estimating the resolution limit of the map equation in community detection

NASA Astrophysics Data System (ADS)

Kawamoto, Tatsuro; Rosvall, Martin

2015-01-01

A community detection algorithm is considered to have a resolution limit if the scale of the smallest modules that can be resolved depends on the size of the analyzed subnetwork. The resolution limit is known to prevent some community detection algorithms from accurately identifying the modular structure of a network. In fact, any global objective function for measuring the quality of a two-level assignment of nodes into modules must have some sort of resolution limit or an external resolution parameter. However, it is yet unknown how the resolution limit affects the so-called map equation, which is known to be an efficient objective function for community detection. We derive an analytical estimate and conclude that the resolution limit of the map equation is set by the total number of links between modules instead of the total number of links in the full network as for modularity. This mechanism makes the resolution limit much less restrictive for the map equation than for modularity; in practice, it is orders of magnitudes smaller. Furthermore, we argue that the effect of the resolution limit often results from shoehorning multilevel modular structures into two-level descriptions. As we show, the hierarchical map equation effectively eliminates the resolution limit for networks with nested multilevel modular structures.

Dual Cross-Linked Biofunctional and Self-Healing Networks to Generate User-Defined Modular Gradient Hydrogel Constructs.

PubMed

Wei, Zhao; Lewis, Daniel M; Xu, Yu; Gerecht, Sharon

2017-08-01

Gradient hydrogels have been developed to mimic the spatiotemporal differences of multiple gradient cues in tissues. Current approaches used to generate such hydrogels are restricted to a single gradient shape and distribution. Here, a hydrogel is designed that includes two chemical cross-linking networks, biofunctional, and self-healing networks, enabling the customizable formation of modular gradient hydrogel construct with various gradient distributions and flexible shapes. The biofunctional networks are formed via Michael addition between the acrylates of oxidized acrylated hyaluronic acid (OAHA) and the dithiol of matrix metalloproteinase (MMP)-sensitive cross-linker and RGD peptides. The self-healing networks are formed via dynamic Schiff base reaction between N-carboxyethyl chitosan (CEC) and OAHA, which drives the modular gradient units to self-heal into an integral modular gradient hydrogel. The CEC-OAHA-MMP hydrogel exhibits excellent flowability at 37 °C under shear stress, enabling its injection to generate gradient distributions and shapes. Furthermore, encapsulated sarcoma cells respond to the gradient cues of RGD peptides and MMP-sensitive cross-linkers in the hydrogel. With these superior properties, the dual cross-linked CEC-OAHA-MMP hydrogel holds significant potential for generating customizable gradient hydrogel constructs, to study and guide cellular responses to their microenvironment such as in tumor mimicking, tissue engineering, and stem cell differentiation and morphogenesis. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Patterns of interactions of a large fish-parasite network in a tropical floodplain.

PubMed

Lima, Dilermando P; Giacomini, Henrique C; Takemoto, Ricardo M; Agostinho, Angelo A; Bini, Luis M

2012-07-01

1. Describing and explaining the structure of species interaction networks is of paramount importance for community ecology. Yet much has to be learned about the mechanisms responsible for major patterns, such as nestedness and modularity in different kinds of systems, of which large and diverse networks are a still underrepresented and scarcely studied fraction. 2. We assembled information on fishes and their parasites living in a large floodplain of key ecological importance for freshwater ecosystems in the Paraná River basin in South America. The resulting fish-parasite network containing 72 and 324 species of fishes and parasites, respectively, was analysed to investigate the patterns of nestedness and modularity as related to fish and parasite features. 3. Nestedness was found in the entire network and among endoparasites, multiple-host life cycle parasites and native hosts, but not in networks of ectoparasites, single-host life cycle parasites and non-native fishes. All networks were significantly modular. Taxonomy was the major host's attribute influencing both nestedness and modularity: more closely related host species tended to be associated with more nested parasite compositions and had greater chance of belonging to the same network module. Nevertheless, host abundance had a positive relationship with nestedness when only native host species pairs of the same network module were considered for analysis. 4. These results highlight the importance of evolutionary history of hosts in linking patterns of nestedness and formation of modules in the network. They also show that functional attributes of parasites (i.e. parasitism mode and life cycle) and origin of host populations (i.e. natives versus non-natives) are crucial to define the relative contribution of these two network properties and their dependence on other ecological factors (e.g. host abundance), with potential implications for community dynamics and stability. © 2012 The Authors. Journal of Animal Ecology © 2012 British Ecological Society.

Finding Correlation between Protein Protein Interaction Modules Using Semantic Web Techniques

NASA Astrophysics Data System (ADS)

Kargar, Mehdi; Moaven, Shahrouz; Abolhassani, Hassan

Many complex networks such as social networks and computer show modular structures, where edges between nodes are much denser within modules than between modules. It is strongly believed that cellular networks are also modular, reflecting the relative independence and coherence of different functional units in a cell. In this paper we used a human curated dataset. In this paper we consider each module in the PPI network as ontology. Using techniques in ontology alignment, we compare each pair of modules in the network. We want to see that is there a correlation between the structure of each module or they have totally different structures. Our results show that there is no correlation between proteins in a protein protein interaction network.

Classification capacity of a modular neural network implementing neurally inspired architecture and training rules.

PubMed

Poirazi, Panayiota; Neocleous, Costas; Pattichis, Costantinos S; Schizas, Christos N

2004-05-01

A three-layer neural network (NN) with novel adaptive architecture has been developed. The hidden layer of the network consists of slabs of single neuron models, where neurons within a slab--but not between slabs--have the same type of activation function. The network activation functions in all three layers have adaptable parameters. The network was trained using a biologically inspired, guided-annealing learning rule on a variety of medical data. Good training/testing classification performance was obtained on all data sets tested. The performance achieved was comparable to that of SVM classifiers. It was shown that the adaptive network architecture, inspired from the modular organization often encountered in the mammalian cerebral cortex, can benefit classification performance.

SensorKit: An End-to-End Solution for Environmental Sensor Networking

NASA Astrophysics Data System (ADS)

Silva, F.; Graham, E.; Deschon, A.; Lam, Y.; Goldman, J.; Wroclawski, J.; Kaiser, W.; Benzel, T.

2008-12-01

Modern day sensor network technology has shown great promise to transform environmental data collection. However, despite the promise, these systems have remained the purview of the engineers and computer scientists who design them rather than a useful tool for the environmental scientists who need them. SensorKit is conceived of as a way to make wireless sensor networks accessible to The People: it is an advanced, powerful tool for sensor data collection that does not require advanced technological know-how. We are aiming to make wireless sensor networks for environmental science as simple as setting up a standard home computer network by providing simple, tested configurations of commercially-available hardware, free and easy-to-use software, and step-by-step tutorials. We designed and built SensorKit using a simplicity-through-sophistication approach, supplying users a powerful sensor to database end-to-end system with a simple and intuitive user interface. Our objective in building SensorKit was to make the prospect of using environmental sensor networks as simple as possible. We built SensorKit from off the shelf hardware components, using the Compact RIO platform from National Instruments for data acquisition due to its modular architecture and flexibility to support a large number of sensor types. In SensorKit, we support various types of analog, digital and networked sensors. Our modular software architecture allows us to abstract sensor details and provide users a common way to acquire data and to command different types of sensors. SensorKit is built on top of the Sensor Processing and Acquisition Network (SPAN), a modular framework for acquiring data in the field, moving it reliably to the scientist institution, and storing it in an easily-accessible database. SPAN allows real-time access to the data in the field by providing various options for long haul communication, such as cellular and satellite links. Our system also features reliable data storage and transmission, using a custody transfer mechanism that ensures data is retained until successful delivery to the scientist can be confirmed. The ability for the scientist to communicate in real-time with the sensor network in the field enables remote sensor reconfiguration and system health and status monitoring. We use a spiral approach of design, test, deploy and revise, and, by going to the field frequently and getting feedback from field scientists, we have been able to include additional functionality that is useful to the scientist while ensuring SensorKit remains intuitive to operate. Users can configure, control, and monitor SensorKit using a number of tools we have developed. An intuitive user interface running on a desktop or laptop allows scientists to setup the system, add and configure sensors, and specify when and how the data will be collected. We also have a mobile version of our interface that runs on a PDA and lets scientists calibrate sensors and "tune" the system while in the field, allowing for data validation before leaving the field and returning to the research lab. SensorKit also features SensorBase, an intuitive user interface built on top of a standard SQL database, which allows scientists to store and share their data with other researchers. SensorKit has been used for diverse scientific applications and deployed throughout the world: from studying mercury cycling in rice paddies in China, to ecological research in the neotropical rainforests of Costa Rica, to monitoring the contamination of salt lakes in Argentina.

Intelligence is associated with the modular structure of intrinsic brain networks.

PubMed

Hilger, Kirsten; Ekman, Matthias; Fiebach, Christian J; Basten, Ulrike

2017-11-22

General intelligence is a psychological construct that captures in a single metric the overall level of behavioural and cognitive performance in an individual. While previous research has attempted to localise intelligence in circumscribed brain regions, more recent work focuses on functional interactions between regions. However, even though brain networks are characterised by substantial modularity, it is unclear whether and how the brain's modular organisation is associated with general intelligence. Modelling subject-specific brain network graphs from functional MRI resting-state data (N = 309), we found that intelligence was not associated with global modularity features (e.g., number or size of modules) or the whole-brain proportions of different node types (e.g., connector hubs or provincial hubs). In contrast, we observed characteristic associations between intelligence and node-specific measures of within- and between-module connectivity, particularly in frontal and parietal brain regions that have previously been linked to intelligence. We propose that the connectivity profile of these regions may shape intelligence-relevant aspects of information processing. Our data demonstrate that not only region-specific differences in brain structure and function, but also the network-topological embedding of fronto-parietal as well as other cortical and subcortical brain regions is related to individual differences in higher cognitive abilities, i.e., intelligence.

A neural network with modular hierarchical learning

NASA Technical Reports Server (NTRS)

Baldi, Pierre F. (Inventor); Toomarian, Nikzad (Inventor)

1994-01-01

This invention provides a new hierarchical approach for supervised neural learning of time dependent trajectories. The modular hierarchical methodology leads to architectures which are more structured than fully interconnected networks. The networks utilize a general feedforward flow of information and sparse recurrent connections to achieve dynamic effects. The advantages include the sparsity of units and connections, the modular organization. A further advantage is that the learning is much more circumscribed learning than in fully interconnected systems. The present invention is embodied by a neural network including a plurality of neural modules each having a pre-established performance capability wherein each neural module has an output outputting present results of the performance capability and an input for changing the present results of the performance capabilitiy. For pattern recognition applications, the performance capability may be an oscillation capability producing a repeating wave pattern as the present results. In the preferred embodiment, each of the plurality of neural modules includes a pre-established capability portion and a performance adjustment portion connected to control the pre-established capability portion.

Detecting communities using asymptotical surprise

NASA Astrophysics Data System (ADS)

Traag, V. A.; Aldecoa, R.; Delvenne, J.-C.

2015-08-01

Nodes in real-world networks are repeatedly observed to form dense clusters, often referred to as communities. Methods to detect these groups of nodes usually maximize an objective function, which implicitly contains the definition of a community. We here analyze a recently proposed measure called surprise, which assesses the quality of the partition of a network into communities. In its current form, the formulation of surprise is rather difficult to analyze. We here therefore develop an accurate asymptotic approximation. This allows for the development of an efficient algorithm for optimizing surprise. Incidentally, this leads to a straightforward extension of surprise to weighted graphs. Additionally, the approximation makes it possible to analyze surprise more closely and compare it to other methods, especially modularity. We show that surprise is (nearly) unaffected by the well-known resolution limit, a particular problem for modularity. However, surprise may tend to overestimate the number of communities, whereas they may be underestimated by modularity. In short, surprise works well in the limit of many small communities, whereas modularity works better in the limit of few large communities. In this sense, surprise is more discriminative than modularity and may find communities where modularity fails to discern any structure.

Cutting the Wires: Modularization of Cellular Networks for Experimental Design

PubMed Central

Lang, Moritz; Summers, Sean; Stelling, Jörg

2014-01-01

Understanding naturally evolved cellular networks requires the consecutive identification and revision of the interactions between relevant molecular species. In this process, initially often simplified and incomplete networks are extended by integrating new reactions or whole subnetworks to increase consistency between model predictions and new measurement data. However, increased consistency with experimental data alone is not sufficient to show the existence of biomolecular interactions, because the interplay of different potential extensions might lead to overall similar dynamics. Here, we present a graph-based modularization approach to facilitate the design of experiments targeted at independently validating the existence of several potential network extensions. Our method is based on selecting the outputs to measure during an experiment, such that each potential network extension becomes virtually insulated from all others during data analysis. Each output defines a module that only depends on one hypothetical network extension, and all other outputs act as virtual inputs to achieve insulation. Given appropriate experimental time-series measurements of the outputs, our modules can be analyzed, simulated, and compared to the experimental data separately. Our approach exemplifies the close relationship between structural systems identification and modularization, an interplay that promises development of related approaches in the future. PMID:24411264

A new hierarchical method to find community structure in networks

NASA Astrophysics Data System (ADS)

Saoud, Bilal; Moussaoui, Abdelouahab

2018-04-01

Community structure is very important to understand a network which represents a context. Many community detection methods have been proposed like hierarchical methods. In our study, we propose a new hierarchical method for community detection in networks based on genetic algorithm. In this method we use genetic algorithm to split a network into two networks which maximize the modularity. Each new network represents a cluster (community). Then we repeat the splitting process until we get one node at each cluster. We use the modularity function to measure the strength of the community structure found by our method, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our method are highly effective at discovering community structure in both computer-generated and real-world network data.

Social Contact Networks and Mixing among Students in K-12 Schools in Pittsburgh, PA

PubMed Central

Guclu, Hasan; Read, Jonathan; Vukotich, Charles J.; Galloway, David D.; Gao, Hongjiang; Rainey, Jeanette J.; Uzicanin, Amra; Zimmer, Shanta M.; Cummings, Derek A. T.

2016-01-01

Students attending schools play an important role in the transmission of influenza. In this study, we present a social network analysis of contacts among 1,828 students in eight different schools in urban and suburban areas in and near Pittsburgh, Pennsylvania, United States of America, including elementary, elementary-middle, middle, and high schools. We collected social contact information of students who wore wireless sensor devices that regularly recorded other devices if they are within a distance of 3 meters. We analyzed these networks to identify patterns of proximal student interactions in different classes and grades, to describe community structure within the schools, and to assess the impact of the physical environment of schools on proximal contacts. In the elementary and middle schools, we observed a high number of intra-grade and intra-classroom contacts and a relatively low number of inter-grade contacts. However, in high schools, contact networks were well connected and mixed across grades. High modularity of lower grades suggests that assumptions of homogeneous mixing in epidemic models may be inappropriate; whereas lower modularity in high schools suggests that homogenous mixing assumptions may be more acceptable in these settings. The results suggest that interventions targeting subsets of classrooms may work better in elementary schools than high schools. Our work presents quantitative measures of age-specific, school-based contacts that can be used as the basis for constructing models of the transmission of infections in schools. PMID:26978780

Social Contact Networks and Mixing among Students in K-12 Schools in Pittsburgh, PA.

PubMed

Guclu, Hasan; Read, Jonathan; Vukotich, Charles J; Galloway, David D; Gao, Hongjiang; Rainey, Jeanette J; Uzicanin, Amra; Zimmer, Shanta M; Cummings, Derek A T

2016-01-01

Students attending schools play an important role in the transmission of influenza. In this study, we present a social network analysis of contacts among 1,828 students in eight different schools in urban and suburban areas in and near Pittsburgh, Pennsylvania, United States of America, including elementary, elementary-middle, middle, and high schools. We collected social contact information of students who wore wireless sensor devices that regularly recorded other devices if they are within a distance of 3 meters. We analyzed these networks to identify patterns of proximal student interactions in different classes and grades, to describe community structure within the schools, and to assess the impact of the physical environment of schools on proximal contacts. In the elementary and middle schools, we observed a high number of intra-grade and intra-classroom contacts and a relatively low number of inter-grade contacts. However, in high schools, contact networks were well connected and mixed across grades. High modularity of lower grades suggests that assumptions of homogeneous mixing in epidemic models may be inappropriate; whereas lower modularity in high schools suggests that homogenous mixing assumptions may be more acceptable in these settings. The results suggest that interventions targeting subsets of classrooms may work better in elementary schools than high schools. Our work presents quantitative measures of age-specific, school-based contacts that can be used as the basis for constructing models of the transmission of infections in schools.

Revealing in-block nestedness: Detection and benchmarking

NASA Astrophysics Data System (ADS)

Solé-Ribalta, Albert; Tessone, Claudio J.; Mariani, Manuel S.; Borge-Holthoefer, Javier

2018-06-01

As new instances of nested organization—beyond ecological networks—are discovered, scholars are debating the coexistence of two apparently incompatible macroscale architectures: nestedness and modularity. The discussion is far from being solved, mainly for two reasons. First, nestedness and modularity appear to emerge from two contradictory dynamics, cooperation and competition. Second, existing methods to assess the presence of nestedness and modularity are flawed when it comes to the evaluation of concurrently nested and modular structures. In this work, we tackle the latter problem, presenting the concept of in-block nestedness, a structural property determining to what extent a network is composed of blocks whose internal connectivity exhibits nestedness. We then put forward a set of optimization methods that allow us to identify such organization successfully, in synthetic and in a large number of real networks. These findings challenge our understanding of the topology of ecological and social systems, calling for new models to explain how such patterns emerge.

Patterns of Twitter Behavior Among Networks of Cannabis Dispensaries in California

PubMed Central

Chew, Robert F; Hsieh, Yuli P; Bieler, Gayle S; Bobashev, Georgiy V; Siege, Christopher; Zarkin, Gary A

2017-01-01

Background Twitter represents a social media platform through which medical cannabis dispensaries can rapidly promote and advertise a multitude of retail products. Yet, to date, no studies have systematically evaluated Twitter behavior among dispensaries and how these behaviors influence the formation of social networks. Objectives This study sought to characterize common cyberbehaviors and shared follower networks among dispensaries operating in two large cannabis markets in California. Methods From a targeted sample of 119 dispensaries in the San Francisco Bay Area and Greater Los Angeles, we collected metadata from the dispensary accounts using the Twitter API. For each city, we characterized the network structure of dispensaries based upon shared followers, then empirically derived communities with the Louvain modularity algorithm. Principal components factor analysis was employed to reduce 12 Twitter measures into a more parsimonious set of cyberbehavioral dimensions. Finally, quadratic discriminant analysis was implemented to verify the ability of the extracted dimensions to classify dispensaries into their derived communities. Results The modularity algorithm yielded three communities in each city with distinct network structures. The principal components factor analysis reduced the 12 cyberbehaviors into five dimensions that encompassed account age, posting frequency, referencing, hyperlinks, and user engagement among the dispensary accounts. In the quadratic discriminant analysis, the dimensions correctly classified 75% (46/61) of the communities in the San Francisco Bay Area and 71% (41/58) in Greater Los Angeles. Conclusions The most centralized and strongly connected dispensaries in both cities had newer accounts, higher daily activity, more frequent user engagement, and increased usage of embedded media, keywords, and hyperlinks. Measures derived from both network structure and cyberbehavioral dimensions can serve as key contextual indicators for the online surveillance of cannabis dispensaries and consumer markets over time. PMID:28676471

Topological dimension tunes activity patterns in hierarchical modular networks

NASA Astrophysics Data System (ADS)

Safari, Ali; Moretti, Paolo; Muñoz, Miguel A.

2017-11-01

Connectivity patterns of relevance in neuroscience and systems biology can be encoded in hierarchical modular networks (HMNs). Recent studies highlight the role of hierarchical modular organization in shaping brain activity patterns, providing an excellent substrate to promote both segregation and integration of neural information. Here, we propose an extensive analysis of the critical spreading rate (or ‘epidemic’ threshold)—separating a phase with endemic persistent activity from one in which activity ceases—on diverse HMNs. By employing analytical and computational techniques we determine the nature of such a threshold and scrutinize how it depends on general structural features of the underlying HMN. We critically discuss the extent to which current graph-spectral methods can be applied to predict the onset of spreading in HMNs and, most importantly, we elucidate the role played by the network topological dimension as a relevant and unifying structural parameter, controlling the epidemic threshold.

Identification of Modules in Protein-Protein Interaction Networks

NASA Astrophysics Data System (ADS)

Erten, Sinan; Koyutürk, Mehmet

In biological systems, most processes are carried out through orchestration of multiple interacting molecules. These interactions are often abstracted using network models. A key feature of cellular networks is their modularity, which contributes significantly to the robustness, as well as adaptability of biological systems. Therefore, modularization of cellular networks is likely to be useful in obtaining insights into the working principles of cellular systems, as well as building tractable models of cellular organization and dynamics. A common, high-throughput source of data on molecular interactions is in the form of physical interactions between proteins, which are organized into protein-protein interaction (PPI) networks. This chapter provides an overview on identification and analysis of functional modules in PPI networks, which has been an active area of research in the last decade.

On Functional Module Detection in Metabolic Networks

PubMed Central

Koch, Ina; Ackermann, Jörg

2013-01-01

Functional modules of metabolic networks are essential for understanding the metabolism of an organism as a whole. With the vast amount of experimental data and the construction of complex and large-scale, often genome-wide, models, the computer-aided identification of functional modules becomes more and more important. Since steady states play a key role in biology, many methods have been developed in that context, for example, elementary flux modes, extreme pathways, transition invariants and place invariants. Metabolic networks can be studied also from the point of view of graph theory, and algorithms for graph decomposition have been applied for the identification of functional modules. A prominent and currently intensively discussed field of methods in graph theory addresses the Q-modularity. In this paper, we recall known concepts of module detection based on the steady-state assumption, focusing on transition-invariants (elementary modes) and their computation as minimal solutions of systems of Diophantine equations. We present the Fourier-Motzkin algorithm in detail. Afterwards, we introduce the Q-modularity as an example for a useful non-steady-state method and its application to metabolic networks. To illustrate and discuss the concepts of invariants and Q-modularity, we apply a part of the central carbon metabolism in potato tubers (Solanum tuberosum) as running example. The intention of the paper is to give a compact presentation of known steady-state concepts from a graph-theoretical viewpoint in the context of network decomposition and reduction and to introduce the application of Q-modularity to metabolic Petri net models. PMID:24958145

Gas Main Sensor and Communications Network System

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

Hagen Schempf

Automatika, Inc. was contracted by the Department of Energy (DOE) and with co-funding from the Northeast Gas Association (NGA), to develop an in-pipe natural gas prototype measurement and wireless communications system for assessing and monitoring distribution networks. This projected was completed in April 2006, and culminated in the installation of more than 2 dozen GasNet nodes in both low- and high-pressure cast-iron and steel mains owned by multiple utilities in the northeastern US. Utilities are currently logging data (off-line) and monitoring data in real time from single and multiple networked sensors over cellular networks and collecting data using wireless bluetoothmore » PDA systems. The system was designed to be modular, using in-pipe sensor-wands capable of measuring, flow, pressure, temperature, water-content and vibration. Internal antennae allowed for the use of the pipe-internals as a waveguide for setting up a sensor network to collect data from multiple nodes simultaneously. Sensor nodes were designed to be installed with low- and no-blow techniques and tools. Using a multi-drop bus technique with a custom protocol, all electronics were designed to be buriable and allow for on-board data-collection (SD-card), wireless relaying and cellular network forwarding. Installation options afforded by the design included direct-burial and external polemounted variants. Power was provided by one or more batteries, direct AC-power (Class I Div.2) and solar-array. The utilities are currently in a data-collection phase and intend to use the collected (and processed) data to make capital improvement decisions, compare it to Stoner model predictions and evaluate the use of such a system for future expansion, technology-improvement and commercialization starting later in 2006.« less

Coordinating an Autonomous Earth-Observing Sensorweb

NASA Technical Reports Server (NTRS)

Sherwood, Robert; Cichy, Benjamin; Tran, Daniel; Chien, Steve; Rabideau, Gregg; Davies, Ashley; Castano, Rebecca; frye, Stuart; Mandl, Dan; Shulman, Seth;

Using a network modularity analysis to inform management of a rare endemic plant in the northern Great Plains, USA

USGS Publications Warehouse

Larson, Diane L.; Droege, Sam; Rabie, Paul A.; Larson, Jennifer L.; Devalez, Jelle; Haar, Milton; McDermott-Kubeczko, Margaret

2014-01-01

1. Analyses of flower-visitor interaction networks allow application of community-level information to conservation problems, but management recommendations that ensue from such analyses are not well characterized. Results of modularity analyses, which detect groups of species (modules) that interact more with each other than with species outside their module, may be particularly applicable to management concerns. 2. We conducted modularity analyses of networks surrounding a rare endemic annual plant, Eriogonum visheri, at Badlands National Park, USA, in 2010 and 2011. Plant species visited were determined by pollen on insect bodies and by flower species upon which insects were captured. Roles within modules (network hub, module hub, connector and peripheral, in decreasing order of network structural importance) were determined for each species. 3. Relationships demonstrated by the modularity analysis, in concert with knowledge of pollen species carried by insects, allowed us to infer effects of two invasive species on E. visheri. Sharing a module increased risk of interspecific pollen transfer to E. visheri. Control of invasive Salsola tragus, which shared a module with E. visheri, is therefore a prudent management objective, but lack of control of invasive Melilotus officinalis, which occupied a different module, is unlikely to negatively affect pollination of E. visheri. Eriogonum pauciflorum may occupy a key position in this network, supporting insects from the E. visheri module when E. visheri is less abundant. 4. Year-to-year variation in species' roles suggests management decisions must be based on observations over several years. Information on pollen deposition on stigmas would greatly strengthen inferences made from the modularity analysis. 5. Synthesis and applications: Assessing the consequences of pollination, whether at the community or individual level, is inherently time-consuming. A trade-off exists: rather than an estimate of fitness effects, the network approach provides a broad understanding of the relationships among insect visitors and other plant species that may affect the focal rare plant. Knowledge of such relationships allows managers to detect, target and prioritize control of only the important subset of invasive species present and identify other species that may augment a rare species' population stability, such as E. pauciflorum in our study.

Mechanisms and dynamics of cooperation and competition emergence in complex networked systems

NASA Astrophysics Data System (ADS)

Gianetto, David A.

Cooperative behavior is a pervasive phenomenon in human interactions and yet how it can evolve and become established, through the selfish process of natural selection, is an enduring puzzle. These behaviors emerge when agents interact in a structured manner; even so, the key structural factors that affect cooperation are not well understood. Moreover, the literature often considers cooperation a single attribute of primitive agents who do not react to environmental changes but real-world actors are more perceptive. The present work moves beyond these assumptions by evolving more realistic game participants, with memories of the past, on complex networks. Agents play repeated games with a three-part Markovian strategy that allows us to separate the cooperation phenomenon into trust, reciprocity, and forgiveness characteristics. Our results show that networks matter most when agents gain the most by acting in a selfish manner, irrespective of how much they may lose by cooperating; since the context provided by neighborhoods inhibits greedy impulses that agents otherwise succumb to in isolation. Network modularity is the most important driver of cooperation emergence in these high-stakes games. However, modularity fails to tell the complete story. Modular scale-free graphs impede cooperation when close coordination is required, partially due to the acyclic nature of scale-free network models. To achieve the highest cooperation in diverse social conditions, both high modularity, low connectivity within modules, and a rich network of long cycles become important. With these findings in hand, we study the influence of networks on coordination and competition within the federal health care insurance exchange. In this applied study, we show that systemic health care coordination is encouraged by the emergent insurance network. The network helps underpin the viability of the exchange and provides an environment of stronger competition once a critical-mass of insurers have entered the market.

Stochastic Effects in Computational Biology of Space Radiation Cancer Risk

NASA Technical Reports Server (NTRS)

Cucinotta, Francis A.; Pluth, Janis; Harper, Jane; O'Neill, Peter

2007-01-01

Estimating risk from space radiation poses important questions on the radiobiology of protons and heavy ions. We are considering systems biology models to study radiation induced repair foci (RIRF) at low doses, in which less than one-track on average transverses the cell, and the subsequent DNA damage processing and signal transduction events. Computational approaches for describing protein regulatory networks coupled to DNA and oxidative damage sites include systems of differential equations, stochastic equations, and Monte-Carlo simulations. We review recent developments in the mathematical description of protein regulatory networks and possible approaches to radiation effects simulation. These include robustness, which states that regulatory networks maintain their functions against external and internal perturbations due to compensating properties of redundancy and molecular feedback controls, and modularity, which leads to general theorems for considering molecules that interact through a regulatory mechanism without exchange of matter leading to a block diagonal reduction of the connecting pathways. Identifying rate-limiting steps, robustness, and modularity in pathways perturbed by radiation damage are shown to be valid techniques for reducing large molecular systems to realistic computer simulations. Other techniques studied are the use of steady-state analysis, and the introduction of composite molecules or rate-constants to represent small collections of reactants. Applications of these techniques to describe spatial and temporal distributions of RIRF and cell populations following low dose irradiation are described.

Disrupted functional connectome in antisocial personality disorder.

PubMed

Jiang, Weixiong; Shi, Feng; Liao, Jian; Liu, Huasheng; Wang, Tao; Shen, Celina; Shen, Hui; Hu, Dewen; Wang, Wei; Shen, Dinggang

2017-08-01

Studies on antisocial personality disorder (ASPD) subjects focus on brain functional alterations in relation to antisocial behaviors. Neuroimaging research has identified a number of focal brain regions with abnormal structures or functions in ASPD. However, little is known about the connections among brain regions in terms of inter-regional whole-brain networks in ASPD patients, as well as possible alterations of brain functional topological organization. In this study, we employ resting-state functional magnetic resonance imaging (R-fMRI) to examine functional connectome of 32 ASPD patients and 35 normal controls by using a variety of network properties, including small-worldness, modularity, and connectivity. The small-world analysis reveals that ASPD patients have increased path length and decreased network efficiency, which implies a reduced ability of global integration of whole-brain functions. Modularity analysis suggests ASPD patients have decreased overall modularity, merged network modules, and reduced intra- and inter-module connectivities related to frontal regions. Also, network-based statistics show that an internal sub-network, composed of 16 nodes and 16 edges, is significantly affected in ASPD patients, where brain regions are mostly located in the fronto-parietal control network. These results suggest that ASPD is associated with both reduced brain integration and segregation in topological organization of functional brain networks, particularly in the fronto-parietal control network. These disruptions may contribute to disturbances in behavior and cognition in patients with ASPD. Our findings may provide insights into a deeper understanding of functional brain networks of ASPD.

Disrupted functional connectome in antisocial personality disorder

PubMed Central

Jiang, Weixiong; Shi, Feng; Liao, Jian; Liu, Huasheng; Wang, Tao; Shen, Celina; Shen, Hui; Hu, Dewen

2017-01-01

Studies on antisocial personality disorder (ASPD) subjects focus on brain functional alterations in relation to antisocial behaviors. Neuroimaging research has identified a number of focal brain regions with abnormal structures or functions in ASPD. However, little is known about the connections among brain regions in terms of inter-regional whole-brain networks in ASPD patients, as well as possible alterations of brain functional topological organization. In this study, we employ resting-state functional magnetic resonance imaging (R-fMRI) to examine functional connectome of 32 ASPD patients and 35 normal controls by using a variety of network properties, including small-worldness, modularity, and connectivity. The small-world analysis reveals that ASPD patients have increased path length and decreased network efficiency, which implies a reduced ability of global integration of whole-brain functions. Modularity analysis suggests ASPD patients have decreased overall modularity, merged network modules, and reduced intra- and inter-module connectivities related to frontal regions. Also, network-based statistics show that an internal sub-network, composed of 16 nodes and 16 edges, is significantly affected in ASPD patients, where brain regions are mostly located in the fronto-parietal control network. These results suggest that ASPD is associated with both reduced brain integration and segregation in topological organization of functional brain networks, particularly in the fronto-parietal control network. These disruptions may contribute to disturbances in behavior and cognition in patients with ASPD. Our findings may provide insights into a deeper understanding of functional brain networks of ASPD. PMID:27541949

Mass media influence spreading in social networks with community structure

NASA Astrophysics Data System (ADS)

Candia, Julián; Mazzitello, Karina I.

2008-07-01

We study an extension of Axelrod's model for social influence, in which cultural drift is represented as random perturbations, while mass media are introduced by means of an external field. In this scenario, we investigate how the modular structure of social networks affects the propagation of mass media messages across a society. The community structure of social networks is represented by coupled random networks, in which two random graphs are connected by intercommunity links. Considering inhomogeneous mass media fields, we study the conditions for successful message spreading and find a novel phase diagram in the multidimensional parameter space. These findings show that social modularity effects are of paramount importance for designing successful, cost-effective advertising campaigns.

SkyNet: Modular nuclear reaction network library

NASA Astrophysics Data System (ADS)

Lippuner, Jonas; Roberts, Luke F.

2017-10-01

The general-purpose nuclear reaction network SkyNet evolves the abundances of nuclear species under the influence of nuclear reactions. SkyNet can be used to compute the nucleosynthesis evolution in all astrophysical scenarios where nucleosynthesis occurs. Any list of isotopes can be evolved and SkyNet supports various different types of nuclear reactions. SkyNet is modular, permitting new or existing physics, such as nuclear reactions or equations of state, to be easily added or modified.

A multilevel layout algorithm for visualizing physical and genetic interaction networks, with emphasis on their modular organization.

PubMed

Tuikkala, Johannes; Vähämaa, Heidi; Salmela, Pekka; Nevalainen, Olli S; Aittokallio, Tero

2012-03-26

Graph drawing is an integral part of many systems biology studies, enabling visual exploration and mining of large-scale biological networks. While a number of layout algorithms are available in popular network analysis platforms, such as Cytoscape, it remains poorly understood how well their solutions reflect the underlying biological processes that give rise to the network connectivity structure. Moreover, visualizations obtained using conventional layout algorithms, such as those based on the force-directed drawing approach, may become uninformative when applied to larger networks with dense or clustered connectivity structure. We implemented a modified layout plug-in, named Multilevel Layout, which applies the conventional layout algorithms within a multilevel optimization framework to better capture the hierarchical modularity of many biological networks. Using a wide variety of real life biological networks, we carried out a systematic evaluation of the method in comparison with other layout algorithms in Cytoscape. The multilevel approach provided both biologically relevant and visually pleasant layout solutions in most network types, hence complementing the layout options available in Cytoscape. In particular, it could improve drawing of large-scale networks of yeast genetic interactions and human physical interactions. In more general terms, the biological evaluation framework developed here enables one to assess the layout solutions from any existing or future graph drawing algorithm as well as to optimize their performance for a given network type or structure. By making use of the multilevel modular organization when visualizing biological networks, together with the biological evaluation of the layout solutions, one can generate convenient visualizations for many network biology applications.

Complexity and dynamics of topological and community structure in complex networks

NASA Astrophysics Data System (ADS)

Berec, Vesna

2017-07-01

Complexity is highly susceptible to variations in the network dynamics, reflected on its underlying architecture where topological organization of cohesive subsets into clusters, system's modular structure and resulting hierarchical patterns, are cross-linked with functional dynamics of the system. Here we study connection between hierarchical topological scales of the simplicial complexes and the organization of functional clusters - communities in complex networks. The analysis reveals the full dynamics of different combinatorial structures of q-th-dimensional simplicial complexes and their Laplacian spectra, presenting spectral properties of resulting symmetric and positive semidefinite matrices. The emergence of system's collective behavior from inhomogeneous statistical distribution is induced by hierarchically ordered topological structure, which is mapped to simplicial complex where local interactions between the nodes clustered into subcomplexes generate flow of information that characterizes complexity and dynamics of the full system.

Development of modularity in the neural activity of childrenʼs brains

NASA Astrophysics Data System (ADS)

Chen, Man; Deem, Michael W.

2015-02-01

We study how modularity of the human brain changes as children develop into adults. Theory suggests that modularity can enhance the response function of a networked system subject to changing external stimuli. Thus, greater cognitive performance might be achieved for more modular neural activity, and modularity might likely increase as children develop. The value of modularity calculated from functional magnetic resonance imaging (fMRI) data is observed to increase during childhood development and peak in young adulthood. Head motion is deconvolved from the fMRI data, and it is shown that the dependence of modularity on age is independent of the magnitude of head motion. A model is presented to illustrate how modularity can provide greater cognitive performance at short times, i.e. task switching. A fitness function is extracted from the model. Quasispecies theory is used to predict how the average modularity evolves with age, illustrating the increase of modularity during development from children to adults that arises from selection for rapid cognitive function in young adults. Experiments exploring the effect of modularity on cognitive performance are suggested. Modularity may be a potential biomarker for injury, rehabilitation, or disease.

Cutting the wires: modularization of cellular networks for experimental design.

PubMed

Lang, Moritz; Summers, Sean; Stelling, Jörg

2014-01-07

Understanding naturally evolved cellular networks requires the consecutive identification and revision of the interactions between relevant molecular species. In this process, initially often simplified and incomplete networks are extended by integrating new reactions or whole subnetworks to increase consistency between model predictions and new measurement data. However, increased consistency with experimental data alone is not sufficient to show the existence of biomolecular interactions, because the interplay of different potential extensions might lead to overall similar dynamics. Here, we present a graph-based modularization approach to facilitate the design of experiments targeted at independently validating the existence of several potential network extensions. Our method is based on selecting the outputs to measure during an experiment, such that each potential network extension becomes virtually insulated from all others during data analysis. Each output defines a module that only depends on one hypothetical network extension, and all other outputs act as virtual inputs to achieve insulation. Given appropriate experimental time-series measurements of the outputs, our modules can be analyzed, simulated, and compared to the experimental data separately. Our approach exemplifies the close relationship between structural systems identification and modularization, an interplay that promises development of related approaches in the future. Copyright © 2014 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Connectome sensitivity or specificity: which is more important?

PubMed

Zalesky, Andrew; Fornito, Alex; Cocchi, Luca; Gollo, Leonardo L; van den Heuvel, Martijn P; Breakspear, Michael

2016-11-15

Connectomes with high sensitivity and high specificity are unattainable with current axonal fiber reconstruction methods, particularly at the macro-scale afforded by magnetic resonance imaging. Tensor-guided deterministic tractography yields sparse connectomes that are incomplete and contain false negatives (FNs), whereas probabilistic methods steered by crossing-fiber models yield dense connectomes, often with low specificity due to false positives (FPs). Densely reconstructed probabilistic connectomes are typically thresholded to improve specificity at the cost of a reduction in sensitivity. What is the optimal tradeoff between connectome sensitivity and specificity? We show empirically and theoretically that specificity is paramount. Our evaluations of the impact of FPs and FNs on empirical connectomes indicate that specificity is at least twice as important as sensitivity when estimating key properties of brain networks, including topological measures of network clustering, network efficiency and network modularity. Our asymptotic analysis of small-world networks with idealized modular structure reveals that as the number of nodes grows, specificity becomes exactly twice as important as sensitivity to the estimation of the clustering coefficient. For the estimation of network efficiency, the relative importance of specificity grows linearly with the number of nodes. The greater importance of specificity is due to FPs occurring more prevalently between network modules rather than within them. These spurious inter-modular connections have a dramatic impact on network topology. We argue that efforts to maximize the sensitivity of connectome reconstruction should be realigned with the need to map brain networks with high specificity. Copyright © 2016 Elsevier Inc. All rights reserved.

The Conundrum of Functional Brain Networks: Small-World Efficiency or Fractal Modularity

PubMed Central

Gallos, Lazaros K.; Sigman, Mariano; Makse, Hernán A.

2012-01-01

The human brain has been studied at multiple scales, from neurons, circuits, areas with well-defined anatomical and functional boundaries, to large-scale functional networks which mediate coherent cognition. In a recent work, we addressed the problem of the hierarchical organization in the brain through network analysis. Our analysis identified functional brain modules of fractal structure that were inter-connected in a small-world topology. Here, we provide more details on the use of network science tools to elaborate on this behavior. We indicate the importance of using percolation theory to highlight the modular character of the functional brain network. These modules present a fractal, self-similar topology, identified through fractal network methods. When we lower the threshold of correlations to include weaker ties, the network as a whole assumes a small-world character. These weak ties are organized precisely as predicted by theory maximizing information transfer with minimal wiring costs. PMID:22586406

Modular multiplication in GF(p) for public-key cryptography

NASA Astrophysics Data System (ADS)

Olszyna, Jakub

Modular multiplication forms the basis of modular exponentiation which is the core operation of the RSA cryptosystem. It is also present in many other cryptographic algorithms including those based on ECC and HECC. Hence, an efficient implementation of PKC relies on efficient implementation of modular multiplication. The paper presents a survey of most common algorithms for modular multiplication along with hardware architectures especially suitable for cryptographic applications in energy constrained environments. The motivation for studying low-power and areaefficient modular multiplication algorithms comes from enabling public-key security for ultra-low power devices that can perform under constrained environments like wireless sensor networks. Serial architectures for GF(p) are analyzed and presented. Finally proposed architectures are verified and compared according to the amount of power dissipated throughout the operation.

DISTILLER: a data integration framework to reveal condition dependency of complex regulons in Escherichia coli.

PubMed

Lemmens, Karen; De Bie, Tijl; Dhollander, Thomas; De Keersmaecker, Sigrid C; Thijs, Inge M; Schoofs, Geert; De Weerdt, Ami; De Moor, Bart; Vanderleyden, Jos; Collado-Vides, Julio; Engelen, Kristof; Marchal, Kathleen

2009-01-01

We present DISTILLER, a data integration framework for the inference of transcriptional module networks. Experimental validation of predicted targets for the well-studied fumarate nitrate reductase regulator showed the effectiveness of our approach in Escherichia coli. In addition, the condition dependency and modularity of the inferred transcriptional network was studied. Surprisingly, the level of regulatory complexity seemed lower than that which would be expected from RegulonDB, indicating that complex regulatory programs tend to decrease the degree of modularity.

Modular biological function is most effectively captured by combining molecular interaction data types.

PubMed

Ames, Ryan M; Macpherson, Jamie I; Pinney, John W; Lovell, Simon C; Robertson, David L

2013-01-01

Large-scale molecular interaction data sets have the potential to provide a comprehensive, system-wide understanding of biological function. Although individual molecules can be promiscuous in terms of their contribution to function, molecular functions emerge from the specific interactions of molecules giving rise to modular organisation. As functions often derive from a range of mechanisms, we demonstrate that they are best studied using networks derived from different sources. Implementing a graph partitioning algorithm we identify subnetworks in yeast protein-protein interaction (PPI), genetic interaction and gene co-regulation networks. Among these subnetworks we identify cohesive subgraphs that we expect to represent functional modules in the different data types. We demonstrate significant overlap between the subgraphs generated from the different data types and show these overlaps can represent related functions as represented by the Gene Ontology (GO). Next, we investigate the correspondence between our subgraphs and the Gene Ontology. This revealed varying degrees of coverage of the biological process, molecular function and cellular component ontologies, dependent on the data type. For example, subgraphs from the PPI show enrichment for 84%, 58% and 93% of annotated GO terms, respectively. Integrating the interaction data into a combined network increases the coverage of GO. Furthermore, the different annotation types of GO are not predominantly associated with one of the interaction data types. Collectively our results demonstrate that successful capture of functional relationships by network data depends on both the specific biological function being characterised and the type of network data being used. We identify functions that require integrated information to be accurately represented, demonstrating the limitations of individual data types. Combining interaction subnetworks across data types is therefore essential for fully understanding the complex and emergent nature of biological function.

Individual nodeʼs contribution to the mesoscale of complex networks

NASA Astrophysics Data System (ADS)

Klimm, Florian; Borge-Holthoefer, Javier; Wessel, Niels; Kurths, Jürgen; Zamora-López, Gorka

2014-12-01

The analysis of complex networks is devoted to the statistical characterization of the topology of graphs at different scales of organization in order to understand their functionality. While the modular structure of networks has become an essential element to better apprehend their complexity, the efforts to characterize the mesoscale of networks have focused on the identification of the modules rather than describing the mesoscale in an informative manner. Here we propose a framework to characterize the position every node takes within the modular configuration of complex networks and to evaluate their function accordingly. For illustration, we apply this framework to a set of synthetic networks, empirical neural networks, and to the transcriptional regulatory network of the Mycobacterium tuberculosis. We find that the architecture of both neuronal and transcriptional networks are optimized for the processing of multisensory information with the coexistence of well-defined modules of specialized components and the presence of hubs conveying information from and to the distinct functional domains.

Integration of multi-interface conversion channel using FPGA for modular photonic network

NASA Astrophysics Data System (ADS)

Janicki, Tomasz; Pozniak, Krzysztof T.; Romaniuk, Ryszard S.

2010-09-01

The article discusses the integration of different types of interfaces with FPGA circuits using a reconfigurable communication platform. The solution has been implemented in practice in a single node of a distributed measurement system. Construction of communication platform has been presented with its selected hardware modules, described in VHDL and implemented in FPGA circuits. The graphical user interface (GUI) has been described that allows a user to control the operation of the system. In the final part of the article selected practical solutions have been introduced. The whole measurement system resides on multi-gigabit optical network. The optical network construction is highly modular, reconfigurable and scalable.

Model of community emergence in weighted social networks

NASA Astrophysics Data System (ADS)

Kumpula, J. M.; Onnela, J.-P.; Saramäki, J.; Kertész, J.; Kaski, K.

2009-04-01

Over the years network theory has proven to be rapidly expanding methodology to investigate various complex systems and it has turned out to give quite unparalleled insight to their structure, function, and response through data analysis, modeling, and simulation. For social systems in particular the network approach has empirically revealed a modular structure due to interplay between the network topology and link weights between network nodes or individuals. This inspired us to develop a simple network model that could catch some salient features of mesoscopic community and macroscopic topology formation during network evolution. Our model is based on two fundamental mechanisms of network sociology for individuals to find new friends, namely cyclic closure and focal closure, which are mimicked by local search-link-reinforcement and random global attachment mechanisms, respectively. In addition we included to the model a node deletion mechanism by removing all its links simultaneously, which corresponds for an individual to depart from the network. Here we describe in detail the implementation of our model algorithm, which was found to be computationally efficient and produce many empirically observed features of large-scale social networks. Thus this model opens a new perspective for studying such collective social phenomena as spreading, structure formation, and evolutionary processes.

A protein domain-based interactome network for C. elegans early embryogenesis

PubMed Central

Boxem, Mike; Maliga, Zoltan; Klitgord, Niels; Li, Na; Lemmens, Irma; Mana, Miyeko; de Lichtervelde, Lorenzo; Mul, Joram D.; van de Peut, Diederik; Devos, Maxime; Simonis, Nicolas; Yildirim, Muhammed A.; Cokol, Murat; Kao, Huey-Ling; de Smet, Anne-Sophie; Wang, Haidong; Schlaitz, Anne-Lore; Hao, Tong; Milstein, Stuart; Fan, Changyu; Tipsword, Mike; Drew, Kevin; Galli, Matilde; Rhrissorrakrai, Kahn; Drechsel, David; Koller, Daphne; Roth, Frederick P.; Iakoucheva, Lilia M.; Dunker, A. Keith; Bonneau, Richard; Gunsalus, Kristin C.; Hill, David E.; Piano, Fabio; Tavernier, Jan; van den Heuvel, Sander; Hyman, Anthony A.; Vidal, Marc

2008-01-01

Summary Many protein-protein interactions are mediated through independently folding modular domains. Proteome-wide efforts to model protein-protein interaction or “interactome” networks have largely ignored this modular organization of proteins. We developed an experimental strategy to efficiently identify interaction domains and generated a domain-based interactome network for proteins involved in C. elegans early embryonic cell divisions. Minimal interacting regions were identified for over 200 proteins, providing important information on their domain organization. Furthermore, our approach increased the sensitivity of the two-hybrid system, resulting in a more complete interactome network. This interactome modeling strategy revealed new insights into C. elegans centrosome function and is applicable to other biological processes in this and other organisms. PMID:18692475

VoIP attacks detection engine based on neural network

NASA Astrophysics Data System (ADS)

Safarik, Jakub; Slachta, Jiri

2015-05-01

The security is crucial for any system nowadays, especially communications. One of the most successful protocols in the field of communication over IP networks is Session Initiation Protocol. It is an open-source project used by different kinds of applications, both open-source and proprietary. High penetration and text-based principle made SIP number one target in IP telephony infrastructure, so security of SIP server is essential. To keep up with hackers and to detect potential malicious attacks, security administrator needs to monitor and evaluate SIP traffic in the network. But monitoring and following evaluation could easily overwhelm the security administrator in networks, typically in networks with a number of SIP servers, users and logically or geographically separated networks. The proposed solution lies in automatic attack detection systems. The article covers detection of VoIP attacks through a distributed network of nodes. Then the gathered data analyze aggregation server with artificial neural network. Artificial neural network means multilayer perceptron network trained with a set of collected attacks. Attack data could also be preprocessed and verified with a self-organizing map. The source data is detected by distributed network of detection nodes. Each node contains a honeypot application and traffic monitoring mechanism. Aggregation of data from each node creates an input for neural networks. The automatic classification on a centralized server with low false positive detection reduce the cost of attack detection resources. The detection system uses modular design for easy deployment in final infrastructure. The centralized server collects and process detected traffic. It also maintains all detection nodes.

Evolution of synthetic signaling scaffolds by recombination of modular protein domains.

PubMed

Lai, Andicus; Sato, Paloma M; Peisajovich, Sergio G

2015-06-19

Signaling scaffolds are proteins that interact via modular domains with multiple partners, regulating signaling networks in space and time and providing an ideal platform from which to alter signaling functions. However, to better exploit scaffolds for signaling engineering, it is necessary to understand the full extent of their modularity. We used a directed evolution approach to identify, from a large library of randomly shuffled protein interaction domains, variants capable of rescuing the signaling defect of a yeast strain in which Ste5, the scaffold in the mating pathway, had been deleted. After a single round of selection, we identified multiple synthetic scaffold variants with diverse domain architectures, able to mediate mating pathway activation in a pheromone-dependent manner. The facility with which this signaling network accommodates changes in scaffold architecture suggests that the mating signaling complex does not possess a single, precisely defined geometry into which the scaffold has to fit. These relaxed geometric constraints may facilitate the evolution of signaling networks, as well as their engineering for applications in synthetic biology.

An algorithm for modularization of MAPK and calcium signaling pathways: comparative analysis among different species.

PubMed

Nayak, Losiana; De, Rajat K

2007-12-01

Signaling pathways are large complex biochemical networks. It is difficult to analyze the underlying mechanism of such networks as a whole. In the present article, we have proposed an algorithm for modularization of signal transduction pathways. Unlike studying a signaling pathway as a whole, this enables one to study the individual modules (less complex smaller units) easily and hence to study the entire pathway better. A comparative study of modules belonging to different species (for the same signaling pathway) has been made, which gives an overall idea about development of the signaling pathways over the taken set of species of calcium and MAPK signaling pathways. The superior performance, in terms of biological significance, of the proposed algorithm over an existing community finding algorithm of Newman [Newman MEJ. Modularity and community structure in networks. Proc Natl Acad Sci USA 2006;103(23):8577-82] has been demonstrated using the aforesaid pathways of H. sapiens.

A multilevel layout algorithm for visualizing physical and genetic interaction networks, with emphasis on their modular organization

PubMed Central

2012-01-01

Background Graph drawing is an integral part of many systems biology studies, enabling visual exploration and mining of large-scale biological networks. While a number of layout algorithms are available in popular network analysis platforms, such as Cytoscape, it remains poorly understood how well their solutions reflect the underlying biological processes that give rise to the network connectivity structure. Moreover, visualizations obtained using conventional layout algorithms, such as those based on the force-directed drawing approach, may become uninformative when applied to larger networks with dense or clustered connectivity structure. Methods We implemented a modified layout plug-in, named Multilevel Layout, which applies the conventional layout algorithms within a multilevel optimization framework to better capture the hierarchical modularity of many biological networks. Using a wide variety of real life biological networks, we carried out a systematic evaluation of the method in comparison with other layout algorithms in Cytoscape. Results The multilevel approach provided both biologically relevant and visually pleasant layout solutions in most network types, hence complementing the layout options available in Cytoscape. In particular, it could improve drawing of large-scale networks of yeast genetic interactions and human physical interactions. In more general terms, the biological evaluation framework developed here enables one to assess the layout solutions from any existing or future graph drawing algorithm as well as to optimize their performance for a given network type or structure. Conclusions By making use of the multilevel modular organization when visualizing biological networks, together with the biological evaluation of the layout solutions, one can generate convenient visualizations for many network biology applications. PMID:22448851

Community Landscapes: An Integrative Approach to Determine Overlapping Network Module Hierarchy, Identify Key Nodes and Predict Network Dynamics

PubMed Central

Kovács, István A.; Palotai, Robin; Szalay, Máté S.; Csermely, Peter

2010-01-01

Background Network communities help the functional organization and evolution of complex networks. However, the development of a method, which is both fast and accurate, provides modular overlaps and partitions of a heterogeneous network, has proven to be rather difficult. Methodology/Principal Findings Here we introduce the novel concept of ModuLand, an integrative method family determining overlapping network modules as hills of an influence function-based, centrality-type community landscape, and including several widely used modularization methods as special cases. As various adaptations of the method family, we developed several algorithms, which provide an efficient analysis of weighted and directed networks, and (1) determine pervasively overlapping modules with high resolution; (2) uncover a detailed hierarchical network structure allowing an efficient, zoom-in analysis of large networks; (3) allow the determination of key network nodes and (4) help to predict network dynamics. Conclusions/Significance The concept opens a wide range of possibilities to develop new approaches and applications including network routing, classification, comparison and prediction. PMID:20824084

A modular optical sensor

NASA Astrophysics Data System (ADS)

Conklin, John Albert

This dissertation presents the design of a modular, fiber-optic sensor and the results obtained from testing the modular sensor. The modular fiber-optic sensor is constructed in such manner that the sensor diaphragm can be replaced with different configurations to detect numerous physical phenomena. Additionally, different fiber-optic detection systems can be attached to the sensor. Initially, the modular sensor was developed to be used by university of students to investigate realistic optical sensors and detection systems to prepare for advance studies of micro-optical mechanical systems (MOMS). The design accomplishes this by doing two things. First, the design significantly lowers the costs associated with studying optical sensors by modularizing the sensor design. Second, the sensor broadens the number of physical phenomena that students can apply optical sensing techniques to in a fiber optics sensor course. The dissertation is divided into seven chapters covering the historical development of fiber-optic sensors, a theoretical overview of fiber-optic sensors, the design, fabrication, and the testing of the modular sensor developed in the course of this work. Chapter 1 discusses, in detail, how this dissertation is organized and states the purpose of the dissertation. Chapter 2 presents an historical overview of the development of optical fibers, optical pressure sensors, and fibers, optical pressure sensors, and optical microphones. Chapter 3 reviews the theory of multi-fiber optic detection systems, optical microphones, and pressure sensors. Chapter 4 presents the design details of the modular, optical sensor. Chapter 5 delves into how the modular sensor is fabricated and how the detection systems are constructed. Chapter 6 presents the data collected from the microphone and pressure sensor configurations of the modular sensor. Finally, Chapter 7 discusses the data collected and draws conclusions about the design based on the data collected. Chapter 7 also presents future work needed to expand the functionality and utility of the modular sensor.

Hierarchical functional modularity in the resting-state human brain.

PubMed

Ferrarini, Luca; Veer, Ilya M; Baerends, Evelinda; van Tol, Marie-José; Renken, Remco J; van der Wee, Nic J A; Veltman, Dirk J; Aleman, André; Zitman, Frans G; Penninx, Brenda W J H; van Buchem, Mark A; Reiber, Johan H C; Rombouts, Serge A R B; Milles, Julien

2009-07-01

Functional magnetic resonance imaging (fMRI) studies have shown that anatomically distinct brain regions are functionally connected during the resting state. Basic topological properties in the brain functional connectivity (BFC) map have highlighted the BFC's small-world topology. Modularity, a more advanced topological property, has been hypothesized to be evolutionary advantageous, contributing to adaptive aspects of anatomical and functional brain connectivity. However, current definitions of modularity for complex networks focus on nonoverlapping clusters, and are seriously limited by disregarding inclusive relationships. Therefore, BFC's modularity has been mainly qualitatively investigated. Here, we introduce a new definition of modularity, based on a recently improved clustering measurement, which overcomes limitations of previous definitions, and apply it to the study of BFC in resting state fMRI of 53 healthy subjects. Results show hierarchical functional modularity in the brain. Copyright 2009 Wiley-Liss, Inc

Emergence of structural patterns out of synchronization in networks with competitive interactions

NASA Astrophysics Data System (ADS)

Assenza, Salvatore; Gutiérrez, Ricardo; Gómez-Gardeñes, Jesús; Latora, Vito; Boccaletti, Stefano

2011-09-01

Synchronization is a collective phenomenon occurring in systems of interacting units, and is ubiquitous in nature, society and technology. Recent studies have enlightened the important role played by the interaction topology on the emergence of synchronized states. However, most of these studies neglect that real world systems change their interaction patterns in time. Here, we analyze synchronization features in networks in which structural and dynamical features co-evolve. The feedback of the node dynamics on the interaction pattern is ruled by the competition of two mechanisms: homophily (reinforcing those interactions with other correlated units in the graph) and homeostasis (preserving the value of the input strength received by each unit). The competition between these two adaptive principles leads to the emergence of key structural properties observed in real world networks, such as modular and scale-free structures, together with a striking enhancement of local synchronization in systems with no global order.

Impact of biased scores on ranking in bipartite competition networks and inference of modular structure via generalized modularity

NASA Astrophysics Data System (ADS)

Jeon, Gyuhyeon; Park, Juyong

2017-02-01

In the common jury-contestant competition format, a jury consisting of multiple judges grade contestants on their performances to determine their ranking. Unlike in another common competition format where two contestants play a head-to-head match to produce the winner such as in football or basketball, the objectivity of judges are often called into question, potentially undermining the public's trust in the fairness of the competition. In this work we show, by modeling the jury-contestant competition format as a weighted bipartite network, how one can identify biased scores and how they impact the competition and its structure. Analyzing the prestigious International Chopin Piano Competition of 2015 as an example with a well-publicized scoring controversy, we show that the presence of even a very small fraction of biased edges can gravely distort our inference of the network structure —in the example a single biased edge is shown to lead to an incorrect “solution” that also wrongly appears to be robust exclusively, dominating other reasonable solutions— highlighting the importance of bias detection and elimination in network inference. In the process our work also presents a modified modularity measure for the one-mode projection of weighted complete bipartite networks.

Modular biowaste monitoring system

NASA Technical Reports Server (NTRS)

Fogal, G. L.

1975-01-01

The objective of the Modular Biowaste Monitoring System Program was to generate and evaluate hardware for supporting shuttle life science experimental and diagnostic programs. An initial conceptual design effort established requirements and defined an overall modular system for the collection, measurement, sampling and storage of urine and feces biowastes. This conceptual design effort was followed by the design, fabrication and performance evaluation of a flight prototype model urine collection, volume measurement and sampling capability. No operational or performance deficiencies were uncovered as a result of the performance evaluation tests.

Dynamic multiprotein assemblies shape the spatial structure of cell signaling.

PubMed

Nussinov, Ruth; Jang, Hyunbum

2014-01-01

Cell signaling underlies critical cellular decisions. Coordination, efficiency as well as fail-safe mechanisms are key elements. How the cell ensures that these hallmarks are at play are important questions. Cell signaling is often viewed as taking place through discrete and cross-talking pathways; oftentimes these are modularized to emphasize distinct functions. While simple, convenient and clear, such models largely neglect the spatial structure of cell signaling; they also convey inter-modular (or inter-protein) spatial separation that may not exist. Here our thesis is that cell signaling is shaped by a network of multiprotein assemblies. While pre-organized, the assemblies and network are loose and dynamic. They contain transiently-associated multiprotein complexes which are often mediated by scaffolding proteins. They are also typically anchored in the membrane, and their continuum may span the cell. IQGAP1 scaffolding protein which binds proteins including Raf, calmodulin, Mek, Erk, actin, and tens more, with actin shaping B-cell (and likely other) membrane-anchored nanoclusters and allosterically polymerizing in dynamic cytoskeleton formation, and Raf anchoring in the membrane along with Ras, provides a striking example. The multivalent network of dynamic proteins and lipids, with specific interactions forming and breaking, can be viewed as endowing gel-like properties. Collectively, this reasons that efficient, productive and reliable cell signaling takes place primarily through transient, preorganized and cooperative protein-protein interactions spanning the cell rather than stochastic, diffusion-controlled processes. Copyright © 2014 Elsevier Ltd. All rights reserved.

Modular Bayesian Networks with Low-Power Wearable Sensors for Recognizing Eating Activities.

PubMed

Kim, Kee-Hoon; Cho, Sung-Bae

2017-12-11

Recently, recognizing a user's daily activity using a smartphone and wearable sensors has become a popular issue. However, in contrast with the ideal definition of an experiment, there could be numerous complex activities in real life with respect to its various background and contexts: time, space, age, culture, and so on. Recognizing these complex activities with limited low-power sensors, considering the power and memory constraints of the wearable environment and the user's obtrusiveness at once is not an easy problem, although it is very crucial for the activity recognizer to be practically useful. In this paper, we recognize activity of eating, which is one of the most typical examples of a complex activity, using only daily low-power mobile and wearable sensors. To organize the related contexts systemically, we have constructed the context model based on activity theory and the "Five W's", and propose a Bayesian network with 88 nodes to predict uncertain contexts probabilistically. The structure of the proposed Bayesian network is designed by a modular and tree-structured approach to reduce the time complexity and increase the scalability. To evaluate the proposed method, we collected the data with 10 different activities from 25 volunteers of various ages, occupations, and jobs, and have obtained 79.71% accuracy, which outperforms other conventional classifiers by 7.54-14.4%. Analyses of the results showed that our probabilistic approach could also give approximate results even when one of contexts or sensor values has a very heterogeneous pattern or is missing.

Essential protein discovery based on a combination of modularity and conservatism.

PubMed

Zhao, Bihai; Wang, Jianxin; Li, Xueyong; Wu, Fang-Xiang

2016-11-01

Essential proteins are indispensable for the survival of a living organism and play important roles in the emerging field of synthetic biology. Many computational methods have been proposed to identify essential proteins by using the topological features of interactome networks. However, most of these methods ignored intrinsic biological meaning of proteins. Researches show that essentiality is tied not only to the protein or gene itself, but also to the molecular modules to which that protein belongs. The results of this study reveal the modularity of essential proteins. On the other hand, essential proteins are more evolutionarily conserved than nonessential proteins and frequently bind each other. That is to say, conservatism is another important feature of essential proteins. Multiple networks are constructed by integrating protein-protein interaction (PPI) networks, time course gene expression data and protein domain information. Based on these networks, a new essential protein identification method is proposed based on a combination of modularity and conservatism of proteins. Experimental results show that the proposed method outperforms other essential protein identification methods in terms of a number essential protein out of top ranked candidates. Copyright © 2016. Published by Elsevier Inc.

Reliability studies of Integrated Modular Engine system designs

NASA Technical Reports Server (NTRS)

Hardy, Terry L.; Rapp, Douglas C.

1993-01-01

A study was performed to evaluate the reliability of Integrated Modular Engine (IME) concepts. Comparisons were made between networked IME systems and non-networked discrete systems using expander cycle configurations. Both redundant and non-redundant systems were analyzed. Binomial approximation and Markov analysis techniques were employed to evaluate total system reliability. In addition, Failure Modes and Effects Analyses (FMEA), Preliminary Hazard Analyses (PHA), and Fault Tree Analysis (FTA) were performed to allow detailed evaluation of the IME concept. A discussion of these system reliability concepts is also presented.

Reliability studies of integrated modular engine system designs

NASA Technical Reports Server (NTRS)

Hardy, Terry L.; Rapp, Douglas C.

1993-01-01

A study was performed to evaluate the reliability of Integrated Modular Engine (IME) concepts. Comparisons were made between networked IME systems and non-networked discrete systems using expander cycle configurations. Both redundant and non-redundant systems were analyzed. Binomial approximation and Markov analysis techniques were employed to evaluate total system reliability. In addition, Failure Modes and Effects Analyses (FMEA), Preliminary Hazard Analyses (PHA), and Fault Tree Analysis (FTA) were performed to allow detailed evaluation of the IME concept. A discussion of these system reliability concepts is also presented.

DISTILLER: a data integration framework to reveal condition dependency of complex regulons in Escherichia coli

PubMed Central

Lemmens, Karen; De Bie, Tijl; Dhollander, Thomas; De Keersmaecker, Sigrid C; Thijs, Inge M; Schoofs, Geert; De Weerdt, Ami; De Moor, Bart; Vanderleyden, Jos; Collado-Vides, Julio; Engelen, Kristof; Marchal, Kathleen

2009-01-01

We present DISTILLER, a data integration framework for the inference of transcriptional module networks. Experimental validation of predicted targets for the well-studied fumarate nitrate reductase regulator showed the effectiveness of our approach in Escherichia coli. In addition, the condition dependency and modularity of the inferred transcriptional network was studied. Surprisingly, the level of regulatory complexity seemed lower than that which would be expected from RegulonDB, indicating that complex regulatory programs tend to decrease the degree of modularity. PMID:19265557

Reliability studies of integrated modular engine system designs

NASA Astrophysics Data System (ADS)

Hardy, Terry L.; Rapp, Douglas C.

1993-06-01

A study was performed to evaluate the reliability of Integrated Modular Engine (IME) concepts. Comparisons were made between networked IME systems and non-networked discrete systems using expander cycle configurations. Both redundant and non-redundant systems were analyzed. Binomial approximation and Markov analysis techniques were employed to evaluate total system reliability. In addition, Failure Modes and Effects Analyses (FMEA), Preliminary Hazard Analyses (PHA), and Fault Tree Analysis (FTA) were performed to allow detailed evaluation of the IME concept. A discussion of these system reliability concepts is also presented.

Reliability studies of Integrated Modular Engine system designs

NASA Astrophysics Data System (ADS)

Hardy, Terry L.; Rapp, Douglas C.

1993-06-01

A study was performed to evaluate the reliability of Integrated Modular Engine (IME) concepts. Comparisons were made between networked IME systems and non-networked discrete systems using expander cycle configurations. Both redundant and non-redundant systems were analyzed. Binomial approximation and Markov analysis techniques were employed to evaluate total system reliability. In addition, Failure Modes and Effects Analyses (FMEA), Preliminary Hazard Analyses (PHA), and Fault Tree Analysis (FTA) were performed to allow detailed evaluation of the IME concept. A discussion of these system reliability concepts is also presented.

Uncovering the effective interval of resolution parameter across multiple community optimization measures

NASA Astrophysics Data System (ADS)

Li, Hui-Jia; Cheng, Qing; Mao, He-Jin; Wang, Huanian; Chen, Junhua

2017-03-01

The study of community structure is a primary focus of network analysis, which has attracted a large amount of attention. In this paper, we focus on two famous functions, i.e., the Hamiltonian function H and the modularity density measure D, and intend to uncover the effective thresholds of their corresponding resolution parameter γ without resolution limit problem. Two widely used example networks are employed, including the ring network of lumps as well as the ad hoc network. In these two networks, we use discrete convex analysis to study the interval of resolution parameter of H and D that will not cause the misidentification. By comparison, we find that in both examples, for Hamiltonian function H, the larger the value of resolution parameter γ, the less resolution limit the network suffers; while for modularity density D, the less resolution limit the network suffers when we decrease the value of γ. Our framework is mathematically strict and efficient and can be applied in a lot of scientific fields.

Experiments in clustered neuronal networks: A paradigm for complex modular dynamics

NASA Astrophysics Data System (ADS)

Teller, Sara; Soriano, Jordi

2016-06-01

Uncovering the interplay activity-connectivity is one of the major challenges in neuroscience. To deepen in the understanding of how a neuronal circuit shapes network dynamics, neuronal cultures have emerged as remarkable systems given their accessibility and easy manipulation. An attractive configuration of these in vitro systems consists in an ensemble of interconnected clusters of neurons. Using calcium fluorescence imaging to monitor spontaneous activity in these clustered neuronal networks, we were able to draw functional maps and reveal their topological features. We also observed that these networks exhibit a hierarchical modular dynamics, in which clusters fire in small groups that shape characteristic communities in the network. The structure and stability of these communities is sensitive to chemical or physical action, and therefore their analysis may serve as a proxy for network health. Indeed, the combination of all these approaches is helping to develop models to quantify damage upon network degradation, with promising applications for the study of neurological disorders in vitro.

Early warning model based on correlated networks in global crude oil markets

NASA Astrophysics Data System (ADS)

Yu, Jia-Wei; Xie, Wen-Jie; Jiang, Zhi-Qiang

2018-01-01

Applying network tools on predicting and warning the systemic risks provides a novel avenue to manage risks in financial markets. Here, we construct a series of global crude oil correlated networks based on the historical 57 oil prices covering a period from 1993 to 2012. Two systemic risk indicators are constructed based on the density and modularity of correlated networks. The local maximums of the risk indicators are found to have the ability to predict the trends of oil prices. In our sample periods, the indicator based on the network density sends five signals and the indicator based on the modularity index sends four signals. The four signals sent by both indicators are able to warn the drop of future oil prices and the signal only sent by the network density is followed by a huge rise of oil prices. Our results deepen the application of network measures on building early warning models of systemic risks and can be applied to predict the trends of future prices in financial markets.

Complex network analysis of resting-state fMRI of the brain.

PubMed

Anwar, Abdul Rauf; Hashmy, Muhammad Yousaf; Imran, Bilal; Riaz, Muhammad Hussnain; Mehdi, Sabtain Muhammad Muntazir; Muthalib, Makii; Perrey, Stephane; Deuschl, Gunther; Groppa, Sergiu; Muthuraman, Muthuraman

2016-08-01

Due to the fact that the brain activity hardly ever diminishes in healthy individuals, analysis of resting state functionality of the brain seems pertinent. Various resting state networks are active inside the idle brain at any time. Based on various neuro-imaging studies, it is understood that various structurally distant regions of the brain could be functionally connected. Regions of the brain, that are functionally connected, during rest constitutes to the resting state network. In the present study, we employed the complex network measures to estimate the presence of community structures within a network. Such estimate is named as modularity. Instead of using a traditional correlation matrix, we used a coherence matrix taken from the causality measure between different nodes. Our results show that in prolonged resting state the modularity starts to decrease. This decrease was observed in all the resting state networks and on both sides of the brain. Our study highlights the usage of coherence matrix instead of correlation matrix for complex network analysis.

Functional architecture of Escherichia coli: new insights provided by a natural decomposition approach.

PubMed

Freyre-González, Julio A; Alonso-Pavón, José A; Treviño-Quintanilla, Luis G; Collado-Vides, Julio

2008-10-27

Previous studies have used different methods in an effort to extract the modular organization of transcriptional regulatory networks. However, these approaches are not natural, as they try to cluster strongly connected genes into a module or locate known pleiotropic transcription factors in lower hierarchical layers. Here, we unravel the transcriptional regulatory network of Escherichia coli by separating it into its key elements, thus revealing its natural organization. We also present a mathematical criterion, based on the topological features of the transcriptional regulatory network, to classify the network elements into one of two possible classes: hierarchical or modular genes. We found that modular genes are clustered into physiologically correlated groups validated by a statistical analysis of the enrichment of the functional classes. Hierarchical genes encode transcription factors responsible for coordinating module responses based on general interest signals. Hierarchical elements correlate highly with the previously studied global regulators, suggesting that this could be the first mathematical method to identify global regulators. We identified a new element in transcriptional regulatory networks never described before: intermodular genes. These are structural genes that integrate, at the promoter level, signals coming from different modules, and therefore from different physiological responses. Using the concept of pleiotropy, we have reconstructed the hierarchy of the network and discuss the role of feedforward motifs in shaping the hierarchical backbone of the transcriptional regulatory network. This study sheds new light on the design principles underpinning the organization of transcriptional regulatory networks, showing a novel nonpyramidal architecture composed of independent modules globally governed by hierarchical transcription factors, whose responses are integrated by intermodular genes.

I(CES)-cubes: a modular self-reconfigurable bipartite robotic system

NASA Astrophysics Data System (ADS)

Unsal, Cem; Kiliccote, Han; Khosla, Pradeep K.

1999-08-01

In this manuscript, we introduce I(CES)-Cubes, a class of 3D modular robotic system that is capable of reconfiguring itself in order to adapt to its environment. This is a bipartite system, i.e. a collection of (i) active elements capable of actuation, and (ii) passive elements acting as connectors between actuated elements. Active elements, called links, are 3-DOF manipulators that are capable of attaching/detaching themselves to/from the passive elements. The cubes can then be positioned and oriented using links, which are independent mechatronic elements. Self- reconfiguration property enables the system to performed locomotion tasks over difficult terrain. For example, the system would be capable of moving over obstacles and climbing stairs. These task are performed by positing and orienting cubes and links to form a 3D network with required shape and position. This paper describes the design of the passive and active elements, the attachment mechanics, and several reconfiguration scenarios. Specifics of the hardware implementation and result of experiments with current prototypes are also given.

Detecting community structure via the maximal sub-graphs and belonging degrees in complex networks

NASA Astrophysics Data System (ADS)

Cui, Yaozu; Wang, Xingyuan; Eustace, Justine

2014-12-01

Community structure is a common phenomenon in complex networks, and it has been shown that some communities in complex networks often overlap each other. So in this paper we propose a new algorithm to detect overlapping community structure in complex networks. To identify the overlapping community structure, our algorithm firstly extracts fully connected sub-graphs which are maximal sub-graphs from original networks. Then two maximal sub-graphs having the key pair-vertices can be merged into a new larger sub-graph using some belonging degree functions. Furthermore we extend the modularity function to evaluate the proposed algorithm. In addition, overlapping nodes between communities are founded successfully. Finally we report the comparison between the modularity and the computational complexity of the proposed algorithm with some other existing algorithms. The experimental results show that the proposed algorithm gives satisfactory results.

The topological requirements for robust perfect adaptation in networks of any size.

PubMed

Araujo, Robyn P; Liotta, Lance A

2018-05-01

Robustness, and the ability to function and thrive amid changing and unfavorable environments, is a fundamental requirement for living systems. Until now it has been an open question how large and complex biological networks can exhibit robust behaviors, such as perfect adaptation to a variable stimulus, since complexity is generally associated with fragility. Here we report that all networks that exhibit robust perfect adaptation (RPA) to a persistent change in stimulus are decomposable into well-defined modules, of which there exist two distinct classes. These two modular classes represent a topological basis for all RPA-capable networks, and generate the full set of topological realizations of the internal model principle for RPA in complex, self-organizing, evolvable bionetworks. This unexpected result supports the notion that evolutionary processes are empowered by simple and scalable modular design principles that promote robust performance no matter how large or complex the underlying networks become.

Selforganization of modular activity of grid cells

PubMed Central

Urdapilleta, Eugenio; Si, Bailu

2017-01-01

Abstract A unique topographical representation of space is found in the concerted activity of grid cells in the rodent medial entorhinal cortex. Many among the principal cells in this region exhibit a hexagonal firing pattern, in which each cell expresses its own set of place fields (spatial phases) at the vertices of a triangular grid, the spacing and orientation of which are typically shared with neighboring cells. Grid spacing, in particular, has been found to increase along the dorso‐ventral axis of the entorhinal cortex but in discrete steps, that is, with a modular structure. In this study, we show that such a modular activity may result from the self‐organization of interacting units, which individually would not show discrete but rather continuously varying grid spacing. Within our “adaptation” network model, the effect of a continuously varying time constant, which determines grid spacing in the isolated cell model, is modulated by recurrent collateral connections, which tend to produce a few subnetworks, akin to magnetic domains, each with its own grid spacing. In agreement with experimental evidence, the modular structure is tightly defined by grid spacing, but also involves grid orientation and distortion, due to interactions across modules. Thus, our study sheds light onto a possible mechanism, other than simply assuming separate networks a priori, underlying the formation of modular grid representations. PMID:28768062

Patterns of Twitter Behavior Among Networks of Cannabis Dispensaries in California.

PubMed

Peiper, Nicholas C; Baumgartner, Peter M; Chew, Robert F; Hsieh, Yuli P; Bieler, Gayle S; Bobashev, Georgiy V; Siege, Christopher; Zarkin, Gary A

2017-07-04

Twitter represents a social media platform through which medical cannabis dispensaries can rapidly promote and advertise a multitude of retail products. Yet, to date, no studies have systematically evaluated Twitter behavior among dispensaries and how these behaviors influence the formation of social networks. This study sought to characterize common cyberbehaviors and shared follower networks among dispensaries operating in two large cannabis markets in California. From a targeted sample of 119 dispensaries in the San Francisco Bay Area and Greater Los Angeles, we collected metadata from the dispensary accounts using the Twitter API. For each city, we characterized the network structure of dispensaries based upon shared followers, then empirically derived communities with the Louvain modularity algorithm. Principal components factor analysis was employed to reduce 12 Twitter measures into a more parsimonious set of cyberbehavioral dimensions. Finally, quadratic discriminant analysis was implemented to verify the ability of the extracted dimensions to classify dispensaries into their derived communities. The modularity algorithm yielded three communities in each city with distinct network structures. The principal components factor analysis reduced the 12 cyberbehaviors into five dimensions that encompassed account age, posting frequency, referencing, hyperlinks, and user engagement among the dispensary accounts. In the quadratic discriminant analysis, the dimensions correctly classified 75% (46/61) of the communities in the San Francisco Bay Area and 71% (41/58) in Greater Los Angeles. The most centralized and strongly connected dispensaries in both cities had newer accounts, higher daily activity, more frequent user engagement, and increased usage of embedded media, keywords, and hyperlinks. Measures derived from both network structure and cyberbehavioral dimensions can serve as key contextual indicators for the online surveillance of cannabis dispensaries and consumer markets over time. ©Nicholas C Peiper, Peter M Baumgartner, Robert F Chew, Yuli P Hsieh, Gayle S Bieler, Georgiy V Bobashev, Christopher Siege, Gary A Zarkin. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 04.07.2017.

Influence of network topology on cooperative problem-solving systems.

PubMed

Fontanari, José F; Rodrigues, Francisco A

2016-09-01

The idea of a collective intelligence behind the complex natural structures built by organisms suggests that the organization of social networks is selected so as to optimize problem-solving competence at the group level. Here we study the influence of the social network topology on the performance of a group of agents whose task is to locate the global maxima of NK fitness landscapes. Agents cooperate by broadcasting messages informing on their fitness and use this information to imitate the fittest agent in their influence networks. In the case those messages convey accurate information on the proximity of the solution (i.e., for smooth fitness landscapes), we find that high connectivity as well as centralization boosts the group performance. For rugged landscapes, however, these characteristics are beneficial for small groups only. For large groups, it is advantageous to slow down the information transmission through the network to avoid local maximum traps. Long-range links and modularity have marginal effects on the performance of the group, except for a very narrow region of the model parameters.

Localizing Tortoise Nests by Neural Networks.

PubMed

Barbuti, Roberto; Chessa, Stefano; Micheli, Alessio; Pucci, Rita

2016-01-01

The goal of this research is to recognize the nest digging activity of tortoises using a device mounted atop the tortoise carapace. The device classifies tortoise movements in order to discriminate between nest digging, and non-digging activity (specifically walking and eating). Accelerometer data was collected from devices attached to the carapace of a number of tortoises during their two-month nesting period. Our system uses an accelerometer and an activity recognition system (ARS) which is modularly structured using an artificial neural network and an output filter. For the purpose of experiment and comparison, and with the aim of minimizing the computational cost, the artificial neural network has been modelled according to three different architectures based on the input delay neural network (IDNN). We show that the ARS can achieve very high accuracy on segments of data sequences, with an extremely small neural network that can be embedded in programmable low power devices. Given that digging is typically a long activity (up to two hours), the application of ARS on data segments can be repeated over time to set up a reliable and efficient system, called Tortoise@, for digging activity recognition.

Understanding and predicting binding between human leukocyte antigens (HLAs) and peptides by network analysis.

PubMed

Luo, Heng; Ye, Hao; Ng, Hui; Shi, Leming; Tong, Weida; Mattes, William; Mendrick, Donna; Hong, Huixiao

2015-01-01

As the major histocompatibility complex (MHC), human leukocyte antigens (HLAs) are one of the most polymorphic genes in humans. Patients carrying certain HLA alleles may develop adverse drug reactions (ADRs) after taking specific drugs. Peptides play an important role in HLA related ADRs as they are the necessary co-binders of HLAs with drugs. Many experimental data have been generated for understanding HLA-peptide binding. However, efficiently utilizing the data for understanding and accurately predicting HLA-peptide binding is challenging. Therefore, we developed a network analysis based method to understand and predict HLA-peptide binding. Qualitative Class I HLA-peptide binding data were harvested and prepared from four major databases. An HLA-peptide binding network was constructed from this dataset and modules were identified by the fast greedy modularity optimization algorithm. To examine the significance of signals in the yielded models, the modularity was compared with the modularity values generated from 1,000 random networks. The peptides and HLAs in the modules were characterized by similarity analysis. The neighbor-edges based and unbiased leverage algorithm (Nebula) was developed for predicting HLA-peptide binding. Leave-one-out (LOO) validations and two-fold cross-validations were conducted to evaluate the performance of Nebula using the constructed HLA-peptide binding network. Nine modules were identified from analyzing the HLA-peptide binding network with a highest modularity compared to all the random networks. Peptide length and functional side chains of amino acids at certain positions of the peptides were different among the modules. HLA sequences were module dependent to some extent. Nebula archived an overall prediction accuracy of 0.816 in the LOO validations and average accuracy of 0.795 in the two-fold cross-validations and outperformed the method reported in the literature. Network analysis is a useful approach for analyzing large and sparse datasets such as the HLA-peptide binding dataset. The modules identified from the network analysis clustered peptides and HLAs with similar sequences and properties of amino acids. Nebula performed well in the predictions of HLA-peptide binding. We demonstrated that network analysis coupled with Nebula is an efficient approach to understand and predict HLA-peptide binding interactions and thus, could further our understanding of ADRs.

Understanding and predicting binding between human leukocyte antigens (HLAs) and peptides by network analysis

PubMed Central

2015-01-01

Background As the major histocompatibility complex (MHC), human leukocyte antigens (HLAs) are one of the most polymorphic genes in humans. Patients carrying certain HLA alleles may develop adverse drug reactions (ADRs) after taking specific drugs. Peptides play an important role in HLA related ADRs as they are the necessary co-binders of HLAs with drugs. Many experimental data have been generated for understanding HLA-peptide binding. However, efficiently utilizing the data for understanding and accurately predicting HLA-peptide binding is challenging. Therefore, we developed a network analysis based method to understand and predict HLA-peptide binding. Methods Qualitative Class I HLA-peptide binding data were harvested and prepared from four major databases. An HLA-peptide binding network was constructed from this dataset and modules were identified by the fast greedy modularity optimization algorithm. To examine the significance of signals in the yielded models, the modularity was compared with the modularity values generated from 1,000 random networks. The peptides and HLAs in the modules were characterized by similarity analysis. The neighbor-edges based and unbiased leverage algorithm (Nebula) was developed for predicting HLA-peptide binding. Leave-one-out (LOO) validations and two-fold cross-validations were conducted to evaluate the performance of Nebula using the constructed HLA-peptide binding network. Results Nine modules were identified from analyzing the HLA-peptide binding network with a highest modularity compared to all the random networks. Peptide length and functional side chains of amino acids at certain positions of the peptides were different among the modules. HLA sequences were module dependent to some extent. Nebula archived an overall prediction accuracy of 0.816 in the LOO validations and average accuracy of 0.795 in the two-fold cross-validations and outperformed the method reported in the literature. Conclusions Network analysis is a useful approach for analyzing large and sparse datasets such as the HLA-peptide binding dataset. The modules identified from the network analysis clustered peptides and HLAs with similar sequences and properties of amino acids. Nebula performed well in the predictions of HLA-peptide binding. We demonstrated that network analysis coupled with Nebula is an efficient approach to understand and predict HLA-peptide binding interactions and thus, could further our understanding of ADRs. PMID:26424483

Using peptide array to identify binding motifs and interaction networks for modular domains.

PubMed

Li, Shawn S-C; Wu, Chenggang

2009-01-01

Specific protein-protein interactions underlie all essential biological processes and form the basis of cellular signal transduction. The recognition of a short, linear peptide sequence in one protein by a modular domain in another represents a common theme of macromolecular recognition in cells, and the importance of this mode of protein-protein interaction is highlighted by the large number of peptide-binding domains encoded by the human genome. This phenomenon also provides a unique opportunity to identify protein-protein binding events using peptide arrays and complementary biochemical assays. Accordingly, high-density peptide array has emerged as a useful tool by which to map domain-mediated protein-protein interaction networks at the proteome level. Using the Src-homology 2 (SH2) and 3 (SH3) domains as examples, we describe the application of oriented peptide array libraries in uncovering specific motifs recognized by an SH2 domain and the use of high-density peptide arrays in identifying interaction networks mediated by the SH3 domain. Methods reviewed here could also be applied to other modular domains, including catalytic domains, that recognize linear peptide sequences.

Coevolutionary diversification creates nested-modular structure in phage–bacteria interaction networks

PubMed Central

Beckett, Stephen J.; Williams, Hywel T. P.

2013-01-01

Phage and their bacterial hosts are the most diverse and abundant biological entities in the oceans, where their interactions have a major impact on marine ecology and ecosystem function. The structure of interaction networks for natural phage–bacteria communities offers insight into their coevolutionary origin. At small phylogenetic scales, observed communities typically show a nested structure, in which both hosts and phages can be ranked by their range of resistance and infectivity, respectively. A qualitatively different multi-scale structure is seen at larger phylogenetic scales; a natural assemblage sampled from the Atlantic Ocean displays large-scale modularity and local nestedness within each module. Here, we show that such ‘nested-modular’ interaction networks can be produced by a simple model of host–phage coevolution in which infection depends on genetic matching. Negative frequency-dependent selection causes diversification of hosts (to escape phages) and phages (to track their evolving hosts). This creates a diverse community of bacteria and phage, maintained by kill-the-winner ecological dynamics. When the resulting communities are visualized as bipartite networks of who infects whom, they show the nested-modular structure characteristic of the Atlantic sample. The statistical significance and strength of this observation varies depending on whether the interaction networks take into account the density of the interacting strains, with implications for interpretation of interaction networks constructed by different methods. Our results suggest that the apparently complex community structures associated with marine bacteria and phage may arise from relatively simple coevolutionary origins. PMID:24516719

Disrupted small world topology and modular organisation of functional networks in late-life depression with and without amnestic mild cognitive impairment.

PubMed

Li, Wenjun; Douglas Ward, B; Liu, Xiaolin; Chen, Gang; Jones, Jennifer L; Antuono, Piero G; Li, Shi-Jiang; Goveas, Joseph S

2015-10-01

The topological architecture of the whole-brain functional networks in those with and without late-life depression (LLD) and amnestic mild cognitive impairment (aMCI) are unknown. To investigate the differences in the small-world measures and the modular community structure of the functional networks between patients with LLD and aMCI when occurring alone or in combination and cognitively healthy non-depressed controls. 79 elderly participants (LLD (n=23), aMCI (n=18), comorbid LLD and aMCI (n=13), and controls (n=25)) completed neuropsychiatric assessments. Graph theoretical methods were employed on resting-state functional connectivity MRI data. LLD and aMCI comorbidity was associated with the greatest disruptions in functional integration measures (decreased global efficiency and increased path length); both LLD groups showed abnormal functional segregation (reduced local efficiency). The modular network organisation was most variable in the comorbid group, followed by patients with LLD-only. Decreased mean global, local and nodal efficiency metrics were associated with greater depressive symptom severity but not memory performance. Considering the whole brain as a complex network may provide unique insights on the neurobiological underpinnings of LLD with and without cognitive impairment. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Exploring anti-community structure in networks with application to incompatibility of traditional Chinese medicine

NASA Astrophysics Data System (ADS)

Zhu, Jiajing; Liu, Yongguo; Zhang, Yun; Liu, Xiaofeng; Xiao, Yonghua; Wang, Shidong; Wu, Xindong

2017-11-01

Community structure is one of the most important properties in networks, in which a node shares its most connections with the others in the same community. On the contrary, the anti-community structure means the nodes in the same group have few or no connections with each other. In Traditional Chinese Medicine (TCM), the incompatibility problem of herbs is a challenge to the clinical medication safety. In this paper, we propose a new anti-community detection algorithm, Random non-nEighboring nOde expansioN (REON), to find anti-communities in networks, in which a new evaluation criterion, anti-modularity, is designed to measure the quality of the obtained anti-community structure. In order to establish anti-communities in REON, we expand the node set by non-neighboring node expansion and regard the node set with the highest anti-modularity as an anti-community. Inspired by the phenomenon that the node with higher degree has greater contribution to the anti-modularity, an improved algorithm called REONI is developed by expanding node set by the non-neighboring node with the maximum degree, which greatly enhances the efficiency of REON. Experiments on synthetic and real-world networks demonstrate the superiority of the proposed algorithms over the existing methods. In addition, by applying REONI to the herb network, we find that it can discover incompatible herb combinations.

Experimental and computational analysis of a large protein network that controls fat storage reveals the design principles of a signaling network.

PubMed

Al-Anzi, Bader; Arpp, Patrick; Gerges, Sherif; Ormerod, Christopher; Olsman, Noah; Zinn, Kai

2015-05-01

An approach combining genetic, proteomic, computational, and physiological analysis was used to define a protein network that regulates fat storage in budding yeast (Saccharomyces cerevisiae). A computational analysis of this network shows that it is not scale-free, and is best approximated by the Watts-Strogatz model, which generates "small-world" networks with high clustering and short path lengths. The network is also modular, containing energy level sensing proteins that connect to four output processes: autophagy, fatty acid synthesis, mRNA processing, and MAP kinase signaling. The importance of each protein to network function is dependent on its Katz centrality score, which is related both to the protein's position within a module and to the module's relationship to the network as a whole. The network is also divisible into subnetworks that span modular boundaries and regulate different aspects of fat metabolism. We used a combination of genetics and pharmacology to simultaneously block output from multiple network nodes. The phenotypic results of this blockage define patterns of communication among distant network nodes, and these patterns are consistent with the Watts-Strogatz model.

Co-extinction in a host-parasite network: identifying key hosts for network stability.

PubMed

Dallas, Tad; Cornelius, Emily

2015-08-17

Parasites comprise a substantial portion of total biodiversity. Ultimately, this means that host extinction could result in many secondary extinctions of obligate parasites and potentially alter host-parasite network structure. Here, we examined a highly resolved fish-parasite network to determine key hosts responsible for maintaining parasite diversity and network structure (quantified here as nestedness and modularity). We evaluated four possible host extinction orders and compared the resulting co-extinction dynamics to random extinction simulations; including host removal based on estimated extinction risk, parasite species richness and host level contributions to nestedness and modularity. We found that all extinction orders, except the one based on realistic extinction risk, resulted in faster declines in parasite diversity and network structure relative to random biodiversity loss. Further, we determined species-level contributions to network structure were best predicted by parasite species richness and host family. Taken together, we demonstrate that a small proportion of hosts contribute substantially to network structure and that removal of these hosts results in rapid declines in parasite diversity and network structure. As network stability can potentially be inferred through measures of network structure, our findings may provide insight into species traits that confer stability.

Modularity and design principles in the sea urchin embryo gene regulatory network

PubMed Central

Peter, Isabelle S.; Davidson, Eric H.

2010-01-01

The gene regulatory network (GRN) established experimentally for the pre-gastrular sea urchin embryo provides causal explanations of the biological functions required for spatial specification of embryonic regulatory states. Here we focus on the structure of the GRN which controls the progressive increase in complexity of territorial regulatory states during embryogenesis; and on the types of modular subcircuits of which the GRN is composed. Each of these subcircuit topologies executes a particular operation of spatial information processing. The GRN architecture reflects the particular mode of embryogenesis represented by sea urchin development. Network structure not only specifies the linkages constituting the genomic regulatory code for development, but also indicates the various regulatory requirements of regional developmental processes. PMID:19932099

The modularity of seed dispersal: differences in structure and robustness between bat- and bird-fruit networks.

PubMed

Mello, Marco Aurelio Ribeiro; Marquitti, Flávia Maria Darcie; Guimarães, Paulo R; Kalko, Elisabeth Klara Viktoria; Jordano, Pedro; de Aguiar, Marcus Aloizio Martinez

2011-09-01

In networks of plant-animal mutualisms, different animal groups interact preferentially with different plants, thus forming distinct modules responsible for different parts of the service. However, what we currently know about seed dispersal networks is based only on birds. Therefore, we wished to fill this gap by studying bat-fruit networks and testing how they differ from bird-fruit networks. As dietary overlap of Neotropical bats and birds is low, they should form distinct mutualistic modules within local networks. Furthermore, since frugivory evolved only once among Neotropical bats, but several times independently among Neotropical birds, greater dietary overlap is expected among bats, and thus connectance and nestedness should be higher in bat-fruit networks. If bat-fruit networks have higher nestedness and connectance, they should be more robust to extinctions. We analyzed 1 mixed network of both bats and birds and 20 networks that consisted exclusively of either bats (11) or birds (9). As expected, the structure of the mixed network was both modular (M = 0.45) and nested (NODF = 0.31); one module contained only birds and two only bats. In 20 datasets with only one disperser group, bat-fruit networks (NODF = 0.53 ± 0.09, C = 0.30 ± 0.11) were more nested and had a higher connectance than bird-fruit networks (NODF = 0.42 ± 0.07, C = 0.22 ± 0.09). Unexpectedly, robustness to extinction of animal species was higher in bird-fruit networks (R = 0.60 ± 0.13) than in bat-fruit networks (R = 0.54 ± 0.09), and differences were explained mainly by species richness. These findings suggest that a modular structure also occurs in seed dispersal networks, similar to pollination networks. The higher nestedness and connectance observed in bat-fruit networks compared with bird-fruit networks may be explained by the monophyletic evolution of frugivory in Neotropical bats, among which the diets of specialists seem to have evolved from the pool of fruits consumed by generalists.

When modularization fails to occur: a developmental perspective.

PubMed

D'Souza, Dean; Karmiloff-Smith, Annette

2011-05-01

We argue that models of adult cognition defined in terms of independently functioning modules cannot be applied to development, whether typical or atypical. The infant brain starts out highly interconnected, and it is only over developmental time that neural networks become increasingly specialized-that is, relatively modularized. In the case of atypical development, even when behavioural scores fall within the normal range, they are frequently underpinned by different cognitive and neural processes. In other words, in neurodevelopmental disorders the gradual process of relative modularization may fail to occur.

Research on collaborative innovation mechanism of green construction supply chain based on united agency

NASA Astrophysics Data System (ADS)

Zhang, Min; He, Weiyi

2018-06-01

Under the guidance of principal-agent theory and modular theory, the collaborative innovation of green technology-based companies, design contractors and project builders based on united agency will provide direction for the development of green construction supply chain in the future. After analyzing the existing independent agencies, this paper proposes the industry-university-research bilateral collaborative innovation network architecture and modularization with the innovative function of engineering design in the context of non-standard transformation interfaces, analyzes the innovation responsibility center, and gives some countermeasures and suggestions to promote the performance of bilateral cooperative innovation network.

Enhanced collective influence: A paradigm to optimize network disruption

NASA Astrophysics Data System (ADS)

Wu, Tao; Chen, Leiting; Zhong, Linfeng; Xian, Xingping

2017-04-01

The function of complex networks typically relies on the integrity of underlying structure. Sometimes, practical applications need to attack networks' function, namely inactivate and fragment networks' underlying structure. To effectively dismantle complex networks and regulate the function of them, a centrality measure, named CI (Morone and Makse, 2015), was proposed for node ranking. We observe that the performance of CI centrality in network disruption problem may deteriorate when it is used in networks with different topology properties. Specifically, the structural features of local network topology are overlooked in CI centrality, even though the local network topology of the nodes with a fixed CI value may have very different organization. To improve the ranking accuracy of CI, this paper proposes a variant ECI to CI by considering loop density and degree diversity of local network topology. And the proposed ECI centrality would degenerate into CI centrality with the reduction of the loop density and the degree diversity level. By comparing ECI with CI and classical centrality measures in both synthetic and real networks, the experimental results suggest that ECI can largely improve the performance of CI for network disruption. Based on the results, we analyze the correlation between the improvement and the properties of the networks. We find that the performance of ECI is positively correlated with assortative coefficient and community modularity and negatively correlated with degree inequality of networks, which can be used as guidance for practical applications.

Overlapping Modularity at the Critical Point of k-Clique Percolation

NASA Astrophysics Data System (ADS)

Tóth, Bálint; Vicsek, Tamás; Palla, Gergely

2013-05-01

One of the most remarkable social phenomena is the formation of communities in social networks corresponding to families, friendship circles, work teams, etc. Since people usually belong to several different communities at the same time, the induced overlaps result in an extremely complicated web of the communities themselves. Thus, uncovering the intricate community structure of social networks is a non-trivial task with great potential for practical applications, gaining a notable interest in the recent years. The Clique Percolation Method (CPM) is one of the earliest overlapping community finding methods, which was already used in the analysis of several different social networks. In this approach the communities correspond to k-clique percolation clusters, and the general heuristic for setting the parameters of the method is to tune the system just below the critical point of k-clique percolation. However, this rule is based on simple physical principles and its validity was never subject to quantitative analysis. Here we examine the quality of the partitioning in the vicinity of the critical point using recently introduced overlapping modularity measures. According to our results on real social and other networks, the overlapping modularities show a maximum close to the critical point, justifying the original criteria for the optimal parameter settings.

Ecological modules and roles of species in heathland plant-insect flower visitor networks.

PubMed

Dupont, Yoko L; Olesen, Jens M

2009-03-01

1. Co-existing plants and flower-visiting animals often form complex interaction networks. A long-standing question in ecology and evolutionary biology is how to detect nonrandom subsets (compartments, blocks, modules) of strongly interacting species within such networks. Here we use a network analytical approach to (i) detect modularity in pollination networks, (ii) investigate species composition of modules, and (iii) assess the stability of modules across sites. 2. Interactions between entomophilous plants and their flower-visitors were recorded throughout the flowering season at three heathland sites in Denmark, separated by >or= 10 km. Among sites, plant communities were similar, but composition of flower-visiting insect faunas differed. Visitation frequencies of visitor species were recorded as a measure of insect abundance. 3. Qualitative (presence-absence) interaction networks were tested for modularity. Modules were identified, and species classified into topological roles (peripherals, connectors, or hubs) using 'functional cartography by simulated annealing', a method recently developed by Guimerà & Amaral (2005a). 4. All networks were significantly modular. Each module consisted of 1-6 plant species and 18-54 insect species. Interactions aggregated around one or two hub plant species, which were largely identical at the three study sites. 5. Insect species were categorized in taxonomic groups, mostly at the level of orders. When weighted by visitation frequency, each module was dominated by one or few insect groups. This pattern was consistent across sites. 6. Our study adds support to the conclusion that certain plant species and flower-visitor groups are nonrandomly and repeatedly associated. Within a network, these strongly interacting subgroups of species may exert reciprocal selection pressures on each other. Thus, modules may be candidates for the long-sought key units of co-evolution.

Complex networks with scale-free nature and hierarchical modularity

NASA Astrophysics Data System (ADS)

Shekatkar, Snehal M.; Ambika, G.

2015-09-01

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

Finding Influential Spreaders from Human Activity beyond Network Location.

PubMed

Min, Byungjoon; Liljeros, Fredrik; Makse, Hernán A

2015-01-01

Most centralities proposed for identifying influential spreaders on social networks to either spread a message or to stop an epidemic require the full topological information of the network on which spreading occurs. In practice, however, collecting all connections between agents in social networks can be hardly achieved. As a result, such metrics could be difficult to apply to real social networks. Consequently, a new approach for identifying influential people without the explicit network information is demanded in order to provide an efficient immunization or spreading strategy, in a practical sense. In this study, we seek a possible way for finding influential spreaders by using the social mechanisms of how social connections are formed in real networks. We find that a reliable immunization scheme can be achieved by asking people how they interact with each other. From these surveys we find that the probabilistic tendency to connect to a hub has the strongest predictive power for influential spreaders among tested social mechanisms. Our observation also suggests that people who connect different communities is more likely to be an influential spreader when a network has a strong modular structure. Our finding implies that not only the effect of network location but also the behavior of individuals is important to design optimal immunization or spreading schemes.

Cancer Transcriptome Dataset Analysis: Comparing Methods of Pathway and Gene Regulatory Network-Based Cluster Identification.

PubMed

Nam, Seungyoon

2017-04-01

Cancer transcriptome analysis is one of the leading areas of Big Data science, biomarker, and pharmaceutical discovery, not to forget personalized medicine. Yet, cancer transcriptomics and postgenomic medicine require innovation in bioinformatics as well as comparison of the performance of available algorithms. In this data analytics context, the value of network generation and algorithms has been widely underscored for addressing the salient questions in cancer pathogenesis. Analysis of cancer trancriptome often results in complicated networks where identification of network modularity remains critical, for example, in delineating the "druggable" molecular targets. Network clustering is useful, but depends on the network topology in and of itself. Notably, the performance of different network-generating tools for network cluster (NC) identification has been little investigated to date. Hence, using gastric cancer (GC) transcriptomic datasets, we compared two algorithms for generating pathway versus gene regulatory network-based NCs, showing that the pathway-based approach better agrees with a reference set of cancer-functional contexts. Finally, by applying pathway-based NC identification to GC transcriptome datasets, we describe cancer NCs that associate with candidate therapeutic targets and biomarkers in GC. These observations collectively inform future research on cancer transcriptomics, drug discovery, and rational development of new analysis tools for optimal harnessing of omics data.

Modular design of metabolic network for robust production of n-butanol from galactose-glucose mixtures.

PubMed

Lim, Hyun Gyu; Lim, Jae Hyung; Jung, Gyoo Yeol

2015-01-01

Refactoring microorganisms for efficient production of advanced biofuel such as n-butanol from a mixture of sugars in the cheap feedstock is a prerequisite to achieve economic feasibility in biorefinery. However, production of biofuel from inedible and cheap feedstock is highly challenging due to the slower utilization of biomass-driven sugars, arising from complex assimilation pathway, difficulties in amplification of biosynthetic pathways for heterologous metabolite, and redox imbalance caused by consuming intracellular reducing power to produce quite reduced biofuel. Even with these problems, the microorganisms should show robust production of biofuel to obtain industrial feasibility. Thus, refactoring microorganisms for efficient conversion is highly desirable in biofuel production. In this study, we engineered robust Escherichia coli to accomplish high production of n-butanol from galactose-glucose mixtures via the design of modular pathway, an efficient and systematic way, to reconstruct the entire metabolic pathway with many target genes. Three modular pathways designed using the predictable genetic elements were assembled for efficient galactose utilization, n-butanol production, and redox re-balancing to robustly produce n-butanol from a sugar mixture of galactose and glucose. Specifically, the engineered strain showed dramatically increased n-butanol production (3.3-fold increased to 6.2 g/L after 48-h fermentation) compared to the parental strain (1.9 g/L) in galactose-supplemented medium. Moreover, fermentation with mixtures of galactose and glucose at various ratios from 2:1 to 1:2 confirmed that our engineered strain was able to robustly produce n-butanol regardless of sugar composition with simultaneous utilization of galactose and glucose. Collectively, modular pathway engineering of metabolic network can be an effective approach in strain development for optimal biofuel production with cost-effective fermentable sugars. To the best of our knowledge, this study demonstrated the first and highest n-butanol production from galactose in E. coli. Moreover, robust production of n-butanol with sugar mixtures with variable composition would facilitate the economic feasibility of the microbial process using a mixture of sugars from cheap biomass in the near future.

Topological distortion and reorganized modular structure of gut microbial co-occurrence networks in inflammatory bowel disease

NASA Astrophysics Data System (ADS)

Baldassano, Steven N.; Bassett, Danielle S.

2016-05-01

The gut microbiome plays a key role in human health, and alterations of the normal gut flora are associated with a variety of distinct disease states. Yet, the natural dependencies between microbes in healthy and diseased individuals remain far from understood. Here we use a network-based approach to characterize microbial co-occurrence in individuals with inflammatory bowel disease (IBD) and healthy (non-IBD control) individuals. We find that microbial networks in patients with IBD differ in both global structure and local connectivity patterns. While a “core” microbiome is preserved, network topology of other densely interconnected microbe modules is distorted, with potent inflammation-mediating organisms assuming roles as integrative and highly connected inter-modular hubs. We show that while both networks display a rich-club organization, in which a small set of microbes commonly co-occur, the healthy network is more easily disrupted by elimination of a small number of key species. Further investigation of network alterations in disease might offer mechanistic insights into the specific pathogens responsible for microbiome-mediated inflammation in IBD.

Modular Bayesian Networks with Low-Power Wearable Sensors for Recognizing Eating Activities

PubMed Central

Kim, Kee-Hoon

2017-01-01

Recently, recognizing a user’s daily activity using a smartphone and wearable sensors has become a popular issue. However, in contrast with the ideal definition of an experiment, there could be numerous complex activities in real life with respect to its various background and contexts: time, space, age, culture, and so on. Recognizing these complex activities with limited low-power sensors, considering the power and memory constraints of the wearable environment and the user’s obtrusiveness at once is not an easy problem, although it is very crucial for the activity recognizer to be practically useful. In this paper, we recognize activity of eating, which is one of the most typical examples of a complex activity, using only daily low-power mobile and wearable sensors. To organize the related contexts systemically, we have constructed the context model based on activity theory and the “Five W’s”, and propose a Bayesian network with 88 nodes to predict uncertain contexts probabilistically. The structure of the proposed Bayesian network is designed by a modular and tree-structured approach to reduce the time complexity and increase the scalability. To evaluate the proposed method, we collected the data with 10 different activities from 25 volunteers of various ages, occupations, and jobs, and have obtained 79.71% accuracy, which outperforms other conventional classifiers by 7.54–14.4%. Analyses of the results showed that our probabilistic approach could also give approximate results even when one of contexts or sensor values has a very heterogeneous pattern or is missing. PMID:29232937

GEECS (Generalized Equipment and Experiment Control System)

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

GONSALVES, ANTHONY; DESHMUKH, AALHAD

2017-01-12

GEECS (Generalized Equipment and Experiment Control System) monitors and controls equipment distributed across a network, performs experiments by scanning input variables, and collects and stores various types of data synchronously from devices. Examples of devices include cameras, motors and pressure gauges. GEEKS is based upon LabView graphical object oriented programming (GOOP), allowing for a modular and scalable framework. Data is published for subscription of an arbitrary number of variables over TCP. A secondary framework allows easy development of graphical user interfaces for a combined control of any available devices on the control system without the need of programming knowledge. Thismore » allows for rapid integration of GEECS into a wide variety of systems. A database interface provides for devise and process configuration while allowing the user to save large quantities of data to local or network drives.« less

Temporal variation in bat-fruit interactions: Foraging strategies influence network structure over time

NASA Astrophysics Data System (ADS)

Zapata-Mesa, Natalya; Montoya-Bustamante, Sebastián; Murillo-García, Oscar E.

2017-11-01

Mutualistic interactions, such as seed dispersal, are important for the maintenance of structure and stability of tropical communities. However, there is a lack of information about spatial and temporal variation in plant-animal interaction networks. Thus, our goal was to assess the effect of bat's foraging strategies on temporal variation in the structure and robustness of bat-fruit networks in both a dry and a rain tropical forest. We evaluated monthly variation in bat-fruit networks by using seven structure metrics: network size, average path length, nestedness, modularity, complementary specialization, normalized degree and betweenness centrality. Seed dispersal networks showed variations in size, species composition and modularity; did not present nested structures and their complementary specialization was high compared to other studies. Both networks presented short path lengths, and a constantly high robustness, despite their monthly variations. Sedentary bat species were recorded during all the study periods and occupied more central positions than nomadic species. We conclude that foraging strategies are important structuring factors that affect the dynamic of networks by determining the functional roles of frugivorous bats over time; thus sedentary bats are more important than nomadic species for the maintenance of the network structure, and their conservation is a must.

Characterizing Variability of Modular Brain Connectivity with Constrained Principal Component Analysis

PubMed Central

Hirayama, Jun-ichiro; Hyvärinen, Aapo; Kiviniemi, Vesa; Kawanabe, Motoaki; Yamashita, Okito

2016-01-01

Characterizing the variability of resting-state functional brain connectivity across subjects and/or over time has recently attracted much attention. Principal component analysis (PCA) serves as a fundamental statistical technique for such analyses. However, performing PCA on high-dimensional connectivity matrices yields complicated “eigenconnectivity” patterns, for which systematic interpretation is a challenging issue. Here, we overcome this issue with a novel constrained PCA method for connectivity matrices by extending the idea of the previously proposed orthogonal connectivity factorization method. Our new method, modular connectivity factorization (MCF), explicitly introduces the modularity of brain networks as a parametric constraint on eigenconnectivity matrices. In particular, MCF analyzes the variability in both intra- and inter-module connectivities, simultaneously finding network modules in a principled, data-driven manner. The parametric constraint provides a compact module-based visualization scheme with which the result can be intuitively interpreted. We develop an optimization algorithm to solve the constrained PCA problem and validate our method in simulation studies and with a resting-state functional connectivity MRI dataset of 986 subjects. The results show that the proposed MCF method successfully reveals the underlying modular eigenconnectivity patterns in more general situations and is a promising alternative to existing methods. PMID:28002474

Modular jet impingement assemblies with passive and active flow control for electronics cooling

DOEpatents

Zhou, Feng; Dede, Ercan Mehmet; Joshi, Shailesh

2016-09-13

Power electronics modules having modular jet impingement assembly utilized to cool heat generating devices are disclosed. The modular jet impingement assemblies include a modular manifold having a distribution recess, one or more angled inlet connection tubes positioned at an inlet end of the modular manifold that fluidly couple the inlet tube to the distribution recess and one or more outlet connection tubes positioned at an outlet end of the modular manifold that fluidly coupling the outlet tube to the distribution recess. The modular jet impingement assemblies include a manifold insert removably positioned within the distribution recess and include one or more inlet branch channels each including an impinging slot and one or more outlet branch channels each including a collecting slot. Further a heat transfer plate coupled to the modular manifold, the heat transfer plate comprising an impingement surface including an array of fins that extend toward the manifold insert.

Modular and Orthogonal Synthesis of Hybrid Polymers and Networks

PubMed Central

Liu, Shuang; Dicker, Kevin T.; Jia, Xinqiao

2015-01-01

Biomaterials scientists strive to develop polymeric materials with distinct chemical make-up, complex molecular architectures, robust mechanical properties and defined biological functions by drawing inspirations from biological systems. Salient features of biological designs include (1) repetitive presentation of basic motifs; and (2) efficient integration of diverse building blocks. Thus, an appealing approach to biomaterials synthesis is to combine synthetic and natural building blocks in a modular fashion employing novel chemical methods. Over the past decade, orthogonal chemistries have become powerful enabling tools for the modular synthesis of advanced biomaterials. These reactions require building blocks with complementary functionalities, occur under mild conditions in the presence of biological molecules and living cells and proceed with high yield and exceptional selectivity. These chemistries have facilitated the construction of complex polymers and networks in a step-growth fashion, allowing facile modulation of materials properties by simple variations of the building blocks. In this review, we first summarize features of several types of orthogonal chemistries. We then discuss recent progress in the synthesis of step growth linear polymers, dendrimers and networks that find application in drug delivery, 3D cell culture and tissue engineering. Overall, orthogonal reactions and modulular synthesis have not only minimized the steps needed for the desired chemical transformations but also maximized the diversity and functionality of the final products. The modular nature of the design, combined with the potential synergistic effect of the hybrid system, will likely result in novel hydrogel matrices with robust structures and defined functions. PMID:25572255

Network community structure and loop coefficient method

NASA Astrophysics Data System (ADS)

Vragović, I.; Louis, E.

2006-07-01

A modular structure, in which groups of tightly connected nodes could be resolved as separate entities, is a property that can be found in many complex networks. In this paper, we propose a algorithm for identifying communities in networks. It is based on a local measure, so-called loop coefficient that is a generalization of the clustering coefficient. Nodes with a large loop coefficient tend to be core inner community nodes, while other vertices are usually peripheral sites at the borders of communities. Our method gives satisfactory results for both artificial and real-world graphs, if they have a relatively pronounced modular structure. This type of algorithm could open a way of interpreting the role of nodes in communities in terms of the local loop coefficient, and could be used as a complement to other methods.

The Budget Guide to Seismic Network Management

NASA Astrophysics Data System (ADS)

Hagerty, M. T.; Ebel, J. E.

2007-05-01

Regardless of their size, there are certain tasks that all seismic networks must perform, including data collection and processing, earthquake location, information dissemination, and quality control. Small seismic networks are unlikely to possess the resources -- manpower and money -- required to do much in-house development. Fortunately, there are a lot of free or inexpensive software solutions available that are able to perform many of the required tasks. Often the available solutions are all-in-one turnkey packages designed and developed for much larger seismic networks, and the cost of adapting them to a smaller network must be weighed against the ease with which other, non-seismic software can be adapted to the same task. We describe here the software and hardware choices we have made for the New England Seismic Network (NESN), a sparse regional seismic network responsible for monitoring and reporting all seismicity within the New England region in the northeastern U.S. We have chosen to use a cost-effective approach to monitoring using free, off-the-shelf solutions where available (e.g., Earthworm, HYP2000) and modifying freeware solutions when it is easier than trying to adapt a large, complicated package. We have selected for use software that is: free, likely to receive continued support from the seismic or, preferably, larger internet community, and modular. Modularity is key to our design because it ensures that if one component of our processing system becomes obsolete, we can insert a suitable replacement with few modifications to the other modules. Our automated event detection, identification and location system is based on a wavelet transform analysis of station data that arrive continuously via TCP/IP transmission over the internet. Our system for interactive analyst review of seismic events and remote system monitoring utilizes a combination of Earthworm modules, Perl cgi-bin scripts, Java, and native Unix commands and can now be carried out via internet browser from anywhere in the world. With our current communication and processing system we are able to achieve a monitoring threshold of about M2.0 for most New England, in spite of high cultural noise and sparse station distribution, and maintain an extremely high rate of data recovery, for minimal cost.

ITEP: an integrated toolkit for exploration of microbial pan-genomes.

PubMed

Benedict, Matthew N; Henriksen, James R; Metcalf, William W; Whitaker, Rachel J; Price, Nathan D

2014-01-03

Comparative genomics is a powerful approach for studying variation in physiological traits as well as the evolution and ecology of microorganisms. Recent technological advances have enabled sequencing large numbers of related genomes in a single project, requiring computational tools for their integrated analysis. In particular, accurate annotations and identification of gene presence and absence are critical for understanding and modeling the cellular physiology of newly sequenced genomes. Although many tools are available to compare the gene contents of related genomes, new tools are necessary to enable close examination and curation of protein families from large numbers of closely related organisms, to integrate curation with the analysis of gain and loss, and to generate metabolic networks linking the annotations to observed phenotypes. We have developed ITEP, an Integrated Toolkit for Exploration of microbial Pan-genomes, to curate protein families, compute similarities to externally-defined domains, analyze gene gain and loss, and generate draft metabolic networks from one or more curated reference network reconstructions in groups of related microbial species among which the combination of core and variable genes constitute the their "pan-genomes". The ITEP toolkit consists of: (1) a series of modular command-line scripts for identification, comparison, curation, and analysis of protein families and their distribution across many genomes; (2) a set of Python libraries for programmatic access to the same data; and (3) pre-packaged scripts to perform common analysis workflows on a collection of genomes. ITEP's capabilities include de novo protein family prediction, ortholog detection, analysis of functional domains, identification of core and variable genes and gene regions, sequence alignments and tree generation, annotation curation, and the integration of cross-genome analysis and metabolic networks for study of metabolic network evolution. ITEP is a powerful, flexible toolkit for generation and curation of protein families. ITEP's modular design allows for straightforward extension as analysis methods and tools evolve. By integrating comparative genomics with the development of draft metabolic networks, ITEP harnesses the power of comparative genomics to build confidence in links between genotype and phenotype and helps disambiguate gene annotations when they are evaluated in both evolutionary and metabolic network contexts.

A community detection algorithm using network topologies and rule-based hierarchical arc-merging strategies

PubMed Central

2017-01-01

The authors use four criteria to examine a novel community detection algorithm: (a) effectiveness in terms of producing high values of normalized mutual information (NMI) and modularity, using well-known social networks for testing; (b) examination, meaning the ability to examine mitigating resolution limit problems using NMI values and synthetic networks; (c) correctness, meaning the ability to identify useful community structure results in terms of NMI values and Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks; and (d) scalability, or the ability to produce comparable modularity values with fast execution times when working with large-scale real-world networks. In addition to describing a simple hierarchical arc-merging (HAM) algorithm that uses network topology information, we introduce rule-based arc-merging strategies for identifying community structures. Five well-studied social network datasets and eight sets of LFR benchmark networks were employed to validate the correctness of a ground-truth community, eight large-scale real-world complex networks were used to measure its efficiency, and two synthetic networks were used to determine its susceptibility to two resolution limit problems. Our experimental results indicate that the proposed HAM algorithm exhibited satisfactory performance efficiency, and that HAM-identified and ground-truth communities were comparable in terms of social and LFR benchmark networks, while mitigating resolution limit problems. PMID:29121100

Impact of individual interest shift on information dissemination in modular networks

NASA Astrophysics Data System (ADS)

Zhao, Narisa; Cui, Xuelian

2017-01-01

Social networks exhibit strong community structure. Many researches have been done to explore the impacts of community structure on information diffusion but few combined with human behaviors together. In this paper, we focus on how the individual interests' changing behavior impacts the dynamics of information propagation. Firstly, we propose an information dissemination model considering both the community structure and individual interest shift where social reinforcement and time decaying are taken into account. The accuracy of the model is evaluated by comparing the simulation and theoretical results. Further, the numerical results illustrate that both the community structure and the interests changing behavior have effects on the outbreak size of the information dissemination. Specially, lower modularity and higher community connection density will accelerate the speed of information propagation especially when the information maximal lifetime is shorter. In addition, the changes of individual interests in the message have a great impact on the final density of the received through increasing or decreasing the number of satisfied individuals directly. What is more, our findings suggest that when the modularity of the network is higher and the community clustering coefficient is lower individual interest shift behavior will have a heavier effect on the spread scope.

A modular network for legged locomotion

NASA Astrophysics Data System (ADS)

Golubitsky, Martin; Stewart, Ian; Buono, Pietro-Luciano; Collins, J. J.

1998-04-01

In this paper we use symmetry methods to study networks of coupled cells, which are models for central pattern generators (CPGs). In these models the cells obey identical systems of differential equations and the network specifies how cells are coupled. Previously, Collins and Stewart showed that the phase relations of many of the standard gaits of quadrupeds and hexapods can be obtained naturally via Hopf bifurcation in small networks. For example, the networks they used to study quadrupeds all had four cells, with the understanding that each cell determined the phase of the motion of one leg. However, in their work it seemed necessary to employ several different four-oscillator networks to obtain all of the standard quadrupedal gaits. We show that this difficulty with four-oscillator networks is unavoidable, but that the problems can be overcome by using a larger network. Specifically, we show that the standard gaits of a quadruped, including walk, trot and pace, cannot all be realized by a single four-cell network without introducing unwanted conjugacies between trot and pace - conjugacies that imply a dynamic equivalence between these gaits that seems inconsistent with observations. In this sense a single network with four cells cannot model the CPG of a quadruped. We also introduce a single eight-cell network that can model all of the primary gaits of quadrupeds without these unwanted conjugacies. Moreover, this network is modular in that it naturally generalizes to provide models of gaits in hexapods, centipedes, and millipedes. The analysis of models for many-legged animals shows that wave-like motions, similar to those obtained by Kopell and Ermentrout, can be expected. However, our network leads to a prediction that the wavelength of the wave motion will divide twice the length of the animal. Indeed, we reproduce illustrations of wave-like motions in centipedes where the animal is approximately one-and-a-half wavelength long - motions that are consistent with this prediction. We discuss the implications of these results for the development of modular control networks for adaptive legged robots.

A host-endoparasite network of Neotropical marine fish: are there organizational patterns?

PubMed

Bellay, Sybelle; Lima, Dilermando P; Takemoto, Ricardo M; Luque, José L

2011-12-01

Properties of ecological networks facilitate the understanding of interaction patterns in host-parasite systems as well as the importance of each species in the interaction structure of a community. The present study evaluates the network structure, functional role of all species and patterns of parasite co-occurrence in a host-parasite network to determine the organization level of a host-parasite system consisting of 170 taxa of gastrointestinal metazoans of 39 marine fish species on the coast of Brazil. The network proved to be nested and modular, with a low degree of connectance. Host-parasite interactions were influenced by host phylogeny. Randomness in parasite co-occurrence was observed in most modules and component communities, although species segregation patterns were also observed. The low degree of connectance in the network may be the cause of properties such as nestedness and modularity, which indicate the presence of a high number of peripheral species. Segregation patterns among parasite species in modules underscore the role of host specificity. Knowledge of ecological networks allows detection of keystone species for the maintenance of biodiversity and the conduction of further studies on the stability of networks in relation to frequent environmental changes.

Stochastic fluctuations and the detectability limit of network communities.

PubMed

Floretta, Lucio; Liechti, Jonas; Flammini, Alessandro; De Los Rios, Paolo

2013-12-01

We have analyzed the detectability limits of network communities in the framework of the popular Girvan and Newman benchmark. By carefully taking into account the inevitable stochastic fluctuations that affect the construction of each and every instance of the benchmark, we come to the conclusion that the native, putative partition of the network is completely lost even before the in-degree/out-degree ratio becomes equal to that of a structureless Erdös-Rényi network. We develop a simple iterative scheme, analytically well described by an infinite branching process, to provide an estimate of the true detectability limit. Using various algorithms based on modularity optimization, we show that all of them behave (semiquantitatively) in the same way, with the same functional form of the detectability threshold as a function of the network parameters. Because the same behavior has also been found by further modularity-optimization methods and for methods based on different heuristics implementations, we conclude that indeed a correct definition of the detectability limit must take into account the stochastic fluctuations of the network construction.

Evidence for dynamically organized modularity in the yeast protein-protein interaction network

NASA Astrophysics Data System (ADS)

Han, Jing-Dong J.; Bertin, Nicolas; Hao, Tong; Goldberg, Debra S.; Berriz, Gabriel F.; Zhang, Lan V.; Dupuy, Denis; Walhout, Albertha J. M.; Cusick, Michael E.; Roth, Frederick P.; Vidal, Marc

2004-07-01

In apparently scale-free protein-protein interaction networks, or `interactome' networks, most proteins interact with few partners, whereas a small but significant proportion of proteins, the `hubs', interact with many partners. Both biological and non-biological scale-free networks are particularly resistant to random node removal but are extremely sensitive to the targeted removal of hubs. A link between the potential scale-free topology of interactome networks and genetic robustness seems to exist, because knockouts of yeast genes encoding hubs are approximately threefold more likely to confer lethality than those of non-hubs. Here we investigate how hubs might contribute to robustness and other cellular properties for protein-protein interactions dynamically regulated both in time and in space. We uncovered two types of hub: `party' hubs, which interact with most of their partners simultaneously, and `date' hubs, which bind their different partners at different times or locations. Both in silico studies of network connectivity and genetic interactions described in vivo support a model of organized modularity in which date hubs organize the proteome, connecting biological processes-or modules -to each other, whereas party hubs function inside modules.

Algorithm for parametric community detection in networks.

PubMed

Bettinelli, Andrea; Hansen, Pierre; Liberti, Leo

2012-07-01

Modularity maximization is extensively used to detect communities in complex networks. It has been shown, however, that this method suffers from a resolution limit: Small communities may be undetectable in the presence of larger ones even if they are very dense. To alleviate this defect, various modifications of the modularity function have been proposed as well as multiresolution methods. In this paper we systematically study a simple model (proposed by Pons and Latapy [Theor. Comput. Sci. 412, 892 (2011)] and similar to the parametric model of Reichardt and Bornholdt [Phys. Rev. E 74, 016110 (2006)]) with a single parameter α that balances the fraction of within community edges and the expected fraction of edges according to the configuration model. An exact algorithm is proposed to find optimal solutions for all values of α as well as the corresponding successive intervals of α values for which they are optimal. This algorithm relies upon a routine for exact modularity maximization and is limited to moderate size instances. An agglomerative hierarchical heuristic is therefore proposed to address parametric modularity detection in large networks. At each iteration the smallest value of α for which it is worthwhile to merge two communities of the current partition is found. Then merging is performed and the data are updated accordingly. An implementation is proposed with the same time and space complexity as the well-known Clauset-Newman-Moore (CNM) heuristic [Phys. Rev. E 70, 066111 (2004)]. Experimental results on artificial and real world problems show that (i) communities are detected by both exact and heuristic methods for all values of the parameter α; (ii) the dendrogram summarizing the results of the heuristic method provides a useful tool for substantive analysis, as illustrated particularly on a Les Misérables data set; (iii) the difference between the parametric modularity values given by the exact method and those given by the heuristic is moderate; (iv) the heuristic version of the proposed parametric method, viewed as a modularity maximization tool, gives better results than the CNM heuristic for large instances.

Elucidation of Sigma Factor-Associated Networks in Pseudomonas aeruginosa Reveals a Modular Architecture with Limited and Function-Specific Crosstalk

PubMed Central

Schulz, Sebastian; Eckweiler, Denitsa; Bielecka, Agata; Nicolai, Tanja; Franke, Raimo; Dötsch, Andreas; Hornischer, Klaus; Bruchmann, Sebastian; Düvel, Juliane; Häussler, Susanne

2015-01-01

Sigma factors are essential global regulators of transcription initiation in bacteria which confer promoter recognition specificity to the RNA polymerase core enzyme. They provide effective mechanisms for simultaneously regulating expression of large numbers of genes in response to challenging conditions, and their presence has been linked to bacterial virulence and pathogenicity. In this study, we constructed nine his-tagged sigma factor expressing and/or deletion mutant strains in the opportunistic pathogen Pseudomonas aeruginosa. To uncover the direct and indirect sigma factor regulons, we performed mRNA profiling, as well as chromatin immunoprecipitation coupled to high-throughput sequencing. We furthermore elucidated the de novo binding motif of each sigma factor, and validated the RNA- and ChIP-seq results by global motif searches in the proximity of transcriptional start sites (TSS). Our integrated approach revealed a highly modular network architecture which is composed of insulated functional sigma factor modules. Analysis of the interconnectivity of the various sigma factor networks uncovered a limited, but highly function-specific, crosstalk which orchestrates complex cellular processes. Our data indicate that the modular structure of sigma factor networks enables P. aeruginosa to function adequately in its environment and at the same time is exploited to build up higher-level functions by specific interconnections that are dominated by a participation of RpoN. PMID:25780925

Motor modules in robot-aided walking

PubMed Central

2012-01-01

Background It is hypothesized that locomotion is achieved by means of rhythm generating networks (central pattern generators) and muscle activation generating networks. This modular organization can be partly identified from the analysis of the muscular activity by means of factorization algorithms. The activity of rhythm generating networks is described by activation signals whilst the muscle intervention generating network is represented by motor modules (muscle synergies). In this study, we extend the analysis of modular organization of walking to the case of robot-aided locomotion, at varying speed and body weight support level. Methods Non Negative Matrix Factorization was applied on surface electromyographic signals of 8 lower limb muscles of healthy subjects walking in gait robotic trainer at different walking velocities (1 to 3km/h) and levels of body weight support (0 to 30%). Results The muscular activity of volunteers could be described by low dimensionality (4 modules), as for overground walking. Moreover, the activation signals during robot-aided walking were bursts of activation timed at specific phases of the gait cycle, underlying an impulsive controller, as also observed in overground walking. This modular organization was consistent across the investigated speeds, body weight support level, and subjects. Conclusions These results indicate that walking in a Lokomat robotic trainer is achieved by similar motor modules and activation signals as overground walking and thus supports the use of robotic training for re-establishing natural walking patterns. PMID:23043818

Control range: a controllability-based index for node significance in directed networks

NASA Astrophysics Data System (ADS)

Wang, Bingbo; Gao, Lin; Gao, Yong

2012-04-01

While a large number of methods for module detection have been developed for undirected networks, it is difficult to adapt them to handle directed networks due to the lack of consensus criteria for measuring the node significance in a directed network. In this paper, we propose a novel structural index, the control range, motivated by recent studies on the structural controllability of large-scale directed networks. The control range of a node quantifies the size of the subnetwork that the node can effectively control. A related index, called the control range similarity, is also introduced to measure the structural similarity between two nodes. When applying the index of control range to several real-world and synthetic directed networks, it is observed that the control range of the nodes is mainly influenced by the network's degree distribution and that nodes with a low degree may have a high control range. We use the index of control range similarity to detect and analyze functional modules in glossary networks and the enzyme-centric network of homo sapiens. Our results, as compared with other approaches to module detection such as modularity optimization algorithm, dynamic algorithm and clique percolation method, indicate that the proposed indices are effective and practical in depicting structural and modular characteristics of sparse directed networks.

Revealing hidden insect-fungus interactions; moderately specialized, modular and anti-nested detritivore networks.

PubMed

Jacobsen, Rannveig M; Sverdrup-Thygeson, Anne; Kauserud, Håvard; Birkemoe, Tone

2018-04-11

Ecological networks are composed of interacting communities that influence ecosystem structure and function. Fungi are the driving force for ecosystem processes such as decomposition and carbon sequestration in terrestrial habitats, and are strongly influenced by interactions with invertebrates. Yet, interactions in detritivore communities have rarely been considered from a network perspective. In the present study, we analyse the interaction networks between three functional guilds of fungi and insects sampled from dead wood. Using DNA metabarcoding to identify fungi, we reveal a diversity of interactions differing in specificity in the detritivore networks, involving three guilds of fungi. Plant pathogenic fungi were relatively unspecialized in their interactions with insects inhabiting dead wood, while interactions between the insects and wood-decay fungi exhibited the highest degree of specialization, which was similar to estimates for animal-mediated seed dispersal networks in previous studies. The low degree of specialization for insect symbiont fungi was unexpected. In general, the pooled insect-fungus networks were significantly more specialized, more modular and less nested than randomized networks. Thus, the detritivore networks had an unusual anti-nested structure. Future studies might corroborate whether this is a common aspect of networks based on interactions with fungi, possibly owing to their often intense competition for substrate. © 2018 The Author(s).

Modular representation of layered neural networks.

PubMed

Watanabe, Chihiro; Hiramatsu, Kaoru; Kashino, Kunio

2018-01-01

Layered neural networks have greatly improved the performance of various applications including image processing, speech recognition, natural language processing, and bioinformatics. However, it is still difficult to discover or interpret knowledge from the inference provided by a layered neural network, since its internal representation has many nonlinear and complex parameters embedded in hierarchical layers. Therefore, it becomes important to establish a new methodology by which layered neural networks can be understood. In this paper, we propose a new method for extracting a global and simplified structure from a layered neural network. Based on network analysis, the proposed method detects communities or clusters of units with similar connection patterns. We show its effectiveness by applying it to three use cases. (1) Network decomposition: it can decompose a trained neural network into multiple small independent networks thus dividing the problem and reducing the computation time. (2) Training assessment: the appropriateness of a trained result with a given hyperparameter or randomly chosen initial parameters can be evaluated by using a modularity index. And (3) data analysis: in practical data it reveals the community structure in the input, hidden, and output layers, which serves as a clue for discovering knowledge from a trained neural network. Copyright © 2017 Elsevier Ltd. All rights reserved.

Detection of protein complex from protein-protein interaction network using Markov clustering

NASA Astrophysics Data System (ADS)

Ochieng, P. J.; Kusuma, W. A.; Haryanto, T.

2017-05-01

Detection of complexes, or groups of functionally related proteins, is an important challenge while analysing biological networks. However, existing algorithms to identify protein complexes are insufficient when applied to dense networks of experimentally derived interaction data. Therefore, we introduced a graph clustering method based on Markov clustering algorithm to identify protein complex within highly interconnected protein-protein interaction networks. Protein-protein interaction network was first constructed to develop geometrical network, the network was then partitioned using Markov clustering to detect protein complexes. The interest of the proposed method was illustrated by its application to Human Proteins associated to type II diabetes mellitus. Flow simulation of MCL algorithm was initially performed and topological properties of the resultant network were analysed for detection of the protein complex. The results indicated the proposed method successfully detect an overall of 34 complexes with 11 complexes consisting of overlapping modules and 20 non-overlapping modules. The major complex consisted of 102 proteins and 521 interactions with cluster modularity and density of 0.745 and 0.101 respectively. The comparison analysis revealed MCL out perform AP, MCODE and SCPS algorithms with high clustering coefficient (0.751) network density and modularity index (0.630). This demonstrated MCL was the most reliable and efficient graph clustering algorithm for detection of protein complexes from PPI networks.

NeAT: a toolbox for the analysis of biological networks, clusters, classes and pathways.

PubMed

Brohée, Sylvain; Faust, Karoline; Lima-Mendez, Gipsi; Sand, Olivier; Janky, Rekin's; Vanderstocken, Gilles; Deville, Yves; van Helden, Jacques

2008-07-01

The network analysis tools (NeAT) (http://rsat.ulb.ac.be/neat/) provide a user-friendly web access to a collection of modular tools for the analysis of networks (graphs) and clusters (e.g. microarray clusters, functional classes, etc.). A first set of tools supports basic operations on graphs (comparison between two graphs, neighborhood of a set of input nodes, path finding and graph randomization). Another set of programs makes the connection between networks and clusters (graph-based clustering, cliques discovery and mapping of clusters onto a network). The toolbox also includes programs for detecting significant intersections between clusters/classes (e.g. clusters of co-expression versus functional classes of genes). NeAT are designed to cope with large datasets and provide a flexible toolbox for analyzing biological networks stored in various databases (protein interactions, regulation and metabolism) or obtained from high-throughput experiments (two-hybrid, mass-spectrometry and microarrays). The web interface interconnects the programs in predefined analysis flows, enabling to address a series of questions about networks of interest. Each tool can also be used separately by entering custom data for a specific analysis. NeAT can also be used as web services (SOAP/WSDL interface), in order to design programmatic workflows and integrate them with other available resources.

Modular assembly for supporting, straining, and directing flow to a core in a nuclear reactor

DOEpatents

Pennell, William E.

1977-01-01

A reactor core support arrangement for supporting, straining, and providing fluid flow to the core and periphery of a nuclear reactor during normal operation. A plurality of removable inlet modular units are contained within permanent liners in the lower supporting plate of the reactor vessel lower internals. During normal operation (1) each inlet modular unit directs main coolant flow to a plurality of core assemblies, the latter being removably supported in receptacles in the upper portion of the modular unit and (2) each inlet modular unit may direct bypass flow to a low pressure annular region of the reactor vessel. Each inlet modular unit may include special fluid seals interposed between mating surfaces of the inlet modular units and the core assemblies and between the inlet modular units and the liners, to minimize leakage and achieve an hydraulic balance. Utilizing the hydraulic balance, the modular units are held in the liners and the assemblies are held in the modular unit receptacles by their own respective weight. Included as part of the permanent liners below the horizontal support plate are generally hexagonal axial debris barriers. The axial debris barriers collectively form a bottom boundary of a secondary high pressure plenum, the upper boundary of which is the bottom surface of the horizontal support plate. Peripheral liners include radial debris barriers which collectively form a barrier against debris entry radially. During normal operation primary coolant inlet openings in the liner, below the axial debris barriers, pass a large amount of coolant into the inlet modular units, and secondary coolant inlet openings in the portion of the liners within the secondary plenum pass a small amount of coolant into the inlet modular units. The secondary coolant inlet openings also provide alternative coolant inlet flow paths in the unlikely event of blockage of the primary inlet openings. The primary inlet openings have characteristics which limit the entry of debris and minimize the potential for debris entering the primary inlets blocking the secondary inlets from inside the modular unit.

Hierarchical thinking in network biology: the unbiased modularization of biochemical networks.

PubMed

Papin, Jason A; Reed, Jennifer L; Palsson, Bernhard O

2004-12-01

As reconstructed biochemical reaction networks continue to grow in size and scope, there is a growing need to describe the functional modules within them. Such modules facilitate the study of biological processes by deconstructing complex biological networks into conceptually simple entities. The definition of network modules is often based on intuitive reasoning. As an alternative, methods are being developed for defining biochemical network modules in an unbiased fashion. These unbiased network modules are mathematically derived from the structure of the whole network under consideration.

A truly Lego®-like modular microfluidics platform

NASA Astrophysics Data System (ADS)

Vittayarukskul, Kevin; Lee, Abraham Phillip

2017-03-01

Ideally, a modular microfluidics platform should be simple to assemble and support 3D configurations for increased versatility. The modular building blocks should also be mass producible like electrical components. These are fundamental features of world-renowned Legos® and why Legos® inspire many existing modular microfluidics platforms. In this paper, a truly Lego®-like microfluidics platform is introduced, and its basic feasibility is demonstrated. Here, PDMS building blocks resembling 2  ×  2 Lego® bricks are cast from 3D-printed master molds. The blocks are pegged and stacked on a traditional Lego® plate to create simple, 3D microfluidic networks, such as a single basket weave. Characteristics of the platform, including reversible sealing and automatic alignment of channels, are also analyzed and discussed in detail.

Ensemble-based modeling and rigidity decomposition of allosteric interaction networks and communication pathways in cyclin-dependent kinases: Differentiating kinase clients of the Hsp90-Cdc37 chaperone

PubMed Central

Stetz, Gabrielle; Tse, Amanda

2017-01-01

The overarching goal of delineating molecular principles underlying differentiation of protein kinase clients and chaperone-based modulation of kinase activity is fundamental to understanding activity of many oncogenic kinases that require chaperoning of Hsp70 and Hsp90 systems to attain a functionally competent active form. Despite structural similarities and common activation mechanisms shared by cyclin-dependent kinase (CDK) proteins, members of this family can exhibit vastly different chaperone preferences. The molecular determinants underlying chaperone dependencies of protein kinases are not fully understood as structurally similar kinases may often elicit distinct regulatory responses to the chaperone. The regulatory divergences observed for members of CDK family are of particular interest as functional diversification among these kinases may be related to variations in chaperone dependencies and can be exploited in drug discovery of personalized therapeutic agents. In this work, we report the results of a computational investigation of several members of CDK family (CDK5, CDK6, CDK9) that represented a broad repertoire of chaperone dependencies—from nonclient CDK5, to weak client CDK6, and strong client CDK9. By using molecular simulations of multiple crystal structures we characterized conformational ensembles and collective dynamics of CDK proteins. We found that the elevated dynamics of CDK9 can trigger imbalances in cooperative collective motions and reduce stability of the active fold, thus creating a cascade of favorable conditions for chaperone intervention. The ensemble-based modeling of residue interaction networks and community analysis determined how differences in modularity of allosteric networks and topography of communication pathways can be linked with the client status of CDK proteins. This analysis unveiled depleted modularity of the allosteric network in CDK9 that alters distribution of communication pathways and leads to impaired signaling in the client kinase. According to our results, these network features may uniquely define chaperone dependencies of CDK clients. The perturbation response scanning and rigidity decomposition approaches identified regulatory hotspots that mediate differences in stability and cooperativity of allosteric interaction networks in the CDK structures. By combining these synergistic approaches, our study revealed dynamic and network signatures that can differentiate kinase clients and rationalize subtle divergences in the activation mechanisms of CDK family members. The therapeutic implications of these results are illustrated by identifying structural hotspots of pathogenic mutations that preferentially target regions of the increased flexibility to enable modulation of activation changes. Our study offers a network-based perspective on dynamic kinase mechanisms and drug design by unravelling relationships between protein kinase dynamics, allosteric communications and chaperone dependencies. PMID:29095844

Unmanned Aerial Vehicle Non Line of Sight Chemical Detection Final Report

DTIC Science & Technology

2016-12-01

aircraft system that is used to perform point detection of chemical warfare agents and collection of vapor, liquid, and solid samples. A modular payload...Standoff Quadcopter Unmanned aircraft system Modular payload 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF...Manufacturing Division, modular payloads are being developed to perform point detection and CBRNE sampling. The available UAS is a quadcopter capable of

CI-KNOW: Cyberinfrastructure Knowledge Networks on the Web. A Social Network Enabled Recommender System for Locating Resources in Cyberinfrastructures

NASA Astrophysics Data System (ADS)

Green, H. D.; Contractor, N. S.; Yao, Y.

2006-12-01

A knowledge network is a multi-dimensional network created from the interactions and interconnections among the scientists, documents, data, analytic tools, and interactive collaboration spaces (like forums and wikis) associated with a collaborative environment. CI-KNOW is a suite of software tools that leverages automated data collection, social network theories, analysis techniques and algorithms to infer an individual's interests and expertise based on their interactions and activities within a knowledge network. The CI-KNOW recommender system mines the knowledge network associated with a scientific community's use of cyberinfrastructure tools and uses relational metadata to record connections among entities in the knowledge network. Recent developments in social network theories and methods provide the backbone for a modular system that creates recommendations from relational metadata. A network navigation portlet allows users to locate colleagues, documents, data or analytic tools in the knowledge network and to explore their networks through a visual, step-wise process. An internal auditing portlet offers administrators diagnostics to assess the growth and health of the entire knowledge network. The first instantiation of the prototype CI-KNOW system is part of the Environmental Cyberinfrastructure Demonstration project at the National Center for Supercomputing Applications, which supports the activities of hydrologic and environmental science communities (CLEANER and CUAHSI) under the umbrella of the WATERS network environmental observatory planning activities (http://cleaner.ncsa.uiuc.edu). This poster summarizes the key aspects of the CI-KNOW system, highlighting the key inputs, calculation mechanisms, and output modalities.

Modular cell biology: retroactivity and insulation

PubMed Central

Del Vecchio, Domitilla; Ninfa, Alexander J; Sontag, Eduardo D

2008-01-01

Modularity plays a fundamental role in the prediction of the behavior of a system from the behavior of its components, guaranteeing that the properties of individual components do not change upon interconnection. Just as electrical, hydraulic, and other physical systems often do not display modularity, nor do many biochemical systems, and specifically, genetic networks. Here, we study the effect of interconnections on the input–output dynamic characteristics of transcriptional components, focusing on a property, which we call ‘retroactivity', that plays a role analogous to non-zero output impedance in electrical systems. In transcriptional networks, retroactivity is large when the amount of transcription factor is comparable to, or smaller than, the amount of promoter-binding sites, or when the affinity of such binding sites is high. To attenuate the effect of retroactivity, we propose a feedback mechanism inspired by the design of amplifiers in electronics. We introduce, in particular, a mechanism based on a phosphorylation–dephosphorylation cycle. This mechanism enjoys a remarkable insulation property, due to the fast timescales of the phosphorylation and dephosphorylation reactions. PMID:18277378

Decomposition of metabolic network into functional modules based on the global connectivity structure of reaction graph.

PubMed

Ma, Hong-Wu; Zhao, Xue-Ming; Yuan, Ying-Jin; Zeng, An-Ping

2004-08-12

Metabolic networks are organized in a modular, hierarchical manner. Methods for a rational decomposition of the metabolic network into relatively independent functional subsets are essential to better understand the modularity and organization principle of a large-scale, genome-wide network. Network decomposition is also necessary for functional analysis of metabolism by pathway analysis methods that are often hampered by the problem of combinatorial explosion due to the complexity of metabolic network. Decomposition methods proposed in literature are mainly based on the connection degree of metabolites. To obtain a more reasonable decomposition, the global connectivity structure of metabolic networks should be taken into account. In this work, we use a reaction graph representation of a metabolic network for the identification of its global connectivity structure and for decomposition. A bow-tie connectivity structure similar to that previously discovered for metabolite graph is found also to exist in the reaction graph. Based on this bow-tie structure, a new decomposition method is proposed, which uses a distance definition derived from the path length between two reactions. An hierarchical classification tree is first constructed from the distance matrix among the reactions in the giant strong component of the bow-tie structure. These reactions are then grouped into different subsets based on the hierarchical tree. Reactions in the IN and OUT subsets of the bow-tie structure are subsequently placed in the corresponding subsets according to a 'majority rule'. Compared with the decomposition methods proposed in literature, ours is based on combined properties of the global network structure and local reaction connectivity rather than, primarily, on the connection degree of metabolites. The method is applied to decompose the metabolic network of Escherichia coli. Eleven subsets are obtained. More detailed investigations of the subsets show that reactions in the same subset are really functionally related. The rational decomposition of metabolic networks, and subsequent studies of the subsets, make it more amenable to understand the inherent organization and functionality of metabolic networks at the modular level. http://genome.gbf.de/bioinformatics/

Evaluating a Modular Design Approach to Collecting Survey Data Using Text Messages

PubMed Central

West, Brady T.; Ghimire, Dirgha; Axinn, William G.

2015-01-01

This article presents analyses of data from a pilot study in Nepal that was designed to provide an initial examination of the errors and costs associated with an innovative methodology for survey data collection. We embedded a randomized experiment within a long-standing panel survey, collecting data on a small number of items with varying sensitivity from a probability sample of 450 young Nepalese adults. Survey items ranged from simple demographics to indicators of substance abuse and mental health problems. Sampled adults were randomly assigned to one of three different modes of data collection: 1) a standard one-time telephone interview, 2) a “single sitting” back-and-forth interview with an interviewer using text messaging, and 3) an interview using text messages within a modular design framework (which generally involves breaking the survey response task into distinct parts over a short period of time). Respondents in the modular group were asked to respond (via text message exchanges with an interviewer) to only one question on a given day, rather than complete the entire survey. Both bivariate and multivariate analyses demonstrate that the two text messaging modes increased the probability of disclosing sensitive information relative to the telephone mode, and that respondents in the modular design group, while responding less frequently, found the survey to be significantly easier. Further, those who responded in the modular group were not unique in terms of available covariates, suggesting that the reduced item response rates only introduced limited nonresponse bias. Future research should consider enhancing this methodology, applying it with other modes of data collection (e. g., web surveys), and continuously evaluating its effectiveness from a total survey error perspective. PMID:26322137

Network portal: a database for storage, analysis and visualization of biological networks

PubMed Central

Turkarslan, Serdar; Wurtmann, Elisabeth J.; Wu, Wei-Ju; Jiang, Ning; Bare, J. Christopher; Foley, Karen; Reiss, David J.; Novichkov, Pavel; Baliga, Nitin S.

2014-01-01

The ease of generating high-throughput data has enabled investigations into organismal complexity at the systems level through the inference of networks of interactions among the various cellular components (genes, RNAs, proteins and metabolites). The wider scientific community, however, currently has limited access to tools for network inference, visualization and analysis because these tasks often require advanced computational knowledge and expensive computing resources. We have designed the network portal (http://networks.systemsbiology.net) to serve as a modular database for the integration of user uploaded and public data, with inference algorithms and tools for the storage, visualization and analysis of biological networks. The portal is fully integrated into the Gaggle framework to seamlessly exchange data with desktop and web applications and to allow the user to create, save and modify workspaces, and it includes social networking capabilities for collaborative projects. While the current release of the database contains networks for 13 prokaryotic organisms from diverse phylogenetic clades (4678 co-regulated gene modules, 3466 regulators and 9291 cis-regulatory motifs), it will be rapidly populated with prokaryotic and eukaryotic organisms as relevant data become available in public repositories and through user input. The modular architecture, simple data formats and open API support community development of the portal. PMID:24271392

Covariance, correlation matrix, and the multiscale community structure of networks.

PubMed

Shen, Hua-Wei; Cheng, Xue-Qi; Fang, Bin-Xing

2010-07-01

Empirical studies show that real world networks often exhibit multiple scales of topological descriptions. However, it is still an open problem how to identify the intrinsic multiple scales of networks. In this paper, we consider detecting the multiscale community structure of network from the perspective of dimension reduction. According to this perspective, a covariance matrix of network is defined to uncover the multiscale community structure through the translation and rotation transformations. It is proved that the covariance matrix is the unbiased version of the well-known modularity matrix. We then point out that the translation and rotation transformations fail to deal with the heterogeneous network, which is very common in nature and society. To address this problem, a correlation matrix is proposed through introducing the rescaling transformation into the covariance matrix. Extensive tests on real world and artificial networks demonstrate that the correlation matrix significantly outperforms the covariance matrix, identically the modularity matrix, as regards identifying the multiscale community structure of network. This work provides a novel perspective to the identification of community structure and thus various dimension reduction methods might be used for the identification of community structure. Through introducing the correlation matrix, we further conclude that the rescaling transformation is crucial to identify the multiscale community structure of network, as well as the translation and rotation transformations.

Modularity and evolutionary constraints in a baculovirus gene regulatory network

PubMed Central

2013-01-01

Background The structure of regulatory networks remains an open question in our understanding of complex biological systems. Interactions during complete viral life cycles present unique opportunities to understand how host-parasite network take shape and behave. The Anticarsia gemmatalis multiple nucleopolyhedrovirus (AgMNPV) is a large double-stranded DNA virus, whose genome may encode for 152 open reading frames (ORFs). Here we present the analysis of the ordered cascade of the AgMNPV gene expression. Results We observed an earlier onset of the expression than previously reported for other baculoviruses, especially for genes involved in DNA replication. Most ORFs were expressed at higher levels in a more permissive host cell line. Genes with more than one copy in the genome had distinct expression profiles, which could indicate the acquisition of new functionalities. The transcription gene regulatory network (GRN) for 149 ORFs had a modular topology comprising five communities of highly interconnected nodes that separated key genes that are functionally related on different communities, possibly maximizing redundancy and GRN robustness by compartmentalization of important functions. Core conserved functions showed expression synchronicity, distinct GRN features and significantly less genetic diversity, consistent with evolutionary constraints imposed in key elements of biological systems. This reduced genetic diversity also had a positive correlation with the importance of the gene in our estimated GRN, supporting a relationship between phylogenetic data of baculovirus genes and network features inferred from expression data. We also observed that gene arrangement in overlapping transcripts was conserved among related baculoviruses, suggesting a principle of genome organization. Conclusions Albeit with a reduced number of nodes (149), the AgMNPV GRN had a topology and key characteristics similar to those observed in complex cellular organisms, which indicates that modularity may be a general feature of biological gene regulatory networks. PMID:24006890

Atmospheric reaction systems as null-models to identify structural traces of evolution in metabolism.

PubMed

Holme, Petter; Huss, Mikael; Lee, Sang Hoon

2011-05-06

The metabolism is the motor behind the biological complexity of an organism. One problem of characterizing its large-scale structure is that it is hard to know what to compare it to. All chemical reaction systems are shaped by the same physics that gives molecules their stability and affinity to react. These fundamental factors cannot be captured by standard null-models based on randomization. The unique property of organismal metabolism is that it is controlled, to some extent, by an enzymatic machinery that is subject to evolution. In this paper, we explore the possibility that reaction systems of planetary atmospheres can serve as a null-model against which we can define metabolic structure and trace the influence of evolution. We find that the two types of data can be distinguished by their respective degree distributions. This is especially clear when looking at the degree distribution of the reaction network (of reaction connected to each other if they involve the same molecular species). For the Earth's atmospheric network and the human metabolic network, we look into more detail for an underlying explanation of this deviation. However, we cannot pinpoint a single cause of the difference, rather there are several concurrent factors. By examining quantities relating to the modular-functional organization of the metabolism, we confirm that metabolic networks have a more complex modular organization than the atmospheric networks, but not much more. We interpret the more variegated modular arrangement of metabolism as a trace of evolved functionality. On the other hand, it is quite remarkable how similar the structures of these two types of networks are, which emphasizes that the constraints from the chemical properties of the molecules has a larger influence in shaping the reaction system than does natural selection.

Using collective variables to drive molecular dynamics simulations

NASA Astrophysics Data System (ADS)

Fiorin, Giacomo; Klein, Michael L.; Hénin, Jérôme

2013-12-01

A software framework is introduced that facilitates the application of biasing algorithms to collective variables of the type commonly employed to drive massively parallel molecular dynamics (MD) simulations. The modular framework that is presented enables one to combine existing collective variables into new ones, and combine any chosen collective variable with available biasing methods. The latter include the classic time-dependent biases referred to as steered MD and targeted MD, the temperature-accelerated MD algorithm, as well as the adaptive free-energy biases called metadynamics and adaptive biasing force. The present modular software is extensible, and portable between commonly used MD simulation engines.

Constructing Social Networks From Secondary Storage With Bulk Analysis Tools

DTIC Science & Technology

2016-06-01

that classic measures of centrality are effective for identifying important nodes and close associates, and that further study of modularity classes...which ground truth was determined by interviews with the owners, and which can be used for future study in this area. Two objectives motivated this thesis...tifying important nodes and close associates, and that further study of modularity classes may be a promising method of partitioning complex components

Modular organization of the white spruce (Picea glauca) transcriptome reveals functional organization and evolutionary signatures.

PubMed

Raherison, Elie S M; Giguère, Isabelle; Caron, Sébastien; Lamara, Mebarek; MacKay, John J

2015-07-01

Transcript profiling has shown the molecular bases of several biological processes in plants but few studies have developed an understanding of overall transcriptome variation. We investigated transcriptome structure in white spruce (Picea glauca), aiming to delineate its modular organization and associated functional and evolutionary attributes. Microarray analyses were used to: identify and functionally characterize groups of co-expressed genes; investigate expressional and functional diversity of vascular tissue preferential genes which were conserved among Picea species, and identify expression networks underlying wood formation. We classified 22 857 genes as variable (79%; 22 coexpression groups) or invariant (21%) by profiling across several vegetative tissues. Modular organization and complex transcriptome restructuring among vascular tissue preferential genes was revealed by their assignment to coexpression groups with partially overlapping profiles and partially distinct functions. Integrated analyses of tissue-based and temporally variable profiles identified secondary xylem gene networks, showed their remodelling over a growing season and identified PgNAC-7 (no apical meristerm (NAM), Arabidopsis transcription activation factor (ATAF) and cup-shaped cotyledon (CUC) transcription factor 007 in Picea glauca) as a major hub gene specific to earlywood formation. Reference profiling identified comprehensive, statistically robust coexpressed groups, revealing that modular organization underpins the evolutionary conservation of the transcriptome structure. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

Simulation and Modeling Capability for Standard Modular Hydropower Technology

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

Stewart, Kevin M.; Smith, Brennan T.; Witt, Adam M.

Grounded in the stakeholder-validated framework established in Oak Ridge National Laboratory’s SMH Exemplary Design Envelope Specification, this report on Simulation and Modeling Capability for Standard Modular Hydropower (SMH) Technology provides insight into the concepts, use cases, needs, gaps, and challenges associated with modeling and simulating SMH technologies. The SMH concept envisions a network of generation, passage, and foundation modules that achieve environmentally compatible, cost-optimized hydropower using standardization and modularity. The development of standardized modeling approaches and simulation techniques for SMH (as described in this report) will pave the way for reliable, cost-effective methods for technology evaluation, optimization, and verification.

Nonparametric Bayesian inference of the microcanonical stochastic block model

NASA Astrophysics Data System (ADS)

Peixoto, Tiago P.

2017-01-01

A principled approach to characterize the hidden modular structure of networks is to formulate generative models and then infer their parameters from data. When the desired structure is composed of modules or "communities," a suitable choice for this task is the stochastic block model (SBM), where nodes are divided into groups, and the placement of edges is conditioned on the group memberships. Here, we present a nonparametric Bayesian method to infer the modular structure of empirical networks, including the number of modules and their hierarchical organization. We focus on a microcanonical variant of the SBM, where the structure is imposed via hard constraints, i.e., the generated networks are not allowed to violate the patterns imposed by the model. We show how this simple model variation allows simultaneously for two important improvements over more traditional inference approaches: (1) deeper Bayesian hierarchies, with noninformative priors replaced by sequences of priors and hyperpriors, which not only remove limitations that seriously degrade the inference on large networks but also reveal structures at multiple scales; (2) a very efficient inference algorithm that scales well not only for networks with a large number of nodes and edges but also with an unlimited number of modules. We show also how this approach can be used to sample modular hierarchies from the posterior distribution, as well as to perform model selection. We discuss and analyze the differences between sampling from the posterior and simply finding the single parameter estimate that maximizes it. Furthermore, we expose a direct equivalence between our microcanonical approach and alternative derivations based on the canonical SBM.

Applying Neural Networks to Air Force Personnel Analysis

DTIC Science & Technology

1992-03-01

Asakawa, Yoda, and Takeoka (1990); Atlas, Cole, Conner, EI-Sharkawi, Marks, Muthusamy, and Barnard (1990); Leung and Zue (1989); and Denker, Gardner...Transactions on Neural Networks, 1(2), 239-242. Kimoto, T., Asakawa, K., Yoda, M., & Takeoka , M. (1990). Stock market prediction with modular neural

Evaluating the Spatio-Temporal Factors that Structure Network Parameters of Plant-Herbivore Interactions

PubMed Central

López-Carretero, Antonio; Díaz-Castelazo, Cecilia; Boege, Karina; Rico-Gray, Víctor

2014-01-01

Despite the dynamic nature of ecological interactions, most studies on species networks offer static representations of their structure, constraining our understanding of the ecological mechanisms involved in their spatio-temporal stability. This is the first study to evaluate plant-herbivore interaction networks on a small spatio-temporal scale. Specifically, we simultaneously assessed the effect of host plant availability, habitat complexity and seasonality on the structure of plant-herbivore networks in a coastal tropical ecosystem. Our results revealed that changes in the host plant community resulting from seasonality and habitat structure are reflected not only in the herbivore community, but also in the emergent properties (network parameters) of the plant-herbivore interaction network such as connectance, selectiveness and modularity. Habitat conditions and periods that are most stressful favored the presence of less selective and susceptible herbivore species, resulting in increased connectance within networks. In contrast, the high degree of selectivennes (i.e. interaction specialization) and modularity of the networks under less stressful conditions was promoted by the diversification in resource use by herbivores. By analyzing networks at a small spatio-temporal scale we identified the ecological factors structuring this network such as habitat complexity and seasonality. Our research offers new evidence on the role of abiotic and biotic factors in the variation of the properties of species interaction networks. PMID:25340790

Low-power cryptographic coprocessor for autonomous wireless sensor networks

NASA Astrophysics Data System (ADS)

Olszyna, Jakub; Winiecki, Wiesław

2013-10-01

The concept of autonomous wireless sensor networks involves energy harvesting, as well as effective management of system resources. Public-key cryptography (PKC) offers the advantage of elegant key agreement schemes with which a secret key can be securely established over unsecure channels. In addition to solving the key management problem, the other major application of PKC is digital signatures, with which non-repudiation of messages exchanges can be achieved. The motivation for studying low-power and area efficient modular arithmetic algorithms comes from enabling public-key security for low-power devices that can perform under constrained environment like autonomous wireless sensor networks. This paper presents a cryptographic coprocessor tailored to the autonomous wireless sensor networks constraints. Such hardware circuit is aimed to support the implementation of different public-key cryptosystems based on modular arithmetic in GF(p) and GF(2m). Key components of the coprocessor are described as GEZEL models and can be easily transformed to VHDL and implemented in hardware.

Stochastic resonance on a modular neuronal network of small-world subnetworks with a subthreshold pacemaker

NASA Astrophysics Data System (ADS)

Yu, Haitao; Wang, Jiang; Liu, Chen; Deng, Bin; Wei, Xile

2011-12-01

We study the phenomenon of stochastic resonance on a modular neuronal network consisting of several small-world subnetworks with a subthreshold periodic pacemaker. Numerical results show that the correlation between the pacemaker frequency and the dynamical response of the network is resonantly dependent on the intensity of additive spatiotemporal noise. This effect of pacemaker-driven stochastic resonance of the system depends extensively on the local and the global network structure, such as the intra- and inter-coupling strengths, rewiring probability of individual small-world subnetwork, the number of links between different subnetworks, and the number of subnetworks. All these parameters play a key role in determining the ability of the network to enhance the noise-induced outreach of the localized subthreshold pacemaker, and only they bounded to a rather sharp interval of values warrant the emergence of the pronounced stochastic resonance phenomenon. Considering the rather important role of pacemakers in real-life, the presented results could have important implications for many biological processes that rely on an effective pacemaker for their proper functioning.

Impulsivity and the Modular Organization of Resting-State Neural Networks

PubMed Central

Davis, F. Caroline; Knodt, Annchen R.; Sporns, Olaf; Lahey, Benjamin B.; Zald, David H.; Brigidi, Bart D.; Hariri, Ahmad R.

2013-01-01

Impulsivity is a complex trait associated with a range of maladaptive behaviors, including many forms of psychopathology. Previous research has implicated multiple neural circuits and neurotransmitter systems in impulsive behavior, but the relationship between impulsivity and organization of whole-brain networks has not yet been explored. Using graph theory analyses, we characterized the relationship between impulsivity and the functional segregation (“modularity”) of the whole-brain network architecture derived from resting-state functional magnetic resonance imaging (fMRI) data. These analyses revealed remarkable differences in network organization across the impulsivity spectrum. Specifically, in highly impulsive individuals, regulatory structures including medial and lateral regions of the prefrontal cortex were isolated from subcortical structures associated with appetitive drive, whereas these brain areas clustered together within the same module in less impulsive individuals. Further exploration of the modular organization of whole-brain networks revealed novel shifts in the functional connectivity between visual, sensorimotor, cortical, and subcortical structures across the impulsivity spectrum. The current findings highlight the utility of graph theory analyses of resting-state fMRI data in furthering our understanding of the neurobiological architecture of complex behaviors. PMID:22645253

Effects of amyloid and small vessel disease on white matter network disruption.

PubMed

Kim, Hee Jin; Im, Kiho; Kwon, Hunki; Lee, Jong Min; Ye, Byoung Seok; Kim, Yeo Jin; Cho, Hanna; Choe, Yearn Seong; Lee, Kyung Han; Kim, Sung Tae; Kim, Jae Seung; Lee, Jae Hong; Na, Duk L; Seo, Sang Won

2015-01-01

There is growing evidence that the human brain is a large scale complex network. The structural network is reported to be disrupted in cognitively impaired patients. However, there have been few studies evaluating the effects of amyloid and small vessel disease (SVD) markers, the common causes of cognitive impairment, on structural networks. Thus, we evaluated the association between amyloid and SVD burdens and structural networks using diffusion tensor imaging (DTI). Furthermore, we determined if network parameters predict cognitive impairments. Graph theoretical analysis was applied to DTI data from 232 cognitively impaired patients with varying degrees of amyloid and SVD burdens. All patients underwent Pittsburgh compound-B (PiB) PET to detect amyloid burden, MRI to detect markers of SVD, including the volume of white matter hyperintensities and the number of lacunes, and detailed neuropsychological testing. The whole-brain network was assessed by network parameters of integration (shortest path length, global efficiency) and segregation (clustering coefficient, transitivity, modularity). PiB retention ratio was not associated with any white matter network parameters. Greater white matter hyperintensity volumes or lacunae numbers were significantly associated with decreased network integration (increased shortest path length, decreased global efficiency) and increased network segregation (increased clustering coefficient, increased transitivity, increased modularity). Decreased network integration or increased network segregation were associated with poor performances in attention, language, visuospatial, memory, and frontal-executive functions. Our results suggest that SVD alters white matter network integration and segregation, which further predicts cognitive dysfunction.

Exploring the topological sources of robustness against invasion in biological and technological networks.

PubMed

Alcalde Cuesta, Fernando; González Sequeiros, Pablo; Lozano Rojo, Álvaro

2016-02-10

For a network, the accomplishment of its functions despite perturbations is called robustness. Although this property has been extensively studied, in most cases, the network is modified by removing nodes. In our approach, it is no longer perturbed by site percolation, but evolves after site invasion. The process transforming resident/healthy nodes into invader/mutant/diseased nodes is described by the Moran model. We explore the sources of robustness (or its counterpart, the propensity to spread favourable innovations) of the US high-voltage power grid network, the Internet2 academic network, and the C. elegans connectome. We compare them to three modular and non-modular benchmark networks, and samples of one thousand random networks with the same degree distribution. It is found that, contrary to what happens with networks of small order, fixation probability and robustness are poorly correlated with most of standard statistics, but they depend strongly on the degree distribution. While community detection techniques are able to detect the existence of a central core in Internet2, they are not effective in detecting hierarchical structures whose topological complexity arises from the repetition of a few rules. Box counting dimension and Rent's rule are applied to show a subtle trade-off between topological and wiring complexity.

Exploring the topological sources of robustness against invasion in biological and technological networks

PubMed Central

Alcalde Cuesta, Fernando; González Sequeiros, Pablo; Lozano Rojo, Álvaro

2016-01-01

For a network, the accomplishment of its functions despite perturbations is called robustness. Although this property has been extensively studied, in most cases, the network is modified by removing nodes. In our approach, it is no longer perturbed by site percolation, but evolves after site invasion. The process transforming resident/healthy nodes into invader/mutant/diseased nodes is described by the Moran model. We explore the sources of robustness (or its counterpart, the propensity to spread favourable innovations) of the US high-voltage power grid network, the Internet2 academic network, and the C. elegans connectome. We compare them to three modular and non-modular benchmark networks, and samples of one thousand random networks with the same degree distribution. It is found that, contrary to what happens with networks of small order, fixation probability and robustness are poorly correlated with most of standard statistics, but they depend strongly on the degree distribution. While community detection techniques are able to detect the existence of a central core in Internet2, they are not effective in detecting hierarchical structures whose topological complexity arises from the repetition of a few rules. Box counting dimension and Rent’s rule are applied to show a subtle trade-off between topological and wiring complexity. PMID:26861189

Modular transcriptional repertoire and MicroRNA target analyses characterize genomic dysregulation in the thymus of Down syndrome infants

PubMed Central

Moreira-Filho, Carlos Alberto; Bando, Silvia Yumi; Bertonha, Fernanda Bernardi; Silva, Filipi Nascimento; da Fontoura Costa, Luciano; Ferreira, Leandro Rodrigues; Furlanetto, Glaucio; Chacur, Paulo; Zerbini, Maria Claudia Nogueira; Carneiro-Sampaio, Magda

2016-01-01

Trisomy 21-driven transcriptional alterations in human thymus were characterized through gene coexpression network (GCN) and miRNA-target analyses. We used whole thymic tissue - obtained at heart surgery from Down syndrome (DS) and karyotipically normal subjects (CT) - and a network-based approach for GCN analysis that allows the identification of modular transcriptional repertoires (communities) and the interactions between all the system's constituents through community detection. Changes in the degree of connections observed for hierarchically important hubs/genes in CT and DS networks corresponded to community changes. Distinct communities of highly interconnected genes were topologically identified in these networks. The role of miRNAs in modulating the expression of highly connected genes in CT and DS was revealed through miRNA-target analysis. Trisomy 21 gene dysregulation in thymus may be depicted as the breakdown and altered reorganization of transcriptional modules. Leading networks acting in normal or disease states were identified. CT networks would depict the “canonical” way of thymus functioning. Conversely, DS networks represent a “non-canonical” way, i.e., thymic tissue adaptation under trisomy 21 genomic dysregulation. This adaptation is probably driven by epigenetic mechanisms acting at chromatin level and through the miRNA control of transcriptional programs involving the networks' high-hierarchy genes. PMID:26848775

Modular architecture of protein structures and allosteric communications: potential implications for signaling proteins and regulatory linkages

PubMed Central

del Sol, Antonio; Araúzo-Bravo, Marcos J; Amoros, Dolors; Nussinov, Ruth

2007-01-01

Background Allosteric communications are vital for cellular signaling. Here we explore a relationship between protein architectural organization and shortcuts in signaling pathways. Results We show that protein domains consist of modules interconnected by residues that mediate signaling through the shortest pathways. These mediating residues tend to be located at the inter-modular boundaries, which are more rigid and display a larger number of long-range interactions than intra-modular regions. The inter-modular boundaries contain most of the residues centrally conserved in the protein fold, which may be crucial for information transfer between amino acids. Our approach to modular decomposition relies on a representation of protein structures as residue-interacting networks, and removal of the most central residue contacts, which are assumed to be crucial for allosteric communications. The modular decomposition of 100 multi-domain protein structures indicates that modules constitute the building blocks of domains. The analysis of 13 allosteric proteins revealed that modules characterize experimentally identified functional regions. Based on the study of an additional functionally annotated dataset of 115 proteins, we propose that high-modularity modules include functional sites and are the basic functional units. We provide examples (the Gαs subunit and P450 cytochromes) to illustrate that the modular architecture of active sites is linked to their functional specialization. Conclusion Our method decomposes protein structures into modules, allowing the study of signal transmission between functional sites. A modular configuration might be advantageous: it allows signaling proteins to expand their regulatory linkages and may elicit a broader range of control mechanisms either via modular combinations or through modulation of inter-modular linkages. PMID:17531094

Rational Modular RNA Engineering Based on In Vivo Profiling of Structural Accessibility.

PubMed

Leistra, Abigail N; Amador, Paul; Buvanendiran, Aishwarya; Moon-Walker, Alex; Contreras, Lydia M

2017-12-15

Bacterial small RNAs (sRNAs) have been established as powerful parts for controlling gene expression. However, development and application of engineered sRNAs has primarily focused on regulating novel synthetic targets. In this work, we demonstrate a rational modular RNA engineering approach that uses in vivo structural accessibility measurements to tune the regulatory activity of a multisubstrate sRNA for differential control of its native target network. Employing the CsrB global sRNA regulator as a model system, we use published in vivo structural accessibility data to infer the contribution of its local structures (substructures) to function and select a subset for engineering. We then modularly recombine the selected substructures, differentially representing those of presumed high or low functional contribution, to build a library of 21 CsrB variants. Using fluorescent translational reporter assays, we demonstrate that the CsrB variants achieve a 5-fold gradient of control of well-characterized Csr network targets. Interestingly, results suggest that less conserved local structures within long, multisubstrate sRNAs may represent better targets for rational engineering than their well-conserved counterparts. Lastly, mapping the impact of sRNA variants on a signature Csr network phenotype indicates the potential of this approach for tuning the activity of global sRNA regulators in the context of metabolic engineering applications.

Markov Networks of Collateral Resistance: National Antimicrobial Resistance Monitoring System Surveillance Results from Escherichia coli Isolates, 2004-2012

PubMed Central

Love, William J.; Zawack, Kelson A.; Booth, James G.; Grӧhn, Yrjo T.

2016-01-01

Surveillance of antimicrobial resistance (AMR) is an important component of public health. Antimicrobial drug use generates selective pressure that may lead to resistance against to the administered drug, and may also select for collateral resistances to other drugs. Analysis of AMR surveillance data has focused on resistance to individual drugs but joint distributions of resistance in bacterial populations are infrequently analyzed and reported. New methods are needed to characterize and communicate joint resistance distributions. Markov networks are a class of graphical models that define connections, or edges, between pairs of variables with non-zero partial correlations and are used here to describe AMR resistance relationships. The graphical least absolute shrinkage and selection operator is used to estimate sparse Markov networks from AMR surveillance data. The method is demonstrated using a subset of Escherichia coli isolates collected by the National Antimicrobial Resistance Monitoring System between 2004 and 2012 which included AMR results for 16 drugs from 14418 isolates. Of the 119 possible unique edges, 33 unique edges were identified at least once during the study period and graphical density ranged from 16.2% to 24.8%. Two frequent dense subgraphs were noted, one containing the five β-lactam drugs and the other containing both sulfonamides, three aminoglycosides, and tetracycline. Density did not appear to change over time (p = 0.71). Unweighted modularity did not appear to change over time (p = 0.18), but a significant decreasing trend was noted in the modularity of the weighted networks (p < 0.005) indicating relationships between drugs of different classes tended to increase in strength and frequency over time compared to relationships between drugs of the same class. The current method provides a novel method to study the joint resistance distribution, but additional work is required to unite the underlying biological and genetic characteristics of the isolates with the current results derived from phenotypic data. PMID:27851767

Markov Networks of Collateral Resistance: National Antimicrobial Resistance Monitoring System Surveillance Results from Escherichia coli Isolates, 2004-2012.

PubMed

Love, William J; Zawack, Kelson A; Booth, James G; Grӧhn, Yrjo T; Lanzas, Cristina

2016-11-01

Surveillance of antimicrobial resistance (AMR) is an important component of public health. Antimicrobial drug use generates selective pressure that may lead to resistance against to the administered drug, and may also select for collateral resistances to other drugs. Analysis of AMR surveillance data has focused on resistance to individual drugs but joint distributions of resistance in bacterial populations are infrequently analyzed and reported. New methods are needed to characterize and communicate joint resistance distributions. Markov networks are a class of graphical models that define connections, or edges, between pairs of variables with non-zero partial correlations and are used here to describe AMR resistance relationships. The graphical least absolute shrinkage and selection operator is used to estimate sparse Markov networks from AMR surveillance data. The method is demonstrated using a subset of Escherichia coli isolates collected by the National Antimicrobial Resistance Monitoring System between 2004 and 2012 which included AMR results for 16 drugs from 14418 isolates. Of the 119 possible unique edges, 33 unique edges were identified at least once during the study period and graphical density ranged from 16.2% to 24.8%. Two frequent dense subgraphs were noted, one containing the five β-lactam drugs and the other containing both sulfonamides, three aminoglycosides, and tetracycline. Density did not appear to change over time (p = 0.71). Unweighted modularity did not appear to change over time (p = 0.18), but a significant decreasing trend was noted in the modularity of the weighted networks (p < 0.005) indicating relationships between drugs of different classes tended to increase in strength and frequency over time compared to relationships between drugs of the same class. The current method provides a novel method to study the joint resistance distribution, but additional work is required to unite the underlying biological and genetic characteristics of the isolates with the current results derived from phenotypic data.

Evolution and development of brain networks: from Caenorhabditis elegans to Homo sapiens.

PubMed

Kaiser, Marcus; Varier, Sreedevi

2011-01-01

Neural networks show a progressive increase in complexity during the time course of evolution. From diffuse nerve nets in Cnidaria to modular, hierarchical systems in macaque and humans, there is a gradual shift from simple processes involving a limited amount of tasks and modalities to complex functional and behavioral processing integrating different kinds of information from highly specialized tissue. However, studies in a range of species suggest that fundamental similarities, in spatial and topological features as well as in developmental mechanisms for network formation, are retained across evolution. 'Small-world' topology and highly connected regions (hubs) are prevalent across the evolutionary scale, ensuring efficient processing and resilience to internal (e.g. lesions) and external (e.g. environment) changes. Furthermore, in most species, even the establishment of hubs, long-range connections linking distant components, and a modular organization, relies on similar mechanisms. In conclusion, evolutionary divergence leads to greater complexity while following essential developmental constraints.

On Event-Triggered Adaptive Architectures for Decentralized and Distributed Control of Large-Scale Modular Systems

PubMed Central

Albattat, Ali; Gruenwald, Benjamin C.; Yucelen, Tansel

2016-01-01

The last decade has witnessed an increased interest in physical systems controlled over wireless networks (networked control systems). These systems allow the computation of control signals via processors that are not attached to the physical systems, and the feedback loops are closed over wireless networks. The contribution of this paper is to design and analyze event-triggered decentralized and distributed adaptive control architectures for uncertain networked large-scale modular systems; that is, systems consist of physically-interconnected modules controlled over wireless networks. Specifically, the proposed adaptive architectures guarantee overall system stability while reducing wireless network utilization and achieving a given system performance in the presence of system uncertainties that can result from modeling and degraded modes of operation of the modules and their interconnections between each other. In addition to the theoretical findings including rigorous system stability and the boundedness analysis of the closed-loop dynamical system, as well as the characterization of the effect of user-defined event-triggering thresholds and the design parameters of the proposed adaptive architectures on the overall system performance, an illustrative numerical example is further provided to demonstrate the efficacy of the proposed decentralized and distributed control approaches. PMID:27537894

On Event-Triggered Adaptive Architectures for Decentralized and Distributed Control of Large-Scale Modular Systems.

PubMed

Albattat, Ali; Gruenwald, Benjamin C; Yucelen, Tansel

2016-08-16

The last decade has witnessed an increased interest in physical systems controlled over wireless networks (networked control systems). These systems allow the computation of control signals via processors that are not attached to the physical systems, and the feedback loops are closed over wireless networks. The contribution of this paper is to design and analyze event-triggered decentralized and distributed adaptive control architectures for uncertain networked large-scale modular systems; that is, systems consist of physically-interconnected modules controlled over wireless networks. Specifically, the proposed adaptive architectures guarantee overall system stability while reducing wireless network utilization and achieving a given system performance in the presence of system uncertainties that can result from modeling and degraded modes of operation of the modules and their interconnections between each other. In addition to the theoretical findings including rigorous system stability and the boundedness analysis of the closed-loop dynamical system, as well as the characterization of the effect of user-defined event-triggering thresholds and the design parameters of the proposed adaptive architectures on the overall system performance, an illustrative numerical example is further provided to demonstrate the efficacy of the proposed decentralized and distributed control approaches.

Modular decomposition of metabolic reaction networks based on flux analysis and pathway projection.

PubMed

Yoon, Jeongah; Si, Yaguang; Nolan, Ryan; Lee, Kyongbum

2007-09-15

The rational decomposition of biochemical networks into sub-structures has emerged as a useful approach to study the design of these complex systems. A biochemical network is characterized by an inhomogeneous connectivity distribution, which gives rise to several organizational features, including modularity. To what extent the connectivity-based modules reflect the functional organization of the network remains to be further explored. In this work, we examine the influence of physiological perturbations on the modular organization of cellular metabolism. Modules were characterized for two model systems, liver and adipocyte primary metabolism, by applying an algorithm for top-down partition of directed graphs with non-uniform edge weights. The weights were set by the engagement of the corresponding reactions as expressed by the flux distribution. For the base case of the fasted rat liver, three modules were found, carrying out the following biochemical transformations: ketone body production, glucose synthesis and transamination. This basic organization was further modified when different flux distributions were applied that describe the liver's metabolic response to whole body inflammation. For the fully mature adipocyte, only a single module was observed, integrating all of the major pathways needed for lipid storage. Weaker levels of integration between the pathways were found for the early stages of adipocyte differentiation. Our results underscore the inhomogeneous distribution of both connectivity and connection strengths, and suggest that global activity data such as the flux distribution can be used to study the organizational flexibility of cellular metabolism. Supplementary data are available at Bioinformatics online.

Increased segregation of brain networks in focal epilepsy: An fMRI graph theory finding.

PubMed

Pedersen, Mangor; Omidvarnia, Amir H; Walz, Jennifer M; Jackson, Graeme D

2015-01-01

Focal epilepsy is conceived of as activating local areas of the brain as well as engaging regional brain networks. Graph theory represents a powerful quantitative framework for investigation of brain networks. Here we investigate whether functional network changes are present in extratemporal focal epilepsy. Task-free functional magnetic resonance imaging data from 15 subjects with extratemporal epilepsy and 26 age and gender matched healthy controls were used for analysis. Local network properties were calculated using local efficiency, clustering coefficient and modularity metrics. Global network properties were assessed with global efficiency and betweenness centrality metrics. Cost-efficiency of the networks at both local and global levels was evaluated by estimating the physical distance between functionally connected nodes, in addition to the overall numbers of connections in the network. Clustering coefficient, local efficiency and modularity were significantly higher in individuals with focal epilepsy than healthy control subjects, while global efficiency and betweenness centrality were not significantly different between the two groups. Local network properties were also highly efficient, at low cost, in focal epilepsy subjects compared to healthy controls. Our results show that functional networks in focal epilepsy are altered in a way that the nodes of the network are more isolated. We postulate that network regularity, or segregation of the nodes of the networks, may be an adaptation that inhibits the conversion of the interictal state to seizures. It remains possible that this may be part of the epileptogenic process or an effect of medications.

Increased segregation of brain networks in focal epilepsy: An fMRI graph theory finding

PubMed Central

Pedersen, Mangor; Omidvarnia, Amir H.; Walz, Jennifer M.; Jackson, Graeme D.

2015-01-01

Focal epilepsy is conceived of as activating local areas of the brain as well as engaging regional brain networks. Graph theory represents a powerful quantitative framework for investigation of brain networks. Here we investigate whether functional network changes are present in extratemporal focal epilepsy. Task-free functional magnetic resonance imaging data from 15 subjects with extratemporal epilepsy and 26 age and gender matched healthy controls were used for analysis. Local network properties were calculated using local efficiency, clustering coefficient and modularity metrics. Global network properties were assessed with global efficiency and betweenness centrality metrics. Cost-efficiency of the networks at both local and global levels was evaluated by estimating the physical distance between functionally connected nodes, in addition to the overall numbers of connections in the network. Clustering coefficient, local efficiency and modularity were significantly higher in individuals with focal epilepsy than healthy control subjects, while global efficiency and betweenness centrality were not significantly different between the two groups. Local network properties were also highly efficient, at low cost, in focal epilepsy subjects compared to healthy controls. Our results show that functional networks in focal epilepsy are altered in a way that the nodes of the network are more isolated. We postulate that network regularity, or segregation of the nodes of the networks, may be an adaptation that inhibits the conversion of the interictal state to seizures. It remains possible that this may be part of the epileptogenic process or an effect of medications. PMID:26110111

A method of Modelling and Simulating the Back-to-Back Modular Multilevel Converter HVDC Transmission System

NASA Astrophysics Data System (ADS)

Wang, Lei; Fan, Youping; Zhang, Dai; Ge, Mengxin; Zou, Xianbin; Li, Jingjiao

2017-09-01

This paper proposes a method to simulate a back-to-back modular multilevel converter (MMC) HVDC transmission system. In this paper we utilize an equivalent networks to simulate the dynamic power system. Moreover, to account for the performance of converter station, core components of model of the converter station gives a basic model of simulation. The proposed method is applied to an equivalent real power system.

Cosmic-ray discrimination capabilities of /ΔE-/E silicon nuclear telescopes using neural networks

NASA Astrophysics Data System (ADS)

Ambriola, M.; Bellotti, R.; Cafagna, F.; Castellano, M.; Ciacio, F.; Circella, M.; Marzo, C. N. D.; Montaruli, T.

2000-02-01

An isotope classifier of cosmic-ray events collected by space detectors has been implemented using a multi-layer perceptron neural architecture. In order to handle a great number of different isotopes a modular architecture of the ``mixture of experts'' type is proposed. The performance of this classifier has been tested on simulated data and has been compared with a ``classical'' classifying procedure. The quantitative comparison with traditional techniques shows that the neural approach has classification performances comparable - within /1% - with that of the classical one, with efficiency of the order of /98%. A possible hardware implementation of such a kind of neural architecture in future space missions is considered.

The influence of phylogeny, social style, and sociodemographic factors on macaque social network structure.

PubMed

Balasubramaniam, Krishna N; Beisner, Brianne A; Berman, Carol M; De Marco, Arianna; Duboscq, Julie; Koirala, Sabina; Majolo, Bonaventura; MacIntosh, Andrew J; McFarland, Richard; Molesti, Sandra; Ogawa, Hideshi; Petit, Odile; Schino, Gabriele; Sosa, Sebastian; Sueur, Cédric; Thierry, Bernard; de Waal, Frans B M; McCowan, Brenda

2018-01-01

Among nonhuman primates, the evolutionary underpinnings of variation in social structure remain debated, with both ancestral relationships and adaptation to current conditions hypothesized to play determining roles. Here we assess whether interspecific variation in higher-order aspects of female macaque (genus: Macaca) dominance and grooming social structure show phylogenetic signals, that is, greater similarity among more closely-related species. We use a social network approach to describe higher-order characteristics of social structure, based on both direct interactions and secondary pathways that connect group members. We also ask whether network traits covary with each other, with species-typical social style grades, and/or with sociodemographic characteristics, specifically group size, sex-ratio, and current living condition (captive vs. free-living). We assembled 34-38 datasets of female-female dyadic aggression and allogrooming among captive and free-living macaques representing 10 species. We calculated dominance (transitivity, certainty), and grooming (centrality coefficient, Newman's modularity, clustering coefficient) network traits as aspects of social structure. Computations of K statistics and randomization tests on multiple phylogenies revealed moderate-strong phylogenetic signals in dominance traits, but moderate-weak signals in grooming traits. GLMMs showed that grooming traits did not covary with dominance traits and/or social style grade. Rather, modularity and clustering coefficient, but not centrality coefficient, were strongly predicted by group size and current living condition. Specifically, larger groups showed more modular networks with sparsely-connected clusters than smaller groups. Further, this effect was independent of variation in living condition, and/or sampling effort. In summary, our results reveal that female dominance networks were more phylogenetically conserved across macaque species than grooming networks, which were more labile to sociodemographic factors. Such findings narrow down the processes that influence interspecific variation in two core aspects of macaque social structure. Future directions should include using phylogeographic approaches, and addressing challenges in examining the effects of socioecological factors on primate social structure. © 2017 Wiley Periodicals, Inc.

Persistency and flexibility of complex brain networks underlie dual-task interference.

PubMed

Alavash, Mohsen; Hilgetag, Claus C; Thiel, Christiane M; Gießing, Carsten

2015-09-01

Previous studies on multitasking suggest that performance decline during concurrent task processing arises from interfering brain modules. Here, we used graph-theoretical network analysis to define functional brain modules and relate the modular organization of complex brain networks to behavioral dual-task costs. Based on resting-state and task fMRI we explored two organizational aspects potentially associated with behavioral interference when human subjects performed a visuospatial and speech task simultaneously: the topological overlap between persistent single-task modules, and the flexibility of single-task modules in adaptation to the dual-task condition. Participants showed a significant decline in visuospatial accuracy in the dual-task compared with single visuospatial task. Global analysis of topological similarity between modules revealed that the overlap between single-task modules significantly correlated with the decline in visuospatial accuracy. Subjects with larger overlap between single-task modules showed higher behavioral interference. Furthermore, lower flexible reconfiguration of single-task modules in adaptation to the dual-task condition significantly correlated with larger decline in visuospatial accuracy. Subjects with lower modular flexibility showed higher behavioral interference. At the regional level, higher overlap between single-task modules and less modular flexibility in the somatomotor cortex positively correlated with the decline in visuospatial accuracy. Additionally, higher modular flexibility in cingulate and frontal control areas and lower flexibility in right-lateralized nodes comprising the middle occipital and superior temporal gyri supported dual-tasking. Our results suggest that persistency and flexibility of brain modules are important determinants of dual-task costs. We conclude that efficient dual-tasking benefits from a specific balance between flexibility and rigidity of functional brain modules. © 2015 Wiley Periodicals, Inc.

FACETS: multi-faceted functional decomposition of protein interaction networks.

PubMed

Seah, Boon-Siew; Bhowmick, Sourav S; Dewey, C Forbes

2012-10-15

The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein-protein interaction (PPI) network using graph theoretic analysis. Despite the recent progress, systems level analysis of high-throughput PPIs remains a daunting task because of the amount of data they present. In this article, we propose a novel PPI network decomposition algorithm called FACETS in order to make sense of the deluge of interaction data using Gene Ontology (GO) annotations. FACETS finds not just a single functional decomposition of the PPI network, but a multi-faceted atlas of functional decompositions that portray alternative perspectives of the functional landscape of the underlying PPI network. Each facet in the atlas represents a distinct interpretation of how the network can be functionally decomposed and organized. Our algorithm maximizes interpretative value of the atlas by optimizing inter-facet orthogonality and intra-facet cluster modularity. We tested our algorithm on the global networks from IntAct, and compared it with gold standard datasets from MIPS and KEGG. We demonstrated the performance of FACETS. We also performed a case study that illustrates the utility of our approach. Supplementary data are available at the Bioinformatics online. Our software is available freely for non-commercial purposes from: http://www.cais.ntu.edu.sg/~assourav/Facets/

Effect of intermodular connection on fast sparse synchronization in clustered small-world neural networks

NASA Astrophysics Data System (ADS)

Kim, Sang-Yoon; Lim, Woochang

2015-11-01

We consider a clustered network with small-world subnetworks of inhibitory fast spiking interneurons and investigate the effect of intermodular connection on the emergence of fast sparsely synchronized rhythms by varying both the intermodular coupling strength Jinter and the average number of intermodular links per interneuron Msyn(inter ). In contrast to the case of nonclustered networks, two kinds of sparsely synchronized states such as modular and global synchronization are found. For the case of modular sparse synchronization, the population behavior reveals the modular structure, because the intramodular dynamics of subnetworks make some mismatching. On the other hand, in the case of global sparse synchronization, the population behavior is globally identical, independently of the cluster structure, because the intramodular dynamics of subnetworks make perfect matching. We introduce a realistic cross-correlation modularity measure, representing the matching degree between the instantaneous subpopulation spike rates of the subnetworks, and examine whether the sparse synchronization is global or modular. Depending on its magnitude, the intermodular coupling strength Jinter seems to play "dual" roles for the pacing between spikes in each subnetwork. For large Jinter, due to strong inhibition it plays a destructive role to "spoil" the pacing between spikes, while for small Jinter it plays a constructive role to "favor" the pacing between spikes. Through competition between the constructive and the destructive roles of Jinter, there exists an intermediate optimal Jinter at which the pacing degree between spikes becomes maximal. In contrast, the average number of intermodular links per interneuron Msyn(inter ) seems to play a role just to favor the pacing between spikes. With increasing Msyn(inter ), the pacing degree between spikes increases monotonically thanks to the increase in the degree of effectiveness of global communication between spikes. Furthermore, we employ the realistic sub- and whole-population order parameters, based on the instantaneous sub- and whole-population spike rates, to determine the threshold values for the synchronization-unsynchronization transition in the sub- and whole populations, and the degrees of global and modular sparse synchronization are also measured in terms of the realistic sub- and whole-population statistical-mechanical spiking measures defined by considering both the occupation and the pacing degrees of spikes. It is expected that our results could have implications for the role of the brain plasticity in some functional behaviors associated with population synchronization.

Investigating brain community structure abnormalities in bipolar disorder using path length associated community estimation.

PubMed

Gadelkarim, Johnson J; Ajilore, Olusola; Schonfeld, Dan; Zhan, Liang; Thompson, Paul M; Feusner, Jamie D; Kumar, Anand; Altshuler, Lori L; Leow, Alex D

2014-05-01

In this article, we present path length associated community estimation (PLACE), a comprehensive framework for studying node-level community structure. Instead of the well-known Q modularity metric, PLACE utilizes a novel metric, Ψ(PL), which measures the difference between intercommunity versus intracommunity path lengths. We compared community structures in human healthy brain networks generated using these two metrics and argued that Ψ(PL) may have theoretical advantages. PLACE consists of the following: (1) extracting community structure using top-down hierarchical binary trees, where a branch at each bifurcation denotes a collection of nodes that form a community at that level, (2) constructing and assessing mean group community structure, and (3) detecting node-level changes in community between groups. We applied PLACE and investigated the structural brain networks obtained from a sample of 25 euthymic bipolar I subjects versus 25 gender- and age-matched healthy controls. Results showed community structural differences in posterior default mode network regions, with the bipolar group exhibiting left-right decoupling. Copyright © 2013 Wiley Periodicals, Inc.

Hierarchical organization of functional connectivity in the mouse brain: a complex network approach.

PubMed

Bardella, Giampiero; Bifone, Angelo; Gabrielli, Andrea; Gozzi, Alessandro; Squartini, Tiziano

2016-08-18

This paper represents a contribution to the study of the brain functional connectivity from the perspective of complex networks theory. More specifically, we apply graph theoretical analyses to provide evidence of the modular structure of the mouse brain and to shed light on its hierarchical organization. We propose a novel percolation analysis and we apply our approach to the analysis of a resting-state functional MRI data set from 41 mice. This approach reveals a robust hierarchical structure of modules persistent across different subjects. Importantly, we test this approach against a statistical benchmark (or null model) which constrains only the distributions of empirical correlations. Our results unambiguously show that the hierarchical character of the mouse brain modular structure is not trivially encoded into this lower-order constraint. Finally, we investigate the modular structure of the mouse brain by computing the Minimal Spanning Forest, a technique that identifies subnetworks characterized by the strongest internal correlations. This approach represents a faster alternative to other community detection methods and provides a means to rank modules on the basis of the strength of their internal edges.

The Modular Organization of Protein Interactions in Escherichia coli

PubMed Central

Peregrín-Alvarez, José M.; Xiong, Xuejian; Su, Chong; Parkinson, John

2009-01-01

Escherichia coli serves as an excellent model for the study of fundamental cellular processes such as metabolism, signalling and gene expression. Understanding the function and organization of proteins within these processes is an important step towards a ‘systems’ view of E. coli. Integrating experimental and computational interaction data, we present a reliable network of 3,989 functional interactions between 1,941 E. coli proteins (∼45% of its proteome). These were combined with a recently generated set of 3,888 high-quality physical interactions between 918 proteins and clustered to reveal 316 discrete modules. In addition to known protein complexes (e.g., RNA and DNA polymerases), we identified modules that represent biochemical pathways (e.g., nitrate regulation and cell wall biosynthesis) as well as batteries of functionally and evolutionarily related processes. To aid the interpretation of modular relationships, several case examples are presented, including both well characterized and novel biochemical systems. Together these data provide a global view of the modular organization of the E. coli proteome and yield unique insights into structural and evolutionary relationships in bacterial networks. PMID:19798435

Hierarchical organization of functional connectivity in the mouse brain: a complex network approach

NASA Astrophysics Data System (ADS)

Bardella, Giampiero; Bifone, Angelo; Gabrielli, Andrea; Gozzi, Alessandro; Squartini, Tiziano

2016-08-01

This paper represents a contribution to the study of the brain functional connectivity from the perspective of complex networks theory. More specifically, we apply graph theoretical analyses to provide evidence of the modular structure of the mouse brain and to shed light on its hierarchical organization. We propose a novel percolation analysis and we apply our approach to the analysis of a resting-state functional MRI data set from 41 mice. This approach reveals a robust hierarchical structure of modules persistent across different subjects. Importantly, we test this approach against a statistical benchmark (or null model) which constrains only the distributions of empirical correlations. Our results unambiguously show that the hierarchical character of the mouse brain modular structure is not trivially encoded into this lower-order constraint. Finally, we investigate the modular structure of the mouse brain by computing the Minimal Spanning Forest, a technique that identifies subnetworks characterized by the strongest internal correlations. This approach represents a faster alternative to other community detection methods and provides a means to rank modules on the basis of the strength of their internal edges.

PAGANI Toolkit: Parallel graph-theoretical analysis package for brain network big data.

PubMed

Du, Haixiao; Xia, Mingrui; Zhao, Kang; Liao, Xuhong; Yang, Huazhong; Wang, Yu; He, Yong

2018-05-01

The recent collection of unprecedented quantities of neuroimaging data with high spatial resolution has led to brain network big data. However, a toolkit for fast and scalable computational solutions is still lacking. Here, we developed the PArallel Graph-theoretical ANalysIs (PAGANI) Toolkit based on a hybrid central processing unit-graphics processing unit (CPU-GPU) framework with a graphical user interface to facilitate the mapping and characterization of high-resolution brain networks. Specifically, the toolkit provides flexible parameters for users to customize computations of graph metrics in brain network analyses. As an empirical example, the PAGANI Toolkit was applied to individual voxel-based brain networks with ∼200,000 nodes that were derived from a resting-state fMRI dataset of 624 healthy young adults from the Human Connectome Project. Using a personal computer, this toolbox completed all computations in ∼27 h for one subject, which is markedly less than the 118 h required with a single-thread implementation. The voxel-based functional brain networks exhibited prominent small-world characteristics and densely connected hubs, which were mainly located in the medial and lateral fronto-parietal cortices. Moreover, the female group had significantly higher modularity and nodal betweenness centrality mainly in the medial/lateral fronto-parietal and occipital cortices than the male group. Significant correlations between the intelligence quotient and nodal metrics were also observed in several frontal regions. Collectively, the PAGANI Toolkit shows high computational performance and good scalability for analyzing connectome big data and provides a friendly interface without the complicated configuration of computing environments, thereby facilitating high-resolution connectomics research in health and disease. © 2018 Wiley Periodicals, Inc.

Non-Stationarity in the “Resting Brain’s” Modular Architecture

PubMed Central

Jones, David T.; Vemuri, Prashanthi; Murphy, Matthew C.; Gunter, Jeffrey L.; Senjem, Matthew L.; Machulda, Mary M.; Przybelski, Scott A.; Gregg, Brian E.; Kantarci, Kejal; Knopman, David S.; Boeve, Bradley F.; Petersen, Ronald C.; Jack, Clifford R.

2012-01-01

Task-free functional magnetic resonance imaging (TF-fMRI) has great potential for advancing the understanding and treatment of neurologic illness. However, as with all measures of neural activity, variability is a hallmark of intrinsic connectivity networks (ICNs) identified by TF-fMRI. This variability has hampered efforts to define a robust metric of connectivity suitable as a biomarker for neurologic illness. We hypothesized that some of this variability rather than representing noise in the measurement process, is related to a fundamental feature of connectivity within ICNs, which is their non-stationary nature. To test this hypothesis, we used a large (n = 892) population-based sample of older subjects to construct a well characterized atlas of 68 functional regions, which were categorized based on independent component analysis network of origin, anatomical locations, and a functional meta-analysis. These regions were then used to construct dynamic graphical representations of brain connectivity within a sliding time window for each subject. This allowed us to demonstrate the non-stationary nature of the brain’s modular organization and assign each region to a “meta-modular” group. Using this grouping, we then compared dwell time in strong sub-network configurations of the default mode network (DMN) between 28 subjects with Alzheimer’s dementia and 56 cognitively normal elderly subjects matched 1∶2 on age, gender, and education. We found that differences in connectivity we and others have previously observed in Alzheimer’s disease can be explained by differences in dwell time in DMN sub-network configurations, rather than steady state connectivity magnitude. DMN dwell time in specific modular configurations may also underlie the TF-fMRI findings that have been described in mild cognitive impairment and cognitively normal subjects who are at risk for Alzheimer’s dementia. PMID:22761880

Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks

PubMed Central

Clune, Jeff

2017-01-01

A long-term goal of AI is to produce agents that can learn a diversity of skills throughout their lifetimes and continuously improve those skills via experience. A longstanding obstacle towards that goal is catastrophic forgetting, which is when learning new information erases previously learned information. Catastrophic forgetting occurs in artificial neural networks (ANNs), which have fueled most recent advances in AI. A recent paper proposed that catastrophic forgetting in ANNs can be reduced by promoting modularity, which can limit forgetting by isolating task information to specific clusters of nodes and connections (functional modules). While the prior work did show that modular ANNs suffered less from catastrophic forgetting, it was not able to produce ANNs that possessed task-specific functional modules, thereby leaving the main theory regarding modularity and forgetting untested. We introduce diffusion-based neuromodulation, which simulates the release of diffusing, neuromodulatory chemicals within an ANN that can modulate (i.e. up or down regulate) learning in a spatial region. On the simple diagnostic problem from the prior work, diffusion-based neuromodulation 1) induces task-specific learning in groups of nodes and connections (task-specific localized learning), which 2) produces functional modules for each subtask, and 3) yields higher performance by eliminating catastrophic forgetting. Overall, our results suggest that diffusion-based neuromodulation promotes task-specific localized learning and functional modularity, which can help solve the challenging, but important problem of catastrophic forgetting. PMID:29145413

Connectomics-based analysis of information flow in the Drosophila brain.

PubMed

Shih, Chi-Tin; Sporns, Olaf; Yuan, Shou-Li; Su, Ta-Shun; Lin, Yen-Jen; Chuang, Chao-Chun; Wang, Ting-Yuan; Lo, Chung-Chuang; Greenspan, Ralph J; Chiang, Ann-Shyn

2015-05-18

Understanding the overall patterns of information flow within the brain has become a major goal of neuroscience. In the current study, we produced a first draft of the Drosophila connectome at the mesoscopic scale, reconstructed from 12,995 images of neuron projections collected in FlyCircuit (version 1.1). Neuron polarities were predicted according to morphological criteria, with nodes of the network corresponding to brain regions designated as local processing units (LPUs). The weight of each directed edge linking a pair of LPUs was determined by the number of neuron terminals that connected one LPU to the other. The resulting network showed hierarchical structure and small-world characteristics and consisted of five functional modules that corresponded to sensory modalities (olfactory, mechanoauditory, and two visual) and the pre-motor center. Rich-club organization was present in this network and involved LPUs in all sensory centers, and rich-club members formed a putative motor center of the brain. Major intra- and inter-modular loops were also identified that could play important roles for recurrent and reverberant information flow. The present analysis revealed whole-brain patterns of network structure and information flow. Additionally, we propose that the overall organizational scheme showed fundamental similarities to the network structure of the mammalian brain. Copyright © 2015 Elsevier Ltd. All rights reserved.

Integrated Module and Gene-Specific Regulatory Inference Implicates Upstream Signaling Networks

PubMed Central

Roy, Sushmita; Lagree, Stephen; Hou, Zhonggang; Thomson, James A.; Stewart, Ron; Gasch, Audrey P.

2013-01-01

Regulatory networks that control gene expression are important in diverse biological contexts including stress response and development. Each gene's regulatory program is determined by module-level regulation (e.g. co-regulation via the same signaling system), as well as gene-specific determinants that can fine-tune expression. We present a novel approach, Modular regulatory network learning with per gene information (MERLIN), that infers regulatory programs for individual genes while probabilistically constraining these programs to reveal module-level organization of regulatory networks. Using edge-, regulator- and module-based comparisons of simulated networks of known ground truth, we find MERLIN reconstructs regulatory programs of individual genes as well or better than existing approaches of network reconstruction, while additionally identifying modular organization of the regulatory networks. We use MERLIN to dissect global transcriptional behavior in two biological contexts: yeast stress response and human embryonic stem cell differentiation. Regulatory modules inferred by MERLIN capture co-regulatory relationships between signaling proteins and downstream transcription factors thereby revealing the upstream signaling systems controlling transcriptional responses. The inferred networks are enriched for regulators with genetic or physical interactions, supporting the inference, and identify modules of functionally related genes bound by the same transcriptional regulators. Our method combines the strengths of per-gene and per-module methods to reveal new insights into transcriptional regulation in stress and development. PMID:24146602

Hierarchical mutual information for the comparison of hierarchical community structures in complex networks

NASA Astrophysics Data System (ADS)

Perotti, Juan Ignacio; Tessone, Claudio Juan; Caldarelli, Guido

2015-12-01

The quest for a quantitative characterization of community and modular structure of complex networks produced a variety of methods and algorithms to classify different networks. However, it is not clear if such methods provide consistent, robust, and meaningful results when considering hierarchies as a whole. Part of the problem is the lack of a similarity measure for the comparison of hierarchical community structures. In this work we give a contribution by introducing the hierarchical mutual information, which is a generalization of the traditional mutual information and makes it possible to compare hierarchical partitions and hierarchical community structures. The normalized version of the hierarchical mutual information should behave analogously to the traditional normalized mutual information. Here the correct behavior of the hierarchical mutual information is corroborated on an extensive battery of numerical experiments. The experiments are performed on artificial hierarchies and on the hierarchical community structure of artificial and empirical networks. Furthermore, the experiments illustrate some of the practical applications of the hierarchical mutual information, namely the comparison of different community detection methods and the study of the consistency, robustness, and temporal evolution of the hierarchical modular structure of networks.

Bayesian module identification from multiple noisy networks.

PubMed

Zamani Dadaneh, Siamak; Qian, Xiaoning

2016-12-01

Module identification has been studied extensively in order to gain deeper understanding of complex systems, such as social networks as well as biological networks. Modules are often defined as groups of vertices in these networks that are topologically cohesive with similar interaction patterns with the rest of the vertices. Most of the existing module identification algorithms assume that the given networks are faithfully measured without errors. However, in many real-world applications, for example, when analyzing protein-protein interaction networks from high-throughput profiling techniques, there is significant noise with both false positive and missing links between vertices. In this paper, we propose a new model for more robust module identification by taking advantage of multiple observed networks with significant noise so that signals in multiple networks can be strengthened and help improve the solution quality by combining information from various sources. We adopt a hierarchical Bayesian model to integrate multiple noisy snapshots that capture the underlying modular structure of the networks under study. By introducing a latent root assignment matrix and its relations to instantaneous module assignments in all the observed networks to capture the underlying modular structure and combine information across multiple networks, an efficient variational Bayes algorithm can be derived to accurately and robustly identify the underlying modules from multiple noisy networks. Experiments on synthetic and protein-protein interaction data sets show that our proposed model enhances both the accuracy and resolution in detecting cohesive modules, and it is less vulnerable to noise in the observed data. In addition, it shows higher power in predicting missing edges compared to individual-network methods.

Modular architectures for quantum networks

NASA Astrophysics Data System (ADS)

Pirker, A.; Wallnöfer, J.; Dür, W.

2018-05-01

We consider the problem of generating multipartite entangled states in a quantum network upon request. We follow a top-down approach, where the required entanglement is initially present in the network in form of network states shared between network devices, and then manipulated in such a way that the desired target state is generated. This minimizes generation times, and allows for network structures that are in principle independent of physical links. We present a modular and flexible architecture, where a multi-layer network consists of devices of varying complexity, including quantum network routers, switches and clients, that share certain resource states. We concentrate on the generation of graph states among clients, which are resources for numerous distributed quantum tasks. We assume minimal functionality for clients, i.e. they do not participate in the complex and distributed generation process of the target state. We present architectures based on shared multipartite entangled Greenberger–Horne–Zeilinger states of different size, and fully connected decorated graph states, respectively. We compare the features of these architectures to an approach that is based on bipartite entanglement, and identify advantages of the multipartite approach in terms of memory requirements and complexity of state manipulation. The architectures can handle parallel requests, and are designed in such a way that the network state can be dynamically extended if new clients or devices join the network. For generation or dynamical extension of the network states, we propose a quantum network configuration protocol, where entanglement purification is used to establish high fidelity states. The latter also allows one to show that the entanglement generated among clients is private, i.e. the network is secure.

Hybrid emergency radiation detection: a wireless sensor network application for consequence management of a radiological release

NASA Astrophysics Data System (ADS)

Kyker, Ronald D.; Berry, Nina; Stark, Doug; Nachtigal, Noel; Kershaw, Chris

2004-08-01

The Hybrid Emergency Radiation Detection (HERD) system is a rapidly deployable ad-hoc wireless sensor network for monitoring the radiation hazard associated with a radiation release. The system is designed for low power, small size, low cost, and rapid deployment in order to provide early notification and minimize exposure. The many design tradeoffs, decisions, and challenges in the implementation of this wireless sensor network design will be presented and compared to the commercial systems available. Our research in a scaleable modular architectural highlights the need and implementation of a system level approach that provides flexibility and adaptability for a variety of applications. This approach seeks to minimize power, provide mission specific specialization, and provide the capability to upgrade the system with the most recent technology advancements by encapsulation and modularity. The implementation of a low power, widely available Real Time Operating System (RTOS) for multitasking with an improvement in code maintenance, portability, and reuse will be presented. Finally future design enhancements technology trends affecting wireless sensor networks will be presented.

From Hippocampus to Whole-Brain: The Role of Integrative Processing in Episodic Memory Retrieval

PubMed Central

Geib, Benjamin R.; Stanley, Matthew L.; Dennis, Nancy A.; Woldorff, Marty G.; Cabeza, Roberto

2017-01-01

Multivariate functional connectivity analyses of neuroimaging data have revealed the importance of complex, distributed interactions between disparate yet interdependent brain regions. Recent work has shown that topological properties of functional brain networks are associated with individual and group differences in cognitive performance, including in episodic memory. After constructing functional whole-brain networks derived from an event-related fMRI study of memory retrieval, we examined differences in functional brain network architecture between forgotten and remembered words. This study yielded three main findings. First, graph theory analyses showed that successfully remembering compared to forgetting was associated with significant changes in the connectivity profile of the left hippocampus and a corresponding increase in efficient communication with the rest of the brain. Second, bivariate functional connectivity analyses indicated stronger interactions between the left hippocampus and a retrieval assembly for remembered versus forgotten items. This assembly included the left precuneus, left caudate, bilateral supramarginal gyrus, and the bilateral dorsolateral superior frontal gyrus. Integrative properties of the retrieval assembly were greater for remembered than forgotten items. Third, whole-brain modularity analyses revealed that successful memory retrieval was marginally significantly associated with a less segregated modular architecture in the network. The magnitude of the decreases in modularity between remembered and forgotten conditions was related to memory performance. These findings indicate that increases in integrative properties at the nodal, retrieval assembly, and whole-brain topological levels facilitate memory retrieval, while also underscoring the potential of multivariate brain connectivity approaches for providing valuable new insights into the neural bases of memory processes. PMID:28112460

Abnormalities in Structural Covariance of Cortical Gyrification in Parkinson's Disease.

PubMed

Xu, Jinping; Zhang, Jiuquan; Zhang, Jinlei; Wang, Yue; Zhang, Yanling; Wang, Jian; Li, Guanglin; Hu, Qingmao; Zhang, Yuanchao

2017-01-01

Although abnormal cortical morphology and connectivity between brain regions (structural covariance) have been reported in Parkinson's disease (PD), the topological organizations of large-scale structural brain networks are still poorly understood. In this study, we investigated large-scale structural brain networks in a sample of 37 PD patients and 34 healthy controls (HC) by assessing the structural covariance of cortical gyrification with local gyrification index (lGI). We demonstrated prominent small-world properties of the structural brain networks for both groups. Compared with the HC group, PD patients showed significantly increased integrated characteristic path length and integrated clustering coefficient, as well as decreased integrated global efficiency in structural brain networks. Distinct distributions of hub regions were identified between the two groups, showing more hub regions in the frontal cortex in PD patients. Moreover, the modular analyses revealed significantly decreased integrated regional efficiency in lateral Fronto-Insula-Temporal module, and increased integrated regional efficiency in Parieto-Temporal module in the PD group as compared to the HC group. In summary, our study demonstrated altered topological properties of structural networks at a global, regional and modular level in PD patients. These findings suggests that the structural networks of PD patients have a suboptimal topological organization, resulting in less effective integration of information between brain regions.

The application of modular protein domains in proteomics

PubMed Central

Jadwin, Joshua A.; Ogiue-Ikeda, Mari; Machida, Kazuya

2012-01-01

The ability of modular protein domains to independently fold and bind short peptide ligands both in vivo and in vitro has allowed a significant number of protein-protein interaction studies to take advantage of them as affinity and detection reagents. Here, we refer to modular domain based proteomics as “domainomics” to draw attention to the potential of using domains and their motifs as tools in proteomics. In this review we describe core concepts of domainomics, established and emerging technologies, and recent studies by functional category. Accumulation of domain-motif binding data should ultimately provide the foundation for domain-specific interactomes, which will likely reveal the underlying substructure of protein networks as well as the selectivity and plasticity of signal transduction. PMID:22710164

Constructing Social Networks from Unstructured Group Dialog in Virtual Worlds

NASA Astrophysics Data System (ADS)

Shah, Fahad; Sukthankar, Gita

Virtual worlds and massively multi-player online games are rich sources of information about large-scale teams and groups, offering the tantalizing possibility of harvesting data about group formation, social networks, and network evolution. However these environments lack many of the cues that facilitate natural language processing in other conversational settings and different types of social media. Public chat data often features players who speak simultaneously, use jargon and emoticons, and only erratically adhere to conversational norms. In this paper, we present techniques for inferring the existence of social links from unstructured conversational data collected from groups of participants in the Second Life virtual world. We present an algorithm for addressing this problem, Shallow Semantic Temporal Overlap (SSTO), that combines temporal and language information to create directional links between participants, and a second approach that relies on temporal overlap alone to create undirected links between participants. Relying on temporal overlap is noisy, resulting in a low precision and networks with many extraneous links. In this paper, we demonstrate that we can ameliorate this problem by using network modularity optimization to perform community detection in the noisy networks and severing cross-community links. Although using the content of the communications still results in the best performance, community detection is effective as a noise reduction technique for eliminating the extra links created by temporal overlap alone.

Separate cortical networks involved in music perception: preliminary functional MRI evidence for modularity of music processing.

PubMed

Schmithorst, Vincent J

2005-04-01

Music perception is a quite complex cognitive task, involving the perception and integration of various elements including melody, harmony, pitch, rhythm, and timbre. A preliminary functional MRI investigation of music perception was performed, using a simplified passive listening task. Group independent component analysis (ICA) was used to separate out various components involved in music processing, as the hemodynamic responses are not known a priori. Various components consistent with auditory processing, expressive language, syntactic processing, and visual association were found. The results are discussed in light of various hypotheses regarding modularity of music processing and its overlap with language processing. The results suggest that, while some networks overlap with ones used for language processing, music processing may involve its own domain-specific processing subsystems.

Peripheral infrastructure vectors and an extended set of plant parts for the Modular Cloning system

PubMed Central

Kretschmer, Carola; Gruetzner, Ramona; Löfke, Christian; Dagdas, Yasin; Bürstenbinder, Katharina; Marillonnet, Sylvestre

2018-01-01

Standardized DNA assembly strategies facilitate the generation of multigene constructs from collections of building blocks in plant synthetic biology. A common syntax for hierarchical DNA assembly following the Golden Gate principle employing Type IIs restriction endonucleases was recently developed, and underlies the Modular Cloning and GoldenBraid systems. In these systems, transcriptional units and/or multigene constructs are assembled from libraries of standardized building blocks, also referred to as phytobricks, in several hierarchical levels and by iterative Golden Gate reactions. Here, a toolkit containing further modules for the novel DNA assembly standards was developed. Intended for use with Modular Cloning, most modules are also compatible with GoldenBraid. Firstly, a collection of approximately 80 additional phytobricks is provided, comprising e.g. modules for inducible expression systems, promoters or epitope tags. Furthermore, DNA modules were developed for connecting Modular Cloning and Gateway cloning, either for toggling between systems or for standardized Gateway destination vector assembly. Finally, first instances of a “peripheral infrastructure” around Modular Cloning are presented: While available toolkits are designed for the assembly of plant transformation constructs, vectors were created to also use coding sequence-containing phytobricks directly in yeast two hybrid interaction or bacterial infection assays. The presented material will further enhance versatility of hierarchical DNA assembly strategies. PMID:29847550

Is functional integration of resting state brain networks an unspecific biomarker for working memory performance?

PubMed

Alavash, Mohsen; Doebler, Philipp; Holling, Heinz; Thiel, Christiane M; Gießing, Carsten

2015-03-01

Is there one optimal topology of functional brain networks at rest from which our cognitive performance would profit? Previous studies suggest that functional integration of resting state brain networks is an important biomarker for cognitive performance. However, it is still unknown whether higher network integration is an unspecific predictor for good cognitive performance or, alternatively, whether specific network organization during rest predicts only specific cognitive abilities. Here, we investigated the relationship between network integration at rest and cognitive performance using two tasks that measured different aspects of working memory; one task assessed visual-spatial and the other numerical working memory. Network clustering, modularity and efficiency were computed to capture network integration on different levels of network organization, and to statistically compare their correlations with the performance in each working memory test. The results revealed that each working memory aspect profits from a different resting state topology, and the tests showed significantly different correlations with each of the measures of network integration. While higher global network integration and modularity predicted significantly better performance in visual-spatial working memory, both measures showed no significant correlation with numerical working memory performance. In contrast, numerical working memory was superior in subjects with highly clustered brain networks, predominantly in the intraparietal sulcus, a core brain region of the working memory network. Our findings suggest that a specific balance between local and global functional integration of resting state brain networks facilitates special aspects of cognitive performance. In the context of working memory, while visual-spatial performance is facilitated by globally integrated functional resting state brain networks, numerical working memory profits from increased capacities for local processing, especially in brain regions involved in working memory performance. Copyright © 2014 Elsevier Inc. All rights reserved.

cPath: open source software for collecting, storing, and querying biological pathways.

PubMed

Cerami, Ethan G; Bader, Gary D; Gross, Benjamin E; Sander, Chris

2006-11-13

Biological pathways, including metabolic pathways, protein interaction networks, signal transduction pathways, and gene regulatory networks, are currently represented in over 220 diverse databases. These data are crucial for the study of specific biological processes, including human diseases. Standard exchange formats for pathway information, such as BioPAX, CellML, SBML and PSI-MI, enable convenient collection of this data for biological research, but mechanisms for common storage and communication are required. We have developed cPath, an open source database and web application for collecting, storing, and querying biological pathway data. cPath makes it easy to aggregate custom pathway data sets available in standard exchange formats from multiple databases, present pathway data to biologists via a customizable web interface, and export pathway data via a web service to third-party software, such as Cytoscape, for visualization and analysis. cPath is software only, and does not include new pathway information. Key features include: a built-in identifier mapping service for linking identical interactors and linking to external resources; built-in support for PSI-MI and BioPAX standard pathway exchange formats; a web service interface for searching and retrieving pathway data sets; and thorough documentation. The cPath software is freely available under the LGPL open source license for academic and commercial use. cPath is a robust, scalable, modular, professional-grade software platform for collecting, storing, and querying biological pathways. It can serve as the core data handling component in information systems for pathway visualization, analysis and modeling.

Proving Stabilization of Biological Systems

NASA Astrophysics Data System (ADS)

Cook, Byron; Fisher, Jasmin; Krepska, Elzbieta; Piterman, Nir

We describe an efficient procedure for proving stabilization of biological systems modeled as qualitative networks or genetic regulatory networks. For scalability, our procedure uses modular proof techniques, where state-space exploration is applied only locally to small pieces of the system rather than the entire system as a whole. Our procedure exploits the observation that, in practice, the form of modular proofs can be restricted to a very limited set. For completeness, our technique falls back on a non-compositional counterexample search. Using our new procedure, we have solved a number of challenging published examples, including: a 3-D model of the mammalian epidermis; a model of metabolic networks operating in type-2 diabetes; a model of fate determination of vulval precursor cells in the C. elegans worm; and a model of pair-rule regulation during segmentation in the Drosophila embryo. Our results show many orders of magnitude speedup in cases where previous stabilization proving techniques were known to succeed, and new results in cases where tools had previously failed.

Critical tipping point distinguishing two types of transitions in modular network structures

NASA Astrophysics Data System (ADS)

Shai, Saray; Kenett, Dror Y.; Kenett, Yoed N.; Faust, Miriam; Dobson, Simon; Havlin, Shlomo

2015-12-01

Modularity is a key organizing principle in real-world large-scale complex networks. The relatively sparse interactions between modules are critical to the functionality of the system and are often the first to fail. We model such failures as site percolation targeting interconnected nodes, those connecting between modules. We find, using percolation theory and simulations, that they lead to a "tipping point" between two distinct regimes. In one regime, removal of interconnected nodes fragments the modules internally and causes the system to collapse. In contrast, in the other regime, while only attacking a small fraction of nodes, the modules remain but become disconnected, breaking the entire system. We show that networks with broader degree distribution might be highly vulnerable to such attacks since only few nodes are needed to interconnect the modules, consequently putting the entire system at high risk. Our model has the potential to shed light on many real-world phenomena, and we briefly consider its implications on recent advances in the understanding of several neurocognitive processes and diseases.

Towards cortex sized artificial neural systems.

PubMed

Johansson, Christopher; Lansner, Anders

2007-01-01

We propose, implement, and discuss an abstract model of the mammalian neocortex. This model is instantiated with a sparse recurrently connected neural network that has spiking leaky integrator units and continuous Hebbian learning. First we study the structure, modularization, and size of neocortex, and then we describe a generic computational model of the cortical circuitry. A characterizing feature of the model is that it is based on the modularization of neocortex into hypercolumns and minicolumns. Both a floating- and fixed-point arithmetic implementation of the model are presented along with simulation results. We conclude that an implementation on a cluster computer is not communication but computation bounded. A mouse and rat cortex sized version of our model executes in 44% and 23% of real-time respectively. Further, an instance of the model with 1.6 x 10(6) units and 2 x 10(11) connections performed noise reduction and pattern completion. These implementations represent the current frontier of large-scale abstract neural network simulations in terms of network size and running speed.

Bechstein's bats maintain individual social links despite a complete reorganisation of their colony structure

NASA Astrophysics Data System (ADS)

Baigger, A.; Perony, N.; Reuter, M.; Leinert, V.; Melber, M.; Grünberger, S.; Fleischmann, D.; Kerth, G.

2013-09-01

Several social mammals, including elephants and some primates, whales and bats, live in multilevel societies that form temporary subgroups. Despite these fission-fusion dynamics, group members often maintain long-term bonds. However, it is unclear whether such individual links and the resulting stable social subunits continue to exist after a complete reorganisation of a society, e.g. following a population crash. Here, we employed a weighted network analysis on 7,109 individual roosting records collected over 4 years in a wild Bechstein's bat colony. We show that, in response to a strong population decline, the colony's two stable social subunits fused into a non-modular social network. Nevertheless, in the first year after the crash, long-term bonds were still detectable, suggesting that the bats remembered previous individual relationships. Our findings are important for understanding the flexibility of animal societies in the face of dramatic changes and for the conservation of social mammals with declining populations.

Reconfigurable modular computer networks for spacecraft on-board processing

NASA Technical Reports Server (NTRS)

Rennels, D. A.

1978-01-01

The core electronics subsystems on unmanned spacecraft, which have been sent over the last 20 years to investigate the moon, Mars, Venus, and Mercury, have progressed through an evolution from simple fixed controllers and analog computers in the 1960's to general-purpose digital computers in current designs. This evolution is now moving in the direction of distributed computer networks. Current Voyager spacecraft already use three on-board computers. One is used to store commands and provide overall spacecraft management. Another is used for instrument control and telemetry collection, and the third computer is used for attitude control and scientific instrument pointing. An examination of the control logic in the instruments shows that, for many, it is cost-effective to replace the sequencing logic with a microcomputer. The Unified Data System architecture considered consists of a set of standard microcomputers connected by several redundant buses. A typical self-checking computer module will contain 23 RAMs, two microprocessors, one memory interface, three bus interfaces, and one core building block.

Bechstein's bats maintain individual social links despite a complete reorganisation of their colony structure.

PubMed

Baigger, A; Perony, N; Reuter, M; Leinert, V; Melber, M; Grünberger, S; Fleischmann, D; Kerth, G

2013-09-01

Several social mammals, including elephants and some primates, whales and bats, live in multilevel societies that form temporary subgroups. Despite these fission-fusion dynamics, group members often maintain long-term bonds. However, it is unclear whether such individual links and the resulting stable social subunits continue to exist after a complete reorganisation of a society, e.g. following a population crash. Here, we employed a weighted network analysis on 7,109 individual roosting records collected over 4 years in a wild Bechstein's bat colony. We show that, in response to a strong population decline, the colony's two stable social subunits fused into a non-modular social network. Nevertheless, in the first year after the crash, long-term bonds were still detectable, suggesting that the bats remembered previous individual relationships. Our findings are important for understanding the flexibility of animal societies in the face of dramatic changes and for the conservation of social mammals with declining populations.

Redundancy Maintenance and Garbage Collection Strategies in Peer-to-Peer Storage Systems

NASA Astrophysics Data System (ADS)

Liu, Xin; Datta, Anwitaman

Maintaining redundancy in P2P storage systems is essential for reliability guarantees. Numerous P2P storage system maintenance algorithms have been proposed in the last years, each supposedly improving upon the previous approaches. We perform a systematic comparative study of the various strategies taking also into account the influence of different garbage collection mechanisms, an issue not studied so far. Our experiments show that while some strategies generally perform better than some others, there is no universally best strategy, and their relative superiority depends on various other design choices as well as the specific evaluation criterion. Our results can be used by P2P storage systems designers to make prudent design decisions, and our exploration of the various evaluation metrics also provides a more comprehensive framework to compare algorithms for P2P storage systems. While there are numerous network simulators specifically developed even to simulate peer-to-peer networks, there existed no P2P storage simulators - a byproduct of this work is a generic modular P2P storage system simulator which we provide as open-source. Different redundancy, maintenance, placement, garbage-collection policies, churn scenarios can be easily integrated to the simulator to try out new schemes in future, and provides a common framework to compare (future) p2p storage systems designs - something which has not been possible so far.

Automatic Identification System modular receiver for academic purposes

NASA Astrophysics Data System (ADS)

Cabrera, F.; Molina, N.; Tichavska, M.; Araña, V.

2016-07-01

The Automatic Identification System (AIS) standard is encompassed within the Global Maritime Distress and Safety System (GMDSS), in force since 1999. The GMDSS is a set of procedures, equipment, and communication protocols designed with the aim of increasing the safety of sea crossings, facilitating navigation, and the rescue of vessels in danger. The use of this system not only is increasingly attractive to security issues but also potentially creates intelligence products throughout the added-value information that this network can transmit from ships on real time (identification, position, course, speed, dimensions, flag, among others). Within the marine electronics market, commercial receivers implement this standard and allow users to access vessel-broadcasted information if in the range of coverage. In addition to satellite services, users may request actionable information from private or public AIS terrestrial networks where real-time feed or historical data can be accessed from its nodes. This paper describes the configuration of an AIS receiver based on a modular design. This modular design facilitates the evaluation of specific modules and also a better understanding of the standard and the possibility of changing hardware modules to improve the performance of the prototype. Thus, the aim of this paper is to describe the system's specifications, its main hardware components, and to present educational didactics on the setup and use of a modular and terrestrial AIS receiver. The latter is for academic purposes and in undergraduate studies such as electrical engineering, telecommunications, and maritime studies.

Sambot II: A self-assembly modular swarm robot

NASA Astrophysics Data System (ADS)

Zhang, Yuchao; Wei, Hongxing; Yang, Bo; Jiang, Cancan

2018-04-01

The new generation of self-assembly modular swarm robot Sambot II, based on the original generation of self-assembly modular swarm robot Sambot, adopting laser and camera module for information collecting, is introduced in this manuscript. The visual control algorithm of Sambot II is detailed and feasibility of the algorithm is verified by the laser and camera experiments. At the end of this manuscript, autonomous docking experiments of two Sambot II robots are presented. The results of experiments are showed and analyzed to verify the feasibility of whole scheme of Sambot II.

Brain connectome modularity in weight-restored anorexia nervosa and body dysmorphic disorder

PubMed Central

Zhang, A; Leow, A; Zhan, L; GadElkarim, J; Moody, T; Khalsa, S; Strober, M; Feusner, JD

2017-01-01

Background Anorexia nervosa (AN) and body dysmorphic disorder (BDD) frequently co-occur, and have several overlapping phenomenological features. Little is known about their shared neurobiology. Aims To compare modular organization of brain structural connectivity. Methods We acquired diffusion-weighted magnetic resonance imaging data on unmedicated individuals with BDD (n=29), weight-restored AN (n=24), and healthy controls (HC) (n=31). We constructed connectivity matrices using whole-brain white matter tractography, and compared modular structures across groups. Results AN showed abnormal modularity involving frontal, basal ganglia, and posterior cingulate nodes. There was a trend in BDD for similar abnormalities, but no significant differences compared with AN. In AN, poor insight correlated with longer path length in right caudal anterior cingulate and right posterior cingulate. Conclusions Abnormal network organization patterns in AN, partially shared with BDD, may have implications for understanding integration between reward and habit/ritual formation, as well as conflict monitoring/error detection. PMID:27429183

Control, responses and modularity of cellular regulatory networks: a control analysis perspective.

PubMed

Bruggeman, F J; Snoep, J L; Westerhoff, H V

2008-11-01

Cells adapt to changes in environmental conditions through the concerted action of signalling, gene expression and metabolic subsystems. The authors will discuss a theoretical framework addressing such integrated systems. This 'hierarchical analysis' was first developed as an extension to a metabolic control analysis. It builds on the phenomenon that often the communication between signalling, gene expression and metabolic subsystems is almost exclusively via regulatory interactions and not via mass flow interactions. This allows for the treatment of the said subsystems as 'levels' in a hierarchical view of the organisation of the molecular reaction network of cells. Such a hierarchical approach has as a major advantage that levels can be analysed conceptually in isolation of each other (from a local intra-level perspective) and at a later stage integrated via their interactions (from a global inter-level perspective). Hereby, it allows for a modular approach with variable scope. A number of different approaches have been developed for the analysis of hierarchical systems, for example hierarchical control analysis and modular response analysis. The authors, here, review these methods and illustrate the strength of these types of analyses using a core model of a system with gene expression, metabolic and signal transduction levels.

A modular modulation method for achieving increases in metabolite production.

PubMed

Acerenza, Luis; Monzon, Pablo; Ortega, Fernando

2015-01-01

Increasing the production of overproducing strains represents a great challenge. Here, we develop a modular modulation method to determine the key steps for genetic manipulation to increase metabolite production. The method consists of three steps: (i) modularization of the metabolic network into two modules connected by linking metabolites, (ii) change in the activity of the modules using auxiliary rates producing or consuming the linking metabolites in appropriate proportions and (iii) determination of the key modules and steps to increase production. The mathematical formulation of the method in matrix form shows that it may be applied to metabolic networks of any structure and size, with reactions showing any kind of rate laws. The results are valid for any type of conservation relationships in the metabolite concentrations or interactions between modules. The activity of the module may, in principle, be changed by any large factor. The method may be applied recursively or combined with other methods devised to perform fine searches in smaller regions. In practice, it is implemented by integrating to the producer strain heterologous reactions or synthetic pathways producing or consuming the linking metabolites. The new procedure may contribute to develop metabolic engineering into a more systematic practice. © 2015 American Institute of Chemical Engineers.

Metabolomic Modularity Analysis (MMA) to Quantify Human Liver Perfusion Dynamics.

PubMed

Sridharan, Gautham Vivek; Bruinsma, Bote Gosse; Bale, Shyam Sundhar; Swaminathan, Anandh; Saeidi, Nima; Yarmush, Martin L; Uygun, Korkut

2017-11-13

Large-scale -omics data are now ubiquitously utilized to capture and interpret global responses to perturbations in biological systems, such as the impact of disease states on cells, tissues, and whole organs. Metabolomics data, in particular, are difficult to interpret for providing physiological insight because predefined biochemical pathways used for analysis are inherently biased and fail to capture more complex network interactions that span multiple canonical pathways. In this study, we introduce a nov-el approach coined Metabolomic Modularity Analysis (MMA) as a graph-based algorithm to systematically identify metabolic modules of reactions enriched with metabolites flagged to be statistically significant. A defining feature of the algorithm is its ability to determine modularity that highlights interactions between reactions mediated by the production and consumption of cofactors and other hub metabolites. As a case study, we evaluated the metabolic dynamics of discarded human livers using time-course metabolomics data and MMA to identify modules that explain the observed physiological changes leading to liver recovery during subnormothermic machine perfusion (SNMP). MMA was performed on a large scale liver-specific human metabolic network that was weighted based on metabolomics data and identified cofactor-mediated modules that would not have been discovered by traditional metabolic pathway analyses.

General software design for multisensor data fusion

NASA Astrophysics Data System (ADS)

Zhang, Junliang; Zhao, Yuming

1999-03-01

In this paper a general method of software design for multisensor data fusion is discussed in detail, which adopts object-oriented technology under UNIX operation system. The software for multisensor data fusion is divided into six functional modules: data collection, database management, GIS, target display and alarming data simulation etc. Furthermore, the primary function, the components and some realization methods of each modular is given. The interfaces among these functional modular relations are discussed. The data exchange among each functional modular is performed by interprocess communication IPC, including message queue, semaphore and shared memory. Thus, each functional modular is executed independently, which reduces the dependence among functional modules and helps software programing and testing. This software for multisensor data fusion is designed as hierarchical structure by the inheritance character of classes. Each functional modular is abstracted and encapsulated through class structure, which avoids software redundancy and enhances readability.

Dopamine precursor depletion impairs structure and efficiency of resting state brain functional networks.

PubMed

Carbonell, Felix; Nagano-Saito, Atsuko; Leyton, Marco; Cisek, Paul; Benkelfat, Chawki; He, Yong; Dagher, Alain

2014-09-01

Spatial patterns of functional connectivity derived from resting brain activity may be used to elucidate the topological properties of brain networks. Such networks are amenable to study using graph theory, which shows that they possess small world properties and can be used to differentiate healthy subjects and patient populations. Of particular interest is the possibility that some of these differences are related to alterations in the dopamine system. To investigate the role of dopamine in the topological organization of brain networks at rest, we tested the effects of reducing dopamine synthesis in 13 healthy subjects undergoing functional magnetic resonance imaging. All subjects were scanned twice, in a resting state, following ingestion of one of two amino acid drinks in a randomized, double-blind manner. One drink was a nutritionally balanced amino acid mixture, and the other was tyrosine and phenylalanine deficient. Functional connectivity between 90 cortical and subcortical regions was estimated for each individual subject under each dopaminergic condition. The lowered dopamine state caused the following network changes: reduced global and local efficiency of the whole brain network, reduced regional efficiency in limbic areas, reduced modularity of brain networks, and greater connection between the normally anti-correlated task-positive and default-mode networks. We conclude that dopamine plays a role in maintaining the efficient small-world properties and high modularity of functional brain networks, and in segregating the task-positive and default-mode networks. This article is part of the Special Issue Section entitled 'Neuroimaging in Neuropharmacology'. Copyright © 2014 Elsevier Ltd. All rights reserved.

From hippocampus to whole-brain: The role of integrative processing in episodic memory retrieval.

PubMed

Geib, Benjamin R; Stanley, Matthew L; Dennis, Nancy A; Woldorff, Marty G; Cabeza, Roberto

2017-04-01

Multivariate functional connectivity analyses of neuroimaging data have revealed the importance of complex, distributed interactions between disparate yet interdependent brain regions. Recent work has shown that topological properties of functional brain networks are associated with individual and group differences in cognitive performance, including in episodic memory. After constructing functional whole-brain networks derived from an event-related fMRI study of memory retrieval, we examined differences in functional brain network architecture between forgotten and remembered words. This study yielded three main findings. First, graph theory analyses showed that successfully remembering compared to forgetting was associated with significant changes in the connectivity profile of the left hippocampus and a corresponding increase in efficient communication with the rest of the brain. Second, bivariate functional connectivity analyses indicated stronger interactions between the left hippocampus and a retrieval assembly for remembered versus forgotten items. This assembly included the left precuneus, left caudate, bilateral supramarginal gyrus, and the bilateral dorsolateral superior frontal gyrus. Integrative properties of the retrieval assembly were greater for remembered than forgotten items. Third, whole-brain modularity analyses revealed that successful memory retrieval was marginally significantly associated with a less segregated modular architecture in the network. The magnitude of the decreases in modularity between remembered and forgotten conditions was related to memory performance. These findings indicate that increases in integrative properties at the nodal, retrieval assembly, and whole-brain topological levels facilitate memory retrieval, while also underscoring the potential of multivariate brain connectivity approaches for providing valuable new insights into the neural bases of memory processes. Hum Brain Mapp 38:2242-2259, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Onset age of L2 acquisition influences language network in early and late Cantonese-Mandarin bilinguals.

PubMed

Liu, Xiaojin; Tu, Liu; Wang, Junjing; Jiang, Bo; Gao, Wei; Pan, Ximin; Li, Meng; Zhong, Miao; Zhu, Zhenzhen; Niu, Meiqi; Li, Yanyan; Zhao, Ling; Chen, Xiaoxi; Liu, Chang; Lu, Zhi; Huang, Ruiwang

2017-11-01

Early second language (L2) experience influences the neural organization of L2 in neuro-plastic terms. Previous studies tried to reveal these plastic effects of age of second language acquisition (AoA-L2) and proficiency-level in L2 (PL-L2) on the neural basis of language processing in bilinguals. Although different activation patterns have been observed during language processing in early and late bilinguals by task-fMRI, few studies reported the effect of AoA-L2 and high PL-L2 on language network at resting state. In this study, we acquired resting-state fMRI (R-fMRI) data from 10 Cantonese (L1)-Mandarin (L2) early bilinguals (acquired L2: 3years old) and 11 late bilinguals (acquired L2: 6years old), and analyzed their topological properties of language networks after controlling the language daily exposure and usage as well as PL in L1 and L2. We found that early bilinguals had significantly a higher clustering coefficient, global and local efficiency, but significantly lower characteristic path length compared to late bilinguals. Modular analysis indicated that compared to late bilinguals, early bilinguals showed significantly stronger intra-modular functional connectivity in the semantic and phonetic modules, stronger inter-modular functional connectivity between the semantic and phonetic modules as well as between the phonetic and syntactic modules. Differences in global and local parameters may reflect different patterns of neuro-plasticity respectively for early and late bilinguals. These results suggested that different L2 experience influences topological properties of language network, even if late bilinguals achieve high PL-L2. Our findings may provide a new perspective of neural mechanisms related to early and late bilinguals. Copyright © 2017 Elsevier Inc. All rights reserved.

Identification of segregated regions in the functional brain connectome of autistic patients by a combination of fuzzy spectral clustering and entropy analysis

PubMed Central

Sato, João Ricardo; Balardin, Joana; Vidal, Maciel Calebe; Fujita, André

2016-01-01

Background Several neuroimaging studies support the model of abnormal development of brain connectivity in patients with autism-spectrum disorders (ASD). In this study, we aimed to test the hypothesis of reduced functional network segregation in autistic patients compared with controls. Methods Functional MRI data from children acquired under a resting-state protocol (Autism Brain Imaging Data Exchange [ABIDE]) were submitted to both fuzzy spectral clustering (FSC) with entropy analysis and graph modularity analysis. Results We included data from 814 children in our analysis. We identified 5 regions of interest comprising the motor, temporal and occipito-temporal cortices with increased entropy (p < 0.05) in the clustering structure (i.e., more segregation in the controls). Moreover, we noticed a statistically reduced modularity (p < 0.001) in the autistic patients compared with the controls. Significantly reduced eigenvector centrality values (p < 0.05) in the patients were observed in the same regions that were identified in the FSC analysis. Limitations There is considerable heterogeneity in the fMRI acquisition protocols among the sites that contributed to the ABIDE data set (e.g., scanner type, pulse sequence, duration of scan and resting-state protocol). Moreover, the sites differed in many variables related to sample characterization (e.g., age, IQ and ASD diagnostic criteria). Therefore, we cannot rule out the possibility that additional differences in functional network organization would be found in a more homogeneous data sample of individuals with ASD. Conclusion Our results suggest that the organization of the whole-brain functional network in patients with ASD is different from that observed in controls, which implies a reduced modularity of the brain functional networks involved in sensorimotor, social, affective and cognitive processing. PMID:26505141

Breakdown of the brain’s functional network modularity with awareness

PubMed Central

Godwin, Douglass; Barry, Robert L.; Marois, René

2015-01-01

Neurobiological theories of awareness propose divergent accounts of the spatial extent of brain changes that support conscious perception. Whereas focal theories posit mostly local regional changes, global theories propose that awareness emerges from the propagation of neural signals across a broad extent of sensory and association cortex. Here we tested the scalar extent of brain changes associated with awareness using graph theoretical analysis applied to functional connectivity data acquired at ultra-high field while subjects performed a simple masked target detection task. We found that awareness of a visual target is associated with a degradation of the modularity of the brain’s functional networks brought about by an increase in intermodular functional connectivity. These results provide compelling evidence that awareness is associated with truly global changes in the brain’s functional connectivity. PMID:25759440

Synthetic biology: exploring and exploiting genetic modularity through the design of novel biological networks.

PubMed

Agapakis, Christina M; Silver, Pamela A

2009-07-01

Synthetic biology has been used to describe many biological endeavors over the past thirty years--from designing enzymes and in vitro systems, to manipulating existing metabolisms and gene expression, to creating entirely synthetic replicating life forms. What separates the current incarnation of synthetic biology from the recombinant DNA technology or metabolic engineering of the past is an emphasis on principles from engineering such as modularity, standardization, and rigorously predictive models. As such, synthetic biology represents a new paradigm for learning about and using biological molecules and data, with applications in basic science, biotechnology, and medicine. This review covers the canonical examples as well as some recent advances in synthetic biology in terms of what we know and what we can learn about the networks underlying biology, and how this endeavor may shape our understanding of living systems.

Critical field-exponents for secure message-passing in modular networks

NASA Astrophysics Data System (ADS)

Shekhtman, Louis M.; Danziger, Michael M.; Bonamassa, Ivan; Buldyrev, Sergey V.; Caldarelli, Guido; Zlatić, Vinko; Havlin, Shlomo

2018-05-01

We study secure message-passing in the presence of multiple adversaries in modular networks. We assume a dominant fraction of nodes in each module have the same vulnerability, i.e., the same entity spying on them. We find both analytically and via simulations that the links between the modules (interlinks) have effects analogous to a magnetic field in a spin-system in that for any amount of interlinks the system no longer undergoes a phase transition. We then define the exponents δ, which relates the order parameter (the size of the giant secure component) at the critical point to the field strength (average number of interlinks per node), and γ, which describes the susceptibility near criticality. These are found to be δ = 2 and γ = 1 (with the scaling of the order parameter near the critical point given by β = 1). When two or more vulnerabilities are equally present in a module we find δ = 1 and γ = 0 (with β ≥ 2). Apart from defining a previously unidentified universality class, these exponents show that increasing connections between modules is more beneficial for security than increasing connections within modules. We also measure the correlation critical exponent ν, and the upper critical dimension d c , finding that ν {d}c=3 as for ordinary percolation, suggesting that for secure message-passing d c = 6. These results provide an interesting analogy between secure message-passing in modular networks and the physics of magnetic spin-systems.

Detecting Network Communities: An Application to Phylogenetic Analysis

PubMed Central

Andrade, Roberto F. S.; Rocha-Neto, Ivan C.; Santos, Leonardo B. L.; de Santana, Charles N.; Diniz, Marcelo V. C.; Lobão, Thierry Petit; Goés-Neto, Aristóteles; Pinho, Suani T. R.; El-Hani, Charbel N.

2011-01-01

This paper proposes a new method to identify communities in generally weighted complex networks and apply it to phylogenetic analysis. In this case, weights correspond to the similarity indexes among protein sequences, which can be used for network construction so that the network structure can be analyzed to recover phylogenetically useful information from its properties. The analyses discussed here are mainly based on the modular character of protein similarity networks, explored through the Newman-Girvan algorithm, with the help of the neighborhood matrix . The most relevant networks are found when the network topology changes abruptly revealing distinct modules related to the sets of organisms to which the proteins belong. Sound biological information can be retrieved by the computational routines used in the network approach, without using biological assumptions other than those incorporated by BLAST. Usually, all the main bacterial phyla and, in some cases, also some bacterial classes corresponded totally (100%) or to a great extent (>70%) to the modules. We checked for internal consistency in the obtained results, and we scored close to 84% of matches for community pertinence when comparisons between the results were performed. To illustrate how to use the network-based method, we employed data for enzymes involved in the chitin metabolic pathway that are present in more than 100 organisms from an original data set containing 1,695 organisms, downloaded from GenBank on May 19, 2007. A preliminary comparison between the outcomes of the network-based method and the results of methods based on Bayesian, distance, likelihood, and parsimony criteria suggests that the former is as reliable as these commonly used methods. We conclude that the network-based method can be used as a powerful tool for retrieving modularity information from weighted networks, which is useful for phylogenetic analysis. PMID:21573202

Habitat fragmentation alters the properties of a host-parasite network: rodents and their helminths in South-East Asia.

PubMed

Bordes, Frédéric; Morand, Serge; Pilosof, Shai; Claude, Julien; Krasnov, Boris R; Cosson, Jean-François; Chaval, Yannick; Ribas, Alexis; Chaisiri, Kittipong; Blasdell, Kim; Herbreteau, Vincent; Dupuy, Stéphane; Tran, Annelise

2015-09-01

1. While the effects of deforestation and habitat fragmentation on parasite prevalence or richness are well investigated, host-parasite networks are still understudied despite their importance in understanding the mechanisms of these major disturbances. Because fragmentation may negatively impact species occupancy, abundance and co-occurrence, we predict a link between spatiotemporal changes in habitat and the architecture of host-parasite networks. 2. For this, we used an extensive data set on 16 rodent species and 29 helminth species from seven localities of South-East Asia. We analysed the effects of rapid deforestation on connectance and modularity of helminth-parasite networks. We estimated both the degree of fragmentation and the rate of deforestation through the development of land uses and their changes through the last 20 to 30 years in order to take into account the dynamics of habitat fragmentation in our statistical analyses. 3. We found that rapid fragmentation does not affect helminth species richness per se but impacts host-parasite interactions as the rodent-helminth network becomes less connected and more modular. 4. Our results suggest that parasite sharing among host species may become more difficult to maintain with the increase of habitat disturbance. © 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society.

An observatory control system for the University of Hawai'i 2.2m Telescope

NASA Astrophysics Data System (ADS)

McKay, Luke; Erickson, Christopher; Mukensnable, Donn; Stearman, Anthony; Straight, Brad

2016-07-01

The University of Hawai'i 2.2m telescope at Maunakea has operated since 1970, and has had several controls upgrades to date. The newest system will operate as a distributed hierarchy of GNU/Linux central server, networked single-board computers, microcontrollers, and a modular motion control processor for the main axes. Rather than just a telescope control system, this new effort is towards a cohesive, modular, and robust whole observatory control system, with design goals of fully robotic unattended operation, high reliability, and ease of maintenance and upgrade.

A modular (almost) automatic set-up for elastic multi-tenants cloud (micro)infrastructures

NASA Astrophysics Data System (ADS)

Amoroso, A.; Astorino, F.; Bagnasco, S.; Balashov, N. A.; Bianchi, F.; Destefanis, M.; Lusso, S.; Maggiora, M.; Pellegrino, J.; Yan, L.; Yan, T.; Zhang, X.; Zhao, X.

2017-10-01

An auto-installing tool on an usb drive can allow for a quick and easy automatic deployment of OpenNebula-based cloud infrastructures remotely managed by a central VMDIRAC instance. A single team, in the main site of an HEP Collaboration or elsewhere, can manage and run a relatively large network of federated (micro-)cloud infrastructures, making an highly dynamic and elastic use of computing resources. Exploiting such an approach can lead to modular systems of cloud-bursting infrastructures addressing complex real-life scenarios.

Modular Subsea Monitoring Network (MSM) - Realizing Integrated Environmental Monitoring Solutions

NASA Astrophysics Data System (ADS)

Mosch, Thomas; Fietzek, Peer

2016-04-01

In a variety of scientific and industrial application areas, ranging i.e. from the supervision of hydrate fields over the detection and localization of fugitive emissions from subsea oil and gas production to fish farming, fixed point observatories are useful and applied means. They monitor the water column and/or are placed at the sea floor over long periods of time. They are essential oceanographic platforms for providing valuable long-term time series data and multi-parameter measurements. Various mooring and observatory endeavors world-wide contribute valuable data needed for understanding our planet's ocean systems and biogeochemical processes. Continuously powered cabled observatories enable real-time data transmission from spots of interest close to the shore or to ocean infrastructures. Independent of the design of the observatories they all rely on sensors which demands for regular maintenance. This work is in most cases associated with cost-intensive maintenance on a regular time basis for the entire sensor carrying fixed platform. It is mandatory to encounter this asset for long-term monitoring by enhancing hardware efficiency. On the basis of two examples of use from the area of hydrate monitoring (off Norway and Japan) we will present the concept of the Modular Subsea Monitoring Network (MSM). The modular, scalable and networking capabilities of the MSM allow for an easy adaptation to different monitoring tasks. Providing intelligent power management, combining chemical and acoustical sensors, adaptation of the payload according to the monitoring tasks, autonomous powering, modular design for easy transportation, storage and mobilization, Vessel of Opportunity-borne launching and recovery capability with a video-guided launcher system and a rope recovery system are key facts addressed during the development of the MSM. Step by step the MSM concept applied to the observatory hardware will also be extended towards the gathered data to maximize the efficiency of subsea monitoring in a variety of applications.

RNA Graph Partitioning for the Discovery of RNA Modularity: A Novel Application of Graph Partition Algorithm to Biology

PubMed Central

Elmetwaly, Shereef; Schlick, Tamar

2014-01-01

Graph representations have been widely used to analyze and design various economic, social, military, political, and biological networks. In systems biology, networks of cells and organs are useful for understanding disease and medical treatments and, in structural biology, structures of molecules can be described, including RNA structures. In our RNA-As-Graphs (RAG) framework, we represent RNA structures as tree graphs by translating unpaired regions into vertices and helices into edges. Here we explore the modularity of RNA structures by applying graph partitioning known in graph theory to divide an RNA graph into subgraphs. To our knowledge, this is the first application of graph partitioning to biology, and the results suggest a systematic approach for modular design in general. The graph partitioning algorithms utilize mathematical properties of the Laplacian eigenvector (µ2) corresponding to the second eigenvalues (λ2) associated with the topology matrix defining the graph: λ2 describes the overall topology, and the sum of µ2′s components is zero. The three types of algorithms, termed median, sign, and gap cuts, divide a graph by determining nodes of cut by median, zero, and largest gap of µ2′s components, respectively. We apply these algorithms to 45 graphs corresponding to all solved RNA structures up through 11 vertices (∼220 nucleotides). While we observe that the median cut divides a graph into two similar-sized subgraphs, the sign and gap cuts partition a graph into two topologically-distinct subgraphs. We find that the gap cut produces the best biologically-relevant partitioning for RNA because it divides RNAs at less stable connections while maintaining junctions intact. The iterative gap cuts suggest basic modules and assembly protocols to design large RNA structures. Our graph substructuring thus suggests a systematic approach to explore the modularity of biological networks. In our applications to RNA structures, subgraphs also suggest design strategies for novel RNA motifs. PMID:25188578

Modular Neuronal Assemblies Embodied in a Closed-Loop Environment: Toward Future Integration of Brains and Machines

PubMed Central

Tessadori, Jacopo; Bisio, Marta; Martinoia, Sergio; Chiappalone, Michela

2012-01-01

Behaviors, from simple to most complex, require a two-way interaction with the environment and the contribution of different brain areas depending on the orchestrated activation of neuronal assemblies. In this work we present a new hybrid neuro-robotic architecture based on a neural controller bi-directionally connected to a virtual robot implementing a Braitenberg vehicle aimed at avoiding obstacles. The robot is characterized by proximity sensors and wheels, allowing it to navigate into a circular arena with obstacles of different sizes. As neural controller, we used hippocampal cultures dissociated from embryonic rats and kept alive over Micro Electrode Arrays (MEAs) for 3–8 weeks. The developed software architecture guarantees a bi-directional exchange of information between the natural and the artificial part by means of simple linear coding/decoding schemes. We used two different kinds of experimental preparation: “random” and “modular” populations. In the second case, the confinement was assured by a polydimethylsiloxane (PDMS) mask placed over the surface of the MEA device, thus defining two populations interconnected via specific microchannels. The main results of our study are: (i) neuronal cultures can be successfully interfaced to an artificial agent; (ii) modular networks show a different dynamics with respect to random culture, both in terms of spontaneous and evoked electrophysiological patterns; (iii) the robot performs better if a reinforcement learning paradigm (i.e., a tetanic stimulation delivered to the network following each collision) is activated, regardless of the modularity of the culture; (iv) the robot controlled by the modular network further enhances its capabilities in avoiding obstacles during the short-term plasticity trial. The developed paradigm offers a new framework for studying, in simplified model systems, neuro-artificial bi-directional interfaces for the development of new strategies for brain-machine interaction. PMID:23248586

Wireless Sensor Networks--A Hands-On Modular Experiments Platform for Enhanced Pedagogical Learning

ERIC Educational Resources Information Center

Taslidere, E.; Cohen, F. S.; Reisman, F. K.

2011-01-01

This paper presents the use of wireless sensor networks (WSNs) in educational research as a platform for enhanced pedagogical learning. The aim here with the use of a WSN platform was to go beyond the implementation stage to the real-life application stage, i.e., linking the implementation to real-life applications, where abstract theory and…

Ectoparasites and endoparasites of fish form networks with different structures.

PubMed

Bellay, S; DE Oliveira, E F; Almeida-Neto, M; Mello, M A R; Takemoto, R M; Luque, J L

2015-06-01

Hosts and parasites interact with each other in a variety of ways, and this diversity of interactions is reflected in the networks they form. To test for differences in interaction patterns of ecto- and endoparasites we analysed subnetworks formed by each kind of parasites and their host fish species in fish-parasite networks for 22 localities. We assessed the proportion of parasite species per host species, the relationship between parasite fauna composition and host taxonomy, connectance, nestedness and modularity of each subnetwork (n = 44). Furthermore, we evaluated the similarity in host species composition among modules in ecto- and endoparasite subnetworks. We found several differences between subnetworks of fish ecto- and endoparasites. The association with a higher number of host species observed among endoparasites resulted in higher connectance and nestedness, and lower values of modularity in their subnetworks than in those of ectoparasites. Taxonomically related host species tended to share ecto- or endoparasites with the same interaction intensity, but the species composition of hosts tended to differ between modules formed by ecto- and endoparasites. Our results suggest that different evolutionary and ecological processes are responsible for organizing the networks formed by ecto- and endoparasites and fish.

An Ancient Gene Network Is Co-opted for Teeth on Old and New Jaws

PubMed Central

Fraser, Gareth J; Hulsey, C. Darrin; Bloomquist, Ryan F; Uyesugi, Kristine; Manley, Nancy R; Streelman, J. Todd

2009-01-01

Vertebrate dentitions originated in the posterior pharynx of jawless fishes more than half a billion years ago. As gnathostomes (jawed vertebrates) evolved, teeth developed on oral jaws and helped to establish the dominance of this lineage on land and in the sea. The advent of oral jaws was facilitated, in part, by absence of hox gene expression in the first, most anterior, pharyngeal arch. Much later in evolutionary time, teleost fishes evolved a novel toothed jaw in the pharynx, the location of the first vertebrate teeth. To examine the evolutionary modularity of dentitions, we asked whether oral and pharyngeal teeth develop using common or independent gene regulatory pathways. First, we showed that tooth number is correlated on oral and pharyngeal jaws across species of cichlid fishes from Lake Malawi (East Africa), suggestive of common regulatory mechanisms for tooth initiation. Surprisingly, we found that cichlid pharyngeal dentitions develop in a region of dense hox gene expression. Thus, regulation of tooth number is conserved, despite distinct developmental environments of oral and pharyngeal jaws; pharyngeal jaws occupy hox-positive, endodermal sites, and oral jaws develop in hox-negative regions with ectodermal cell contributions. Next, we studied the expression of a dental gene network for tooth initiation, most genes of which are similarly deployed across the two disparate jaw sites. This collection of genes includes members of the ectodysplasin pathway, eda and edar, expressed identically during the patterning of oral and pharyngeal teeth. Taken together, these data suggest that pharyngeal teeth of jawless vertebrates utilized an ancient gene network before the origin of oral jaws, oral teeth, and ectodermal appendages. The first vertebrate dentition likely appeared in a hox-positive, endodermal environment and expressed a genetic program including ectodysplasin pathway genes. This ancient regulatory circuit was co-opted and modified for teeth in oral jaws of the first jawed vertebrate, and subsequently deployed as jaws enveloped teeth on novel pharyngeal jaws. Our data highlight an amazing modularity of jaws and teeth as they coevolved during the history of vertebrates. We exploit this diversity to infer a core dental gene network, common to the first tooth and all of its descendants. PMID:19215146

Toward Developmental Connectomics of the Human Brain

PubMed Central

Cao, Miao; Huang, Hao; Peng, Yun; Dong, Qi; He, Yong

2016-01-01

Imaging connectomics based on graph theory has become an effective and unique methodological framework for studying structural and functional connectivity patterns of the developing brain. Normal brain development is characterized by continuous and significant network evolution throughout infancy, childhood, and adolescence, following specific maturational patterns. Disruption of these normal changes is associated with neuropsychiatric developmental disorders, such as autism spectrum disorders or attention-deficit hyperactivity disorder. In this review, we focused on the recent progresses regarding typical and atypical development of human brain networks from birth to early adulthood, using a connectomic approach. Specifically, by the time of birth, structural networks already exhibit adult-like organization, with global efficient small-world and modular structures, as well as hub regions and rich-clubs acting as communication backbones. During development, the structure networks are fine-tuned, with increased global integration and robustness and decreased local segregation, as well as the strengthening of the hubs. In parallel, functional networks undergo more dramatic changes during maturation, with both increased integration and segregation during development, as brain hubs shift from primary regions to high order functioning regions, and the organization of modules transitions from a local anatomical emphasis to a more distributed architecture. These findings suggest that structural networks develop earlier than functional networks; meanwhile functional networks demonstrate more dramatic maturational changes with the evolution of structural networks serving as the anatomical backbone. In this review, we also highlighted topologically disorganized characteristics in structural and functional brain networks in several major developmental neuropsychiatric disorders (e.g., autism spectrum disorders, attention-deficit hyperactivity disorder and developmental dyslexia). Collectively, we showed that delineation of the brain network from a connectomics perspective offers a unique and refreshing view of both normal development and neuropsychiatric disorders. PMID:27064378

Graph theoretical analysis of functional network for comprehension of sign language.

PubMed

Liu, Lanfang; Yan, Xin; Liu, Jin; Xia, Mingrui; Lu, Chunming; Emmorey, Karen; Chu, Mingyuan; Ding, Guosheng

2017-09-15

Signed languages are natural human languages using the visual-motor modality. Previous neuroimaging studies based on univariate activation analysis show that a widely overlapped cortical network is recruited regardless whether the sign language is comprehended (for signers) or not (for non-signers). Here we move beyond previous studies by examining whether the functional connectivity profiles and the underlying organizational structure of the overlapped neural network may differ between signers and non-signers when watching sign language. Using graph theoretical analysis (GTA) and fMRI, we compared the large-scale functional network organization in hearing signers with non-signers during the observation of sentences in Chinese Sign Language. We found that signed sentences elicited highly similar cortical activations in the two groups of participants, with slightly larger responses within the left frontal and left temporal gyrus in signers than in non-signers. Crucially, further GTA revealed substantial group differences in the topologies of this activation network. Globally, the network engaged by signers showed higher local efficiency (t (24) =2.379, p=0.026), small-worldness (t (24) =2.604, p=0.016) and modularity (t (24) =3.513, p=0.002), and exhibited different modular structures, compared to the network engaged by non-signers. Locally, the left ventral pars opercularis served as a network hub in the signer group but not in the non-signer group. These findings suggest that, despite overlap in cortical activation, the neural substrates underlying sign language comprehension are distinguishable at the network level from those for the processing of gestural action. Copyright © 2017 Elsevier B.V. All rights reserved.

Convergent evolution of gene networks by single-gene duplications in higher eukaryotes.

PubMed

Amoutzias, Gregory D; Robertson, David L; Oliver, Stephen G; Bornberg-Bauer, Erich

2004-03-01

By combining phylogenetic, proteomic and structural information, we have elucidated the evolutionary driving forces for the gene-regulatory interaction networks of basic helix-loop-helix transcription factors. We infer that recurrent events of single-gene duplication and domain rearrangement repeatedly gave rise to distinct networks with almost identical hub-based topologies, and multiple activators and repressors. We thus provide the first empirical evidence for scale-free protein networks emerging through single-gene duplications, the dominant importance of molecular modularity in the bottom-up construction of complex biological entities, and the convergent evolution of networks.

A Diagrammatic Language for Biochemical Networks

NASA Astrophysics Data System (ADS)

Maimon, Ron

2002-03-01

I present a diagrammatic language for representing the structure of biochemical networks. The language is designed to represent modular structure in a computational fasion, with composition of reactions replacing functional composition. This notation is used to represent arbitrarily large networks efficiently. The notation finds its most natural use in representing biological interaction networks, but it is a general computing language appropriate to any naturally occuring computation. Unlike lambda-calculus, or text-derived languages, it does not impose a tree-structure on the diagrams, and so is more effective at representing biological fucntion than competing notations.

Structural Preferential Attachment: Network Organization beyond the Link

NASA Astrophysics Data System (ADS)

Hébert-Dufresne, Laurent; Allard, Antoine; Marceau, Vincent; Noël, Pierre-André; Dubé, Louis J.

2011-10-01

We introduce a mechanism which models the emergence of the universal properties of complex networks, such as scale independence, modularity and self-similarity, and unifies them under a scale-free organization beyond the link. This brings a new perspective on network organization where communities, instead of links, are the fundamental building blocks of complex systems. We show how our simple model can reproduce social and information networks by predicting their community structure and more importantly, how their nodes or communities are interconnected, often in a self-similar manner.

Modern network science of neurological disorders.

PubMed

Stam, Cornelis J

2014-10-01

Modern network science has revealed fundamental aspects of normal brain-network organization, such as small-world and scale-free patterns, hierarchical modularity, hubs and rich clubs. The next challenge is to use this knowledge to gain a better understanding of brain disease. Recent developments in the application of network science to conditions such as Alzheimer's disease, multiple sclerosis, traumatic brain injury and epilepsy have challenged the classical concept of neurological disorders being either 'local' or 'global', and have pointed to the overload and failure of hubs as a possible final common pathway in neurological disorders.

A novel joint-processing adaptive nonlinear equalizer using a modular recurrent neural network for chaotic communication systems.

PubMed

Zhao, Haiquan; Zeng, Xiangping; Zhang, Jiashu; Liu, Yangguang; Wang, Xiaomin; Li, Tianrui

2011-01-01

To eliminate nonlinear channel distortion in chaotic communication systems, a novel joint-processing adaptive nonlinear equalizer based on a pipelined recurrent neural network (JPRNN) is proposed, using a modified real-time recurrent learning (RTRL) algorithm. Furthermore, an adaptive amplitude RTRL algorithm is adopted to overcome the deteriorating effect introduced by the nesting process. Computer simulations illustrate that the proposed equalizer outperforms the pipelined recurrent neural network (PRNN) and recurrent neural network (RNN) equalizers. Copyright © 2010 Elsevier Ltd. All rights reserved.

Network clustering and community detection using modulus of families of loops.

PubMed

Shakeri, Heman; Poggi-Corradini, Pietro; Albin, Nathan; Scoglio, Caterina

2017-01-01

We study the structure of loops in networks using the notion of modulus of loop families. We introduce an alternate measure of network clustering by quantifying the richness of families of (simple) loops. Modulus tries to minimize the expected overlap among loops by spreading the expected link usage optimally. We propose weighting networks using these expected link usages to improve classical community detection algorithms. We show that the proposed method enhances the performance of certain algorithms, such as spectral partitioning and modularity maximization heuristics, on standard benchmarks.

Empirical research on complex networks modeling of combat SoS based on data from real war-game, Part I: Statistical characteristics

NASA Astrophysics Data System (ADS)

Chen, Lei; Kou, Yingxin; Li, Zhanwu; Xu, An; Wu, Cheng

2018-01-01

We build a complex networks model of combat System-of-Systems (SoS) based on empirical data from a real war-game, this model is a combination of command & control (C2) subnetwork, sensors subnetwork, influencers subnetwork and logistical support subnetwork, each subnetwork has idiographic components and statistical characteristics. The C2 subnetwork is the core of whole combat SoS, it has a hierarchical structure with no modularity, of which robustness is strong enough to maintain normal operation after any two nodes is destroyed; the sensors subnetwork and influencers subnetwork are like sense organ and limbs of whole combat SoS, they are both flat modular networks of which degree distribution obey GEV distribution and power-law distribution respectively. The communication network is the combination of all subnetworks, it is an assortative Small-World network with core-periphery structure, the Intelligence & Communication Stations/Command Center integrated with C2 nodes in the first three level act as the hub nodes in communication network, and all the fourth-level C2 nodes, sensors, influencers and logistical support nodes have communication capability, they act as the periphery nodes in communication network, its degree distribution obeys exponential distribution in the beginning, Gaussian distribution in the middle, and power-law distribution in the end, and its path length obeys GEV distribution. The betweenness centrality distribution, closeness centrality distribution and eigenvector centrality are also been analyzed to measure the vulnerability of nodes.

FACETS: multi-faceted functional decomposition of protein interaction networks

PubMed Central

Seah, Boon-Siew; Bhowmick, Sourav S.; Forbes Dewey, C.

2012-01-01

Motivation: The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein–protein interaction (PPI) network using graph theoretic analysis. Despite the recent progress, systems level analysis of high-throughput PPIs remains a daunting task because of the amount of data they present. In this article, we propose a novel PPI network decomposition algorithm called FACETS in order to make sense of the deluge of interaction data using Gene Ontology (GO) annotations. FACETS finds not just a single functional decomposition of the PPI network, but a multi-faceted atlas of functional decompositions that portray alternative perspectives of the functional landscape of the underlying PPI network. Each facet in the atlas represents a distinct interpretation of how the network can be functionally decomposed and organized. Our algorithm maximizes interpretative value of the atlas by optimizing inter-facet orthogonality and intra-facet cluster modularity. Results: We tested our algorithm on the global networks from IntAct, and compared it with gold standard datasets from MIPS and KEGG. We demonstrated the performance of FACETS. We also performed a case study that illustrates the utility of our approach. Contact: seah0097@ntu.edu.sg or assourav@ntu.edu.sg Supplementary information: Supplementary data are available at the Bioinformatics online. Availability: Our software is available freely for non-commercial purposes from: http://www.cais.ntu.edu.sg/∼assourav/Facets/ PMID:22908217

Physiology Teaching and Learning Experience in a New Modular Curriculum at the National University of Rwanda

ERIC Educational Resources Information Center

Gahutu, Jean Bosco

2010-01-01

In the present article, I report on my experience in teaching and learning physiology in the first year of a new modular curriculum at the Faculty of Medicine of the National University of Rwanda. With self-reported questionnaires, I collected learning experience perceptions from 112 students who attended the module of physiology in 2008. The…

Standardized Modular Power Interfaces for Future Space Explorations Missions

NASA Technical Reports Server (NTRS)

Oeftering, Richard

2015-01-01

Earlier studies show that future human explorations missions are composed of multi-vehicle assemblies with interconnected electric power systems. Some vehicles are often intended to serve as flexible multi-purpose or multi-mission platforms. This drives the need for power architectures that can be reconfigured to support this level of flexibility. Power system developmental costs can be reduced, program wide, by utilizing a common set of modular building blocks. Further, there are mission operational and logistics cost benefits of using a common set of modular spares. These benefits are the goals of the Advanced Exploration Systems (AES) Modular Power System (AMPS) project. A common set of modular blocks requires a substantial level of standardization in terms of the Electrical, Data System, and Mechanical interfaces. The AMPS project is developing a set of proposed interface standards that will provide useful guidance for modular hardware developers but not needlessly constrain technology options, or limit future growth in capability. In 2015 the AMPS project focused on standardizing the interfaces between the elements of spacecraft power distribution and energy storage. The development of the modular power standard starts with establishing mission assumptions and ground rules to define design application space. The standards are defined in terms of AMPS objectives including Commonality, Reliability-Availability, Flexibility-Configurability and Supportability-Reusability. The proposed standards are aimed at assembly and sub-assembly level building blocks. AMPS plans to adopt existing standards for spacecraft command and data, software, network interfaces, and electrical power interfaces where applicable. Other standards including structural encapsulation, heat transfer, and fluid transfer, are governed by launch and spacecraft environments and bound by practical limitations of weight and volume. Developing these mechanical interface standards is more difficult but an essential part of defining physical building blocks of modular power. This presentation describes the AMPS projects progress towards standardized modular power interfaces.

The pandemonium system of reflective agents.

PubMed

Smieja, F

1996-01-01

The Pandemonium system of reflective MINOS agents solves problems by automatic dynamic modularization of the input space. The agents contain feedforward neural networks which adapt using the backpropagation algorithm. We demonstrate the performance of Pandemonium on various categories of problems. These include learning continuous functions with discontinuities, separating two spirals, learning the parity function, and optical character recognition. It is shown how strongly the advantages gained from using a modularization technique depend on the nature of the problem. The superiority of the Pandemonium method over a single net on the first two test categories is contrasted with its limited advantages for the second two categories. In the first case the system converges quicker with modularization and is seen to lead to simpler solutions. For the second case the problem is not significantly simplified through flat decomposition of the input space, although convergence is still quicker.

Integromics network meta-analysis on cardiac aging offers robust multi-layer modular signatures and reveals micronome synergism.

PubMed

Dimitrakopoulou, Konstantina; Vrahatis, Aristidis G; Bezerianos, Anastasios

2015-03-04

The avalanche of integromics and panomics approaches shifted the deciphering of aging mechanisms from single molecular entities to communities of them. In this orientation, we explore the cardiac aging mechanisms - risk factor for multiple cardiovascular diseases - by capturing the micronome synergism and detecting longevity signatures in the form of communities (modules). For this, we developed a meta-analysis scheme that integrates transcriptome expression data from multiple cardiac-specific independent studies in mouse and human along with proteome and micronome interaction data in the form of multiple independent weighted networks. Modularization of each weighted network produced modules, which in turn were further analyzed so as to define consensus modules across datasets that change substantially during lifespan. Also, we established a metric that determines - from the modular perspective - the synergism of microRNA-microRNA interactions as defined by significantly functionally associated targets. The meta-analysis provided 40 consensus integromics modules across mouse datasets and revealed microRNA relations with substantial collective action during aging. Three modules were reproducible, based on homology, when mapped against human-derived modules. The respective homologs mainly represent NADH dehydrogenases, ATP synthases, cytochrome oxidases, Ras GTPases and ribosomal proteins. Among various observations, we corroborate to the involvement of miR-34a (included in consensus modules) as proposed recently; yet we report that has no synergistic effect. Moving forward, we determined its age-related neighborhood in which HCN3, a known heart pacemaker channel, was included. Also, miR-125a-5p/-351, miR-200c/-429, miR-106b/-17, miR-363/-92b, miR-181b/-181d, miR-19a/-19b, let-7d/-7f, miR-18a/-18b, miR-128/-27b and miR-106a/-291a-3p pairs exhibited significant synergy and their association to aging and/or cardiovascular diseases is supported in many cases by a disease database and previous studies. On the contrary, we suggest that miR-22 has not substantial impact on heart longevity as proposed recently. We revised several proteins and microRNAs recently implicated in cardiac aging and proposed for the first time modules as signatures. The integromics meta-analysis approach can serve as an efficient subvening signature tool for more-oriented better-designed experiments. It can also promote the combinational multi-target microRNA therapy of age-related cardiovascular diseases along the continuum from prevention to detection, diagnosis, treatment and outcome.

Comparative Analysis of Disruption Tolerant Network Routing Simulations in the One and NS-3

DTIC Science & Technology

2017-12-01

real systems with less work compared to ns-2. In order to meet the design goals of ns-3, the entire code structure changed to a modular design . As a...NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS COMPARATIVE ANALYSIS OF DISRUPTION TOLERANT NETWORK ROUTING SIMULATIONS IN THE ONE AND NS-3...Thesis 03-23-2016 to 12-15-2017 4. TITLE AND SUBTITLE COMPARATIVE ANALYSIS OF DISRUPTION TOLERANT NETWORK ROUTING SIMULATIONS IN THE ONE AND NS-3 5

E-Center: A Collaborative Platform for Wide Area Network Users

NASA Astrophysics Data System (ADS)

Grigoriev, M.; DeMar, P.; Tierney, B.; Lake, A.; Metzger, J.; Frey, M.; Calyam, P.

2012-12-01

The E-Center is a social collaborative web-based platform for assisting network users in understanding network conditions across network paths of interest to them. It is designed to give a user the necessary tools to isolate, identify, and resolve network performance-related problems. E-Center provides network path information on a link-by-link level, as well as from an end-to-end perspective. In addition to providing current and recent network path data, E-Center is intended to provide a social media environment for them to share issues, ideas, concerns, and problems. The product has a modular design that accommodates integration of other network services that make use of the same network path and performance data.

Low-complexity nonlinear adaptive filter based on a pipelined bilinear recurrent neural network.

PubMed

Zhao, Haiquan; Zeng, Xiangping; He, Zhengyou

2011-09-01

To reduce the computational complexity of the bilinear recurrent neural network (BLRNN), a novel low-complexity nonlinear adaptive filter with a pipelined bilinear recurrent neural network (PBLRNN) is presented in this paper. The PBLRNN, inheriting the modular architectures of the pipelined RNN proposed by Haykin and Li, comprises a number of BLRNN modules that are cascaded in a chained form. Each module is implemented by a small-scale BLRNN with internal dynamics. Since those modules of the PBLRNN can be performed simultaneously in a pipelined parallelism fashion, it would result in a significant improvement of computational efficiency. Moreover, due to nesting module, the performance of the PBLRNN can be further improved. To suit for the modular architectures, a modified adaptive amplitude real-time recurrent learning algorithm is derived on the gradient descent approach. Extensive simulations are carried out to evaluate the performance of the PBLRNN on nonlinear system identification, nonlinear channel equalization, and chaotic time series prediction. Experimental results show that the PBLRNN provides considerably better performance compared to the single BLRNN and RNN models.

Functional modules of sigma factor regulons guarantee adaptability and evolvability

PubMed Central

Binder, Sebastian C.; Eckweiler, Denitsa; Schulz, Sebastian; Bielecka, Agata; Nicolai, Tanja; Franke, Raimo; Häussler, Susanne; Meyer-Hermann, Michael

2016-01-01

The focus of modern molecular biology turns from assigning functions to individual genes towards understanding the expression and regulation of complex sets of molecules. Here, we provide evidence that alternative sigma factor regulons in the pathogen Pseudomonas aeruginosa largely represent insulated functional modules which provide a critical level of biological organization involved in general adaptation and survival processes. Analysis of the operational state of the sigma factor network revealed that transcription factors functionally couple the sigma factor regulons and significantly modulate the transcription levels in the face of challenging environments. The threshold quality of newly evolved transcription factors was reached faster and more robustly in in silico testing when the structural organization of sigma factor networks was taken into account. These results indicate that the modular structures of alternative sigma factor regulons provide P. aeruginosa with a robust framework to function adequately in its environment and at the same time facilitate evolutionary change. Our data support the view that widespread modularity guarantees robustness of biological networks and is a key driver of evolvability. PMID:26915971

cPath: open source software for collecting, storing, and querying biological pathways

PubMed Central

Cerami, Ethan G; Bader, Gary D; Gross, Benjamin E; Sander, Chris

2006-01-01

Background Biological pathways, including metabolic pathways, protein interaction networks, signal transduction pathways, and gene regulatory networks, are currently represented in over 220 diverse databases. These data are crucial for the study of specific biological processes, including human diseases. Standard exchange formats for pathway information, such as BioPAX, CellML, SBML and PSI-MI, enable convenient collection of this data for biological research, but mechanisms for common storage and communication are required. Results We have developed cPath, an open source database and web application for collecting, storing, and querying biological pathway data. cPath makes it easy to aggregate custom pathway data sets available in standard exchange formats from multiple databases, present pathway data to biologists via a customizable web interface, and export pathway data via a web service to third-party software, such as Cytoscape, for visualization and analysis. cPath is software only, and does not include new pathway information. Key features include: a built-in identifier mapping service for linking identical interactors and linking to external resources; built-in support for PSI-MI and BioPAX standard pathway exchange formats; a web service interface for searching and retrieving pathway data sets; and thorough documentation. The cPath software is freely available under the LGPL open source license for academic and commercial use. Conclusion cPath is a robust, scalable, modular, professional-grade software platform for collecting, storing, and querying biological pathways. It can serve as the core data handling component in information systems for pathway visualization, analysis and modeling. PMID:17101041

An Approach to Automated Fusion System Design and Adaptation

PubMed Central

Fritze, Alexander; Mönks, Uwe; Holst, Christoph-Alexander; Lohweg, Volker

2017-01-01

Industrial applications are in transition towards modular and flexible architectures that are capable of self-configuration and -optimisation. This is due to the demand of mass customisation and the increasing complexity of industrial systems. The conversion to modular systems is related to challenges in all disciplines. Consequently, diverse tasks such as information processing, extensive networking, or system monitoring using sensor and information fusion systems need to be reconsidered. The focus of this contribution is on distributed sensor and information fusion systems for system monitoring, which must reflect the increasing flexibility of fusion systems. This contribution thus proposes an approach, which relies on a network of self-descriptive intelligent sensor nodes, for the automatic design and update of sensor and information fusion systems. This article encompasses the fusion system configuration and adaptation as well as communication aspects. Manual interaction with the flexibly changing system is reduced to a minimum. PMID:28300762

An Approach to Automated Fusion System Design and Adaptation.

PubMed

Fritze, Alexander; Mönks, Uwe; Holst, Christoph-Alexander; Lohweg, Volker

2017-03-16

Industrial applications are in transition towards modular and flexible architectures that are capable of self-configuration and -optimisation. This is due to the demand of mass customisation and the increasing complexity of industrial systems. The conversion to modular systems is related to challenges in all disciplines. Consequently, diverse tasks such as information processing, extensive networking, or system monitoring using sensor and information fusion systems need to be reconsidered. The focus of this contribution is on distributed sensor and information fusion systems for system monitoring, which must reflect the increasing flexibility of fusion systems. This contribution thus proposes an approach, which relies on a network of self-descriptive intelligent sensor nodes, for the automatic design and update of sensor and information fusion systems. This article encompasses the fusion system configuration and adaptation as well as communication aspects. Manual interaction with the flexibly changing system is reduced to a minimum.

Aphasia and the Diagram Makers Revisited: an Update of Information Processing Models

PubMed Central

2006-01-01

Aphasic syndromes from diseases such as stroke and degenerative disorders are still common and disabling neurobehavioral disorders. Diagnosis, management and treatment of these communication disorders are often dependent upon understanding the neuropsychological mechanisms that underlie these disorders. Since the work of Broca it has been recognized that the human brain is organized in a modular fashion. Wernicke realized that the types of signs and symptoms displayed by aphasic patients reflect the degradation or disconnection of the modules that comprise this speech-language network. Thus, he was the first to propose a diagrammatic or information processing model of this modular language-speech network. Since he first published this model many new aphasic syndromes have been discovered and this has led to modifications of this model. This paper reviews some of the early (nineteenth century) models and then attempts to develop a more up-to-date and complete model. PMID:20396501

Cyberinfrastructure for remote environmental observatories: a model homogeneous sensor network in the Great Basin, USA

NASA Astrophysics Data System (ADS)

Strachan, Scotty; Slater, David; Fritzinger, Eric; Lyles, Bradley; Kent, Graham; Smith, Kenneth; Dascalu, Sergiu; Harris, Frederick

2017-04-01

Sensor-based data collection has changed the potential scale and resolution of in-situ environmental studies by orders of magnitude, increasing expertise and management requirements accordingly. Cost-effective management of these observing systems is possible by leveraging cyberinfrastructure resources. Presented is a case study environmental observation network in the Great Basin region, USA, the Nevada Climate-ecohydrological Assessment Network (NevCAN). NevCAN stretches hundreds of kilometers across several mountain ranges and monitors climate and ecohydrological conditions from low desert (900 m ASL) to high subalpine treeline (3360 m ASL) down to 1-minute timescales. The network has been operating continuously since 2010, collecting billions of sensor data points and millions of camera images that record hourly conditions at each site, despite requiring relatively low annual maintenance expenditure. These data have provided unique insight into fine-scale processes across mountain gradients, which is crucial scientific information for a water-scarce region. The key to maintaining data continuity for these remotely-located study sites has been use of uniform data transport and management systems, coupled with high-reliability power system designs. Enabling non-proprietary digital communication paths to all study sites and sensors allows the research team to acquire data in near-real-time, troubleshoot problems, and diversify sensor hardware. A wide-area network design based on common Internet Protocols (IP) has been extended into each study site, providing production bandwidth of between 2 Mbps and 60 Mbps, depending on local conditions. The network architecture and site-level support systems (such as power generation) have been implemented with the core objectives of capacity, redundancy, and modularity. NevCAN demonstrates that by following simple but uniform "best practices", the next generation of regionally-specific environmental observatories can evolve to provide dramatically improved levels of scientific and hazard monitoring that span complex topographies and remote geography.

The Network Structure of Human Personality According to the NEO-PI-R: Matching Network Community Structure to Factor Structure

PubMed Central

Goekoop, Rutger; Goekoop, Jaap G.; Scholte, H. Steven

2012-01-01

Introduction Human personality is described preferentially in terms of factors (dimensions) found using factor analysis. An alternative and highly related method is network analysis, which may have several advantages over factor analytic methods. Aim To directly compare the ability of network community detection (NCD) and principal component factor analysis (PCA) to examine modularity in multidimensional datasets such as the neuroticism-extraversion-openness personality inventory revised (NEO-PI-R). Methods 434 healthy subjects were tested on the NEO-PI-R. PCA was performed to extract factor structures (FS) of the current dataset using both item scores and facet scores. Correlational network graphs were constructed from univariate correlation matrices of interactions between both items and facets. These networks were pruned in a link-by-link fashion while calculating the network community structure (NCS) of each resulting network using the Wakita Tsurumi clustering algorithm. NCSs were matched against FS and networks of best matches were kept for further analysis. Results At facet level, NCS showed a best match (96.2%) with a ‘confirmatory’ 5-FS. At item level, NCS showed a best match (80%) with the standard 5-FS and involved a total of 6 network clusters. Lesser matches were found with ‘confirmatory’ 5-FS and ‘exploratory’ 6-FS of the current dataset. Network analysis did not identify facets as a separate level of organization in between items and clusters. A small-world network structure was found in both item- and facet level networks. Conclusion We present the first optimized network graph of personality traits according to the NEO-PI-R: a ‘Personality Web’. Such a web may represent the possible routes that subjects can take during personality development. NCD outperforms PCA by producing plausible modularity at item level in non-standard datasets, and can identify the key roles of individual items and clusters in the network. PMID:23284713

The network structure of human personality according to the NEO-PI-R: matching network community structure to factor structure.

PubMed

Goekoop, Rutger; Goekoop, Jaap G; Scholte, H Steven

2012-01-01

Human personality is described preferentially in terms of factors (dimensions) found using factor analysis. An alternative and highly related method is network analysis, which may have several advantages over factor analytic methods. To directly compare the ability of network community detection (NCD) and principal component factor analysis (PCA) to examine modularity in multidimensional datasets such as the neuroticism-extraversion-openness personality inventory revised (NEO-PI-R). 434 healthy subjects were tested on the NEO-PI-R. PCA was performed to extract factor structures (FS) of the current dataset using both item scores and facet scores. Correlational network graphs were constructed from univariate correlation matrices of interactions between both items and facets. These networks were pruned in a link-by-link fashion while calculating the network community structure (NCS) of each resulting network using the Wakita Tsurumi clustering algorithm. NCSs were matched against FS and networks of best matches were kept for further analysis. At facet level, NCS showed a best match (96.2%) with a 'confirmatory' 5-FS. At item level, NCS showed a best match (80%) with the standard 5-FS and involved a total of 6 network clusters. Lesser matches were found with 'confirmatory' 5-FS and 'exploratory' 6-FS of the current dataset. Network analysis did not identify facets as a separate level of organization in between items and clusters. A small-world network structure was found in both item- and facet level networks. We present the first optimized network graph of personality traits according to the NEO-PI-R: a 'Personality Web'. Such a web may represent the possible routes that subjects can take during personality development. NCD outperforms PCA by producing plausible modularity at item level in non-standard datasets, and can identify the key roles of individual items and clusters in the network.

Surveillance and reconnaissance ground system architecture

NASA Astrophysics Data System (ADS)

Devambez, Francois

2001-12-01

Modern conflicts induces various modes of deployment, due to the type of conflict, the type of mission, and phase of conflict. It is then impossible to define fixed architecture systems for surveillance ground segments. Thales has developed a structure for a ground segment based on the operational functions required, and on the definition of modules and networks. Theses modules are software and hardware modules, including communications and networks. This ground segment is called MGS (Modular Ground Segment), and is intended for use in airborne reconnaissance systems, surveillance systems, and U.A.V. systems. Main parameters for the definition of a modular ground image exploitation system are : Compliance with various operational configurations, Easy adaptation to the evolution of theses configurations, Interoperability with NATO and multinational forces, Security, Multi-sensors, multi-platforms capabilities, Technical modularity, Evolutivity Reduction of life cycle cost The general performances of the MGS are presented : type of sensors, acquisition process, exploitation of images, report generation, data base management, dissemination, interface with C4I. The MGS is then described as a set of hardware and software modules, and their organization to build numerous operational configurations. Architectures are from minimal configuration intended for a mono-sensor image exploitation system, to a full image intelligence center, for a multilevel exploitation of multi-sensor.

Retroactivity in the Context of Modularly Structured Biomolecular Systems

PubMed Central

Pantoja-Hernández, Libertad; Martínez-García, Juan Carlos

2015-01-01

Synthetic biology has intensively promoted the technical implementation of modular strategies in the fabrication of biological devices. Modules are considered as networks of reactions. The behavior displayed by biomolecular systems results from the information processes carried out by the interconnection of the involved modules. However, in natural systems, module wiring is not a free-of-charge process; as a consequence of interconnection, a reactive phenomenon called retroactivity emerges. This phenomenon is characterized by signals that propagate from downstream modules (the modules that receive the incoming signals upon interconnection) to upstream ones (the modules that send the signals upon interconnection). Such retroactivity signals, depending of their strength, may change and sometimes even disrupt the behavior of modular biomolecular systems. Thus, analysis of retroactivity effects in natural biological and biosynthetic systems is crucial to achieve a deeper understanding of how this interconnection between functionally characterized modules takes place and how it impacts the overall behavior of the involved cell. By discussing the modules interconnection in natural and synthetic biomolecular systems, we propose that such systems should be considered as quasi-modular. PMID:26137457

Individual-Based Ant-Plant Networks: Diurnal-Nocturnal Structure and Species-Area Relationship

PubMed Central

Dáttilo, Wesley; Fagundes, Roberth; Gurka, Carlos A. Q.; Silva, Mara S. A.; Vieira, Marisa C. L.; Izzo, Thiago J.; Díaz-Castelazo, Cecília; Del-Claro, Kleber; Rico-Gray, Victor

2014-01-01

Despite the importance and increasing knowledge of ecological networks, sampling effort and intrapopulation variation has been widely overlooked. Using continuous daily sampling of ants visiting three plant species in the Brazilian Neotropical savanna, we evaluated for the first time the topological structure over 24 h and species-area relationships (based on the number of extrafloral nectaries available) in individual-based ant-plant networks. We observed that diurnal and nocturnal ant-plant networks exhibited the same pattern of interactions: a nested and non-modular pattern and an average level of network specialization. Despite the high similarity in the ants’ composition between the two collection periods, ant species found in the central core of highly interacting species totally changed between diurnal and nocturnal sampling for all plant species. In other words, this “night-turnover” suggests that the ecological dynamics of these ant-plant interactions can be temporally partitioned (day and night) at a small spatial scale. Thus, it is possible that in some cases processes shaping mutualistic networks formed by protective ants and plants may be underestimated by diurnal sampling alone. Moreover, we did not observe any effect of the number of extrafloral nectaries on ant richness and their foraging on such plants in any of the studied ant-plant networks. We hypothesize that competitively superior ants could monopolize individual plants and allow the coexistence of only a few other ant species, however, other alternative hypotheses are also discussed. Thus, sampling period and species-area relationship produces basic information that increases our confidence in how individual-based ant-plant networks are structured, and the need to consider nocturnal records in ant-plant network sampling design so as to decrease inappropriate inferences. PMID:24918750

A modular architecture for transparent computation in recurrent neural networks.

PubMed

Carmantini, Giovanni S; Beim Graben, Peter; Desroches, Mathieu; Rodrigues, Serafim

2017-01-01

Computation is classically studied in terms of automata, formal languages and algorithms; yet, the relation between neural dynamics and symbolic representations and operations is still unclear in traditional eliminative connectionism. Therefore, we suggest a unique perspective on this central issue, to which we would like to refer as transparent connectionism, by proposing accounts of how symbolic computation can be implemented in neural substrates. In this study we first introduce a new model of dynamics on a symbolic space, the versatile shift, showing that it supports the real-time simulation of a range of automata. We then show that the Gödelization of versatile shifts defines nonlinear dynamical automata, dynamical systems evolving on a vectorial space. Finally, we present a mapping between nonlinear dynamical automata and recurrent artificial neural networks. The mapping defines an architecture characterized by its granular modularity, where data, symbolic operations and their control are not only distinguishable in activation space, but also spatially localizable in the network itself, while maintaining a distributed encoding of symbolic representations. The resulting networks simulate automata in real-time and are programmed directly, in the absence of network training. To discuss the unique characteristics of the architecture and their consequences, we present two examples: (i) the design of a Central Pattern Generator from a finite-state locomotive controller, and (ii) the creation of a network simulating a system of interactive automata that supports the parsing of garden-path sentences as investigated in psycholinguistics experiments. Copyright © 2016 Elsevier Ltd. All rights reserved.

Disrupted topological organization of resting-state functional brain network in subcortical vascular mild cognitive impairment.

PubMed

Yi, Li-Ye; Liang, Xia; Liu, Da-Ming; Sun, Bo; Ying, Sun; Yang, Dong-Bo; Li, Qing-Bin; Jiang, Chuan-Lu; Han, Ying

2015-10-01

Neuroimaging studies have demonstrated both structural and functional abnormalities in widespread brain regions in patients with subcortical vascular mild cognitive impairment (svMCI). However, whether and how these changes alter functional brain network organization remains largely unknown. We recruited 21 patients with svMCI and 26 healthy control (HC) subjects who underwent resting-state functional magnetic resonance imaging scans. Graph theory-based network analyses were used to investigate alterations in the topological organization of functional brain networks. Compared with the HC individuals, the patients with svMCI showed disrupted global network topology with significantly increased path length and modularity. Modular structure was also impaired in the svMCI patients with a notable rearrangement of the executive control module, where the parietal regions were split out and grouped as a separate module. The svMCI patients also revealed deficits in the intra- and/or intermodule connectivity of several brain regions. Specifically, the within-module degree was decreased in the middle cingulate gyrus while it was increased in the left anterior insula, medial prefrontal cortex and cuneus. Additionally, increased intermodule connectivity was observed in the inferior and superior parietal gyrus, which was associated with worse cognitive performance in the svMCI patients. Together, our results indicate that svMCI patients exhibit dysregulation of the topological organization of functional brain networks, which has important implications for understanding the pathophysiological mechanism of svMCI. © 2015 John Wiley & Sons Ltd.

Implementing TCP/IP and a socket interface as a server in a message-passing operating system

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

Hipp, E.; Wiltzius, D.

1990-03-01

The UNICOS 4.3BSD network code and socket transport interface are the basis of an explicit network server for NLTSS, a message passing operating system on the Cray YMP. A BSD socket user library provides access to the network server using an RPC mechanism. The advantages of this server methodology are its modularity and extensibility to migrate to future protocol suites (e.g. OSI) and transport interfaces. In addition, the network server is implemented in an explicit multi-tasking environment to take advantage of the Cray YMP multi-processor platform. 19 refs., 5 figs.

Epidemic Threshold in Structured Scale-Free Networks

NASA Astrophysics Data System (ADS)

EguíLuz, VíCtor M.; Klemm, Konstantin

2002-08-01

We analyze the spreading of viruses in scale-free networks with high clustering and degree correlations, as found in the Internet graph. For the susceptible-infected-susceptible model of epidemics the prevalence undergoes a phase transition at a finite threshold of the transmission probability. Comparing with the absence of a finite threshold in networks with purely random wiring, our result suggests that high clustering (modularity) and degree correlations protect scale-free networks against the spreading of viruses. We introduce and verify a quantitative description of the epidemic threshold based on the connectivity of the neighborhoods of the hubs.

Sparse dictionary learning of resting state fMRI networks.

PubMed

Eavani, Harini; Filipovych, Roman; Davatzikos, Christos; Satterthwaite, Theodore D; Gur, Raquel E; Gur, Ruben C

2012-07-02

Research in resting state fMRI (rsfMRI) has revealed the presence of stable, anti-correlated functional subnetworks in the brain. Task-positive networks are active during a cognitive process and are anti-correlated with task-negative networks, which are active during rest. In this paper, based on the assumption that the structure of the resting state functional brain connectivity is sparse, we utilize sparse dictionary modeling to identify distinct functional sub-networks. We propose two ways of formulating the sparse functional network learning problem that characterize the underlying functional connectivity from different perspectives. Our results show that the whole-brain functional connectivity can be concisely represented with highly modular, overlapping task-positive/negative pairs of sub-networks.

A small world of weak ties provides optimal global integration of self-similar modules in functional brain networks.

PubMed

Gallos, Lazaros K; Makse, Hernán A; Sigman, Mariano

2012-02-21

The human brain is organized in functional modules. Such an organization presents a basic conundrum: Modules ought to be sufficiently independent to guarantee functional specialization and sufficiently connected to bind multiple processors for efficient information transfer. It is commonly accepted that small-world architecture of short paths and large local clustering may solve this problem. However, there is intrinsic tension between shortcuts generating small worlds and the persistence of modularity, a global property unrelated to local clustering. Here, we present a possible solution to this puzzle. We first show that a modified percolation theory can define a set of hierarchically organized modules made of strong links in functional brain networks. These modules are "large-world" self-similar structures and, therefore, are far from being small-world. However, incorporating weaker ties to the network converts it into a small world preserving an underlying backbone of well-defined modules. Remarkably, weak ties are precisely organized as predicted by theory maximizing information transfer with minimal wiring cost. This trade-off architecture is reminiscent of the "strength of weak ties" crucial concept of social networks. Such a design suggests a natural solution to the paradox of efficient information flow in the highly modular structure of the brain.

A small world of weak ties provides optimal global integration of self-similar modules in functional brain networks

PubMed Central

Gallos, Lazaros K.; Makse, Hernán A.; Sigman, Mariano

2012-01-01

The human brain is organized in functional modules. Such an organization presents a basic conundrum: Modules ought to be sufficiently independent to guarantee functional specialization and sufficiently connected to bind multiple processors for efficient information transfer. It is commonly accepted that small-world architecture of short paths and large local clustering may solve this problem. However, there is intrinsic tension between shortcuts generating small worlds and the persistence of modularity, a global property unrelated to local clustering. Here, we present a possible solution to this puzzle. We first show that a modified percolation theory can define a set of hierarchically organized modules made of strong links in functional brain networks. These modules are “large-world” self-similar structures and, therefore, are far from being small-world. However, incorporating weaker ties to the network converts it into a small world preserving an underlying backbone of well-defined modules. Remarkably, weak ties are precisely organized as predicted by theory maximizing information transfer with minimal wiring cost. This trade-off architecture is reminiscent of the “strength of weak ties” crucial concept of social networks. Such a design suggests a natural solution to the paradox of efficient information flow in the highly modular structure of the brain. PMID:22308319

Modeling information diffusion in time-varying community networks

NASA Astrophysics Data System (ADS)

Cui, Xuelian; Zhao, Narisa

2017-12-01

Social networks are rarely static, and they typically have time-varying network topologies. A great number of studies have modeled temporal networks and explored social contagion processes within these models; however, few of these studies have considered community structure variations. In this paper, we present a study of how the time-varying property of a modular structure influences the information dissemination. First, we propose a continuous-time Markov model of information diffusion where two parameters, mobility rate and community attractiveness, are introduced to address the time-varying nature of the community structure. The basic reproduction number is derived, and the accuracy of this model is evaluated by comparing the simulation and theoretical results. Furthermore, numerical results illustrate that generally both the mobility rate and community attractiveness significantly promote the information diffusion process, especially in the initial outbreak stage. Moreover, the strength of this promotion effect is much stronger when the modularity is higher. Counterintuitively, it is found that when all communities have the same attractiveness, social mobility no longer accelerates the diffusion process. In addition, we show that the local spreading in the advantage group has been greatly enhanced due to the agglomeration effect caused by the social mobility and community attractiveness difference, which thus increases the global spreading.

Phase transition of Surprise optimization in community detection

NASA Astrophysics Data System (ADS)

Xiang, Ju; Tang, Yan-Ni; Gao, Yuan-Yuan; Liu, Lang; Hao, Yi; Li, Jian-Ming; Zhang, Yan; Chen, Shi

2018-02-01

Community detection is one of important issues in the research of complex networks. In literatures, many methods have been proposed to detect community structures in the networks, while they also have the scope of application themselves. In this paper, we investigate an important measure for community detection, Surprise (Aldecoa and Marín, Sci. Rep. 3 (2013) 1060), by focusing on the critical points in the merging and splitting of communities. We firstly analyze the critical behavior of Surprise and give the phase diagrams in community-partition transition. The results show that the critical number of communities for Surprise has a super-exponential increase with the increase of the link-density difference, while it is close to that of Modularity for small difference between inter- and intra-community link densities. By directly optimizing Surprise, we experimentally test the results on various networks, following a series of comparisons with other classical methods, and further find that the heterogeneity of networks could quicken the splitting of communities. On the whole, the results show that Surprise tends to split communities due to various reasons such as the heterogeneity in link density, degree and community size, and it thus exhibits higher resolution than other methods, e.g., Modularity, in community detection. Finally, we provide several approaches for enhancing Surprise.

The economics of data acquisition computers for ST and MST radars

NASA Technical Reports Server (NTRS)

Watkins, B. J.

1983-01-01

Some low cost options for data acquisition computers for ST (stratosphere, troposphere) and MST (mesosphere, stratosphere, troposphere) are presented. The particular equipment discussed reflects choices made by the University of Alaska group but of course many other options exist. The low cost microprocessor and array processor approach presented here has several advantages because of its modularity. An inexpensive system may be configured for a minimum performance ST radar, whereas a multiprocessor and/or a multiarray processor system may be used for a higher performance MST radar. This modularity is important for a network of radars because the initial cost is minimized while future upgrades will still be possible at minimal expense. This modularity also aids in lowering the cost of software development because system expansions should rquire little software changes. The functions of the radar computer will be to obtain Doppler spectra in near real time with some minor analysis such as vector wind determination.

A spatially localized architecture for fast and modular DNA computing

NASA Astrophysics Data System (ADS)

Chatterjee, Gourab; Dalchau, Neil; Muscat, Richard A.; Phillips, Andrew; Seelig, Georg

2017-09-01

Cells use spatial constraints to control and accelerate the flow of information in enzyme cascades and signalling networks. Synthetic silicon-based circuitry similarly relies on spatial constraints to process information. Here, we show that spatial organization can be a similarly powerful design principle for overcoming limitations of speed and modularity in engineered molecular circuits. We create logic gates and signal transmission lines by spatially arranging reactive DNA hairpins on a DNA origami. Signal propagation is demonstrated across transmission lines of different lengths and orientations and logic gates are modularly combined into circuits that establish the universality of our approach. Because reactions preferentially occur between neighbours, identical DNA hairpins can be reused across circuits. Co-localization of circuit elements decreases computation time from hours to minutes compared to circuits with diffusible components. Detailed computational models enable predictive circuit design. We anticipate our approach will motivate using spatial constraints for future molecular control circuit designs.

Loads Bias Genetic and Signaling Switches in Synthetic and Natural Systems

PubMed Central

Medford, June; Prasad, Ashok

2014-01-01

Biological protein interactions networks such as signal transduction or gene transcription networks are often treated as modular, allowing motifs to be analyzed in isolation from the rest of the network. Modularity is also a key assumption in synthetic biology, where it is similarly expected that when network motifs are combined together, they do not lose their essential characteristics. However, the interactions that a network module has with downstream elements change the dynamical equations describing the upstream module and thus may change the dynamic and static properties of the upstream circuit even without explicit feedback. In this work we analyze the behavior of a ubiquitous motif in gene transcription and signal transduction circuits: the switch. We show that adding an additional downstream component to the simple genetic toggle switch changes its dynamical properties by changing the underlying potential energy landscape, and skewing it in favor of the unloaded side, and in some situations adding loads to the genetic switch can also abrogate bistable behavior. We find that an additional positive feedback motif found in naturally occurring toggle switches could tune the potential energy landscape in a desirable manner. We also analyze autocatalytic signal transduction switches and show that a ubiquitous positive feedback switch can lose its switch-like properties when connected to a downstream load. Our analysis underscores the necessity of incorporating the effects of downstream components when understanding the physics of biochemical network motifs, and raises the question as to how these effects are managed in real biological systems. This analysis is particularly important when scaling synthetic networks to more complex organisms. PMID:24676102

The Double-Stranded DNA Virosphere as a Modular Hierarchical Network of Gene Sharing

PubMed Central

Iranzo, Jaime

2016-01-01

ABSTRACT Virus genomes are prone to extensive gene loss, gain, and exchange and share no universal genes. Therefore, in a broad-scale study of virus evolution, gene and genome network analyses can complement traditional phylogenetics. We performed an exhaustive comparative analysis of the genomes of double-stranded DNA (dsDNA) viruses by using the bipartite network approach and found a robust hierarchical modularity in the dsDNA virosphere. Bipartite networks consist of two classes of nodes, with nodes in one class, in this case genomes, being connected via nodes of the second class, in this case genes. Such a network can be partitioned into modules that combine nodes from both classes. The bipartite network of dsDNA viruses includes 19 modules that form 5 major and 3 minor supermodules. Of these modules, 11 include tailed bacteriophages, reflecting the diversity of this largest group of viruses. The module analysis quantitatively validates and refines previously proposed nontrivial evolutionary relationships. An expansive supermodule combines the large and giant viruses of the putative order “Megavirales” with diverse moderate-sized viruses and related mobile elements. All viruses in this supermodule share a distinct morphogenetic tool kit with a double jelly roll major capsid protein. Herpesviruses and tailed bacteriophages comprise another supermodule, held together by a distinct set of morphogenetic proteins centered on the HK97-like major capsid protein. Together, these two supermodules cover the great majority of currently known dsDNA viruses. We formally identify a set of 14 viral hallmark genes that comprise the hubs of the network and account for most of the intermodule connections. PMID:27486193

Integration of white matter network is associated with interindividual differences in psychologically mediated placebo response in migraine patients.

PubMed

Liu, Jixin; Ma, Shaohui; Mu, Junya; Chen, Tao; Xu, Qing; Dun, Wanghuan; Tian, Jie; Zhang, Ming

2017-10-01

Individual differences of brain changes of neural communication and integration in the modular architecture of the human brain network exist for the repeated migraine attack and physical or psychological stressors. However, whether the interindividual variability in the migraine brain connectome predicts placebo response to placebo treatment is still unclear. Using DTI and graph theory approaches, we systematically investigated the topological organization of white matter networks in 71 patients with migraine without aura (MO) and 50 matched healthy controls at three levels: global network measure, nodal efficiency, and nodal intramodule/intermodule efficiency. All patients participated in an 8-week sham acupuncture treatment to induce analgesia. In our results, 30% (n = 21) of patients had 50% change in migraine days from baseline after placebo treatment. At baseline, abnormal increased network integration was found in MO patients as compared with the HC group, and the increased global efficiency before starting clinical treatment was associated with their following placebo response. For nodal efficiency, significantly increased within-subnetwork nodal efficiency and intersubnetwork connectivity of the hippocampus and middle frontal gyrus in patients' white matter network were correlated with the responses of follow-up placebo treatment. Our findings suggested that the trait-like individual differences in pain-related maladaptive stress interfered with and diminished the capacity of chronic pain modulation differently, and the placebo response for treatment could be predicted from a prior white matter network modular structure in migraineurs. Hum Brain Mapp 38:5250-5259, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Evolution of a Patient Information Management System in a Local Area Network Environment at Loyola University of Chicago Medical Center

PubMed Central

Price, Ronald N; Chandrasekhar, Arcot J; Tamirisa, Balaji

1990-01-01

The Department of Medicine at Loyola University Medical Center (LUMC) of Chicago has implemented a local area network (LAN) based Patient Information Management System (PIMS) as part of its integrated departmental database management system. PIMS consists of related database applications encompassing demographic information, current medications, problem lists, clinical data, prior events, and on-line procedure results. Integration into the existing departmental database system permits PIMS to capture and manipulate data in other departmental applications. Standardization of clinical data is accomplished through three data tables that verify diagnosis codes, procedures codes and a standardized set of clinical data elements. The modularity of the system, coupled with standardized data formats, allowed the development of a Patient Information Protocol System (PIPS). PIPS, a userdefinable protocol processor, provides physicians with individualized data entry or review screens customized for their specific research protocols or practice habits. Physician feedback indicates that the PIMS/PIPS combination enhances their ability to collect and review specific patient information by filtering large amount of clinical data.

Recent Technology Advances in Distributed Engine Control

NASA Technical Reports Server (NTRS)

Culley, Dennis

2017-01-01

This presentation provides an overview of the work performed at NASA Glenn Research Center in distributed engine control technology. This is control system hardware technology that overcomes engine system constraints by modularizing control hardware and integrating the components over communication networks.

A Modular Framework for Modeling Hardware Elements in Distributed Engine Control Systems

NASA Technical Reports Server (NTRS)

Zinnecker, Alicia M.; Culley, Dennis E.; Aretskin-Hariton, Eliot D.

2014-01-01

Progress toward the implementation of distributed engine control in an aerospace application may be accelerated through the development of a hardware-in-the-loop (HIL) system for testing new control architectures and hardware outside of a physical test cell environment. One component required in an HIL simulation system is a high-fidelity model of the control platform: sensors, actuators, and the control law. The control system developed for the Commercial Modular Aero-Propulsion System Simulation 40k (C-MAPSS40k) provides a verifiable baseline for development of a model for simulating a distributed control architecture. This distributed controller model will contain enhanced hardware models, capturing the dynamics of the transducer and the effects of data processing, and a model of the controller network. A multilevel framework is presented that establishes three sets of interfaces in the control platform: communication with the engine (through sensors and actuators), communication between hardware and controller (over a network), and the physical connections within individual pieces of hardware. This introduces modularity at each level of the model, encouraging collaboration in the development and testing of various control schemes or hardware designs. At the hardware level, this modularity is leveraged through the creation of a Simulink(R) library containing blocks for constructing smart transducer models complying with the IEEE 1451 specification. These hardware models were incorporated in a distributed version of the baseline C-MAPSS40k controller and simulations were run to compare the performance of the two models. The overall tracking ability differed only due to quantization effects in the feedback measurements in the distributed controller. Additionally, it was also found that the added complexity of the smart transducer models did not prevent real-time operation of the distributed controller model, a requirement of an HIL system.

Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks.

PubMed

Ertosun, Mehmet Günhan; Rubin, Daniel L

2015-01-01

Brain glioma is the most common primary malignant brain tumors in adults with different pathologic subtypes: Lower Grade Glioma (LGG) Grade II, Lower Grade Glioma (LGG) Grade III, and Glioblastoma Multiforme (GBM) Grade IV. The survival and treatment options are highly dependent of this glioma grade. We propose a deep learning-based, modular classification pipeline for automated grading of gliomas using digital pathology images. Whole tissue digitized images of pathology slides obtained from The Cancer Genome Atlas (TCGA) were used to train our deep learning modules. Our modular pipeline provides diagnostic quality statistics, such as precision, sensitivity and specificity, of the individual deep learning modules, and (1) facilitates training given the limited data in this domain, (2) enables exploration of different deep learning structures for each module, (3) leads to developing less complex modules that are simpler to analyze, and (4) provides flexibility, permitting use of single modules within the framework or use of other modeling or machine learning applications, such as probabilistic graphical models or support vector machines. Our modular approach helps us meet the requirements of minimum accuracy levels that are demanded by the context of different decision points within a multi-class classification scheme. Convolutional Neural Networks are trained for each module for each sub-task with more than 90% classification accuracies on validation data set, and achieved classification accuracy of 96% for the task of GBM vs LGG classification, 71% for further identifying the grade of LGG into Grade II or Grade III on independent data set coming from new patients from the multi-institutional repository.

Reduced modeling of signal transduction – a modular approach

PubMed Central

Koschorreck, Markus; Conzelmann, Holger; Ebert, Sybille; Ederer, Michael; Gilles, Ernst Dieter

2007-01-01

Background Combinatorial complexity is a challenging problem in detailed and mechanistic mathematical modeling of signal transduction. This subject has been discussed intensively and a lot of progress has been made within the last few years. A software tool (BioNetGen) was developed which allows an automatic rule-based set-up of mechanistic model equations. In many cases these models can be reduced by an exact domain-oriented lumping technique. However, the resulting models can still consist of a very large number of differential equations. Results We introduce a new reduction technique, which allows building modularized and highly reduced models. Compared to existing approaches further reduction of signal transduction networks is possible. The method also provides a new modularization criterion, which allows to dissect the model into smaller modules that are called layers and can be modeled independently. Hallmarks of the approach are conservation relations within each layer and connection of layers by signal flows instead of mass flows. The reduced model can be formulated directly without previous generation of detailed model equations. It can be understood and interpreted intuitively, as model variables are macroscopic quantities that are converted by rates following simple kinetics. The proposed technique is applicable without using complex mathematical tools and even without detailed knowledge of the mathematical background. However, we provide a detailed mathematical analysis to show performance and limitations of the method. For physiologically relevant parameter domains the transient as well as the stationary errors caused by the reduction are negligible. Conclusion The new layer based reduced modeling method allows building modularized and strongly reduced models of signal transduction networks. Reduced model equations can be directly formulated and are intuitively interpretable. Additionally, the method provides very good approximations especially for macroscopic variables. It can be combined with existing reduction methods without any difficulties. PMID:17854494

A Modular Framework for Modeling Hardware Elements in Distributed Engine Control Systems

NASA Technical Reports Server (NTRS)

Zinnecker, Alicia M.; Culley, Dennis E.; Aretskin-Hariton, Eliot D.

2015-01-01

Progress toward the implementation of distributed engine control in an aerospace application may be accelerated through the development of a hardware-in-the-loop (HIL) system for testing new control architectures and hardware outside of a physical test cell environment. One component required in an HIL simulation system is a high-fidelity model of the control platform: sensors, actuators, and the control law. The control system developed for the Commercial Modular Aero-Propulsion System Simulation 40k (C-MAPSS40k) provides a verifiable baseline for development of a model for simulating a distributed control architecture. This distributed controller model will contain enhanced hardware models, capturing the dynamics of the transducer and the effects of data processing, and a model of the controller network. A multilevel framework is presented that establishes three sets of interfaces in the control platform: communication with the engine (through sensors and actuators), communication between hardware and controller (over a network), and the physical connections within individual pieces of hardware. This introduces modularity at each level of the model, encouraging collaboration in the development and testing of various control schemes or hardware designs. At the hardware level, this modularity is leveraged through the creation of a SimulinkR library containing blocks for constructing smart transducer models complying with the IEEE 1451 specification. These hardware models were incorporated in a distributed version of the baseline C-MAPSS40k controller and simulations were run to compare the performance of the two models. The overall tracking ability differed only due to quantization effects in the feedback measurements in the distributed controller. Additionally, it was also found that the added complexity of the smart transducer models did not prevent real-time operation of the distributed controller model, a requirement of an HIL system.

Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks

PubMed Central

Ertosun, Mehmet Günhan; Rubin, Daniel L.

2015-01-01

Brain glioma is the most common primary malignant brain tumors in adults with different pathologic subtypes: Lower Grade Glioma (LGG) Grade II, Lower Grade Glioma (LGG) Grade III, and Glioblastoma Multiforme (GBM) Grade IV. The survival and treatment options are highly dependent of this glioma grade. We propose a deep learning-based, modular classification pipeline for automated grading of gliomas using digital pathology images. Whole tissue digitized images of pathology slides obtained from The Cancer Genome Atlas (TCGA) were used to train our deep learning modules. Our modular pipeline provides diagnostic quality statistics, such as precision, sensitivity and specificity, of the individual deep learning modules, and (1) facilitates training given the limited data in this domain, (2) enables exploration of different deep learning structures for each module, (3) leads to developing less complex modules that are simpler to analyze, and (4) provides flexibility, permitting use of single modules within the framework or use of other modeling or machine learning applications, such as probabilistic graphical models or support vector machines. Our modular approach helps us meet the requirements of minimum accuracy levels that are demanded by the context of different decision points within a multi-class classification scheme. Convolutional Neural Networks are trained for each module for each sub-task with more than 90% classification accuracies on validation data set, and achieved classification accuracy of 96% for the task of GBM vs LGG classification, 71% for further identifying the grade of LGG into Grade II or Grade III on independent data set coming from new patients from the multi-institutional repository. PMID:26958289

A Modular Framework for Modeling Hardware Elements in Distributed Engine Control Systems

NASA Technical Reports Server (NTRS)

Zinnecker, Alicia Mae; Culley, Dennis E.; Aretskin-Hariton, Eliot D.

2014-01-01

Progress toward the implementation of distributed engine control in an aerospace application may be accelerated through the development of a hardware-in-the-loop (HIL) system for testing new control architectures and hardware outside of a physical test cell environment. One component required in an HIL simulation system is a high-fidelity model of the control platform: sensors, actuators, and the control law. The control system developed for the Commercial Modular Aero-Propulsion System Simulation 40k (40,000 pound force thrust) (C-MAPSS40k) provides a verifiable baseline for development of a model for simulating a distributed control architecture. This distributed controller model will contain enhanced hardware models, capturing the dynamics of the transducer and the effects of data processing, and a model of the controller network. A multilevel framework is presented that establishes three sets of interfaces in the control platform: communication with the engine (through sensors and actuators), communication between hardware and controller (over a network), and the physical connections within individual pieces of hardware. This introduces modularity at each level of the model, encouraging collaboration in the development and testing of various control schemes or hardware designs. At the hardware level, this modularity is leveraged through the creation of a Simulink (R) library containing blocks for constructing smart transducer models complying with the IEEE 1451 specification. These hardware models were incorporated in a distributed version of the baseline C-MAPSS40k controller and simulations were run to compare the performance of the two models. The overall tracking ability differed only due to quantization effects in the feedback measurements in the distributed controller. Additionally, it was also found that the added complexity of the smart transducer models did not prevent real-time operation of the distributed controller model, a requirement of an HIL system.

A Hybrid CPU-GPU Accelerated Framework for Fast Mapping of High-Resolution Human Brain Connectome

PubMed Central

Ren, Ling; Xu, Mo; Xie, Teng; Gong, Gaolang; Xu, Ningyi; Yang, Huazhong; He, Yong

2013-01-01

Recently, a combination of non-invasive neuroimaging techniques and graph theoretical approaches has provided a unique opportunity for understanding the patterns of the structural and functional connectivity of the human brain (referred to as the human brain connectome). Currently, there is a very large amount of brain imaging data that have been collected, and there are very high requirements for the computational capabilities that are used in high-resolution connectome research. In this paper, we propose a hybrid CPU-GPU framework to accelerate the computation of the human brain connectome. We applied this framework to a publicly available resting-state functional MRI dataset from 197 participants. For each subject, we first computed Pearson’s Correlation coefficient between any pairs of the time series of gray-matter voxels, and then we constructed unweighted undirected brain networks with 58 k nodes and a sparsity range from 0.02% to 0.17%. Next, graphic properties of the functional brain networks were quantified, analyzed and compared with those of 15 corresponding random networks. With our proposed accelerating framework, the above process for each network cost 80∼150 minutes, depending on the network sparsity. Further analyses revealed that high-resolution functional brain networks have efficient small-world properties, significant modular structure, a power law degree distribution and highly connected nodes in the medial frontal and parietal cortical regions. These results are largely compatible with previous human brain network studies. Taken together, our proposed framework can substantially enhance the applicability and efficacy of high-resolution (voxel-based) brain network analysis, and have the potential to accelerate the mapping of the human brain connectome in normal and disease states. PMID:23675425

Analysing published global Ebola Virus Disease research using social network analysis

PubMed Central

Hagel, Christiane; Weidemann, Felix; Gauch, Stephan; Edwards, Suzanne

2017-01-01

Introduction The 2014/2015 West African Ebola Virus Disease (EVD) outbreak attracted global attention. Numerous opinions claimed that the global response was impaired, in part because, the EVD research was neglected, although quantitative or qualitative studies did not exist. Our objective was to analyse how the EVD research landscape evolved by exploring the existing research network and its communities before and during the outbreak in West Africa. Methods/ Principal findings Social network analysis (SNA) was used to analyse collaborations between institutions named by co-authors as affiliations in publications on EVD. Bibliometric data of publications on EVD between 1976 and 2015 was collected from Thomson Reuters’ Web of Science Core Collection (WoS). Freely available software was used for network analysis at a global-level and for 10-year periods. The networks are presented as undirected-weighted graphs. Rankings by degree and betweenness were calculated to identify central and powerful network positions; modularity function was used to identify research communities. Overall 4,587 publications were identified, of which 2,528 were original research articles. Those yielded 1,644 authors’ affiliated institutions and 9,907 connections for co-authorship network construction. The majority of institutions were from the USA, Canada and Europe. Collaborations with research partners on the African continent did exist, but less frequently. Around six highly connected organisations in the network were identified with powerful and broker positions. Network characteristics varied widely among the 10-year periods and evolved from 30 to 1,489 institutions and 60 to 9,176 connections respectively. Most influential actors are from public or governmental institutions whereas private sector actors, in particular the pharmaceutical industry, are largely absent. Conclusion/ Significance Research output on EVD has increased over time and surged during the 2014/2015 outbreak. The overall EVD research network is organised around a few key actors, signalling a concentration of expertise but leaving room for increased cooperation with other institutions especially from affected countries. Finding innovative ways to maintain support for these pivotal actors while steering the global EVD research network towards an agenda driven by agreed, prioritized needs and finding ways to better integrate currently peripheral and newer expertise may accelerate the translation of research into the development of necessary live saving products for EVD ahead of the next outbreak. PMID:28991915

Analysing published global Ebola Virus Disease research using social network analysis.

PubMed

Hagel, Christiane; Weidemann, Felix; Gauch, Stephan; Edwards, Suzanne; Tinnemann, Peter

2017-10-01

The 2014/2015 West African Ebola Virus Disease (EVD) outbreak attracted global attention. Numerous opinions claimed that the global response was impaired, in part because, the EVD research was neglected, although quantitative or qualitative studies did not exist. Our objective was to analyse how the EVD research landscape evolved by exploring the existing research network and its communities before and during the outbreak in West Africa. Social network analysis (SNA) was used to analyse collaborations between institutions named by co-authors as affiliations in publications on EVD. Bibliometric data of publications on EVD between 1976 and 2015 was collected from Thomson Reuters' Web of Science Core Collection (WoS). Freely available software was used for network analysis at a global-level and for 10-year periods. The networks are presented as undirected-weighted graphs. Rankings by degree and betweenness were calculated to identify central and powerful network positions; modularity function was used to identify research communities. Overall 4,587 publications were identified, of which 2,528 were original research articles. Those yielded 1,644 authors' affiliated institutions and 9,907 connections for co-authorship network construction. The majority of institutions were from the USA, Canada and Europe. Collaborations with research partners on the African continent did exist, but less frequently. Around six highly connected organisations in the network were identified with powerful and broker positions. Network characteristics varied widely among the 10-year periods and evolved from 30 to 1,489 institutions and 60 to 9,176 connections respectively. Most influential actors are from public or governmental institutions whereas private sector actors, in particular the pharmaceutical industry, are largely absent. Research output on EVD has increased over time and surged during the 2014/2015 outbreak. The overall EVD research network is organised around a few key actors, signalling a concentration of expertise but leaving room for increased cooperation with other institutions especially from affected countries. Finding innovative ways to maintain support for these pivotal actors while steering the global EVD research network towards an agenda driven by agreed, prioritized needs and finding ways to better integrate currently peripheral and newer expertise may accelerate the translation of research into the development of necessary live saving products for EVD ahead of the next outbreak.

Evolutionary versatility of eukaryotic protein domains revealed by their bigram networks

PubMed Central

2011-01-01

Background Protein domains are globular structures of independently folded polypeptides that exert catalytic or binding activities. Their sequences are recognized as evolutionary units that, through genome recombination, constitute protein repertoires of linkage patterns. Via mutations, domains acquire modified functions that contribute to the fitness of cells and organisms. Recent studies have addressed the evolutionary selection that may have shaped the functions of individual domains and the emergence of particular domain combinations, which led to new cellular functions in multi-cellular animals. This study focuses on modeling domain linkage globally and investigates evolutionary implications that may be revealed by novel computational analysis. Results A survey of 77 completely sequenced eukaryotic genomes implies a potential hierarchical and modular organization of biological functions in most living organisms. Domains in a genome or multiple genomes are modeled as a network of hetero-duplex covalent linkages, termed bigrams. A novel computational technique is introduced to decompose such networks, whereby the notion of domain "networking versatility" is derived and measured. The most and least "versatile" domains (termed "core domains" and "peripheral domains" respectively) are examined both computationally via sequence conservation measures and experimentally using selected domains. Our study suggests that such a versatility measure extracted from the bigram networks correlates with the adaptivity of domains during evolution, where the network core domains are highly adaptive, significantly contrasting the network peripheral domains. Conclusions Domain recombination has played a major part in the evolution of eukaryotes attributing to genome complexity. From a system point of view, as the results of selection and constant refinement, networks of domain linkage are structured in a hierarchical modular fashion. Domains with high degree of networking versatility appear to be evolutionary adaptive, potentially through functional innovations. Domain bigram networks are informative as a model of biological functions. The networking versatility indices extracted from such networks for individual domains reflect the strength of evolutionary selection that the domains have experienced. PMID:21849086

Evolutionary versatility of eukaryotic protein domains revealed by their bigram networks.

PubMed

Xie, Xueying; Jin, Jing; Mao, Yongyi

2011-08-18

Protein domains are globular structures of independently folded polypeptides that exert catalytic or binding activities. Their sequences are recognized as evolutionary units that, through genome recombination, constitute protein repertoires of linkage patterns. Via mutations, domains acquire modified functions that contribute to the fitness of cells and organisms. Recent studies have addressed the evolutionary selection that may have shaped the functions of individual domains and the emergence of particular domain combinations, which led to new cellular functions in multi-cellular animals. This study focuses on modeling domain linkage globally and investigates evolutionary implications that may be revealed by novel computational analysis. A survey of 77 completely sequenced eukaryotic genomes implies a potential hierarchical and modular organization of biological functions in most living organisms. Domains in a genome or multiple genomes are modeled as a network of hetero-duplex covalent linkages, termed bigrams. A novel computational technique is introduced to decompose such networks, whereby the notion of domain "networking versatility" is derived and measured. The most and least "versatile" domains (termed "core domains" and "peripheral domains" respectively) are examined both computationally via sequence conservation measures and experimentally using selected domains. Our study suggests that such a versatility measure extracted from the bigram networks correlates with the adaptivity of domains during evolution, where the network core domains are highly adaptive, significantly contrasting the network peripheral domains. Domain recombination has played a major part in the evolution of eukaryotes attributing to genome complexity. From a system point of view, as the results of selection and constant refinement, networks of domain linkage are structured in a hierarchical modular fashion. Domains with high degree of networking versatility appear to be evolutionary adaptive, potentially through functional innovations. Domain bigram networks are informative as a model of biological functions. The networking versatility indices extracted from such networks for individual domains reflect the strength of evolutionary selection that the domains have experienced.

Driving and driven architectures of directed small-world human brain functional networks.

PubMed

Yan, Chaogan; He, Yong

2011-01-01

Recently, increasing attention has been focused on the investigation of the human brain connectome that describes the patterns of structural and functional connectivity networks of the human brain. Many studies of the human connectome have demonstrated that the brain network follows a small-world topology with an intrinsically cohesive modular structure and includes several network hubs in the medial parietal regions. However, most of these studies have only focused on undirected connections between regions in which the directions of information flow are not taken into account. How the brain regions causally influence each other and how the directed network of human brain is topologically organized remain largely unknown. Here, we applied linear multivariate Granger causality analysis (GCA) and graph theoretical approaches to a resting-state functional MRI dataset with a large cohort of young healthy participants (n = 86) to explore connectivity patterns of the population-based whole-brain functional directed network. This directed brain network exhibited prominent small-world properties, which obviously improved previous results of functional MRI studies showing weak small-world properties in the directed brain networks in terms of a kernel-based GCA and individual analysis. This brain network also showed significant modular structures associated with 5 well known subsystems: fronto-parietal, visual, paralimbic/limbic, subcortical and primary systems. Importantly, we identified several driving hubs predominantly located in the components of the attentional network (e.g., the inferior frontal gyrus, supplementary motor area, insula and fusiform gyrus) and several driven hubs predominantly located in the components of the default mode network (e.g., the precuneus, posterior cingulate gyrus, medial prefrontal cortex and inferior parietal lobule). Further split-half analyses indicated that our results were highly reproducible between two independent subgroups. The current study demonstrated the directions of spontaneous information flow and causal influences in the directed brain networks, thus providing new insights into our understanding of human brain functional connectome.

Consequences of plant invasions on compartmentalization and species’ roles in plant–pollinator networks

PubMed Central

Albrecht, Matthias; Padrón, Benigno; Bartomeus, Ignasi; Traveset, Anna

2014-01-01

Compartmentalization—the organization of ecological interaction networks into subsets of species that do not interact with other subsets (true compartments) or interact more frequently among themselves than with other species (modules)—has been identified as a key property for the functioning, stability and evolution of ecological communities. Invasions by entomophilous invasive plants may profoundly alter the way interaction networks are compartmentalized. We analysed a comprehensive dataset of 40 paired plant–pollinator networks (invaded versus uninvaded) to test this hypothesis. We show that invasive plants have higher generalization levels with respect to their pollinators than natives. The consequences for network topology are that—rather than displacing native species from the network—plant invaders attracting pollinators into invaded modules tend to play new important topological roles (i.e. network hubs, module hubs and connectors) and cause role shifts in native species, creating larger modules that are more connected among each other. While the number of true compartments was lower in invaded compared with uninvaded networks, the effect of invasion on modularity was contingent on the study system. Interestingly, the generalization level of the invasive plants partially explains this pattern, with more generalized invaders contributing to a lower modularity. Our findings indicate that the altered interaction structure of invaded networks makes them more robust against simulated random secondary species extinctions, but more vulnerable when the typically highly connected invasive plants go extinct first. The consequences and pathways by which biological invasions alter the interaction structure of plant–pollinator communities highlighted in this study may have important dynamical and functional implications, for example, by influencing multi-species reciprocal selection regimes and coevolutionary processes. PMID:24943368

Sensor Data Qualification Technique Applied to Gas Turbine Engines

NASA Technical Reports Server (NTRS)

Csank, Jeffrey T.; Simon, Donald L.

2013-01-01

This paper applies a previously developed sensor data qualification technique to a commercial aircraft engine simulation known as the Commercial Modular Aero-Propulsion System Simulation 40,000 (C-MAPSS40k). The sensor data qualification technique is designed to detect, isolate, and accommodate faulty sensor measurements. It features sensor networks, which group various sensors together and relies on an empirically derived analytical model to relate the sensor measurements. Relationships between all member sensors of the network are analyzed to detect and isolate any faulty sensor within the network.

A Healthy Brain in a Healthy Body: Brain Network Correlates of Physical and Mental Fitness

PubMed Central

Douw, Linda; Nieboer, Dagmar; van Dijk, Bob W.; Stam, Cornelis J.; Twisk, Jos W. R.

2014-01-01

A healthy lifestyle is an important focus in today's society. The physical benefits of regular exercise are abundantly clear, but physical fitness is also associated with better cognitive performance. How these two factors together relate to characteristics of the brain is still incompletely understood. By applying mathematical concepts from ‘network theory’, insights in the organization and dynamics of brain functioning can be obtained. We test the hypothesis that neural network organization mediates the association between cardio respiratory fitness (i.e. VO2 max) and cognitive functioning. A healthy cohort was studied (n = 219, 113 women, age range 41–44 years). Subjects underwent resting-state eyes-closed magneto-encephalography (MEG). Five artifact-free epochs were analyzed and averaged in six frequency bands (delta-gamma). The phase lag index (PLI) was used as a measure of functional connectivity between all sensors. Modularity analysis was performed, and both within and between-module connectivity of each sensor was calculated. Subjects underwent a maximum oxygen uptake (VO2 max) measurement as an indicator of cardio respiratory fitness. All subjects were tested with a commonly used Dutch intelligence test. Intelligence quotient (IQ) was related to VO2 max. In addition, VO2 max was negatively associated with upper alpha and beta band modularity. Particularly increased intermodular connectivity in the beta band was associated with higher VO2 max and IQ, further indicating a benefit of more global network integration as opposed to local connections. Within-module connectivity showed a spatially varied pattern of correlation, while average connectivity did not show significant results. Mediation analysis was not significant. The occurrence of less modularity in the resting-state is associated with better cardio respiratory fitness, while having increased intermodular connectivity, as opposed to within-module connections, is related to better physical and mental fitness. PMID:24498438

Solar thermal power systems point-focusing distributed receiver technology project. Volume 2: Detailed report

NASA Technical Reports Server (NTRS)

1980-01-01

The accomplishments of the Point-Focusing Distributed Receiver Technology Project during fiscal year 1979 are detailed. Present studies involve designs of modular units that collect and concentrate solar energy via highly reflective, parabolic-shaped dishes. The concentrated energy is then converted to heat in a working fluid, such as hot gas. In modules designed to produce heat for industrial applications, a flexible line conveys the heated fluid from the module to a heat transfer network. In modules designed to produce electricity the fluid carries the heat directly to an engine in a power conversion unit located at the focus of the concentrator. The engine is mechanically linked to an electric generator. A Brayton-cycle engine is currently being developed as the most promising electrical energy converter to meet near-future needs.

Conceptual Design of a 100 MWe Modular Molten Salt Power Tower Plant

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

James E. Pacheco; Carter Moursund, Dale Rogers, David Wasyluk

2011-09-20

A conceptual design of a 100 MWe modular molten salt solar power tower plant has been developed which can provide capacity factors in the range of 35 to 75%. Compared to single tower plants, the modular design provides a higher degree of flexibility in achieving the desired customer's capacity factor and is obtained simply by adjusting the number of standard modules. Each module consists of a standard size heliostat field and receiver system, hence reengineering and associated unacceptable performance uncertainties due to scaling are eliminated. The modular approach with multiple towers also improves plant availability. Heliostat field components, receivers andmore » towers are shop assembled allowing for high quality and minimal field assembly. A centralized thermal-storage system stores hot salt from the receivers, allowing nearly continuous power production, independent of solar energy collection, and improved parity with the grid. A molten salt steam generator converts the stored thermal energy into steam, which powers a steam turbine generator to produce electricity. This paper describes the conceptual design of the plant, the advantages of modularity, expected performance, pathways to cost reductions, and environmental impact.« less

Differentially categorized structural brain hubs are involved in different microstructural, functional, and cognitive characteristics and contribute to individual identification.

PubMed

Wang, Xindi; Lin, Qixiang; Xia, Mingrui; He, Yong

2018-04-01

Very little is known regarding whether structural hubs of human brain networks that enable efficient information communication may be classified into different categories. Using three multimodal neuroimaging data sets, we construct individual structural brain networks and further identify hub regions based on eight widely used graph-nodal metrics, followed by comprehensive characteristics and reproducibility analyses. We show the three categories of structural hubs in the brain network, namely, aggregated, distributed, and connector hubs. Spatially, these distinct categories of hubs are primarily located in the default-mode system and additionally in the visual and limbic systems for aggregated hubs, in the frontoparietal system for distributed hubs, and in the sensorimotor and ventral attention systems for connector hubs. These categorized hubs exhibit various distinct characteristics to support their differentiated roles, involving microstructural organization, wiring costs, topological vulnerability, functional modular integration, and cognitive flexibility; moreover, these characteristics are better in the hubs than nonhubs. Finally, all three categories of hubs display high across-session spatial similarities and act as structural fingerprints with high predictive rates (100%, 100%, and 84.2%) for individual identification. Collectively, we highlight three categories of brain hubs with differential microstructural, functional and, cognitive associations, which shed light on topological mechanisms of the human connectome. © 2018 Wiley Periodicals, Inc.

Modular Seafloor and Water Column Systems for the Ocean Observatories Initiative Cabled Array

NASA Astrophysics Data System (ADS)

Delaney, J. R.; Manalang, D.; Harrington, M.; Tilley, J.; Dosher, J.; Cram, G.; Harkins, G.; McGuire, C.; Waite, P.; McRae, E.; McGinnis, T.; Kenney, M.; Siani, C.; Michel-Hart, N.; Denny, S.; Boget, E.; Kawka, O. E.; Daly, K. L.; Luther, D. S.; Kelley, D. S.; Milcic, M.

2016-02-01

Over the past decade, cabled ocean observatories have become an increasingly important way to collect continuous real-time data at remote subsea locations. This has led to the development of a class of subsea systems designed and built specifically to distribute power and bandwidth among sensing instrumentation on the seafloor and throughout the water column. Such systems are typically powered by shore-based infrastructure and involve networks of fiber optic and electrical cabling that provide real-time data access and control of remotely deployed instrumentation. Several subsea node types were developed and/or adapted for cabled use in order to complete the installation of the largest North American scientific cabled observatory in Oct, 2014. The Ocean Observatories Initiative (OOI) Cabled Array, funded by the US National Science Foundation, consists of a core infrastructure that includes 900 km of fiber optic/electrical cables, seven primary nodes, 18 seafloor junction boxes, three mooring-mounted winched profiling systems, and three wire-crawling profiler systems. In aggregate, the installed infrastructure has 200 dedicated scientific instrument ports (of which 120 are currently assigned), and is capable of further expansion. The installed system has a 25-year design life for reliable, sustained monitoring; and all nodes, profilers and instrument packages are ROV-serviceable. Now in it's second year of operation, the systems that comprise the Cabled Array are providing reliable, 24/7 real-time data collection from deployed instrumentation, and offer a modular and scalable class of subsea systems for ocean observing. This presentation will provide an overview of the observatory-class subsystems of the OOI Cabled Array, focusing on the junction boxes, moorings and profilers that power and communicate with deployed instrumentation.

Modular Neural Networks for Speech Recognition.

DTIC Science & Technology

1996-08-01

automatic speech rccogni- tion, understanding and translation since the early 1950’ s . Although researchers have demonstrated impressive results with...nodes. It serves only as a data source for the following hidden layer( s ). Finally, the networks output is computed by neurons in the output layer. The...following update rule for weights in the hidden layer: w (,,•+I) ("’) E/V S (W W k- = wj, -- 7 - / v It is easy to generalize the backpropagation

Information Extraction from Large-Multi-Layer Social Networks

DTIC Science & Technology

2015-08-06

mization [4]. Methods that fall into this category include spec- tral algorithms, modularity methods, and methods that rely on statistical inference...Snijders and Chris Baerveldt, “A multilevel network study of the effects of delinquent behavior on friendship evolution,” Journal of mathematical sociol- ogy...1970. [10] Ulrike Luxburg, “A tutorial on spectral clustering,” Statistics and Computing, vol. 17, no. 4, pp. 395–416, Dec. 2007. [11] R. A. Fisher, “On

Neurobiologically realistic determinants of self-organized criticality in networks of spiking neurons.

PubMed

Rubinov, Mikail; Sporns, Olaf; Thivierge, Jean-Philippe; Breakspear, Michael

2011-06-01

Self-organized criticality refers to the spontaneous emergence of self-similar dynamics in complex systems poised between order and randomness. The presence of self-organized critical dynamics in the brain is theoretically appealing and is supported by recent neurophysiological studies. Despite this, the neurobiological determinants of these dynamics have not been previously sought. Here, we systematically examined the influence of such determinants in hierarchically modular networks of leaky integrate-and-fire neurons with spike-timing-dependent synaptic plasticity and axonal conduction delays. We characterized emergent dynamics in our networks by distributions of active neuronal ensemble modules (neuronal avalanches) and rigorously assessed these distributions for power-law scaling. We found that spike-timing-dependent synaptic plasticity enabled a rapid phase transition from random subcritical dynamics to ordered supercritical dynamics. Importantly, modular connectivity and low wiring cost broadened this transition, and enabled a regime indicative of self-organized criticality. The regime only occurred when modular connectivity, low wiring cost and synaptic plasticity were simultaneously present, and the regime was most evident when between-module connection density scaled as a power-law. The regime was robust to variations in other neurobiologically relevant parameters and favored systems with low external drive and strong internal interactions. Increases in system size and connectivity facilitated internal interactions, permitting reductions in external drive and facilitating convergence of postsynaptic-response magnitude and synaptic-plasticity learning rate parameter values towards neurobiologically realistic levels. We hence infer a novel association between self-organized critical neuronal dynamics and several neurobiologically realistic features of structural connectivity. The central role of these features in our model may reflect their importance for neuronal information processing.

Evo-Devo insights from pathological networks: exploring craniosynostosis as a developmental mechanism for modularity and complexity in the human skull.

PubMed

Esteve-Altava, Borja; Rasskin-Gutman, Diego

2015-07-20

Bone fusion has occurred repeatedly during skull evolution in all tetrapod lineages, leading to a reduction in the number of bones and an increase in their morphological complexity. The ontogeny of the human skull includes also bone fusions as part of its normal developmental process. However, several disruptions might cause premature closure of cranial sutures (craniosynostosis), reducing the number of bones and producing new skull growth patterns that causes shape changes. Here, we compare skull network models of a normal newborn with different craniosynostosis conditions, the normal adult stage, and phylogenetically reconstructed forms of a primitive tetrapod, a synapsid, and a placental mammal. Changes in morphological complexity of newborn-to-synostosed skulls are two to three times less than in newborn-to-adult; and even smaller when we compare them to the increases among the reconstructed ancestors in the evolutionary transitions. In addition, normal, synostosed, and adult human skulls show the same connectivity modules: facial and cranial. Differences arise in the internal structure of these modules. In the adult skull the facial module has an internal hierarchical organization, whereas the cranial module has a regular network organization. However, all newborn forms, normal and synostosed, do not reach such kind of internal organization. We conclude that the subtle changes in skull complexity at the developmental scale can change the modular substructure of the newborn skull to more integrated modules in the adult skull, but is not enough to generate radical changes as it occurs at a macroevolutionary scale. The timing of closure of craniofacial sutures, together with the conserved patterns of morphological modularity, highlights a potential relation between the premature fusion of bones and the evolution of the shape of the skull in hominids.

Incorporating profile information in community detection for online social networks

NASA Astrophysics Data System (ADS)

Fan, W.; Yeung, K. H.

2014-07-01

Community structure is an important feature in the study of complex networks. It is because nodes of the same community may have similar properties. In this paper we extend two popular community detection methods to partition online social networks. In our extended methods, the profile information of users is used for partitioning. We apply the extended methods in several sample networks of Facebook. Compared with the original methods, the community structures we obtain have higher modularity. Our results indicate that users' profile information is consistent with the community structure of their friendship network to some extent. To the best of our knowledge, this paper is the first to discuss how profile information can be used to improve community detection in online social networks.

The 'wired' universe of organic chemistry.

PubMed

Grzybowski, Bartosz A; Bishop, Kyle J M; Kowalczyk, Bartlomiej; Wilmer, Christopher E

2009-04-01

The millions of reactions performed and compounds synthesized by organic chemists over the past two centuries connect to form a network larger than the metabolic networks of higher organisms and rivalling the complexity of the World Wide Web. Despite its apparent randomness, the network of chemistry has a well-defined, modular architecture. The network evolves in time according to trends that have not changed since the inception of the discipline, and thus project into chemistry's future. Analysis of organic chemistry using the tools of network theory enables the identification of most 'central' organic molecules, and for the prediction of which and how many molecules will be made in the future. Statistical analyses based on network connectivity are useful in optimizing parallel syntheses, in estimating chemical reactivity, and more.

Resting state functional MRI reveals abnormal network connectivity in neurofibromatosis 1.

PubMed

Tomson, Steffie N; Schreiner, Matthew J; Narayan, Manjari; Rosser, Tena; Enrique, Nicole; Silva, Alcino J; Allen, Genevera I; Bookheimer, Susan Y; Bearden, Carrie E

2015-11-01

Neurofibromatosis type I (NF1) is a genetic disorder caused by mutations in the neurofibromin 1 gene at locus 17q11.2. Individuals with NF1 have an increased incidence of learning disabilities, attention deficits, and autism spectrum disorders. As a single-gene disorder, NF1 represents a valuable model for understanding gene-brain-behavior relationships. While mouse models have elucidated molecular and cellular mechanisms underlying learning deficits associated with this mutation, little is known about functional brain architecture in human subjects with NF1. To address this question, we used resting state functional connectivity magnetic resonance imaging (rs-fcMRI) to elucidate the intrinsic network structure of 30 NF1 participants compared with 30 healthy demographically matched controls during an eyes-open rs-fcMRI scan. Novel statistical methods were employed to quantify differences in local connectivity (edge strength) and modularity structure, in combination with traditional global graph theory applications. Our findings suggest that individuals with NF1 have reduced anterior-posterior connectivity, weaker bilateral edges, and altered modularity clustering relative to healthy controls. Further, edge strength and modular clustering indices were correlated with IQ and internalizing symptoms. These findings suggest that Ras signaling disruption may lead to abnormal functional brain connectivity; further investigation into the functional consequences of these alterations in both humans and in animal models is warranted. © 2015 Wiley Periodicals, Inc.

Resting state functional MRI reveals abnormal network connectivity in Neurofibromatosis 1

PubMed Central

Tomson, S.N.; Schreiner, M.; Narayan, M.; Rosser, Tena; Enrique, Nicole; Silva, Alcino J.; Allen, G.I.; Bookheimer, S.Y.; Bearden, C.E.

2015-01-01

Neurofibromatosis type I (NF1) is a genetic disorder caused by mutations in the neurofibromin 1 gene at locus 17q11.2. Individuals with NF1 have an increased incidence of learning disabilities, attention deficits and autism spectrum disorders. As a single gene disorder, NF1 represents a valuable model for understanding gene-brain-behavior relationships. While mouse models have elucidated molecular and cellular mechanisms underlying learning deficits associated with this mutation, little is known about functional brain architecture in human subjects with NF1. To address this question, we used resting state functional connectivity MRI (rs-fcMRI) to elucidate the intrinsic network structure of 30 NF1 participants compared with 30 healthy demographically matched controls during an eyes-open rs-fcMRI scan. Novel statistical methods were employed to quantify differences in local connectivity (edge strength) and modularity structure, in combination with traditional global graph theory applications. Our findings suggest that individuals with NF1 have reduced anterior-posterior connectivity, weaker bilateral edges, and altered modularity clustering relative to healthy controls. Further, edge strength and modular clustering indices were correlated with IQ and internalizing symptoms. These findings suggest that Ras signaling disruption may lead to abnormal functional brain connectivity; further investigation into the functional consequences of these alterations in both humans and in animal models is warranted. PMID:26304096

A networked modular hardware and software system for MRI-guided robotic prostate interventions

NASA Astrophysics Data System (ADS)

Su, Hao; Shang, Weijian; Harrington, Kevin; Camilo, Alex; Cole, Gregory; Tokuda, Junichi; Hata, Nobuhiko; Tempany, Clare; Fischer, Gregory S.

2012-02-01

Magnetic resonance imaging (MRI) provides high resolution multi-parametric imaging, large soft tissue contrast, and interactive image updates making it an ideal modality for diagnosing prostate cancer and guiding surgical tools. Despite a substantial armamentarium of apparatuses and systems has been developed to assist surgical diagnosis and therapy for MRI-guided procedures over last decade, the unified method to develop high fidelity robotic systems in terms of accuracy, dynamic performance, size, robustness and modularity, to work inside close-bore MRI scanner still remains a challenge. In this work, we develop and evaluate an integrated modular hardware and software system to support the surgical workflow of intra-operative MRI, with percutaneous prostate intervention as an illustrative case. Specifically, the distinct apparatuses and methods include: 1) a robot controller system for precision closed loop control of piezoelectric motors, 2) a robot control interface software that connects the 3D Slicer navigation software and the robot controller to exchange robot commands and coordinates using the OpenIGTLink open network communication protocol, and 3) MRI scan plane alignment to the planned path and imaging of the needle as it is inserted into the target location. A preliminary experiment with ex-vivo phantom validates the system workflow, MRI-compatibility and shows that the robotic system has a better than 0.01mm positioning accuracy.

Tired and misconnected: A breakdown of brain modularity following sleep deprivation.

PubMed

Ben Simon, Eti; Maron-Katz, Adi; Lahav, Nir; Shamir, Ron; Hendler, Talma

2017-06-01

Sleep deprivation (SD) critically affects a range of cognitive and affective functions, typically assessed during task performance. Whether such impairments stem from changes to the brain's intrinsic functional connectivity remain largely unknown. To examine this hypothesis, we applied graph theoretical analysis on resting-state fMRI data derived from 18 healthy participants, acquired during both sleep-rested and sleep-deprived states. We hypothesized that parameters indicative of graph connectivity, such as modularity, will be impaired by sleep deprivation and that these changes will correlate with behavioral outcomes elicited by sleep loss. As expected, our findings point to a profound reduction in network modularity without sleep, evident in the limbic, default-mode, salience and executive modules. These changes were further associated with behavioral impairments elicited by SD: a decrease in salience module density was associated with worse task performance, an increase in limbic module density was predictive of stronger amygdala activation in a subsequent emotional-distraction task and a shift in frontal hub lateralization (from left to right) was associated with increased negative mood. Altogether, these results portray a loss of functional segregation within the brain and a shift towards a more random-like network without sleep, already detected in the spontaneous activity of the sleep-deprived brain. Hum Brain Mapp 38:3300-3314, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

SA-SOM algorithm for detecting communities in complex networks

NASA Astrophysics Data System (ADS)

Chen, Luogeng; Wang, Yanran; Huang, Xiaoming; Hu, Mengyu; Hu, Fang

2017-10-01

Currently, community detection is a hot topic. This paper, based on the self-organizing map (SOM) algorithm, introduced the idea of self-adaptation (SA) that the number of communities can be identified automatically, a novel algorithm SA-SOM of detecting communities in complex networks is proposed. Several representative real-world networks and a set of computer-generated networks by LFR-benchmark are utilized to verify the accuracy and the efficiency of this algorithm. The experimental findings demonstrate that this algorithm can identify the communities automatically, accurately and efficiently. Furthermore, this algorithm can also acquire higher values of modularity, NMI and density than the SOM algorithm does.

Memory Network For Distributed Data Processors

NASA Technical Reports Server (NTRS)

Bolen, David; Jensen, Dean; Millard, ED; Robinson, Dave; Scanlon, George

1992-01-01

Universal Memory Network (UMN) is modular, digital data-communication system enabling computers with differing bus architectures to share 32-bit-wide data between locations up to 3 km apart with less than one millisecond of latency. Makes it possible to design sophisticated real-time and near-real-time data-processing systems without data-transfer "bottlenecks". This enterprise network permits transmission of volume of data equivalent to an encyclopedia each second. Facilities benefiting from Universal Memory Network include telemetry stations, simulation facilities, power-plants, and large laboratories or any facility sharing very large volumes of data. Main hub of UMN is reflection center including smaller hubs called Shared Memory Interfaces.

Spectra of random networks in the weak clustering regime

NASA Astrophysics Data System (ADS)

Peron, Thomas K. DM.; Ji, Peng; Kurths, Jürgen; Rodrigues, Francisco A.

2018-03-01

The asymptotic behavior of dynamical processes in networks can be expressed as a function of spectral properties of the corresponding adjacency and Laplacian matrices. Although many theoretical results are known for the spectra of traditional configuration models, networks generated through these models fail to describe many topological features of real-world networks, in particular non-null values of the clustering coefficient. Here we study effects of cycles of order three (triangles) in network spectra. By using recent advances in random matrix theory, we determine the spectral distribution of the network adjacency matrix as a function of the average number of triangles attached to each node for networks without modular structure and degree-degree correlations. Implications to network dynamics are discussed. Our findings can shed light in the study of how particular kinds of subgraphs influence network dynamics.

Using a Simple Neural Network to Delineate Some Principles of Distributed Economic Choice.

PubMed

Balasubramani, Pragathi P; Moreno-Bote, Rubén; Hayden, Benjamin Y

2018-01-01

The brain uses a mixture of distributed and modular organization to perform computations and generate appropriate actions. While the principles under which the brain might perform computations using modular systems have been more amenable to modeling, the principles by which the brain might make choices using distributed principles have not been explored. Our goal in this perspective is to delineate some of those distributed principles using a neural network method and use its results as a lens through which to reconsider some previously published neurophysiological data. To allow for direct comparison with our own data, we trained the neural network to perform binary risky choices. We find that value correlates are ubiquitous and are always accompanied by non-value information, including spatial information (i.e., no pure value signals). Evaluation, comparison, and selection were not distinct processes; indeed, value signals even in the earliest stages contributed directly, albeit weakly, to action selection. There was no place, other than at the level of action selection, at which dimensions were fully integrated. No units were specialized for specific offers; rather, all units encoded the values of both offers in an anti-correlated format, thus contributing to comparison. Individual network layers corresponded to stages in a continuous rotation from input to output space rather than to functionally distinct modules. While our network is likely to not be a direct reflection of brain processes, we propose that these principles should serve as hypotheses to be tested and evaluated for future studies.

Applying network analysis and Nebula (neighbor-edges based and unbiased leverage algorithm) to ToxCast data.

PubMed

Ye, Hao; Luo, Heng; Ng, Hui Wen; Meehan, Joe; Ge, Weigong; Tong, Weida; Hong, Huixiao

2016-01-01

ToxCast data have been used to develop models for predicting in vivo toxicity. To predict the in vivo toxicity of a new chemical using a ToxCast data based model, its ToxCast bioactivity data are needed but not normally available. The capability of predicting ToxCast bioactivity data is necessary to fully utilize ToxCast data in the risk assessment of chemicals. We aimed to understand and elucidate the relationships between the chemicals and bioactivity data of the assays in ToxCast and to develop a network analysis based method for predicting ToxCast bioactivity data. We conducted modularity analysis on a quantitative network constructed from ToxCast data to explore the relationships between the assays and chemicals. We further developed Nebula (neighbor-edges based and unbiased leverage algorithm) for predicting ToxCast bioactivity data. Modularity analysis on the network constructed from ToxCast data yielded seven modules. Assays and chemicals in the seven modules were distinct. Leave-one-out cross-validation yielded a Q(2) of 0.5416, indicating ToxCast bioactivity data can be predicted by Nebula. Prediction domain analysis showed some types of ToxCast assay data could be more reliably predicted by Nebula than others. Network analysis is a promising approach to understand ToxCast data. Nebula is an effective algorithm for predicting ToxCast bioactivity data, helping fully utilize ToxCast data in the risk assessment of chemicals. Published by Elsevier Ltd.

Using a Simple Neural Network to Delineate Some Principles of Distributed Economic Choice

PubMed Central

Balasubramani, Pragathi P.; Moreno-Bote, Rubén; Hayden, Benjamin Y.

2018-01-01

The brain uses a mixture of distributed and modular organization to perform computations and generate appropriate actions. While the principles under which the brain might perform computations using modular systems have been more amenable to modeling, the principles by which the brain might make choices using distributed principles have not been explored. Our goal in this perspective is to delineate some of those distributed principles using a neural network method and use its results as a lens through which to reconsider some previously published neurophysiological data. To allow for direct comparison with our own data, we trained the neural network to perform binary risky choices. We find that value correlates are ubiquitous and are always accompanied by non-value information, including spatial information (i.e., no pure value signals). Evaluation, comparison, and selection were not distinct processes; indeed, value signals even in the earliest stages contributed directly, albeit weakly, to action selection. There was no place, other than at the level of action selection, at which dimensions were fully integrated. No units were specialized for specific offers; rather, all units encoded the values of both offers in an anti-correlated format, thus contributing to comparison. Individual network layers corresponded to stages in a continuous rotation from input to output space rather than to functionally distinct modules. While our network is likely to not be a direct reflection of brain processes, we propose that these principles should serve as hypotheses to be tested and evaluated for future studies. PMID:29643773

Functional Brain Networks Develop from a “Local to Distributed” Organization

PubMed Central

Power, Jonathan D.; Dosenbach, Nico U. F.; Church, Jessica A.; Miezin, Francis M.; Schlaggar, Bradley L.; Petersen, Steven E.

2009-01-01

The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI), graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward ‘segregation’ (a general decrease in correlation strength) between regions close in anatomical space and ‘integration’ (an increased correlation strength) between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more “distributed” architecture in young adults. We argue that this “local to distributed” developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths) are similar in child and adult graphs, with both showing “small-world”-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults both have relatively efficient systems that may solve similar information processing problems in divergent ways. PMID:19412534

Nestedness across biological scales

PubMed Central

Marquitti, Flavia M. D.; Raimundo, Rafael L. G.; Sebastián-González, Esther; Coltri, Patricia P.; Perez, S. Ivan; Brandt, Débora Y. C.; Nunes, Kelly; Daura-Jorge, Fábio G.; Floeter, Sergio R.; Guimarães, Paulo R.

2017-01-01

Biological networks pervade nature. They describe systems throughout all levels of biological organization, from molecules regulating metabolism to species interactions that shape ecosystem dynamics. The network thinking revealed recurrent organizational patterns in complex biological systems, such as the formation of semi-independent groups of connected elements (modularity) and non-random distributions of interactions among elements. Other structural patterns, such as nestedness, have been primarily assessed in ecological networks formed by two non-overlapping sets of elements; information on its occurrence on other levels of organization is lacking. Nestedness occurs when interactions of less connected elements form proper subsets of the interactions of more connected elements. Only recently these properties began to be appreciated in one-mode networks (where all elements can interact) which describe a much wider variety of biological phenomena. Here, we compute nestedness in a diverse collection of one-mode networked systems from six different levels of biological organization depicting gene and protein interactions, complex phenotypes, animal societies, metapopulations, food webs and vertebrate metacommunities. Our findings suggest that nestedness emerge independently of interaction type or biological scale and reveal that disparate systems can share nested organization features characterized by inclusive subsets of interacting elements with decreasing connectedness. We primarily explore the implications of a nested structure for each of these studied systems, then theorize on how nested networks are assembled. We hypothesize that nestedness emerges across scales due to processes that, although system-dependent, may share a general compromise between two features: specificity (the number of interactions the elements of the system can have) and affinity (how these elements can be connected to each other). Our findings suggesting occurrence of nestedness throughout biological scales can stimulate the debate on how pervasive nestedness may be in nature, while the theoretical emergent principles can aid further research on commonalities of biological networks. PMID:28166284

Functional brain networks develop from a "local to distributed" organization.

PubMed

Fair, Damien A; Cohen, Alexander L; Power, Jonathan D; Dosenbach, Nico U F; Church, Jessica A; Miezin, Francis M; Schlaggar, Bradley L; Petersen, Steven E

2009-05-01

The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI), graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward 'segregation' (a general decrease in correlation strength) between regions close in anatomical space and 'integration' (an increased correlation strength) between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more "distributed" architecture in young adults. We argue that this "local to distributed" developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths) are similar in child and adult graphs, with both showing "small-world"-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults both have relatively efficient systems that may solve similar information processing problems in divergent ways.

Social networks in primates: smart and tolerant species have more efficient networks.

PubMed

Pasquaretta, Cristian; Levé, Marine; Claidière, Nicolas; van de Waal, Erica; Whiten, Andrew; MacIntosh, Andrew J J; Pelé, Marie; Bergstrom, Mackenzie L; Borgeaud, Christèle; Brosnan, Sarah F; Crofoot, Margaret C; Fedigan, Linda M; Fichtel, Claudia; Hopper, Lydia M; Mareno, Mary Catherine; Petit, Odile; Schnoell, Anna Viktoria; di Sorrentino, Eugenia Polizzi; Thierry, Bernard; Tiddi, Barbara; Sueur, Cédric

2014-12-23

Network optimality has been described in genes, proteins and human communicative networks. In the latter, optimality leads to the efficient transmission of information with a minimum number of connections. Whilst studies show that differences in centrality exist in animal networks with central individuals having higher fitness, network efficiency has never been studied in animal groups. Here we studied 78 groups of primates (24 species). We found that group size and neocortex ratio were correlated with network efficiency. Centralisation (whether several individuals are central in the group) and modularity (how a group is clustered) had opposing effects on network efficiency, showing that tolerant species have more efficient networks. Such network properties affecting individual fitness could be shaped by natural selection. Our results are in accordance with the social brain and cultural intelligence hypotheses, which suggest that the importance of network efficiency and information flow through social learning relates to cognitive abilities.

Social networks in primates: smart and tolerant species have more efficient networks

PubMed Central

Pasquaretta, Cristian; Levé, Marine; Claidière, Nicolas; van de Waal, Erica; Whiten, Andrew; MacIntosh, Andrew J. J.; Pelé, Marie; Bergstrom, Mackenzie L.; Borgeaud, Christèle; Brosnan, Sarah F.; Crofoot, Margaret C.; Fedigan, Linda M.; Fichtel, Claudia; Hopper, Lydia M.; Mareno, Mary Catherine; Petit, Odile; Schnoell, Anna Viktoria; di Sorrentino, Eugenia Polizzi; Thierry, Bernard; Tiddi, Barbara; Sueur, Cédric

2014-01-01

Network optimality has been described in genes, proteins and human communicative networks. In the latter, optimality leads to the efficient transmission of information with a minimum number of connections. Whilst studies show that differences in centrality exist in animal networks with central individuals having higher fitness, network efficiency has never been studied in animal groups. Here we studied 78 groups of primates (24 species). We found that group size and neocortex ratio were correlated with network efficiency. Centralisation (whether several individuals are central in the group) and modularity (how a group is clustered) had opposing effects on network efficiency, showing that tolerant species have more efficient networks. Such network properties affecting individual fitness could be shaped by natural selection. Our results are in accordance with the social brain and cultural intelligence hypotheses, which suggest that the importance of network efficiency and information flow through social learning relates to cognitive abilities. PMID:25534964

Networks in Cell Biology

NASA Astrophysics Data System (ADS)

Buchanan, Mark; Caldarelli, Guido; De Los Rios, Paolo; Rao, Francesco; Vendruscolo, Michele

2010-05-01

Introduction; 1. Network views of the cell Paolo De Los Rios and Michele Vendruscolo; 2. Transcriptional regulatory networks Sarath Chandra Janga and M. Madan Babu; 3. Transcription factors and gene regulatory networks Matteo Brilli, Elissa Calistri and Pietro Lió; 4. Experimental methods for protein interaction identification Peter Uetz, Björn Titz, Seesandra V. Rajagopala and Gerard Cagney; 5. Modeling protein interaction networks Francesco Rao; 6. Dynamics and evolution of metabolic networks Daniel Segré; 7. Hierarchical modularity in biological networks: the case of metabolic networks Erzsébet Ravasz Regan; 8. Signalling networks Gian Paolo Rossini; Appendix 1. Complex networks: from local to global properties D. Garlaschelli and G. Caldarelli; Appendix 2. Modelling the local structure of networks D. Garlaschelli and G. Caldarelli; Appendix 3. Higher-order topological properties S. Ahnert, T. Fink and G. Caldarelli; Appendix 4. Elementary mathematical concepts A. Gabrielli and G. Caldarelli; References.

Network-Physics (NP) BEC DIGITAL(#)-VULNERABILITY; ``Q-Computing"=Simple-Arithmetic;Modular-Congruences=SignalXNoise PRODUCTS=Clock-model;BEC-Factorization;RANDOM-# Definition;P=/=NP TRIVIAL Proof!!!

NASA Astrophysics Data System (ADS)

Pi, E. I.; Siegel, E.

2010-03-01

Siegel[AMS Natl.Mtg.(2002)-Abs.973-60-124] digits logarithmic- law inversion to ONLY BEQS BEC:Quanta/Bosons=#: EMP-like SEVERE VULNERABILITY of ONLY #-networks(VS.ANALOG INvulnerability) via Barabasi NP(VS.dynamics[Not.AMS(5/2009)] critique);(so called)``quantum-computing''(QC) = simple-arithmetic (sansdivision);algorithmiccomplexities:INtractibility/UNdecidabi lity/INefficiency/NONcomputability/HARDNESS(so MIScalled) ``noise''-induced-phase-transition(NIT)ACCELERATION:Cook-Levin theorem Reducibility = RG fixed-points; #-Randomness DEFINITION via WHAT? Query(VS. Goldreich[Not.AMS(2002)] How? mea culpa)= ONLY MBCS hot-plasma v #-clumping NON-random BEC; Modular-Arithmetic Congruences = Signal x Noise PRODUCTS = clock-model; NON-Shor[Physica A,341,586(04)]BEC logarithmic-law inversion factorization: Watkins #-theory U statistical- physics); P=/=NP C-S TRIVIAL Proof: Euclid!!! [(So Miscalled) computational-complexity J-O obviation(3 millennia AGO geometry: NO:CC,``CS'';``Feet of Clay!!!'']; Query WHAT?:Definition: (so MIScalled)``complexity''=UTTER-SIMPLICITY!! v COMPLICATEDNESS MEASURE(S).

Disentangling the multigenic and pleiotropic nature of molecular function

PubMed Central

2015-01-01

Background Biological processes at the molecular level are usually represented by molecular interaction networks. Function is organised and modularity identified based on network topology, however, this approach often fails to account for the dynamic and multifunctional nature of molecular components. For example, a molecule engaging in spatially or temporally independent functions may be inappropriately clustered into a single functional module. To capture biologically meaningful sets of interacting molecules, we use experimentally defined pathways as spatial/temporal units of molecular activity. Results We defined functional profiles of Saccharomyces cerevisiae based on a minimal set of Gene Ontology terms sufficient to represent each pathway's genes. The Gene Ontology terms were used to annotate 271 pathways, accounting for pathway multi-functionality and gene pleiotropy. Pathways were then arranged into a network, linked by shared functionality. Of the genes in our data set, 44% appeared in multiple pathways performing a diverse set of functions. Linking pathways by overlapping functionality revealed a modular network with energy metabolism forming a sparse centre, surrounded by several denser clusters comprised of regulatory and metabolic pathways. Signalling pathways formed a relatively discrete cluster connected to the centre of the network. Genetic interactions were enriched within the clusters of pathways by a factor of 5.5, confirming the organisation of our pathway network is biologically significant. Conclusions Our representation of molecular function according to pathway relationships enables analysis of gene/protein activity in the context of specific functional roles, as an alternative to typical molecule-centric graph-based methods. The pathway network demonstrates the cooperation of multiple pathways to perform biological processes and organises pathways into functionally related clusters with interdependent outcomes. PMID:26678917

Dynamics on networks: the role of local dynamics and global networks on the emergence of hypersynchronous neural activity.

PubMed

Schmidt, Helmut; Petkov, George; Richardson, Mark P; Terry, John R

2014-11-01

Graph theory has evolved into a useful tool for studying complex brain networks inferred from a variety of measures of neural activity, including fMRI, DTI, MEG and EEG. In the study of neurological disorders, recent work has discovered differences in the structure of graphs inferred from patient and control cohorts. However, most of these studies pursue a purely observational approach; identifying correlations between properties of graphs and the cohort which they describe, without consideration of the underlying mechanisms. To move beyond this necessitates the development of computational modeling approaches to appropriately interpret network interactions and the alterations in brain dynamics they permit, which in the field of complexity sciences is known as dynamics on networks. In this study we describe the development and application of this framework using modular networks of Kuramoto oscillators. We use this framework to understand functional networks inferred from resting state EEG recordings of a cohort of 35 adults with heterogeneous idiopathic generalized epilepsies and 40 healthy adult controls. Taking emergent synchrony across the global network as a proxy for seizures, our study finds that the critical strength of coupling required to synchronize the global network is significantly decreased for the epilepsy cohort for functional networks inferred from both theta (3-6 Hz) and low-alpha (6-9 Hz) bands. We further identify left frontal regions as a potential driver of seizure activity within these networks. We also explore the ability of our method to identify individuals with epilepsy, observing up to 80% predictive power through use of receiver operating characteristic analysis. Collectively these findings demonstrate that a computer model based analysis of routine clinical EEG provides significant additional information beyond standard clinical interpretation, which should ultimately enable a more appropriate mechanistic stratification of people with epilepsy leading to improved diagnostics and therapeutics.

Observability and Estimation of Distributed Space Systems via Local Information-Exchange Networks

NASA Technical Reports Server (NTRS)

Fathpour, Nanaz; Hadaegh, Fred Y.; Mesbahi, Mehran; Rahmani, Amirreza

2011-01-01

Spacecraft formation flying involves the coordination of states among multiple spacecraft through relative sensing, inter-spacecraft communication, and control. Most existing formation-flying estimation algorithms can only be supported via highly centralized, all-to-all, static relative sensing. New algorithms are proposed that are scalable, modular, and robust to variations in the topology and link characteristics of the formation exchange network. These distributed algorithms rely on a local information exchange network, relaxing the assumptions on existing algorithms. Distributed space systems rely on a signal transmission network among multiple spacecraft for their operation. Control and coordination among multiple spacecraft in a formation is facilitated via a network of relative sensing and interspacecraft communications. Guidance, navigation, and control rely on the sensing network. This network becomes more complex the more spacecraft are added, or as mission requirements become more complex. The observability of a formation state was observed by a set of local observations from a particular node in the formation. Formation observability can be parameterized in terms of the matrices appearing in the formation dynamics and observation matrices. An agreement protocol was used as a mechanism for observing formation states from local measurements. An agreement protocol is essentially an unforced dynamic system whose trajectory is governed by the interconnection geometry and initial condition of each node, with a goal of reaching a common value of interest. The observability of the interconnected system depends on the geometry of the network, as well as the position of the observer relative to the topology. For the first time, critical GN&C (guidance, navigation, and control estimation) subsystems are synthesized by bringing the contribution of the spacecraft information-exchange network to the forefront of algorithmic analysis and design. The result is a formation estimation algorithm that is modular and robust to variations in the topology and link properties of the underlying formation network.

Functional vs. Structural Modularity: do they imply each other?

NASA Astrophysics Data System (ADS)

Toroczkai, Zoltan

2009-03-01

While many deterministic and stochastic processes have been proposed to produce heterogeneous graphs mimicking real-world networks, only a handful of studies attempt to connect structure and dynamics with the function(s) performed by the network. In this talk I will present an approach built on the premise that structure, dynamics, and their observed heterogeneity, are implementations of various functions and their compositions. After a brief review of real-world networks where this connection can explicitly be made, I will focus on biological networks. Biological networks are known to possess functionally specialized modules, which perform tasks almost independently of each other. While proposals have been made for the evolutionary emergence of modularity, it is far from clear that adaptation on evolutionary timescales is the sole mechanism leading to functional specialization. We show that non-evolutionary learning can also lead to the formation of functionally specialized modules in a system exposed to multiple environmental constraints. A natural example suggesting that this is possible is the cerebral cortex, where there are clearly delineated functional areas in spite of the largely uniform anatomical construction of the cortical tissue. However, as numerous experiments show, when damaged, regions specialized for a certain function can be retrained to perform functions normally attributed to other regions. We use the paradigm of neural networks to represent a multitasking system, and use several non-evolutionary learning algorithms as mechanisms for phenotypic adaptation. We show that for a network learning to perform multiple tasks, the degree of independence between the tasks dictates the degree of functional specialization emerging in the network. To uncover the functional modules, we introduce a method of node knockouts that explicitly rates the contribution of each node to different tasks (differential robustness). Through a concrete example we also demonstrate the potential inability of purely topology-based clustering methods to detect functional modules. The robustness of these results suggests that similar mechanisms might be responsible for the emergence of functional specialization in other multitasking networks, as well, including social networks.

78 FR 31897 - Agency Information Collection Activities; Proposed Collection; Comment Request; CPSC Table Saw...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-28

... Assisted Telephone Interviewing (CATI) survey about their usage of, and opinions about, the modular blade... Activities; Proposed Collection; Comment Request; CPSC Table Saw User Survey AGENCY: Consumer Product Safety... comment in response to the notice. This notice solicits comments on a survey of table saw users to...

Active-passive bistatic surveillance for long range air defense

NASA Astrophysics Data System (ADS)

Wardrop, B.; Molyneux-Berry, M. R. B.

1992-06-01

A hypothetical mobile support receiver capable of working within existing and future air defense networks as a means to maintain essential surveillance functions is considered. It is shown how multibeam receiver architecture supported by digital signal processing can substantially improve surveillance performance against chaff and jamming threats. A dual-mode support receiver concept is proposed which is based on the state-of-the-art phased-array technology, modular processing in industry standard hardware and existing networks.

Integration of Virtual Machine Technologies into Hastily Formed Networks in Support of Humanitarian Relief and Disaster Recovery Missions

DTIC Science & Technology

2011-12-01

and measures of effectiveness (MOE). New technologies that offer solid-state hard drives built into modular VDI devices known as appliances ...Joint Reconfigurable Vehicle LAN Local Area Network LOS Line of Sight LTE Long Term Evolution MB Megabyte MOP Measure of Performance MOE Measure ...re-usable measures of performance and measures of effectiveness (MOP and MOE) and evaluation procedures will be applied to this research. A

Development of an Efficient Identifier for Nuclear Power Plant Transients Based on Latest Advances of Error Back-Propagation Learning Algorithm

NASA Astrophysics Data System (ADS)

Moshkbar-Bakhshayesh, Khalil; Ghofrani, Mohammad B.

2014-02-01

This study aims to improve the performance of nuclear power plants (NPPs) transients training and identification using the latest advances of error back-propagation (EBP) learning algorithm. To this end, elements of EBP, including input data, initial weights, learning rate, cost function, activation function, and weights updating procedure are investigated and an efficient neural network is developed. Usefulness of modular networks is also examined and appropriate identifiers, one for each transient, are employed. Furthermore, the effect of transient type on transient identifier performance is illustrated. Subsequently, the developed transient identifier is applied to Bushehr nuclear power plant (BNPP). Seven types of the plant events are probed to analyze the ability of the proposed identifier. The results reveal that identification occurs very early with only five plant variables, whilst in the previous studies a larger number of variables (typically 15 to 20) were required. Modular networks facilitated identification due to its sole dependency on the sign of each network output signal. Fast training of input patterns, extendibility for identification of more transients and reduction of false identification are other advantageous of the proposed identifier. Finally, the balance between the correct answer to the trained transients (memorization) and reasonable response to the test transients (generalization) is improved, meeting one of the primary design criteria of identifiers.

Engineering genetic circuit interactions within and between synthetic minimal cells

NASA Astrophysics Data System (ADS)

Adamala, Katarzyna P.; Martin-Alarcon, Daniel A.; Guthrie-Honea, Katriona R.; Boyden, Edward S.

2017-05-01

Genetic circuits and reaction cascades are of great importance for synthetic biology, biochemistry and bioengineering. An open question is how to maximize the modularity of their design to enable the integration of different reaction networks and to optimize their scalability and flexibility. One option is encapsulation within liposomes, which enables chemical reactions to proceed in well-isolated environments. Here we adapt liposome encapsulation to enable the modular, controlled compartmentalization of genetic circuits and cascades. We demonstrate that it is possible to engineer genetic circuit-containing synthetic minimal cells (synells) to contain multiple-part genetic cascades, and that these cascades can be controlled by external signals as well as inter-liposomal communication without crosstalk. We also show that liposomes that contain different cascades can be fused in a controlled way so that the products of incompatible reactions can be brought together. Synells thus enable a more modular creation of synthetic biology cascades, an essential step towards their ultimate programmability.

Evolutionary and Developmental Modules

PubMed Central

Lacquaniti, Francesco; Ivanenko, Yuri P.; d’Avella, Andrea; Zelik, Karl E.; Zago, Myrka

2013-01-01

The identification of biological modules at the systems level often follows top-down decomposition of a task goal, or bottom-up decomposition of multidimensional data arrays into basic elements or patterns representing shared features. These approaches traditionally have been applied to mature, fully developed systems. Here we review some results from two other perspectives on modularity, namely the developmental and evolutionary perspective. There is growing evidence that modular units of development were highly preserved and recombined during evolution. We first consider a few examples of modules well identifiable from morphology. Next we consider the more difficult issue of identifying functional developmental modules. We dwell especially on modular control of locomotion to argue that the building blocks used to construct different locomotor behaviors are similar across several animal species, presumably related to ancestral neural networks of command. A recurrent theme from comparative studies is that the developmental addition of new premotor modules underlies the postnatal acquisition and refinement of several different motor behaviors in vertebrates. PMID:23730285

Evolutionary and developmental modules.

PubMed

Lacquaniti, Francesco; Ivanenko, Yuri P; d'Avella, Andrea; Zelik, Karl E; Zago, Myrka

2013-01-01

The identification of biological modules at the systems level often follows top-down decomposition of a task goal, or bottom-up decomposition of multidimensional data arrays into basic elements or patterns representing shared features. These approaches traditionally have been applied to mature, fully developed systems. Here we review some results from two other perspectives on modularity, namely the developmental and evolutionary perspective. There is growing evidence that modular units of development were highly preserved and recombined during evolution. We first consider a few examples of modules well identifiable from morphology. Next we consider the more difficult issue of identifying functional developmental modules. We dwell especially on modular control of locomotion to argue that the building blocks used to construct different locomotor behaviors are similar across several animal species, presumably related to ancestral neural networks of command. A recurrent theme from comparative studies is that the developmental addition of new premotor modules underlies the postnatal acquisition and refinement of several different motor behaviors in vertebrates.

FunBlocks. A modular framework for AmI system development.

PubMed

Baquero, Rafael; Rodríguez, José; Mendoza, Sonia; Decouchant, Dominique; Papis, Alfredo Piero Mateos

2012-01-01

The last decade has seen explosive growth in the technologies required to implement Ambient Intelligence (AmI) systems. Technologies such as facial and speech recognition, home networks, household cleaning robots, to name a few, have become commonplace. However, due to the multidisciplinary nature of AmI systems and the distinct requirements of different user groups, integrating these developments into full-scale systems is not an easy task. In this paper we propose FunBlocks, a minimalist modular framework for the development of AmI systems based on the function module abstraction used in the IEC 61499 standard for distributed control systems. FunBlocks provides a framework for the development of AmI systems through the integration of modules loosely joined by means of an event-driven middleware and a module and sensor/actuator catalog. The modular design of the FunBlocks framework allows the development of AmI systems which can be customized to a wide variety of usage scenarios.

FunBlocks. A Modular Framework for AmI System Development

PubMed Central

Baquero, Rafael; Rodríguez, José; Mendoza, Sonia; Decouchant, Dominique; Papis, Alfredo Piero Mateos

2012-01-01

The last decade has seen explosive growth in the technologies required to implement Ambient Intelligence (AmI) systems. Technologies such as facial and speech recognition, home networks, household cleaning robots, to name a few, have become commonplace. However, due to the multidisciplinary nature of AmI systems and the distinct requirements of different user groups, integrating these developments into full-scale systems is not an easy task. In this paper we propose FunBlocks, a minimalist modular framework for the development of AmI systems based on the function module abstraction used in the IEC 61499 standard for distributed control systems. FunBlocks provides a framework for the development of AmI systems through the integration of modules loosely joined by means of an event-driven middleware and a module and sensor/actuator catalog. The modular design of the FunBlocks framework allows the development of AmI systems which can be customized to a wide variety of usage scenarios. PMID:23112599

Implementing neural nets with programmable logic

NASA Technical Reports Server (NTRS)

Vidal, Jacques J.

1988-01-01

Networks of Boolean programmable logic modules are presented as one purely digital class of artificial neural nets. The approach contrasts with the continuous analog framework usually suggested. Programmable logic networks are capable of handling many neural-net applications. They avoid some of the limitations of threshold logic networks and present distinct opportunities. The network nodes are called dynamically programmable logic modules. They can be implemented with digitally controlled demultiplexers. Each node performs a Boolean function of its inputs which can be dynamically assigned. The overall network is therefore a combinational circuit and its outputs are Boolean global functions of the network's input variables. The approach offers definite advantages for VLSI implementation, namely, a regular architecture with limited connectivity, simplicity of the control machinery, natural modularity, and the support of a mature technology.

Community structure in networks

NASA Astrophysics Data System (ADS)

Newman, Mark

2004-03-01

Many networked systems, including physical, biological, social, and technological networks, appear to contain ``communities'' -- groups of nodes within which connections are dense, but between which they are sparser. The ability to find such communities in an automated fashion could be of considerable use. Communities in a web graph for instance might correspond to sets of web sites dealing with related topics, while communities in a biochemical network or an electronic circuit might correspond to functional units of some kind. We present a number of new methods for community discovery, including methods based on ``betweenness'' measures and methods based on modularity optimization. We also give examples of applications of these methods to both computer-generated and real-world network data, and show how our techniques can be used to shed light on the sometimes dauntingly complex structure of networked systems.

A seismic-network mission proposal as an example for modular robotic lunar exploration missions

NASA Astrophysics Data System (ADS)

Lange, C.; Witte, L.; Rosta, R.; Sohl, F.; Heffels, A.; Knapmeyer, M.

2017-05-01

In this paper it is intended to discuss an approach to reduce design costs for subsequent missions by introducing modularity, commonality and multi-mission capability and thereby reuse of mission individual investments into the design of lunar exploration infrastructural systems. The presented approach has been developed within the German Helmholtz-Alliance on Robotic Exploration of Extreme Environments (ROBEX), a research alliance bringing together deep-sea and space research to jointly develop technologies and investigate problems for the exploration of highly inaccessible terrain - be it in the deep sea and polar regions or on the Moon and other planets. Although overall costs are much smaller for deep sea missions as compared to lunar missions, a lot can be learned from modularity approaches in deep sea research infrastructure design, which allows a high operational flexibility in the planning phase of a mission as well as during its implementation. The research presented here is based on a review of existing modular solutions in Earth orbiting satellites as well as science and exploration systems. This is followed by an investigation of lunar exploration scenarios from which we derive requirements for a multi-mission modular architecture. After analyzing possible options, an approach using a bus modular architecture for dedicated subsystems is presented. The approach is based on exchangeable modules e.g. incorporating instruments, which are added to the baseline system platform according to the demands of the specific scenario. It will be described in more detail, including arising problems e.g. in the power or thermal domain. Finally, technological building blocks to put the architecture into practical use will be described more in detail.

Default mode network abnormalities in posttraumatic stress disorder: A novel network-restricted topology approach.

PubMed

Akiki, Teddy J; Averill, Christopher L; Wrocklage, Kristen M; Scott, J Cobb; Averill, Lynnette A; Schweinsburg, Brian; Alexander-Bloch, Aaron; Martini, Brenda; Southwick, Steven M; Krystal, John H; Abdallah, Chadi G

2018-08-01

Disruption in the default mode network (DMN) has been implicated in numerous neuropsychiatric disorders, including posttraumatic stress disorder (PTSD). However, studies have largely been limited to seed-based methods and involved inconsistent definitions of the DMN. Recent advances in neuroimaging and graph theory now permit the systematic exploration of intrinsic brain networks. In this study, we used resting-state functional magnetic resonance imaging (fMRI), diffusion MRI, and graph theoretical analyses to systematically examine the DMN connectivity and its relationship with PTSD symptom severity in a cohort of 65 combat-exposed US Veterans. We employed metrics that index overall connectivity strength, network integration (global efficiency), and network segregation (clustering coefficient). Then, we conducted a modularity and network-based statistical analysis to identify DMN regions of particular importance in PTSD. Finally, structural connectivity analyses were used to probe whether white matter abnormalities are associated with the identified functional DMN changes. We found decreased DMN functional connectivity strength to be associated with increased PTSD symptom severity. Further topological characterization suggests decreased functional integration and increased segregation in subjects with severe PTSD. Modularity analyses suggest a spared connectivity in the posterior DMN community (posterior cingulate, precuneus, angular gyrus) despite overall DMN weakened connections with increasing PTSD severity. Edge-wise network-based statistical analyses revealed a prefrontal dysconnectivity. Analysis of the diffusion networks revealed no alterations in overall strength or prefrontal structural connectivity. DMN abnormalities in patients with severe PTSD symptoms are characterized by decreased overall interconnections. On a finer scale, we found a pattern of prefrontal dysconnectivity, but increased cohesiveness in the posterior DMN community and relative sparing of connectivity in this region. The DMN measures established in this study may serve as a biomarker of disease severity and could have potential utility in developing circuit-based therapeutics. Published by Elsevier Inc.

Fluid design studies of integrated modular engine system

NASA Technical Reports Server (NTRS)

Frankenfield, Bruce; Carek, Jerry

1993-01-01

A study was performed to develop a fluid system design and show the feasibility of constructing an integrated modular engine (IME) configuration, using an expander cycle engine. The primary design goal of the IME configuration was to improve the propulsion system reliability. The IME fluid system was designed as a single fault tolerant system, while minimizing the required fluid components. This study addresses the design of the high pressure manifolds, turbopumps and thrust chambers for the IME configuration. A physical layout drawing was made, which located each of the fluid system components, manifolds and thrust chambers. Finally, a comparison was made between the fluid system designs of an IME system and a non-network (clustered) engine system.

A technological infrastructure to sustain Internetworked Enterprises

NASA Astrophysics Data System (ADS)

La Mattina, Ernesto; Savarino, Vincenzo; Vicari, Claudia; Storelli, Davide; Bianchini, Devis

In the Web 3.0 scenario, where information and services are connected by means of their semantics, organizations can improve their competitive advantage by publishing their business and service descriptions. In this scenario, Semantic Peer to Peer (P2P) can play a key role in defining dynamic and highly reconfigurable infrastructures. Organizations can share knowledge and services, using this infrastructure to move towards value networks, an emerging organizational model characterized by fluid boundaries and complex relationships. This chapter collects and defines the technological requirements and architecture of a modular and multi-Layer Peer to Peer infrastructure for SOA-based applications. This technological infrastructure, based on the combination of Semantic Web and P2P technologies, is intended to sustain Internetworked Enterprise configurations, defining a distributed registry and enabling more expressive queries and efficient routing mechanisms. The following sections focus on the overall architecture, while describing the layers that form it.

Understanding emotion with brain networks.

PubMed

Pessoa, Luiz

2018-02-01

Emotional processing appears to be interlocked with perception, cognition, motivation, and action. These interactions are supported by the brain's large-scale non-modular anatomical and functional architectures. An important component of this organization involves characterizing the brain in terms of networks. Two aspects of brain networks are discussed: brain networks should be considered as inherently overlapping (not disjoint) and dynamic (not static). Recent work on multivariate pattern analysis shows that affective dimensions can be detected in the activity of distributed neural systems that span cortical and subcortical regions. More broadly, the paper considers how we should think of causation in complex systems like the brain, so as to inform the relationship between emotion and other mental aspects, such as cognition.

Flexible detection optics for light scattering

NASA Astrophysics Data System (ADS)

Taratuta, Victor G.; Hurd, Alan J.; Meyer, Robert B.

1984-05-01

We have designed and built a compact, modular apparatus for the collection, viewing, and detection of scattered light for less than 1200, based on a commercially available optical bench. The novelty of our instrument is that it has the flexibility of modular design while allowing the user to see exactly what is happening: both the real image of the sample and the spatial coherence of the scattered light can be examined. There is built-in control over polarization, filtering, magnification, and other parameters.

Analysis of Garment Production Methods. Part 2: Comparison of Cost and Production between a Traditional Bundle System and Modular Manufacturing

DTIC Science & Technology

1992-02-01

configuration. We have spent the last year observing two firms as they experimented with modular manufacturing. The following report will track the progress of...the transitions as they I moved through the year . Incorporated into the analysis is the statistical interpretation of data collected from each firm, as...during the year . FEBRUARY The most noticeable change this month was the introduction of the new ergonomic chairs for the operators. Previously the

A configurable sensor network applied to ambient assisted living.

PubMed

Villacorta, Juan J; Jiménez, María I; Del Val, Lara; Izquierdo, Alberto

2011-01-01

The rising older people population has increased the interest in ambient assisted living systems. This article presents a system for monitoring the disabled or older persons developed from an existing surveillance system. The modularity and adaptability characteristics of the system allow an easy adaptation for a different purpose. The proposed system uses a network of sensors capable of motion detection that includes fall warning, identification of persons and a configurable control system which allows its use in different scenarios.

Implementation of a Modular Fly away Kits (FLAK) for C4ISR in order to counter Asymmetric Threats in the Coalition Riverine and Maritime Theatres

DTIC Science & Technology

2006-06-01

integrated scenario tests of the completed FLAK in Chiang Mai , Thailand in May 2006. Measures of Effectiveness and Measures of Performance will be...Royal Thai Air Force Headquarters – Bangkok, Thailand Maritime Intelligence Fusion Center – Alameda, CA IIFC – Chiang Mai , Thailand Figure 22...to the Mae Ngat Dam, 60 kilometers north of Chiang Mai , Thailand was implemented. 2. Network Architecture The network that was designed for

TRANSDIAGNOSTIC DIMENSIONS OF ANXIETY AND DEPRESSION MODERATE MOTIVATION-RELATED BRAIN NETWORKS DURING GOAL MAINTENANCE

PubMed Central

Spielberg, Jeffrey M.; Miller, Gregory A.; Warren, Stacie L.; Sutton, Bradley P.; Banich, Marie; Heller, Wendy

2015-01-01

Background Advancing research on the etiology, prevention, and treatment of psychopathology requires the field to move beyond modular conceptualizations of neural dysfunction toward understanding disturbance in key brain networks. Although some studies of anxiety and depression have begun doing so, they typically suffer from several drawbacks, including: (1) a categorical approach ignoring transdiagnostic processes, (2) failure to account for substantial anxiety and depression comorbidity, (3) examination of networks at rest, which overlooks disruption manifesting only when networks are challenged. Accordingly, the present study examined relationships between transdiagnostic dimensions of anxiety/depression and patterns of functional connectivity while goal maintenance was challenged. Methods Participants (n = 179, unselected community members and undergraduates selected to be high/low on anxiety/depression) performed a task in which goal maintenance was challenged (color-word Stroop) while fMRI data were collected. Analyses examined moderation by anxiety/depression of condition-dependent coupling between regions of dorsolateral prefrontal cortex (dlPFC) previously associated with approach and avoidance motivation and amygdala/orbitofrontal cortex (OFC). Results Anxious arousal was positively associated with amygdala↔right dlPFC coupling. Depression was positively associated with OFC↔right dlPFC coupling and negatively associated with OFC↔left dlPFC coupling. Conclusions Findings advance the field toward an integrative model of the neural instantiation of anxiety/depression by identifying specific, distinct dysfunctions associated with anxiety and depression in networks important for maintaining approach and avoidance goals. Specifically, findings shed light on potential neural mechanisms involved in attentional biases in anxiety and valuation biases in depression and underscore the importance of examining transdiagnostic dimensions of anxiety/depression while networks are challenged. PMID:24753242

Transdiagnostic dimensions of anxiety and depression moderate motivation-related brain networks during goal maintenance.

PubMed

Spielberg, Jeffrey M; Miller, Gregory A; Warren, Stacie L; Sutton, Bradley P; Banich, Marie; Heller, Wendy

2014-10-01

Advancing research on the etiology, prevention, and treatment of psychopathology requires the field to move beyond modular conceptualizations of neural dysfunction toward understanding disturbance in key brain networks. Although some studies of anxiety and depression have begun doing so, they typically suffer from several drawbacks, including: (1) a categorical approach ignoring transdiagnostic processes, (2) failure to account for substantial anxiety and depression comorbidity, (3) examination of networks at rest, which overlooks disruption manifesting only when networks are challenged. Accordingly, the present study examined relationships between transdiagnostic dimensions of anxiety/depression and patterns of functional connectivity while goal maintenance was challenged. Participants (n = 179, unselected community members and undergraduates selected to be high/low on anxiety/depression) performed a task in which goal maintenance was challenged (color-word Stroop) while fMRI data were collected. Analyses examined moderation by anxiety/depression of condition-dependent coupling between regions of dorsolateral prefrontal cortex (dlPFC) previously associated with approach and avoidance motivation and amygdala/orbitofrontal cortex (OFC). Anxious arousal was positively associated with amygdala↔right dlPFC coupling. Depression was positively associated with OFC↔right dlPFC coupling and negatively associated with OFC↔left dlPFC coupling. Findings advance the field toward an integrative model of the neural instantiation of anxiety/depression by identifying specific, distinct dysfunctions associated with anxiety and depression in networks important for maintaining approach and avoidance goals. Specifically, findings shed light on potential neural mechanisms involved in attentional biases in anxiety and valuation biases in depression and underscore the importance of examining transdiagnostic dimensions of anxiety/depression while networks are challenged. © 2014 Wiley Periodicals, Inc.

The development of functional network organization in early childhood and early adolescence: A resting-state fNIRS study.

PubMed

Cai, Lin; Dong, Qi; Niu, Haijing

2018-04-01

Early childhood (7-8 years old) and early adolescence (11-12 years old) constitute two landmark developmental stages that comprise considerable changes in neural cognition. However, very limited information from functional neuroimaging studies exists on the functional topological configuration of the human brain during specific developmental periods. In the present study, we utilized continuous resting-state functional near-infrared spectroscopy (rs-fNIRS) imaging data to examine topological changes in network organization during development from early childhood and early adolescence to adulthood. Our results showed that the properties of small-worldness and modularity were not significantly different across development, demonstrating the developmental maturity of important functional brain organization in early childhood. Intriguingly, young children had a significantly lower global efficiency than early adolescents and adults, which revealed that the integration of the distributed networks strengthens across the developmental stages underlying cognitive development. Moreover, local efficiency of young children and adolescents was significantly lower than that of adults, while there was no difference between these two younger groups. This finding demonstrated that functional segregation remained relatively steady from early childhood to early adolescence, and the brain in these developmental periods possesses no optimal network configuration. Furthermore, we found heterogeneous developmental patterns in the regional nodal properties in various brain regions, such as linear increased nodal properties in the frontal cortex, indicating increasing cognitive capacity over development. Collectively, our results demonstrated that significant topological changes in functional network organization occurred during these two critical developmental stages, and provided a novel insight into elucidating subtle changes in brain functional networks across development. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

A Modularity-Based Method Reveals Mixed Modules from Chemical-Gene Heterogeneous Network

PubMed Central

Song, Jianglong; Tang, Shihuan; Liu, Xi; Gao, Yibo; Yang, Hongjun; Lu, Peng

2015-01-01

For a multicomponent therapy, molecular network is essential to uncover its specific mode of action from a holistic perspective. The molecular system of a Traditional Chinese Medicine (TCM) formula can be represented by a 2-class heterogeneous network (2-HN), which typically includes chemical similarities, chemical-target interactions and gene interactions. An important premise of uncovering the molecular mechanism is to identify mixed modules from complex chemical-gene heterogeneous network of a TCM formula. We thus proposed a novel method (MixMod) based on mixed modularity to detect accurate mixed modules from 2-HNs. At first, we compared MixMod with Clauset-Newman-Moore algorithm (CNM), Markov Cluster algorithm (MCL), Infomap and Louvain on benchmark 2-HNs with known module structure. Results showed that MixMod was superior to other methods when 2-HNs had promiscuous module structure. Then these methods were tested on a real drug-target network, in which 88 disease clusters were regarded as real modules. MixMod could identify the most accurate mixed modules from the drug-target 2-HN (normalized mutual information 0.62 and classification accuracy 0.4524). In the end, MixMod was applied to the 2-HN of Buchang naoxintong capsule (BNC) and detected 49 mixed modules. By using enrichment analysis, we investigated five mixed modules that contained primary constituents of BNC intestinal absorption liquid. As a matter of fact, the findings of in vitro experiments using BNC intestinal absorption liquid were found to highly accord with previous analysis. Therefore, MixMod is an effective method to detect accurate mixed modules from chemical-gene heterogeneous networks and further uncover the molecular mechanism of multicomponent therapies, especially TCM formulae. PMID:25927435

Animal social networks as substrate for cultural behavioural diversity.

PubMed

Whitehead, Hal; Lusseau, David

2012-02-07

We used individual-based stochastic models to examine how social structure influences the diversity of socially learned behaviour within a non-human population. For continuous behavioural variables we modelled three forms of dyadic social learning, averaging the behavioural value of the two individuals, random transfer of information from one individual to the other, and directional transfer from the individual with highest behavioural value to the other. Learning had potential error. We also examined the transfer of categorical behaviour between individuals with random directionality and two forms of error, the adoption of a randomly chosen existing behavioural category or the innovation of a new type of behaviour. In populations without social structuring the diversity of culturally transmitted behaviour increased with learning error and population size. When the populations were structured socially either by making individuals members of permanent social units or by giving them overlapping ranges, behavioural diversity increased with network modularity under all scenarios, although the proportional increase varied considerably between continuous and categorical behaviour, with transmission mechanism, and population size. Although functions of the form e(c)¹(m)⁻(c)² + (c)³(Log(N)) predicted the mean increase in diversity with modularity (m) and population size (N), behavioural diversity could be highly unpredictable both between simulations with the same set of parameters, and within runs. Errors in social learning and social structuring generally promote behavioural diversity. Consequently, social learning may be considered to produce culture in populations whose social structure is sufficiently modular. Copyright © 2011 Elsevier Ltd. All rights reserved.

Community detection in complex networks by using membrane algorithm

NASA Astrophysics Data System (ADS)

Liu, Chuang; Fan, Linan; Liu, Zhou; Dai, Xiang; Xu, Jiamei; Chang, Baoren

Community detection in complex networks is a key problem of network analysis. In this paper, a new membrane algorithm is proposed to solve the community detection in complex networks. The proposed algorithm is based on membrane systems, which consists of objects, reaction rules, and a membrane structure. Each object represents a candidate partition of a complex network, and the quality of objects is evaluated according to network modularity. The reaction rules include evolutionary rules and communication rules. Evolutionary rules are responsible for improving the quality of objects, which employ the differential evolutionary algorithm to evolve objects. Communication rules implement the information exchanged among membranes. Finally, the proposed algorithm is evaluated on synthetic, real-world networks with real partitions known and the large-scaled networks with real partitions unknown. The experimental results indicate the superior performance of the proposed algorithm in comparison with other experimental algorithms.

Working Memory and Reasoning Benefit from Different Modes of Large-scale Brain Dynamics in Healthy Older Adults.

PubMed

Lebedev, Alexander V; Nilsson, Jonna; Lövdén, Martin

2018-07-01

Researchers have proposed that solving complex reasoning problems, a key indicator of fluid intelligence, involves the same cognitive processes as solving working memory tasks. This proposal is supported by an overlap of the functional brain activations associated with the two types of tasks and by high correlations between interindividual differences in performance. We replicated these findings in 53 older participants but also showed that solving reasoning and working memory problems benefits from different configurations of the functional connectome and that this dissimilarity increases with a higher difficulty load. Specifically, superior performance in a typical working memory paradigm ( n-back) was associated with upregulation of modularity (increased between-network segregation), whereas performance in the reasoning task was associated with effective downregulation of modularity. We also showed that working memory training promotes task-invariant increases in modularity. Because superior reasoning performance is associated with downregulation of modular dynamics, training may thus have fostered an inefficient way of solving the reasoning tasks. This could help explain why working memory training does little to promote complex reasoning performance. The study concludes that complex reasoning abilities cannot be reduced to working memory and suggests the need to reconsider the feasibility of using working memory training interventions to attempt to achieve effects that transfer to broader cognition.

Modeling languages for biochemical network simulation: reaction vs equation based approaches.

PubMed

Wiechert, Wolfgang; Noack, Stephan; Elsheikh, Atya

2010-01-01

Biochemical network modeling and simulation is an essential task in any systems biology project. The systems biology markup language (SBML) was established as a standardized model exchange language for mechanistic models. A specific strength of SBML is that numerous tools for formulating, processing, simulation and analysis of models are freely available. Interestingly, in the field of multidisciplinary simulation, the problem of model exchange between different simulation tools occurred much earlier. Several general modeling languages like Modelica have been developed in the 1990s. Modelica enables an equation based modular specification of arbitrary hierarchical differential algebraic equation models. Moreover, libraries for special application domains can be rapidly developed. This contribution compares the reaction based approach of SBML with the equation based approach of Modelica and explains the specific strengths of both tools. Several biological examples illustrating essential SBML and Modelica concepts are given. The chosen criteria for tool comparison are flexibility for constraint specification, different modeling flavors, hierarchical, modular and multidisciplinary modeling. Additionally, support for spatially distributed systems, event handling and network analysis features is discussed. As a major result it is shown that the choice of the modeling tool has a strong impact on the expressivity of the specified models but also strongly depends on the requirements of the application context.

An improved fault detection classification and location scheme based on wavelet transform and artificial neural network for six phase transmission line using single end data only.

PubMed

Koley, Ebha; Verma, Khushaboo; Ghosh, Subhojit

2015-01-01

Restrictions on right of way and increasing power demand has boosted development of six phase transmission. It offers a viable alternative for transmitting more power, without major modification in existing structure of three phase double circuit transmission system. Inspite of the advantages, low acceptance of six phase system is attributed to the unavailability of a proper protection scheme. The complexity arising from large number of possible faults in six phase lines makes the protection quite challenging. The proposed work presents a hybrid wavelet transform and modular artificial neural network based fault detector, classifier and locator for six phase lines using single end data only. The standard deviation of the approximate coefficients of voltage and current signals obtained using discrete wavelet transform are applied as input to the modular artificial neural network for fault classification and location. The proposed scheme has been tested for all 120 types of shunt faults with variation in location, fault resistance, fault inception angles. The variation in power system parameters viz. short circuit capacity of the source and its X/R ratio, voltage, frequency and CT saturation has also been investigated. The result confirms the effectiveness and reliability of the proposed protection scheme which makes it ideal for real time implementation.

Graph theoretic analysis of protein interaction networks of eukaryotes

NASA Astrophysics Data System (ADS)

Goh, K.-I.; Kahng, B.; Kim, D.

2005-11-01

Owing to the recent progress in high-throughput experimental techniques, the datasets of large-scale protein interactions of prototypical multicellular species, the nematode worm Caenorhabditis elegans and the fruit fly Drosophila melanogaster, have been assayed. The datasets are obtained mainly by using the yeast hybrid method, which contains false-positive and false-negative simultaneously. Accordingly, while it is desirable to test such datasets through further wet experiments, here we invoke recent developed network theory to test such high-throughput datasets in a simple way. Based on the fact that the key biological processes indispensable to maintaining life are conserved across eukaryotic species, and the comparison of structural properties of the protein interaction networks (PINs) of the two species with those of the yeast PIN, we find that while the worm and yeast PIN datasets exhibit similar structural properties, the current fly dataset, though most comprehensively screened ever, does not reflect generic structural properties correctly as it is. The modularity is suppressed and the connectivity correlation is lacking. Addition of interologs to the current fly dataset increases the modularity and enhances the occurrence of triangular motifs as well. The connectivity correlation function of the fly, however, remains distinct under such interolog additions, for which we present a possible scenario through an in silico modeling.

Statistical Physics of Cascading Failures in Complex Networks

NASA Astrophysics Data System (ADS)

Panduranga, Nagendra Kumar

Systems such as the power grid, world wide web (WWW), and internet are categorized as complex systems because of the presence of a large number of interacting elements. For example, the WWW is estimated to have a billion webpages and understanding the dynamics of such a large number of individual agents (whose individual interactions might not be fully known) is a challenging task. Complex network representations of these systems have proved to be of great utility. Statistical physics is the study of emergence of macroscopic properties of systems from the characteristics of the interactions between individual molecules. Hence, statistical physics of complex networks has been an effective approach to study these systems. In this dissertation, I have used statistical physics to study two distinct phenomena in complex systems: i) Cascading failures and ii) Shortest paths in complex networks. Understanding cascading failures is considered to be one of the "holy grails" in the study of complex systems such as the power grid, transportation networks, and economic systems. Studying failures of these systems as percolation on complex networks has proved to be insightful. Previously, cascading failures have been studied extensively using two different models: k-core percolation and interdependent networks. The first part of this work combines the two models into a general model, solves it analytically, and validates the theoretical predictions through extensive computer simulations. The phase diagram of the percolation transition has been systematically studied as one varies the average local k-core threshold and the coupling between networks. The phase diagram of the combined processes is very rich and includes novel features that do not appear in the models which study each of the processes separately. For example, the phase diagram consists of first- and second-order transition regions separated by two tricritical lines that merge together and enclose a two-stage transition region. In the two-stage transition, the size of the giant component undergoes a first-order jump at a certain occupation probability followed by a continuous second-order transition at a smaller occupation probability. Furthermore, at certain fixed interdependencies, the percolation transition cycles from first-order to second-order to two-stage to first-order as the k-core threshold is increased. We setup the analytical equations describing the phase boundaries of the two-stage transition region and we derive the critical exponents for each type of transition. Understanding the shortest paths between individual elements in systems like communication networks and social media networks is important in the study of information cascades in these systems. Often, large heterogeneity can be present in the connections between nodes in these networks. Certain sets of nodes can be more highly connected among themselves than with the nodes from other sets. These sets of nodes are often referred to as 'communities'. The second part of this work studies the effect of the presence of communities on the distribution of shortest paths in a network using a modular Erdős-Renyi network model. In this model, the number of communities and the degree of modularity of the network can be tuned using the parameters of the model. We find that the model reaches a percolation threshold while tuning the degree of modularity of the network and the distribution of the shortest paths in the network can be used as an indicator of how the communities are connected.

Development of Improved Models, Stochasticity, and Frameworks for the MIT Extensible Air Network Simulation

NASA Technical Reports Server (NTRS)

Clarke, John-Paul

2004-01-01

MEANS, the MIT Extensible Air Network Simulation, was created in February of 2001, and has been developed with support from NASA Ames since August of 2001. MEANS is a simulation tool which is designed to maximize fidelity without requiring data of such a low level as to preclude easy examination of alternative scenarios. To this end, MEANS is structured in a modular fashion to allow more detailed components to be brought in when desired, and left out when they would only be an impediment. Traditionally, one of the difficulties with high-fidelity models is that they require a level of detail in their data that is difficult to obtain. For analysis of past scenarios, the required data may not have been collected, or may be considered proprietary and thus difficult for independent researchers to obtain. For hypothetical scenarios, generation of the data is sufficiently difficult to be a task in and of itself. Often, simulations designed by a researcher will model exactly one element of the problem well and in detail, while assuming away other parts of the problem which are not of interest or for which data is not available. While these models are useful for working with the task at hand, they are very often not applicable to future problems. The MEAN Simulation attempts to address these problems by using a modular design which provides components of varying fidelity for each aspect of the simulation. This allows for the most accurate model for which data is available to be used. It also provides for easy analysis of sensitivity to data accuracy. This can be particularly useful in the case where accurate data is available for some subset of the situations that are to be considered. Furthermore, the ability to use the same model while examining effects on different parts of a system reduces the time spent learning the simulation, and provides for easier comparisons between changes to different parts of the system.

Abasy Atlas: a comprehensive inventory of systems, global network properties and systems-level elements across bacteria

PubMed Central

Ibarra-Arellano, Miguel A.; Campos-González, Adrián I.; Treviño-Quintanilla, Luis G.; Tauch, Andreas; Freyre-González, Julio A.

2016-01-01

The availability of databases electronically encoding curated regulatory networks and of high-throughput technologies and methods to discover regulatory interactions provides an invaluable source of data to understand the principles underpinning the organization and evolution of these networks responsible for cellular regulation. Nevertheless, data on these sources never goes beyond the regulon level despite the fact that regulatory networks are complex hierarchical-modular structures still challenging our understanding. This brings the necessity for an inventory of systems across a large range of organisms, a key step to rendering feasible comparative systems biology approaches. In this work, we take the first step towards a global understanding of the regulatory networks organization by making a cartography of the functional architectures of diverse bacteria. Abasy (Across-bacteria systems) Atlas provides a comprehensive inventory of annotated functional systems, global network properties and systems-level elements (global regulators, modular genes shaping functional systems, basal machinery genes and intermodular genes) predicted by the natural decomposition approach for reconstructed and meta-curated regulatory networks across a large range of bacteria, including pathogenically and biotechnologically relevant organisms. The meta-curation of regulatory datasets provides the most complete and reliable set of regulatory interactions currently available, which can even be projected into subsets by considering the force or weight of evidence supporting them or the systems that they belong to. Besides, Abasy Atlas provides data enabling large-scale comparative systems biology studies aimed at understanding the common principles and particular lifestyle adaptions of systems across bacteria. Abasy Atlas contains systems and system-level elements for 50 regulatory networks comprising 78 649 regulatory interactions covering 42 bacteria in nine taxa, containing 3708 regulons and 1776 systems. All this brings together a large corpus of data that will surely inspire studies to generate hypothesis regarding the principles governing the evolution and organization of systems and the functional architectures controlling them. Database URL: http://abasy.ccg.unam.mx PMID:27242034

Refaunation and the reinstatement of the seed-dispersal function in Gorongosa National Park.

PubMed

Correia, Marta; Timóteo, Sérgio; Rodríguez-Echeverría, Susana; Mazars-Simon, Alban; Heleno, Ruben

2017-02-01

Large animals are important seed dispersers; however, they tend to be under a high extinction risk worldwide. There is compelling evidence that the global biodiversity crisis is leading to the deterioration of several ecosystem functions, but there is virtually no information on how large-scale refaunation efforts can reinstate seed dispersal. We evaluated the effectiveness of a 62-km 2 wildlife sanctuary, which was established to recover populations of large mammals in Gorongosa National Park (Mozambique), in restoring seed dispersal. We collected animal scats during the dry season of 2014 (June-August) along 5 transects inside and 5 transects outside the sanctuary fence (50 km total) with the same type of plant community, identified animal and plant species in the transects, and quantified the number of seeds in each scat. Based on these data, we built bipartite networks and calculated network and species-level descriptor values, and we compared data collected inside and outside the sanctuary. There were more scats (268 vs. 207) and more scats containing seeds (132 vs. 94) inside than outside the sanctuary. The number of mammal dispersers was also higher inside (17) than outside the sanctuary (11). Similarly, more seeds (2413 vs. 2124) and plant species (33 vs. 26) were dispersed inside than outside the sanctuary. Overall, the seed-dispersal network was less specialized (0.38 vs. 0.44) and there was a greater overlap (0.16 vs. 0.07) inside than outside the sanctuary. Both networks were significantly modular and antinested. The high number and richness of seeds dispersed inside the sanctuary was explained mostly by a higher abundance of dispersers rather than by disperser identity. Our results suggest conservation efforts aimed at recovering populations of large mammals are helping to reestablish not only target mammal species but also their functional roles as seed dispersers in the ecosystem. © 2016 Society for Conservation Biology.

International migration network: Topology and modeling

NASA Astrophysics Data System (ADS)

Fagiolo, Giorgio; Mastrorillo, Marina

2013-07-01

This paper studies international migration from a complex-network perspective. We define the international migration network (IMN) as the weighted-directed graph where nodes are world countries and links account for the stock of migrants originated in a given country and living in another country at a given point in time. We characterize the binary and weighted architecture of the network and its evolution over time in the period 1960-2000. We find that the IMN is organized around a modular structure with a small-world binary pattern displaying disassortativity and high clustering, with power-law distributed weighted-network statistics. We also show that a parsimonious gravity model of migration can account for most of observed IMN topological structure. Overall, our results suggest that socioeconomic, geographical, and political factors are more important than local-network properties in shaping the structure of the IMN.

International migration network: topology and modeling.

PubMed

Fagiolo, Giorgio; Mastrorillo, Marina

2013-07-01

This paper studies international migration from a complex-network perspective. We define the international migration network (IMN) as the weighted-directed graph where nodes are world countries and links account for the stock of migrants originated in a given country and living in another country at a given point in time. We characterize the binary and weighted architecture of the network and its evolution over time in the period 1960-2000. We find that the IMN is organized around a modular structure with a small-world binary pattern displaying disassortativity and high clustering, with power-law distributed weighted-network statistics. We also show that a parsimonious gravity model of migration can account for most of observed IMN topological structure. Overall, our results suggest that socioeconomic, geographical, and political factors are more important than local-network properties in shaping the structure of the IMN.

SkyNet: A Modular Nuclear Reaction Network Library

NASA Astrophysics Data System (ADS)

Lippuner, Jonas; Roberts, Luke F.

2017-12-01

Almost all of the elements heavier than hydrogen that are present in our solar system were produced by nuclear burning processes either in the early universe or at some point in the life cycle of stars. In all of these environments, there are dozens to thousands of nuclear species that interact with each other to produce successively heavier elements. In this paper, we present SkyNet, a new general-purpose nuclear reaction network that evolves the abundances of nuclear species under the influence of nuclear reactions. SkyNet can be used to compute the nucleosynthesis evolution in all astrophysical scenarios where nucleosynthesis occurs. SkyNet is free and open source, and aims to be easy to use and flexible. Any list of isotopes can be evolved, and SkyNet supports different types of nuclear reactions. SkyNet is modular so that new or existing physics, like nuclear reactions or equations of state, can easily be added or modified. Here, we present in detail the physics implemented in SkyNet with a focus on a self-consistent transition to and from nuclear statistical equilibrium to non-equilibrium nuclear burning, our implementation of electron screening, and coupling of the network to an equation of state. We also present comprehensive code tests and comparisons with existing nuclear reaction networks. We find that SkyNet agrees with published results and other codes to an accuracy of a few percent. Discrepancies, where they exist, can be traced to differences in the physics implementations.

pyNBS: A Python implementation for network-based stratification of tumor mutations.

PubMed

Huang, Justin K; Jia, Tongqiu; Carlin, Daniel E; Ideker, Trey

2018-03-28

We present pyNBS: a modularized Python 2.7 implementation of the network-based stratification (NBS) algorithm for stratifying tumor somatic mutation profiles into molecularly and clinically relevant subtypes. In addition to release of the software, we benchmark its key parameters and provide a compact cancer reference network that increases the significance of tumor stratification using the NBS algorithm. The structure of the code exposes key steps of the algorithm to foster further collaborative development. The package, along with examples and data, can be downloaded and installed from the URL http://www.github.com/huangger/pyNBS/. jkh013@ucsd.edu.

Experimental nonlocality-based network diagnostics of multipartite entangled states.

PubMed

Ciampini, Mario A; Vigliar, Caterina; Cimini, Valeria; Paesani, Stefano; Sciarrino, Fabio; Crespi, Andrea; Corrielli, Giacomo; Osellame, Roberto; Mataloni, Paolo; Paternostro, Mauro; Barbieri, Marco

2017-12-07

We introduce a novel diagnostic scheme for multipartite networks of entangled particles, aimed at assessing the quality of the gates used for the engineering of their state. Using the information gathered from a set of suitably chosen multiparticle Bell tests, we identify conditions bounding the quality of the entangled bonds among the elements of a register. We illustrate the effectiveness of our proposal by characterizing a quantum resource engineered combining two-photon hyperentanglement and photonic-chip technology. Our approach opens up future studies on medium-sized networks due to the intrinsically modular nature of cluster states, and paves the way to section-by-section analysis of larger photonics resources.

Parameterized centrality metric for network analysis

NASA Astrophysics Data System (ADS)

Ghosh, Rumi; Lerman, Kristina

2011-06-01

A variety of metrics have been proposed to measure the relative importance of nodes in a network. One of these, alpha-centrality [P. Bonacich, Am. J. Sociol.0002-960210.1086/228631 92, 1170 (1987)], measures the number of attenuated paths that exist between nodes. We introduce a normalized version of this metric and use it to study network structure, for example, to rank nodes and find community structure of the network. Specifically, we extend the modularity-maximization method for community detection to use this metric as the measure of node connectivity. Normalized alpha-centrality is a powerful tool for network analysis, since it contains a tunable parameter that sets the length scale of interactions. Studying how rankings and discovered communities change when this parameter is varied allows us to identify locally and globally important nodes and structures. We apply the proposed metric to several benchmark networks and show that it leads to better insights into network structure than alternative metrics.

Computing the shape of brain networks using graph filtration and Gromov-Hausdorff metric.

PubMed

Lee, Hyekyoung; Chung, Moo K; Kang, Hyejin; Kim, Boong-Nyun; Lee, Dong Soo

2011-01-01

The difference between networks has been often assessed by the difference of global topological measures such as the clustering coefficient, degree distribution and modularity. In this paper, we introduce a new framework for measuring the network difference using the Gromov-Hausdorff (GH) distance, which is often used in shape analysis. In order to apply the GH distance, we define the shape of the brain network by piecing together the patches of locally connected nearest neighbors using the graph filtration. The shape of the network is then transformed to an algebraic form called the single linkage matrix. The single linkage matrix is subsequently used in measuring network differences using the GH distance. As an illustration, we apply the proposed framework to compare the FDG-PET based functional brain networks out of 24 attention deficit hyperactivity disorder (ADHD) children, 26 autism spectrum disorder (ASD) children and 11 pediatric control subjects.

MoniQA: a general approach to monitor quality assurance

NASA Astrophysics Data System (ADS)

Jacobs, J.; Deprez, T.; Marchal, G.; Bosmans, H.

2006-03-01

MoniQA ("Monitor Quality Assurance") is a new, non-commercial, independent quality assurance software application developed in our medical physics team. It is a complete Java TM - based modular environment for the evaluation of radiological viewing devices and it thus fits in the global quality assurance network of our (film less) radiology department. The purpose of the software tool is to guide the medical physicist through an acceptance protocol and the radiologist through a constancy check protocol by presentation of the necessary test patterns and by automated data collection. Data are then sent to a central management system for further analysis. At the moment more than 55 patterns have been implemented, which can be grouped in schemes to implement protocols (i.e. AAPMtg18, DIN and EUREF). Some test patterns are dynamically created and 'drawn' on the viewing device with random parameters as is the case in a recently proposed new pattern for constancy testing. The software is installed on 35 diagnostic stations (70 monitors) in a film less radiology department. Learning time was very limited. A constancy check -with the new pattern that assesses luminance decrease, resolution problems and geometric distortion- takes only 2 minutes and 28 seconds per monitor. The modular approach of the software allows the evaluation of new or emerging test patterns. We will report on the software and its usability: practicality of the constancy check tests in our hospital and on the results from acceptance tests of viewing stations for digital mammography.

Fingerprinting Software Defined Networks and Controllers

DTIC Science & Technology

2015-03-01

24 2.5.3 Intrusion Prevention System with SDN . . . . . . . . . . . . . . . 25 2.5.4 Modular Security Services...Control Message Protocol IDS Intrusion Detection System IPS Intrusion Prevention System ISP Internet Service Provider LLDP Link Layer Discovery Protocol...layer functions (e.g., web proxies, firewalls, intrusion detection/prevention, load balancers, etc.). The increase in switch capabilities combined

A Public/Private Sector Partnership for Adult Literacy Education in a Rural Environment. Final Performance Report.

ERIC Educational Resources Information Center

McKenna, Michael C.

The Job Network Center of Georgia Southern College provided assistance to the Grinnell Corporation in a partnership agreement addressing illiteracy within Grinnell's work force. A self-contained, modularized curriculum was used to facilitate monitoring of participants' progress and compensating employees for achievement. Other businesses were…

A Hybrid TDMA/CSMA-Based Wireless Sensor and Data Transmission Network for ORS Intra-Microsatellite Applications

PubMed Central

Wang, Long; Liu, Yong; Yin, Zengshan

2018-01-01

To achieve launch-on-demand for Operationally Responsive Space (ORS) missions, in this article, an intra-satellite wireless network (ISWN) is presented. It provides a wireless and modularized scheme for intra-spacecraft sensing and data buses. By removing the wired data bus, the commercial off-the-shelf (COTS) based wireless modular architecture will reduce both the volume and weight of the satellite platform, thus achieving rapid design and cost savings in development and launching. Based on the on-orbit data demand analysis, a hybrid time division multiple access/carrier sense multiple access (TDMA/CSMA) protocol is proposed. It includes an improved clear channel assessment (CCA) mechanism and a traffic adaptive slot allocation method. To analyze the access process, a Markov model is constructed. Then a detailed calculation is given in which the unsaturated cases are considered. Through simulations, the proposed protocol is proved to commendably satisfy the demands and performs better than existing schemes. It helps to build a full-wireless satellite instead of the current wired ones, and will contribute to provide dynamic space capabilities for ORS missions. PMID:29757243

A multi-period capacitated school location problem with modular equipment and closest assignment considerations

NASA Astrophysics Data System (ADS)

Delmelle, Eric M.; Thill, Jean-Claude; Peeters, Dominique; Thomas, Isabelle

2014-07-01

In rapidly growing urban areas, it is deemed vital to expand (or contract) an existing network of public facilities to meet anticipated changes in the level of demand. We present a multi-period capacitated median model for school network facility location planning that minimizes transportation costs, while functional costs are subject to a budget constraint. The proposed Vintage Flexible Capacitated Location Problem (ViFCLP) has the flexibility to account for a minimum school-age closing requirement, while the maximum capacity of each school can be adjusted by the addition of modular units. Non-closest assignments are controlled by the introduction of a parameter penalizing excess travel. The applicability of the ViFCLP is illustrated on a large US school system (Charlotte-Mecklenburg, North Carolina) where high school demand is expected to grow faster with distance to the city center. Higher school capacities and greater penalty on travel impedance parameter reduce the number of non-closest assignments. The proposed model is beneficial to policy makers seeking to improve the provision and efficiency of public services over a multi-period planning horizon.

A Hybrid TDMA/CSMA-Based Wireless Sensor and Data Transmission Network for ORS Intra-Microsatellite Applications.

PubMed

Wang, Long; Liu, Yong; Yin, Zengshan

2018-05-12

To achieve launch-on-demand for Operationally Responsive Space (ORS) missions, in this article, an intra-satellite wireless network (ISWN) is presented. It provides a wireless and modularized scheme for intra-spacecraft sensing and data buses. By removing the wired data bus, the commercial off-the-shelf (COTS) based wireless modular architecture will reduce both the volume and weight of the satellite platform, thus achieving rapid design and cost savings in development and launching. Based on the on-orbit data demand analysis, a hybrid time division multiple access/carrier sense multiple access (TDMA/CSMA) protocol is proposed. It includes an improved clear channel assessment (CCA) mechanism and a traffic adaptive slot allocation method. To analyze the access process, a Markov model is constructed. Then a detailed calculation is given in which the unsaturated cases are considered. Through simulations, the proposed protocol is proved to commendably satisfy the demands and performs better than existing schemes. It helps to build a full-wireless satellite instead of the current wired ones, and will contribute to provide dynamic space capabilities for ORS missions.

Redefining the modular organization of the core Mediator complex.

PubMed

Wang, Xuejuan; Sun, Qianqian; Ding, Zhenrui; Ji, Jinhua; Wang, Jianye; Kong, Xiao; Yang, Jianghong; Cai, Gang

2014-07-01

The Mediator complex plays an essential role in the regulation of eukaryotic transcription. The Saccharomyces cerevisiae core Mediator comprises 21 subunits, which are organized into Head, Middle and Tail modules. Previously, the Head module was assigned to a distinct dense domain at the base, and the Middle and Tail modules were identified to form a tight structure above the Head module, which apparently contradicted findings from many biochemical and functional studies. Here, we compared the structures of the core Mediator and its subcomplexes, especially the first 3D structure of the Head + Middle modules, which permitted an unambiguous assignment of the three modules. Furthermore, nanogold labeling pinpointing four Mediator subunits from different modules conclusively validated the modular assignment, in which the Head and Middle modules fold back on one another and form the upper portion of the core Mediator, while the Tail module forms a distinct dense domain at the base. The new modular model of the core Mediator has reconciled the previous inconsistencies between the structurally and functionally defined Mediator modules. Collectively, these analyses completely redefine the modular organization of the core Mediator, which allow us to integrate the structural and functional information into a coherent mechanism for the Mediator's modularity and regulation in transcription initiation.

Redefining the modular organization of the core Mediator complex

PubMed Central

Wang, Xuejuan; Sun, Qianqian; Ding, Zhenrui; Ji, Jinhua; Wang, Jianye; Kong, Xiao; Yang, Jianghong; Cai, Gang

2014-01-01

The Mediator complex plays an essential role in the regulation of eukaryotic transcription. The Saccharomyces cerevisiae core Mediator comprises 21 subunits, which are organized into Head, Middle and Tail modules. Previously, the Head module was assigned to a distinct dense domain at the base, and the Middle and Tail modules were identified to form a tight structure above the Head module, which apparently contradicted findings from many biochemical and functional studies. Here, we compared the structures of the core Mediator and its subcomplexes, especially the first 3D structure of the Head + Middle modules, which permitted an unambiguous assignment of the three modules. Furthermore, nanogold labeling pinpointing four Mediator subunits from different modules conclusively validated the modular assignment, in which the Head and Middle modules fold back on one another and form the upper portion of the core Mediator, while the Tail module forms a distinct dense domain at the base. The new modular model of the core Mediator has reconciled the previous inconsistencies between the structurally and functionally defined Mediator modules. Collectively, these analyses completely redefine the modular organization of the core Mediator, which allow us to integrate the structural and functional information into a coherent mechanism for the Mediator's modularity and regulation in transcription initiation. PMID:24810298

Factor analysis of regional brain activation in bipolar and healthy individuals reveals a consistent modular structure.

PubMed

Fleck, David E; Welge, Jeffrey A; Eliassen, James C; Adler, Caleb M; DelBello, Melissa P; Strakowski, Stephen M

2018-07-01

The neurophysiological substrates of cognition and emotion, as seen with fMRI, are generally explained using modular structures. The present study was designed to probe the modular structure of cognitive-emotional processing in bipolar and healthy individuals using factor analysis and compare the results with current conceptions of the neurophysiology of bipolar disorder. Exploratory factor analysis was used to assess patterns of covariation among brain regions-of-interest activated during the Continuous Performance Task with Emotional and Neutral Distractors in healthy and bipolar individuals without a priori constraints on the number or composition of latent factors. Results indicated a common cognitive-emotional network consisting of prefrontal, medial temporal, limbic, parietal, anterior cingulate and posterior cingulate modules. However, reduced brain activation to emotional stimuli in the frontal, medial temporal and limbic modules was apparent in the bipolar relative to the healthy group, potentially accounting for emotional dysregulation in bipolar disorder. This study is limited by a relatively small sample size recruited at a single site. The results have yet to be validated on a larger independent sample. Although the modular structure of cognitive-emotional processing is similar in bipolar and healthy individuals, activation in response to emotional/neutral cues varies. These findings are not only consistent with recent conceptions of mood regulation in bipolar disorder, but also suggest that regional activation can be considered within tighter modular structures without compromising data interpretation. This demonstration may serve as a template for data reduction in future region-of-interest analyses to increase statistical power. Copyright © 2018 Elsevier B.V. All rights reserved.

Knowledge network model of the energy consumption in discrete manufacturing system

NASA Astrophysics Data System (ADS)

Xu, Binzi; Wang, Yan; Ji, Zhicheng

2017-07-01

Discrete manufacturing system generates a large amount of data and information because of the development of information technology. Hence, a management mechanism is urgently required. In order to incorporate knowledge generated from manufacturing data and production experience, a knowledge network model of the energy consumption in the discrete manufacturing system was put forward based on knowledge network theory and multi-granularity modular ontology technology. This model could provide a standard representation for concepts, terms and their relationships, which could be understood by both human and computer. Besides, the formal description of energy consumption knowledge elements (ECKEs) in the knowledge network was also given. Finally, an application example was used to verify the feasibility of the proposed method.