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

Science.gov (United States)

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

2018-05-01

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

Modification and co-option of leaf developmental programs for the acquisition of flat structures in monocots: Unifacial leaves in Juncus and cladodes in Asparagus

Directory of Open Access Journals (Sweden)

Hokuto eNakayama

2013-07-01

Full Text Available It has been suggested that modification and co-option of existing gene regulatory networks (GRNs play an important role in the morphological diversity. In plants, leaf development is one of active research areas, and the basic GRN for leaf development is beginning to be understood. Moreover, leaves show wide variation in their form, and some of this variation is thought to be the result of adaptation. Thus, leaves and leaf-like organs are an emerging and interesting model to reveal how existing GRNs give rise to novel forms and architectures during evolution. In this review, we highlight recent findings in Evo-Devo studies, especially on Juncus unifacial leaves, which are composed of lamina with abaxialized identities, and Asparagus cladodes, which are leaf-like organs at the axils of scale leaves. Based on these studies, we discuss how flat structures have evolved and morphologically diversified in shoot systems of monocot species, focusing on the modification and co-option of GRN for leaf development.

How do leaf veins influence the worldwide leaf economic spectrum? Review and synthesis.

Science.gov (United States)

Sack, Lawren; Scoffoni, Christine; John, Grace P; Poorter, Hendrik; Mason, Chase M; Mendez-Alonzo, Rodrigo; Donovan, Lisa A

2013-10-01

Leaf vein traits are implicated in the determination of gas exchange rates and plant performance. These traits are increasingly considered as causal factors affecting the 'leaf economic spectrum' (LES), which includes the light-saturated rate of photosynthesis, dark respiration, foliar nitrogen concentration, leaf dry mass per area (LMA) and leaf longevity. This article reviews the support for two contrasting hypotheses regarding a key vein trait, vein length per unit leaf area (VLA). Recently, Blonder et al. (2011, 2013) proposed that vein traits, including VLA, can be described as the 'origin' of the LES by structurally determining LMA and leaf thickness, and thereby vein traits would predict LES traits according to specific equations. Careful re-examination of leaf anatomy, published datasets, and a newly compiled global database for diverse species did not support the 'vein origin' hypothesis, and moreover showed that the apparent power of those equations to predict LES traits arose from circularity. This review provides a 'flux trait network' hypothesis for the effects of vein traits on the LES and on plant performance, based on a synthesis of the previous literature. According to this hypothesis, VLA, while virtually independent of LMA, strongly influences hydraulic conductance, and thus stomatal conductance and photosynthetic rate. We also review (i) the specific physiological roles of VLA; (ii) the role of leaf major veins in influencing LES traits; and (iii) the role of VLA in determining photosynthetic rate per leaf dry mass and plant relative growth rate. A clear understanding of leaf vein traits provides a new perspective on plant function independently of the LES and can enhance the ability to explain and predict whole plant performance under dynamic conditions, with applications towards breeding improved crop varieties.

Leaf structural traits of tropical woody species resistant to cement dust.

Science.gov (United States)

Siqueira-Silva, Advanio Inácio; Pereira, Eduardo Gusmão; Modolo, Luzia Valentina; Paiva, Elder Antonio Sousa

2016-08-01

Cement industries located nearby limestone outcrops in Brazil have contributed to the coating of cement dust over native plant species. However, little is known about the extent of the response of tropical woody plants to such environmental pollutant particularly during the first stages of plant development and establishment. This work focused on the investigation of possible alterations in leaf structural and ultrastructural traits of 5-month-old Guazuma ulmifolia Lam. (Malvaceae), 6-month-old Myracrodruon urundeuva Allemão (Anacardiaceae), and 9-month-old Trichilia hirta L. (Meliaceae) challenged superficially with cement dust during new leaf development. Leaf surface of plants, the soil or both (leaf plus soil), were treated (or not) for 60 days, under controlled conditions, with cement dust at 2.5 or 5.0 mg cm(-2). After exposure, no significant structural changes were observed in plant leaves. Also, no plant death was recorded by the end of the experiment. There was also some evidence of localized leaf necrosis in G. ulmifolia and T. hirta, leaf curling in M. urundeuva and T. hirta, and bulges formation on epidermal surface of T. hirta, after cement dust contact with plant shoots. All species studied exhibited stomata obliteration while T. hirta, in particular, presented early leaf abscission, changes in cellular relief, and organization and content of midrib cells. No significant ultrastructural alterations were detected under the experimental conditions studied. Indeed, mesophyll cells presented plastids with intact membrane systems. The high plant survival rates, together with mild morphoanatomic traits alterations in leaves, indicate that G. ulmifolia is more resistant to cement dust pollutant, followed by M. urundeuva and T. hirta. Thus, the three plant species are promising for being used to revegetate areas impacted by cement industries activities.

Maize YABBY genes drooping leaf1 and drooping leaf2 affect agronomic traits by regulating leaf architecture

Science.gov (United States)

Leaf architectural traits, such as length, width and angle, directly influence canopy structure and light penetration, photosynthate production and overall yield. We discovered and characterized a maize (Zea mays) mutant with aberrant leaf architecture we named drooping leaf1 (drl1), as leaf blades ...

Leaf Wetness Evaluation Using Artificial Neural Network for Improving Apple Scab Fight

Directory of Open Access Journals (Sweden)

Alessandro Stella

2017-06-01

Full Text Available Precision agriculture represents a promising technological trend in which governments and local authorities are increasingly investing. In particular, optimising the use of pesticides and having localised models of plant disease are the most important goals for the farmers of the future. The Trentino province in Italy is known as a strong national producer of apples. Apple production has to face many issues, however, among which is apple scab. This disease depends mainly on leaf wetness data typically acquired by fixed sensors. Based on the exploitation of artificial neural networks, this work aims to spatially extend the measurements of such sensors across uncovered areas (areas deprived of sensors. Achieved results have been validated comparing the apple scab risk of the same zone using either real leaf wetness data and estimated data. Thanks to the proposed method, it is possible to get the most relevant parameter of apple scab risk in places where no leaf wetness sensor is available. Moreover, our method permits having a specific risk evaluation of apple scab infection for each orchard, leading to an optimization of the use of chemical pesticides.

Nanofibers-based nanoweb promise superhydrophobic polyaniline: from star-shaped to leaf-shaped structures.

Science.gov (United States)

Fan, Haosen; Wang, Hao; Guo, Jing; Zhao, Ning; Xu, Jian

2013-11-01

Star-shaped and leaf-shaped polyaniline (PANI) hierarchical structures with interlaced nanofibers on the surface were successfully prepared by chemical polymerization of aniline in the presence of lithium triflate (LT). Chemical structure and composition of the star-like PANI obtained were characterized by FTIR and UV-vis spectra. PANI 2D architectures can be tailored from star-shaped to leaf-shaped structures by change the concentration of LT. The synthesized star-like and leaf-like polyaniline show good superhydrophobicity with water contact angles of both above 150° due to the combination of the rough nanoweb structure and the low surface tension of fluorinated chain of dopant. This method is a facile and applicable strategy for a large-scale fabrication of 2D PANI micro/nanostructures. Many potential applications such as self-cleaning and antifouling coating can be expected based on the superhydrophobic PANI micro/nanostructures. Crown Copyright © 2013. Published by Elsevier Inc. All rights reserved.

Genetic diversity and population structure of leaf-nosed bat ...

African Journals Online (AJOL)

Genetic variation and population structure of the leaf-nosed bat Hipposideros speoris were estimated using 16S rRNA sequence and microsatellite analysis. Twenty seven distinct mitochondrial haplotypes were identified from 186 individuals, sampled from eleven populations. FST test revealed significant variations ...

Lotus-leaf-like structured chitosan–polyvinyl pyrrolidone films as an anti-adhesion barrier

Energy Technology Data Exchange (ETDEWEB)

Lim, Jin Ik; Kang, Min Ji; Lee, Woo-Kul, E-mail: leewo@dankook.ac.kr

2014-11-30

Highlights: • Improved mechanical properties by hydrogen bond between chitosan and PVP chains. • Improved anti-adhesion effect by lotus-leaf-like structured chitosan–PVP (L-chitosan–PVP) film. • L-Chitosan–PVP film as a blood/tissue anti-adhesion barrier for post-surgical treatment. - Abstract: For postsurgical anti-adhesion barrier applications, lotus-leaf-like structured chitosan–PVP films were prepared using a solution casting method with dodecyltrichloro-immobilized SiO{sub 2} nanoparticles. We evaluated whether the lotus-leaf-like structured chitosan–PVP films (L-chitosan–PVP) could be applied as postsurgical anti-adhesion barriers. A recovery test using a tensile strength testing machine and measurement of crystallinity using X-ray diffraction indicated that films with 75% PVP were the optimal composition of the chitosan–PVP films. Also, dodecyltrichloro-immobilized SiO{sub 2} nanoparticles were synthesized and sprayed on the film after pretreatment with the instant bio-glue. Analysis of cell adhesion, proliferation, and anti-thrombus efficiency were performed via a WST assay, field emission scanning electron microscopy, and hemacytometry. The contact angle with the lotus-leaf-like surface was of approximately 150°. Furthermore, the L-chitosan–PVP film yielded a lower cell and platelet adhesion rate (around less than 4%) than that yielded by the untreated film. These results indicate that the lotus-leaf-like structure has a unique property and that this novel L-chitosan–PVP film can be applied as a blood/tissue-compatible, biodegradable material for implantable medical devices that need an anti-adhesion barrier.

Lotus-leaf-like structured chitosan–polyvinyl pyrrolidone films as an anti-adhesion barrier

International Nuclear Information System (INIS)

Lim, Jin Ik; Kang, Min Ji; Lee, Woo-Kul

2014-01-01

Highlights: • Improved mechanical properties by hydrogen bond between chitosan and PVP chains. • Improved anti-adhesion effect by lotus-leaf-like structured chitosan–PVP (L-chitosan–PVP) film. • L-Chitosan–PVP film as a blood/tissue anti-adhesion barrier for post-surgical treatment. - Abstract: For postsurgical anti-adhesion barrier applications, lotus-leaf-like structured chitosan–PVP films were prepared using a solution casting method with dodecyltrichloro-immobilized SiO 2 nanoparticles. We evaluated whether the lotus-leaf-like structured chitosan–PVP films (L-chitosan–PVP) could be applied as postsurgical anti-adhesion barriers. A recovery test using a tensile strength testing machine and measurement of crystallinity using X-ray diffraction indicated that films with 75% PVP were the optimal composition of the chitosan–PVP films. Also, dodecyltrichloro-immobilized SiO 2 nanoparticles were synthesized and sprayed on the film after pretreatment with the instant bio-glue. Analysis of cell adhesion, proliferation, and anti-thrombus efficiency were performed via a WST assay, field emission scanning electron microscopy, and hemacytometry. The contact angle with the lotus-leaf-like surface was of approximately 150°. Furthermore, the L-chitosan–PVP film yielded a lower cell and platelet adhesion rate (around less than 4%) than that yielded by the untreated film. These results indicate that the lotus-leaf-like structure has a unique property and that this novel L-chitosan–PVP film can be applied as a blood/tissue-compatible, biodegradable material for implantable medical devices that need an anti-adhesion barrier

Influence of Vegetation Structure on Lidar-derived Canopy Height and Fractional Cover in Forested Riparian Buffers During Leaf-Off and Leaf-On Conditions

Science.gov (United States)

Wasser, Leah; Day, Rick; Chasmer, Laura; Taylor, Alan

2013-01-01

Estimates of canopy height (H) and fractional canopy cover (FC) derived from lidar data collected during leaf-on and leaf-off conditions are compared with field measurements from 80 forested riparian buffer plots. The purpose is to determine if existing lidar data flown in leaf-off conditions for applications such as terrain mapping can effectively estimate forested riparian buffer H and FC within a range of riparian vegetation types. Results illustrate that: 1) leaf-off and leaf-on lidar percentile estimates are similar to measured heights in all plots except those dominated by deciduous compound-leaved trees where lidar underestimates H during leaf off periods; 2) canopy height models (CHMs) underestimate H by a larger margin compared to percentile methods and are influenced by vegetation type (conifer needle, deciduous simple leaf or deciduous compound leaf) and canopy height variability, 3) lidar estimates of FC are within 10% of plot measurements during leaf-on periods, but are underestimated during leaf-off periods except in mixed and conifer plots; and 4) depth of laser pulse penetration lower in the canopy is more variable compared to top of the canopy penetration which may influence within canopy vegetation structure estimates. This study demonstrates that leaf-off lidar data can be used to estimate forested riparian buffer canopy height within diverse vegetation conditions and fractional canopy cover within mixed and conifer forests when leaf-on lidar data are not available. PMID:23382966

Network structure exploration in networks with node attributes

Science.gov (United States)

Chen, Yi; Wang, Xiaolong; Bu, Junzhao; Tang, Buzhou; Xiang, Xin

2016-05-01

Complex networks provide a powerful way to represent complex systems and have been widely studied during the past several years. One of the most important tasks of network analysis is to detect structures (also called structural regularities) embedded in networks by determining group number and group partition. Most of network structure exploration models only consider network links. However, in real world networks, nodes may have attributes that are useful for network structure exploration. In this paper, we propose a novel Bayesian nonparametric (BNP) model to explore structural regularities in networks with node attributes, called Bayesian nonparametric attribute (BNPA) model. This model does not only take full advantage of both links between nodes and node attributes for group partition via shared hidden variables, but also determine group number automatically via the Bayesian nonparametric theory. Experiments conducted on a number of real and synthetic networks show that our BNPA model is able to automatically explore structural regularities in networks with node attributes and is competitive with other state-of-the-art models.

Can Leaf Spectroscopy Predict Leaf and Forest Traits Along a Peruvian Tropical Forest Elevation Gradient?

Science.gov (United States)

Doughty, Christopher E.; Santos-Andrade, P. E.; Goldsmith, G. R.; Blonder, B.; Shenkin, A.; Bentley, L. P.; Chavana-Bryant, C.; Huaraca-Huasco, W.; Díaz, S.; Salinas, N.; Enquist, B. J.; Martin, R.; Asner, G. P.; Malhi, Y.

2017-11-01

High-resolution spectroscopy can be used to measure leaf chemical and structural traits. Such leaf traits are often highly correlated to other traits, such as photosynthesis, through the leaf economics spectrum. We measured VNIR (visible-near infrared) leaf reflectance (400-1,075 nm) of sunlit and shaded leaves in 150 dominant species across ten, 1 ha plots along a 3,300 m elevation gradient in Peru (on 4,284 individual leaves). We used partial least squares (PLS) regression to compare leaf reflectance to chemical traits, such as nitrogen and phosphorus, structural traits, including leaf mass per area (LMA), branch wood density and leaf venation, and "higher-level" traits such as leaf photosynthetic capacity, leaf water repellency, and woody growth rates. Empirical models using leaf reflectance predicted leaf N and LMA (r2 > 30% and %RMSE < 30%), weakly predicted leaf venation, photosynthesis, and branch density (r2 between 10 and 35% and %RMSE between 10% and 65%), and did not predict leaf water repellency or woody growth rates (r2<5%). Prediction of higher-level traits such as photosynthesis and branch density is likely due to these traits correlations with LMA, a trait readily predicted with leaf spectroscopy.

Characterization and 2D structural model of corn straw and poplar leaf biochars.

Science.gov (United States)

Zhao, Nan; Lv, YiZhong; Yang, XiXiang; Huang, Feng; Yang, JianWen

2017-12-22

The integrated experimental methods were used to analyze the physicochemical properties and structural characteristics and to build the 2D structural model of two kinds of biochars. Corn straw and poplar leaf biochars were gained by pyrolysing the raw materials slowly in a furnace at 300, 500, and 700 °C under oxygen-deficient conditions. Scanning electron microscope was applied to observe the surface morphology of the biochars. High temperatures destroyed the pore structures of the biochars, forming a particle mixture of varying sizes. The ash content, yield, pH, and surface area were also observed to describe the biochars' properties. The yield decreases as the pyrolysis temperature increases. The biochars are neutral to alkaline. The biggest surface area is 251.11 m 2 /g for 700 °C corn straw biochar. Elemental analysis, infrared microspectroscopy, solid-state C-13 NMR spectroscopy, and pyrolysis gas chromatography-mass spectrometry (Py-GC-MS) were also used to study the structural characteristics and build the 2D structural models of biochars. The C content in the corn straw and poplar leaf biochars increases with the increase of the pyrolysis temperature. A higher pyrolysis temperature makes the aryl carbon increase, and C=O, OH, and aliphatic hydrocarbon content decrease in the IR spectra. Solid-state C-13 NMR spectra show that a higher pyrolysis temperature makes the alkyl carbon and alkoxy carbon decrease and the aryl carbon increase. The results of IR microspectra and solid-state C-13 NMR spectra reveal that some noticeable differences exist in these two kinds of biochars and in the same type of biochar but under different pyrolysis temperatures. The conceptual elemental compositions of 500 °C corn straw and poplar leaf biochars are C 61 H 33 NO 13 and C 59 H 41 N 3 O 12 , respectively. Significant differences exist in the SEM images, physicochemical properties, and structural characteristics of corn straw and poplar leaf biochars.

vhv supply networks, problems of network structure

Energy Technology Data Exchange (ETDEWEB)

Raimbault, J

1966-04-01

The present and future power requirements of the Paris area and the structure of the existing networks are discussed. The various limitations that will have to be allowed for to lay down the structure of a regional transmission network leading in the power of the large national transmission network to within the Paris built up area are described. The theoretical solution that has been adopted, and the features of its final achievement, which is planned for about the year 2000, and the intermediate stages are given. The problem of the structure of the National Power Transmission network which is to supply the regional network was studied. To solve this problem, a 730 kV voltage network will have to be introduced.

[Network structures in biological systems].

Science.gov (United States)

Oleskin, A V

2013-01-01

Network structures (networks) that have been extensively studied in the humanities are characterized by cohesion, a lack of a central control unit, and predominantly fractal properties. They are contrasted with structures that contain a single centre (hierarchies) as well as with those whose elements predominantly compete with one another (market-type structures). As far as biological systems are concerned, their network structures can be subdivided into a number of types involving different organizational mechanisms. Network organization is characteristic of various structural levels of biological systems ranging from single cells to integrated societies. These networks can be classified into two main subgroups: (i) flat (leaderless) network structures typical of systems that are composed of uniform elements and represent modular organisms or at least possess manifest integral properties and (ii) three-dimensional, partly hierarchical structures characterized by significant individual and/or intergroup (intercaste) differences between their elements. All network structures include an element that performs structural, protective, and communication-promoting functions. By analogy to cell structures, this element is denoted as the matrix of a network structure. The matrix includes a material and an immaterial component. The material component comprises various structures that belong to the whole structure and not to any of its elements per se. The immaterial (ideal) component of the matrix includes social norms and rules regulating network elements' behavior. These behavioral rules can be described in terms of algorithms. Algorithmization enables modeling the behavior of various network structures, particularly of neuron networks and their artificial analogs.

Regression and artificial neural network modeling for the prediction of gray leaf spot of maize.

Science.gov (United States)

Paul, P A; Munkvold, G P

2005-04-01

ABSTRACT Regression and artificial neural network (ANN) modeling approaches were combined to develop models to predict the severity of gray leaf spot of maize, caused by Cercospora zeae-maydis. In all, 329 cases consisting of environmental, cultural, and location-specific variables were collected for field plots in Iowa between 1998 and 2002. Disease severity on the ear leaf at the dough to dent plant growth stage was used as the response variable. Correlation and regression analyses were performed to select potentially useful predictor variables. Predictors from the best 9 of 80 regression models were used to develop ANN models. A random sample of 60% of the cases was used to train the networks, and 20% each for testing and validation. Model performance was evaluated based on coefficient of determination (R(2)) and mean square error (MSE) for the validation data set. The best models had R(2) ranging from 0.70 to 0.75 and MSE ranging from 174.7 to 202.8. The most useful predictor variables were hours of daily temperatures between 22 and 30 degrees C (85.50 to 230.50 h) and hours of nightly relative humidity >/=90% (122 to 330 h) for the period between growth stages V4 and V12, mean nightly temperature (65.26 to 76.56 degrees C) for the period between growth stages V12 and R2, longitude (90.08 to 95.14 degrees W), maize residue on the soil surface (0 to 100%), planting date (in day of the year; 112 to 182), and gray leaf spot resistance rating (2 to 7; based on a 1-to-9 scale, where 1 = most susceptible to 9 = most resistant).

Leaf ontogeny of Schinus molle L. plants under cadmium contamination: the meristematic origin of leaf structural changes.

Science.gov (United States)

Pereira, Marcio Paulo; Corrêa, Felipe Fogaroli; de Castro, Evaristo Mauro; de Oliveira, Jean Paulo Vitor; Pereira, Fabricio José

2017-11-01

Previous works show the development of thicker leaves on tolerant plants growing under cadmium (Cd 2+ ) contamination. The aim of this study was to evaluate the Cd 2+ effects on the leaf meristems of the tolerant species Schinus molle. Plants were grown in nutrient solution containing 0, 10, and 50 μM of Cd 2+ . Anatomical analysis was performed on leaf primordia sampled at regular time intervals. Under the lowest Cd 2+ level (10 μM), increased ground meristem thickness, diameter of the cells, cell elongation rate, and leaf dry mass were found. However, 50 μM of Cd 2+ reduced all these variables. In addition, the ground meristem cells became larger when exposed to any Cd 2+ level. The epidermis, palisade parenchyma, and vascular tissues developed earlier in Cd 2+ -exposed leaves. The modifications found on the ground meristem may be related to the development of thicker leaves on S. molle plants exposed to low Cd 2+ levels. Furthermore, older leaves showed higher Cd 2+ content when compared to the younger ones, preventing the Cd 2+ toxicity to these leaves. Thus, low Cd 2+ concentrations change the ground meristem structure and function reflecting on the development of thicker and enhanced leaves.

Chemical and structural analysis of Eucalyptus globulus and E. camaldulensis leaf cuticles: a lipidized cell wall region

Directory of Open Access Journals (Sweden)

Paula eGuzmán

2014-09-01

Full Text Available The plant cuticle has traditionally been conceived as an independent hydrophobic layer that covers the external epidermal cell wall. Due to its complexity, the existing relationship between cuticle chemical composition and ultra-structure remains unclear to date. This study aimed to examine the link between chemical composition and structure of isolated, adaxial leaf cuticles of Eucalyptus camaldulensis and E. globulus by the gradual extraction and identification of lipid constituents (cutin and soluble lipids, coupled to spectroscopic and microscopic analyses. The soluble compounds and cutin monomers identified could not be assigned to a concrete internal cuticle ultra-structure. After cutin depolymerization, a cellulose network resembling the cell wall was observed, with different structural patterns in the regions ascribed to the cuticle proper and cuticular layer, respectively. Our results suggest that the current cuticle model should be revised, stressing the presence and major role of cell wall polysaccharides. It is concluded that the cuticle may be interpreted as a modified cell wall region which contains additional lipids. The major heterogeneity of the plant cuticle makes it difficult to establish a direct link between cuticle chemistry and structure with the existing methodologies.

Managing Network Partitions in Structured P2P Networks

Science.gov (United States)

Shafaat, Tallat M.; Ghodsi, Ali; Haridi, Seif

Structured overlay networks form a major class of peer-to-peer systems, which are touted for their abilities to scale, tolerate failures, and self-manage. Any long-lived Internet-scale distributed system is destined to face network partitions. Consequently, the problem of network partitions and mergers is highly related to fault-tolerance and self-management in large-scale systems. This makes it a crucial requirement for building any structured peer-to-peer systems to be resilient to network partitions. Although the problem of network partitions and mergers is highly related to fault-tolerance and self-management in large-scale systems, it has hardly been studied in the context of structured peer-to-peer systems. Structured overlays have mainly been studied under churn (frequent joins/failures), which as a side effect solves the problem of network partitions, as it is similar to massive node failures. Yet, the crucial aspect of network mergers has been ignored. In fact, it has been claimed that ring-based structured overlay networks, which constitute the majority of the structured overlays, are intrinsically ill-suited for merging rings. In this chapter, we motivate the problem of network partitions and mergers in structured overlays. We discuss how a structured overlay can automatically detect a network partition and merger. We present an algorithm for merging multiple similar ring-based overlays when the underlying network merges. We examine the solution in dynamic conditions, showing how our solution is resilient to churn during the merger, something widely believed to be difficult or impossible. We evaluate the algorithm for various scenarios and show that even when falsely detecting a merger, the algorithm quickly terminates and does not clutter the network with many messages. The algorithm is flexible as the tradeoff between message complexity and time complexity can be adjusted by a parameter.

Feedback topology and XOR-dynamics in Boolean networks with varying input structure.

Science.gov (United States)

Ciandrini, L; Maffi, C; Motta, A; Bassetti, B; Cosentino Lagomarsino, M

2009-08-01

We analyze a model of fixed in-degree random Boolean networks in which the fraction of input-receiving nodes is controlled by the parameter gamma. We investigate analytically and numerically the dynamics of graphs under a parallel XOR updating scheme. This scheme is interesting because it is accessible analytically and its phenomenology is at the same time under control and as rich as the one of general Boolean networks. We give analytical formulas for the dynamics on general graphs, showing that with a XOR-type evolution rule, dynamic features are direct consequences of the topological feedback structure, in analogy with the role of relevant components in Kauffman networks. Considering graphs with fixed in-degree, we characterize analytically and numerically the feedback regions using graph decimation algorithms (Leaf Removal). With varying gamma , this graph ensemble shows a phase transition that separates a treelike graph region from one in which feedback components emerge. Networks near the transition point have feedback components made of disjoint loops, in which each node has exactly one incoming and one outgoing link. Using this fact, we provide analytical estimates of the maximum period starting from topological considerations.

Feedback topology and XOR-dynamics in Boolean networks with varying input structure

Science.gov (United States)

Ciandrini, L.; Maffi, C.; Motta, A.; Bassetti, B.; Cosentino Lagomarsino, M.

2009-08-01

We analyze a model of fixed in-degree random Boolean networks in which the fraction of input-receiving nodes is controlled by the parameter γ . We investigate analytically and numerically the dynamics of graphs under a parallel XOR updating scheme. This scheme is interesting because it is accessible analytically and its phenomenology is at the same time under control and as rich as the one of general Boolean networks. We give analytical formulas for the dynamics on general graphs, showing that with a XOR-type evolution rule, dynamic features are direct consequences of the topological feedback structure, in analogy with the role of relevant components in Kauffman networks. Considering graphs with fixed in-degree, we characterize analytically and numerically the feedback regions using graph decimation algorithms (Leaf Removal). With varying γ , this graph ensemble shows a phase transition that separates a treelike graph region from one in which feedback components emerge. Networks near the transition point have feedback components made of disjoint loops, in which each node has exactly one incoming and one outgoing link. Using this fact, we provide analytical estimates of the maximum period starting from topological considerations.

Reading the Leaves: A Comparison of Leaf Rank and Automated Areole Measurement for Quantifying Aspects of Leaf Venation

Directory of Open Access Journals (Sweden)

Walton A. Green

2014-08-01

Full Text Available The reticulate venation that is characteristic of a dicot leaf has excited interest from systematists for more than a century, and from physiological and developmental botanists for decades. The tools of digital image acquisition and computer image analysis, however, are only now approaching the sophistication needed to quantify aspects of the venation network found in real leaves quickly, easily, accurately, and reliably enough to produce biologically meaningful data. In this paper, we examine 120 leaves distributed across vascular plants (representing 118 genera and 80 families using two approaches: a semiquantitative scoring system called “leaf ranking,” devised by the late Leo Hickey, and an automated image-analysis protocol. In the process of comparing these approaches, we review some methodological issues that arise in trying to quantify a vein network, and discuss the strengths and weaknesses of automatic data collection and human pattern recognition. We conclude that subjective leaf rank provides a relatively consistent, semiquantitative measure of areole size among other variables; that modal areole size is generally consistent across large sections of a leaf lamina; and that both approaches—semiquantitative, subjective scoring; and fully quantitative, automated measurement—have appropriate places in the study of leaf venation.

Effects of structural complexity on within-canopy light environments and leaf traits in a northern mixed deciduous forest.

Science.gov (United States)

Fotis, Alexander T; Curtis, Peter S

2017-10-01

Canopy structure influences forest productivity through its effects on the distribution of radiation and the light-induced changes in leaf physiological traits. Due to the difficulty of accessing and measuring forest canopies, few field-based studies have quantitatively linked these divergent scales of canopy functioning. The objective of our study was to investigate how canopy structure affects light profiles within a forest canopy and whether leaves of mature trees adjust morphologically and biochemically to the light environments characteristic of canopies with different structural complexity. We used a combination of light detection and ranging (LiDAR) data and hemispherical photographs to quantify canopy structure and light environments, respectively, and a telescoping pole to sample leaves. Leaf mass per area (LMA), nitrogen on an area basis (Narea) and chlorophyll on a mass basis (Chlmass) were measured in red maple (Acer rubrum), american beech (Fagus grandifolia), white pine (Pinus strobus), and northern red oak (Quercus rubra) at different heights in plots with similar leaf area index but contrasting canopy complexity (rugosity). We found that more complex canopies had greater porosity and reduced light variability in the midcanopy while total light interception was unchanged relative to less complex canopies. Leaf phenotypes of F. grandifolia, Q. rubra and P. strobus were more sun-acclimated in the midstory of structurally complex canopies while leaf phenotypes of A. rubrum were more shade-acclimated (lower LMA) in the upper canopy of more complex stands, despite no differences in total light interception. Broadleaf species showed further differences in acclimation with increased Narea and reduced Chlmass in leaves with higher LMA, while P. strobus showed no change in Narea and Chlmass with higher LMA. Our results provide new insight on how light distribution and leaf acclimation in mature trees might be altered when natural and anthropogenic

Validation of Leaf Area Index measurements based on the Wireless Sensor Network platform

Science.gov (United States)

Song, Q.; Li, X.; Liu, Q.

2017-12-01

The leaf area index (LAI) is one of the important parameters for estimating plant canopy function, which has significance for agricultural analysis such as crop yield estimation and disease evaluation. The quick and accurate access to acquire crop LAI is particularly vital. In the study, LAI measurement of corn crops is mainly through three kinds of methods: the leaf length and width method (LAILLW), the instruments indirect measurement method (LAII) and the leaf area index sensor method(LAIS). Among them, LAI value obtained from LAILLW can be regarded as approximate true value. LAI-2200,the current widespread LAI canopy analyzer,is used in LAII. LAIS based on wireless sensor network can realize the automatic acquisition of crop images,simplifying the data collection work,while the other two methods need person to carry out field measurements.Through the comparison of LAIS and other two methods, the validity and reliability of LAIS observation system is verified. It is found that LAI trend changes are similar in three methods, and the rate of change of LAI has an increase with time in the first two months of corn growth when LAIS costs less manpower, energy and time. LAI derived from LAIS is more accurate than LAII in the early growth stage,due to the small blade especially under the strong light. Besides, LAI processed from a false color image with near infrared information is much closer to the true value than true color picture after the corn growth period up to one and half months.

Leaf venation, as a resistor, to optimize a switchable IR absorber.

Science.gov (United States)

Alston, M E; Barber, R

2016-08-24

Leaf vascular patterns are the mechanisms and mechanical support for the transportation of fluidics for photosynthesis and leaf development properties. Vascular hierarchical networks in leaves have far-reaching functions in optimal transport efficiency of functional fluidics. Embedding leaf morphogenesis as a resistor network is significant in the optimization of a translucent thermally functional material. This will enable regulation through pressure equalization by diminishing flow pressure variation. This paper investigates nature's vasculature networks that exhibit hierarchical branching scaling applied to microfluidics. To enable optimum potential for pressure drop regulation by algorithm design. This code analysis of circuit conduit optimization for transport fluidic flow resistance is validated against CFD simulation, within a closed loop network. The paper will propose this self-optimization, characterization by resistance seeking targeting to determine a microfluidic network as a resistor. To advance a thermally function material as a switchable IR absorber.

The heterogeneity and spatial patterning of structure and physiology across the leaf surface in giant leaves of Alocasia macrorrhiza.

Directory of Open Access Journals (Sweden)

Shuai Li

Full Text Available Leaf physiology determines the carbon acquisition of the whole plant, but there can be considerable variation in physiology and carbon acquisition within individual leaves. Alocasia macrorrhiza (L. Schott is an herbaceous species that can develop very large leaves of up to 1 m in length. However, little is known about the hydraulic and photosynthetic design of such giant leaves. Based on previous studies of smaller leaves, and on the greater surface area for trait variation in large leaves, we hypothesized that A. macrorrhiza leaves would exhibit significant heterogeneity in structure and function. We found evidence of reduced hydraulic supply and demand in the outer leaf regions; leaf mass per area, chlorophyll concentration, and guard cell length decreased, as did stomatal conductance, net photosynthetic rate and quantum efficiency of photosystem II. This heterogeneity in physiology was opposite to that expected from a thinner boundary layer at the leaf edge, which would have led to greater rates of gas exchange. Leaf temperature was 8.8°C higher in the outer than in the central region in the afternoon, consistent with reduced stomatal conductance and transpiration caused by a hydraulic limitation to the outer lamina. The reduced stomatal conductance in the outer regions would explain the observed homogeneous distribution of leaf water potential across the leaf surface. These findings indicate substantial heterogeneity in gas exchange across the leaf surface in large leaves, greater than that reported for smaller-leafed species, though the observed structural differences across the lamina were within the range reported for smaller-leafed species. Future work will determine whether the challenge of transporting water to the outer regions can limit leaf size for plants experiencing drought, and whether the heterogeneity of function across the leaf surface represents a particular disadvantage for large simple leaves that might explain their

Leaf colleters in Tontelea micrantha (Celastraceae, Salacioideae): ecological, morphological and structural aspects.

Science.gov (United States)

Mercadante-Simões, Maria Olívia; Paiva, Elder Antônio Sousa

2013-08-01

The colleter secretion can be useful to protect plants of Cerrado (Brazilian savanna) biome during the long and pronounced dry season. This study describes the presence of colleters in Tontelea micrantha and represents the first record of these structures in Celastraceae. To investigate colleter structure and their secretory processes, young leaves were collected, fixed, and processed according to conventional techniques for light, and electron microscopy. Colleters were observed at the marginal teeth on the leaf. They produce mucilaginous secretions that spread over the leaf surface. After secretory phase, colleters abscise. The secretory epithelium is uniseriate and composed of elongated cells whose dense cytoplasm is rich in organelles. The ultrastructure of the secretory cells is compatible with the pectin-rich secretion. Observations of the young leaves surface revealed the presence of superficial hydrophilic secretion films that appeared to have the function of maintaining the water status of those organs. Copyright © 2013 Académie des sciences. Published by Elsevier SAS. All rights reserved.

Dependence of leaf structural indices in two forest maple species from within-crown irradiance

Directory of Open Access Journals (Sweden)

N.A. Belyavskaya

2012-03-01

Full Text Available The main leaf structural parameters of two genus Acer L. representatives ( A. platanoides and A. tataricum have been characterized. The responses of structural indices to within-crown light level have been studied. Inter-species differences have been revealed in irradiance adaptation at the cellular level.

Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification

Directory of Open Access Journals (Sweden)

Srdjan Sladojevic

2016-01-01

Full Text Available The latest generation of convolutional neural networks (CNNs has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%.

From network structure to network reorganization: implications for adult neurogenesis

International Nuclear Information System (INIS)

Schneider-Mizell, Casey M; Zochowski, Michal R; Sander, Leonard M; Parent, Jack M; Ben-Jacob, Eshel

2010-01-01

Networks can be dynamical systems that undergo functional and structural reorganization. One example of such a process is adult hippocampal neurogenesis, in which new cells are continuously born and incorporate into the existing network of the dentate gyrus region of the hippocampus. Many of these introduced cells mature and become indistinguishable from established neurons, joining the existing network. Activity in the network environment is known to promote birth, survival and incorporation of new cells. However, after epileptogenic injury, changes to the connectivity structure around the neurogenic niche are known to correlate with aberrant neurogenesis. The possible role of network-level changes in the development of epilepsy is not well understood. In this paper, we use a computational model to investigate how the structural and functional outcomes of network reorganization, driven by addition of new cells during neurogenesis, depend on the original network structure. We find that there is a stable network topology that allows the network to incorporate new neurons in a manner that enhances activity of the persistently active region, but maintains global network properties. In networks having other connectivity structures, new cells can greatly alter the distribution of firing activity and destroy the initial activity patterns. We thus find that new cells are able to provide focused enhancement of network only for small-world networks with sufficient inhibition. Network-level deviations from this topology, such as those caused by epileptogenic injury, can set the network down a path that develops toward pathological dynamics and aberrant structural integration of new cells

Two-leaf wall structures under 'soft' impact load - aircraft crash

International Nuclear Information System (INIS)

Eibl, J.; Block, K.

1982-01-01

The article describes a mechanical model with which the load conditions associated with aircraft crash on a two-leaf wall or roof structure can be analysed quite simply. The necessary assumptions for the material behaviour governing the contact of the two slabs and, in general, the maximum limit deformations of reinforced concrete slabs are more particularly dealt with. Treating the problem the authors make use, inter alia, of some of their own experimental results. (orig.)

In Situ Nondestructive Analysis of Kalanchoe pinnata Leaf Surface Structure by Polarization-Modulation Infrared Reflection-Absorption Spectroscopy.

Science.gov (United States)

Hama, Tetsuya; Kouchi, Akira; Watanabe, Naoki; Enami, Shinichi; Shimoaka, Takafumi; Hasegawa, Takeshi

2017-12-14

The outermost surface of the leaves of land plants is covered with a lipid membrane called the cuticle that protects against various stress factors. Probing the molecular-level structure of the intact cuticle is highly desirable for understanding its multifunctional properties. We report the in situ characterization of the surface structure of Kalanchoe pinnata leaves using polarization-modulation infrared reflection-absorption spectroscopy (PM-IRRAS). Without sample pretreatment, PM-IRRAS measures the IR spectra of the leaf cuticle of a potted K. pinnata plant. The peak position of the CH 2 -related modes shows that the cuticular waxes on the leaf surface are mainly crystalline, and the alkyl chains are highly packed in an all-trans zigzag conformation. The surface selection rule of PM-IRRAS revealed the average orientation of the cuticular molecules, as indicated by the positive and negative signals of the IR peaks. This unique property of PM-IRRAS revealed that the alkyl chains of the waxes and the main chains of polysaccharides are oriented almost perpendicular to the leaf surface. The nondestructive, background-free, and environmental gas-free nature of PM-IRRAS allows the structure and chemistry of the leaf cuticle to be studied directly in its native environment.

Evaluation of Methane from Sisal Leaf Residue and Palash Leaf Litter

Science.gov (United States)

Arisutha, S.; Baredar, P.; Deshpande, D. M.; Suresh, S.

2014-12-01

The aim of this study is to evaluate methane production from sisal leaf residue and palash leaf litter mixed with different bulky materials such as vegetable market waste, hostel kitchen waste and digested biogas slurry in a laboratory scale anaerobic reactor. The mixture was prepared with 1:1 proportion. Maximum methane content of 320 ml/day was observed in the case of sisal leaf residue mixed with vegetable market waste as the feed. Methane content was minimum (47 ml/day), when palash leaf litter was used as feed. This was due to the increased content of lignin and polyphenol in the feedstock which were of complex structure and did not get degraded directly by microorganisms. Sisal leaf residue mixtures also showed highest content of volatile fatty acids (VFAs) as compared to palash leaf litter mixtures. It was observed that VFA concentration in the digester first increased, reached maximum (when pH was minimum) and then decreased.

Biological transportation networks: Modeling and simulation

KAUST Repository

Albi, Giacomo

2015-09-15

We present a model for biological network formation originally introduced by Cai and Hu [Adaptation and optimization of biological transport networks, Phys. Rev. Lett. 111 (2013) 138701]. The modeling of fluid transportation (e.g., leaf venation and angiogenesis) and ion transportation networks (e.g., neural networks) is explained in detail and basic analytical features like the gradient flow structure of the fluid transportation network model and the impact of the model parameters on the geometry and topology of network formation are analyzed. We also present a numerical finite-element based discretization scheme and discuss sample cases of network formation simulations.

Leaf-litter amount as a factor in the structure of a ponerine ants community (Hymenoptera, Formicidae, Ponerinae in an eastern Amazonian rainforest, Brazil

Directory of Open Access Journals (Sweden)

Alexandro Herbert dos Santos Bastos

2011-12-01

Full Text Available Leaf-litter amount as a factor in the structure of a ponerine ants community (Hymenoptera, Formicidae, Ponerinae in an eastern Amazonian rainforest, Brazil. Leaf-litter may be an important factor in structuring ponerine ant communities (Hymenoptera, Formicidae, Ponerinae in tropical rainforests. We specifically examined how leaf-litter affects the structure of a ponerine ant community in primary Amazonian rainforest sites at the Ferreira Penna Scientific Station, Pará, Brazil. A total of 53 species belonging to eight genera of three ponerine tribes were collected with mini-Winkler extractors. The amount of leaf-litter positively affected the abundance and richness of the ponerine ant community, and also influenced species composition. Nearby samples often had low species similarity, especially when adjacent samples differed in the amount of leaf-litter. Leaf-litter availability in Amazonian primary forests is a key factor for distribution of ground-dwelling ponerine species, even at small scales.

Cytohistological study of the leaf structures of Panax ginseng Meyer and Panax quinquefolius L.

Directory of Open Access Journals (Sweden)

Ok Ran Lee

2017-10-01

Conclusion: The anatomical leaf structure of both P. ginseng and P. quinquefolius shows that they are typical shade-loving sciophytes. Slight differences in chloroplast structure suggests that the two different species can be authenticated using transmission electron microscopy images, and light-resistant cultivar breeding can be performed via controlling photosynthesis efficiency.

An examination of a reciprocal relationship between network governance and network structure

DEFF Research Database (Denmark)

Bergenholtz, Carsten; Goduscheit, René Chester

The present article examines the network structure and governance of inter-organisational innovation networks. Network governance refers to the issue of how to manage and coordinate the relational activities and processes in the network while research on network structure deals with the overall...... structural relations between the actors in the network. These streams of research do contain references to each other but mostly rely on a static conception of the relationship between network structure and the applied network governance. The paper is based on a primarily qualitative case study of a loosely...... coupled Danish inter-organisational innovation network. The proposition is that a reciprocal relation between network governance and network structure can be identified....

Network structure exploration via Bayesian nonparametric models

International Nuclear Information System (INIS)

Chen, Y; Wang, X L; Xiang, X; Tang, B Z; Bu, J Z

2015-01-01

Complex networks provide a powerful mathematical representation of complex systems in nature and society. To understand complex networks, it is crucial to explore their internal structures, also called structural regularities. The task of network structure exploration is to determine how many groups there are in a complex network and how to group the nodes of the network. Most existing structure exploration methods need to specify either a group number or a certain type of structure when they are applied to a network. In the real world, however, the group number and also the certain type of structure that a network has are usually unknown in advance. To explore structural regularities in complex networks automatically, without any prior knowledge of the group number or the certain type of structure, we extend a probabilistic mixture model that can handle networks with any type of structure but needs to specify a group number using Bayesian nonparametric theory. We also propose a novel Bayesian nonparametric model, called the Bayesian nonparametric mixture (BNPM) model. Experiments conducted on a large number of networks with different structures show that the BNPM model is able to explore structural regularities in networks automatically with a stable, state-of-the-art performance. (paper)

Microbial community structure of leaf-cutter ant fungus gardens and refuse dumps.

Science.gov (United States)

Scott, Jarrod J; Budsberg, Kevin J; Suen, Garret; Wixon, Devin L; Balser, Teri C; Currie, Cameron R

2010-03-29

Leaf-cutter ants use fresh plant material to grow a mutualistic fungus that serves as the ants' primary food source. Within fungus gardens, various plant compounds are metabolized and transformed into nutrients suitable for ant consumption. This symbiotic association produces a large amount of refuse consisting primarily of partly degraded plant material. A leaf-cutter ant colony is thus divided into two spatially and chemically distinct environments that together represent a plant biomass degradation gradient. Little is known about the microbial community structure in gardens and dumps or variation between lab and field colonies. Using microbial membrane lipid analysis and a variety of community metrics, we assessed and compared the microbiota of fungus gardens and refuse dumps from both laboratory-maintained and field-collected colonies. We found that gardens contained a diverse and consistent community of microbes, dominated by Gram-negative bacteria, particularly gamma-Proteobacteria and Bacteroidetes. These findings were consistent across lab and field gardens, as well as host ant taxa. In contrast, dumps were enriched for Gram-positive and anaerobic bacteria. Broad-scale clustering analyses revealed that community relatedness between samples reflected system component (gardens/dumps) rather than colony source (lab/field). At finer scales samples clustered according to colony source. Here we report the first comparative analysis of the microbiota from leaf-cutter ant colonies. Our work reveals the presence of two distinct communities: one in the fungus garden and the other in the refuse dump. Though we find some effect of colony source on community structure, our data indicate the presence of consistently associated microbes within gardens and dumps. Substrate composition and system component appear to be the most important factor in structuring the microbial communities. These results thus suggest that resident communities are shaped by the plant degradation

Networks: structure and action : steering in and steering by policy networks

NARCIS (Netherlands)

Dassen, A.

2010-01-01

This thesis explores the opportunities to build a structural policy network model that is rooted in social network theories. By making a distinction between a process of steering in networks, and a process of steering by networks, it addresses the effects of network structures on network dynamics as

Estimating leaf area and leaf biomass of open-grown deciduous urban trees

Science.gov (United States)

David J. Nowak

1996-01-01

Logarithmic regression equations were developed to predict leaf area and leaf biomass for open-grown deciduous urban trees based on stem diameter and crown parameters. Equations based on crown parameters produced more reliable estimates. The equations can be used to help quantify forest structure and functions, particularly in urbanizing and urban/suburban areas.

Detecting Hierarchical Structure in Networks

DEFF Research Database (Denmark)

Herlau, Tue; Mørup, Morten; Schmidt, Mikkel Nørgaard

2012-01-01

Many real-world networks exhibit hierarchical organization. Previous models of hierarchies within relational data has focused on binary trees; however, for many networks it is unknown whether there is hierarchical structure, and if there is, a binary tree might not account well for it. We propose...... a generative Bayesian model that is able to infer whether hierarchies are present or not from a hypothesis space encompassing all types of hierarchical tree structures. For efficient inference we propose a collapsed Gibbs sampling procedure that jointly infers a partition and its hierarchical structure....... On synthetic and real data we demonstrate that our model can detect hierarchical structure leading to better link-prediction than competing models. Our model can be used to detect if a network exhibits hierarchical structure, thereby leading to a better comprehension and statistical account the network....

European networks in structural integrity

International Nuclear Information System (INIS)

Crutzen, S.; Davies, M.; Hemsworth, B.; Hurst, R.; Kussmaul, K.

1994-01-01

Several institutions and electrical utilities in Europe, including the Joint Research Centre (JRC) have the capability to deal problems posed by the operation and ageing of structural components and with their structural integrity assessment. These institutions and the JRC have developed cooperative programmes now organised in networks. They include utilities, engineering companies, R and D laboratories and Regulatory Bodies. Networks are organised and managed like the successful PISC programme: The Institute for Advanced Materials of JRC plays the role of Operating Agent and Manager of these networks: ENIQ, AMES, NESC, each of them dealing with a specific aspect of fitness for purpose of materials in structural components. There exist strong links between the networks and EC Working Groups on Structural Integrity Codes and Standards. (orig.)

Collective network for computer structures

Science.gov (United States)

Blumrich, Matthias A [Ridgefield, CT; Coteus, Paul W [Yorktown Heights, NY; Chen, Dong [Croton On Hudson, NY; Gara, Alan [Mount Kisco, NY; Giampapa, Mark E [Irvington, NY; Heidelberger, Philip [Cortlandt Manor, NY; Hoenicke, Dirk [Ossining, NY; Takken, Todd E [Brewster, NY; Steinmacher-Burow, Burkhard D [Wernau, DE; Vranas, Pavlos M [Bedford Hills, NY

2011-08-16

A system and method for enabling high-speed, low-latency global collective communications among interconnected processing nodes. The global collective network optimally enables collective reduction operations to be performed during parallel algorithm operations executing in a computer structure having a plurality of the interconnected processing nodes. Router devices ate included that interconnect the nodes of the network via links to facilitate performance of low-latency global processing operations at nodes of the virtual network and class structures. The global collective network may be configured to provide global barrier and interrupt functionality in asynchronous or synchronized manner. When implemented in a massively-parallel supercomputing structure, the global collective network is physically and logically partitionable according to needs of a processing algorithm.

Patterning of leaf vein networks by convergent auxin transport pathways.

Science.gov (United States)

Sawchuk, Megan G; Edgar, Alexander; Scarpella, Enrico

2013-01-01

The formation of leaf vein patterns has fascinated biologists for centuries. Transport of the plant signal auxin has long been implicated in vein patterning, but molecular details have remained unclear. Varied evidence suggests a central role for the plasma-membrane (PM)-localized PIN-FORMED1 (PIN1) intercellular auxin transporter of Arabidopsis thaliana in auxin-transport-dependent vein patterning. However, in contrast to the severe vein-pattern defects induced by auxin transport inhibitors, pin1 mutant leaves have only mild vein-pattern defects. These defects have been interpreted as evidence of redundancy between PIN1 and the other four PM-localized PIN proteins in vein patterning, redundancy that underlies many developmental processes. By contrast, we show here that vein patterning in the Arabidopsis leaf is controlled by two distinct and convergent auxin-transport pathways: intercellular auxin transport mediated by PM-localized PIN1 and intracellular auxin transport mediated by the evolutionarily older, endoplasmic-reticulum-localized PIN6, PIN8, and PIN5. PIN6 and PIN8 are expressed, as PIN1 and PIN5, at sites of vein formation. pin6 synthetically enhances pin1 vein-pattern defects, and pin8 quantitatively enhances pin1pin6 vein-pattern defects. Function of PIN6 is necessary, redundantly with that of PIN8, and sufficient to control auxin response levels, PIN1 expression, and vein network formation; and the vein pattern defects induced by ectopic PIN6 expression are mimicked by ectopic PIN8 expression. Finally, vein patterning functions of PIN6 and PIN8 are antagonized by PIN5 function. Our data define a new level of control of vein patterning, one with repercussions on other patterning processes in the plant, and suggest a mechanism to select cell files specialized for vascular function that predates evolution of PM-localized PIN proteins.

Patterning of leaf vein networks by convergent auxin transport pathways.

Directory of Open Access Journals (Sweden)

Megan G Sawchuk

Full Text Available The formation of leaf vein patterns has fascinated biologists for centuries. Transport of the plant signal auxin has long been implicated in vein patterning, but molecular details have remained unclear. Varied evidence suggests a central role for the plasma-membrane (PM-localized PIN-FORMED1 (PIN1 intercellular auxin transporter of Arabidopsis thaliana in auxin-transport-dependent vein patterning. However, in contrast to the severe vein-pattern defects induced by auxin transport inhibitors, pin1 mutant leaves have only mild vein-pattern defects. These defects have been interpreted as evidence of redundancy between PIN1 and the other four PM-localized PIN proteins in vein patterning, redundancy that underlies many developmental processes. By contrast, we show here that vein patterning in the Arabidopsis leaf is controlled by two distinct and convergent auxin-transport pathways: intercellular auxin transport mediated by PM-localized PIN1 and intracellular auxin transport mediated by the evolutionarily older, endoplasmic-reticulum-localized PIN6, PIN8, and PIN5. PIN6 and PIN8 are expressed, as PIN1 and PIN5, at sites of vein formation. pin6 synthetically enhances pin1 vein-pattern defects, and pin8 quantitatively enhances pin1pin6 vein-pattern defects. Function of PIN6 is necessary, redundantly with that of PIN8, and sufficient to control auxin response levels, PIN1 expression, and vein network formation; and the vein pattern defects induced by ectopic PIN6 expression are mimicked by ectopic PIN8 expression. Finally, vein patterning functions of PIN6 and PIN8 are antagonized by PIN5 function. Our data define a new level of control of vein patterning, one with repercussions on other patterning processes in the plant, and suggest a mechanism to select cell files specialized for vascular function that predates evolution of PM-localized PIN proteins.

Cross-linked structure of network evolution

International Nuclear Information System (INIS)

Bassett, Danielle S.; Wymbs, Nicholas F.; Grafton, Scott T.; Porter, Mason A.; Mucha, Peter J.

2014-01-01

We study the temporal co-variation of network co-evolution via the cross-link structure of networks, for which we take advantage of the formalism of hypergraphs to map cross-link structures back to network nodes. We investigate two sets of temporal network data in detail. In a network of coupled nonlinear oscillators, hyperedges that consist of network edges with temporally co-varying weights uncover the driving co-evolution patterns of edge weight dynamics both within and between oscillator communities. In the human brain, networks that represent temporal changes in brain activity during learning exhibit early co-evolution that then settles down with practice. Subsequent decreases in hyperedge size are consistent with emergence of an autonomous subgraph whose dynamics no longer depends on other parts of the network. Our results on real and synthetic networks give a poignant demonstration of the ability of cross-link structure to uncover unexpected co-evolution attributes in both real and synthetic dynamical systems. This, in turn, illustrates the utility of analyzing cross-links for investigating the structure of temporal networks

Cross-linked structure of network evolution

Energy Technology Data Exchange (ETDEWEB)

Bassett, Danielle S., E-mail: dsb@seas.upenn.edu [Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States); Department of Physics, University of California, Santa Barbara, California 93106 (United States); Sage Center for the Study of the Mind, University of California, Santa Barbara, California 93106 (United States); Wymbs, Nicholas F.; Grafton, Scott T. [Department of Psychology and UCSB Brain Imaging Center, University of California, Santa Barbara, California 93106 (United States); Porter, Mason A. [Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford OX2 6GG (United Kingdom); CABDyN Complexity Centre, University of Oxford, Oxford, OX1 1HP (United Kingdom); Mucha, Peter J. [Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, North Carolina 27599 (United States); Department of Applied Physical Sciences, University of North Carolina, Chapel Hill, North Carolina 27599 (United States)

2014-03-15

We study the temporal co-variation of network co-evolution via the cross-link structure of networks, for which we take advantage of the formalism of hypergraphs to map cross-link structures back to network nodes. We investigate two sets of temporal network data in detail. In a network of coupled nonlinear oscillators, hyperedges that consist of network edges with temporally co-varying weights uncover the driving co-evolution patterns of edge weight dynamics both within and between oscillator communities. In the human brain, networks that represent temporal changes in brain activity during learning exhibit early co-evolution that then settles down with practice. Subsequent decreases in hyperedge size are consistent with emergence of an autonomous subgraph whose dynamics no longer depends on other parts of the network. Our results on real and synthetic networks give a poignant demonstration of the ability of cross-link structure to uncover unexpected co-evolution attributes in both real and synthetic dynamical systems. This, in turn, illustrates the utility of analyzing cross-links for investigating the structure of temporal networks.

Ecophysiological Remote Sensing of Leaf-Canopy Photosynthetic Characteristics in a Cool-Temperate Deciduous Forest in Japan

Science.gov (United States)

Noda, H. M.; Muraoka, H.

2014-12-01

Satellite remote sensing of structure and function of canopy is crucial to detect temporal and spatial distributions of forest ecosystems dynamics in changing environments. The spectral reflectance of the canopy is determined by optical properties (spectral reflectance and transmittance) of single leaves and their spatial arrangements in the canopy. The optical properties of leaves reflect their pigments contents and anatomical structures. Thus detailed information and understandings of the consequence between ecophysiological traits and optical properties from single leaf to canopy level are essential for remote sensing of canopy ecophysiology. To develop the ecophysiological remote sensing of forest canopy, we have been promoting multiple and cross-scale measurements in "Takayama site" belonging to AsiaFlux and JaLTER networks, located in a cool-temperate deciduous broadleaf forest on a mountainous landscape in Japan. In this forest, in situ measurement of canopy spectral reflectance has been conducted continuously by a spectroradiometer as part of the "Phenological Eyes Network (PEN)" since 2004. To analyze the canopy spectral reflectance from leaf ecophysiological viewpoints, leaf mass per area, nitrogen content, chlorophyll contents, photosynthetic capacities and the optical properties have been measured for dominant canopy tree species Quercus crispla and Betula ermanii throughout the seasons for multiple years.Photosynthetic capacity was largely correlated with chlorophyll contents throughout the growing season in both Q. crispla and B. ermanii. In these leaves, the reflectance at "red edge" (710 nm) changed by corresponding to the changes of chlorophyll contents throughout the seasons. Our canopy-level examination showed that vegetation indices obtained by red edge reflectance have linear relationship with leaf chlorophyll contents and photosynthetic capacity. Finally we apply this knowledge to the Rapid Eye satellite imagery around Takayama site to scale

Pinning Control Strategy of Multicommunity Structure Networks

Directory of Open Access Journals (Sweden)

Chao Ding

2017-01-01

Full Text Available In order to investigate the effects of community structure on synchronization, a pinning control strategy is researched in a class of complex networks with community structure in this paper. A feedback control law is designed based on the network community structure information. The stability condition is given and proved by using Lyapunov stability theory. Our research shows that as to community structure networks, there being a threshold hT≈5, when coupling strength bellows this threshold, the stronger coupling strength corresponds to higher synchronizability; vice versa, the stronger coupling strength brings lower synchronizability. In addition the synchronizability of overlapping and nonoverlapping community structure networks was simulated and analyzed; while the nodes were controlled randomly and intensively, the results show that intensive control strategy is better than the random one. The network will reach synchronization easily when the node with largest betweenness was controlled. Furthermore, four difference networks’ synchronizability, such as Barabási-Albert network, Watts-Strogatz network, Erdös-Rényi network, and community structure network, are simulated; the research shows that the community structure network is more easily synchronized under the same control strength.

True Nature of Supply Network Communication Structure

Directory of Open Access Journals (Sweden)

Lokhman Hakim bin Osman

2016-04-01

Full Text Available Globalization of world economy has altered the definition of organizational structure. Global supply chain can no longer be viewed as an arm-length structure. It has become more complex. The complexity demands deeper research and understanding. This research analyzed a structure of supply network in an attempt to elucidate the true structure of the supply network. Using the quantitative Social Network Analysis methodology, findings of this study indicated that, the structure of the supply network differs depending on the types of network relations. An important implication of these findings would be a more focus resource management upon network relationship development that is based on firms’ positions in the different network structure. This research also contributes to the various strategies of effective and efficient supply chain management.

Airline network structure in competitive market

Directory of Open Access Journals (Sweden)

Babić Danica D.

2014-01-01

Full Text Available Airline's network is the key element of its business strategy and selected network structure will not have influence only on the airline's costs but could gain some advantage in revenues, too. Network designing implies that an airline has to make decisions about markets that it will serve and how to serve those markets. Network choice raises the following questions for an airline: a what markets to serve, b how to serve selected markets, c what level of service to offer, d what are the benefits/cost of the that decisions and e what is the influence of the competition. We analyzed the existing airline business models and corresponding network structure. The paper highlights the relationship between the network structures and the airline business strategies. Using a simple model we examine the relationship between the network structure and service quality in deregulated market.

Towards structural controllability of local-world networks

International Nuclear Information System (INIS)

Sun, Shiwen; Ma, Yilin; Wu, Yafang; Wang, Li; Xia, Chengyi

2016-01-01

Controlling complex networks is of vital importance in science and engineering. Meanwhile, local-world effect is an important ingredient which should be taken into consideration in the complete description of real-world complex systems. In this letter, structural controllability of a class of local-world networks is investigated. Through extensive numerical simulations, firstly, effects of local world size M and network size N on structural controllability are examined. For local-world networks with sparse topological configuration, compared to network size, local-world size can induce stronger influence on controllability, however, for dense networks, controllability is greatly affected by network size and local-world effect can be neglected. Secondly, relationships between controllability and topological properties are analyzed. Lastly, the robustness of local-world networks under targeted attacks regarding structural controllability is discussed. These results can help to deepen the understanding of structural complexity and connectivity patterns of complex systems. - Highlights: • Structural controllability of a class of local-world networks is investigated. • For sparse local-world networks, compared to network size, local-world size can bring stronger influence on controllability. • For dense networks, controllability is greatly affected by network size and the effect of local-world size can be neglected. • Structural controllability against targeted node attacks is discussed.

Towards structural controllability of local-world networks

Energy Technology Data Exchange (ETDEWEB)

Sun, Shiwen, E-mail: sunsw80@126.com [Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384 (China); Key Laboratory of Computer Vision and System (Tianjin University of Technology), Ministry of Education, Tianjin 300384 (China); Ma, Yilin; Wu, Yafang; Wang, Li; Xia, Chengyi [Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384 (China); Key Laboratory of Computer Vision and System (Tianjin University of Technology), Ministry of Education, Tianjin 300384 (China)

2016-05-20

Controlling complex networks is of vital importance in science and engineering. Meanwhile, local-world effect is an important ingredient which should be taken into consideration in the complete description of real-world complex systems. In this letter, structural controllability of a class of local-world networks is investigated. Through extensive numerical simulations, firstly, effects of local world size M and network size N on structural controllability are examined. For local-world networks with sparse topological configuration, compared to network size, local-world size can induce stronger influence on controllability, however, for dense networks, controllability is greatly affected by network size and local-world effect can be neglected. Secondly, relationships between controllability and topological properties are analyzed. Lastly, the robustness of local-world networks under targeted attacks regarding structural controllability is discussed. These results can help to deepen the understanding of structural complexity and connectivity patterns of complex systems. - Highlights: • Structural controllability of a class of local-world networks is investigated. • For sparse local-world networks, compared to network size, local-world size can bring stronger influence on controllability. • For dense networks, controllability is greatly affected by network size and the effect of local-world size can be neglected. • Structural controllability against targeted node attacks is discussed.

Exploring biological network structure with clustered random networks

Directory of Open Access Journals (Sweden)

Bansal Shweta

2009-12-01

Full Text Available Abstract Background Complex biological systems are often modeled as networks of interacting units. Networks of biochemical interactions among proteins, epidemiological contacts among hosts, and trophic interactions in ecosystems, to name a few, have provided useful insights into the dynamical processes that shape and traverse these systems. The degrees of nodes (numbers of interactions and the extent of clustering (the tendency for a set of three nodes to be interconnected are two of many well-studied network properties that can fundamentally shape a system. Disentangling the interdependent effects of the various network properties, however, can be difficult. Simple network models can help us quantify the structure of empirical networked systems and understand the impact of various topological properties on dynamics. Results Here we develop and implement a new Markov chain simulation algorithm to generate simple, connected random graphs that have a specified degree sequence and level of clustering, but are random in all other respects. The implementation of the algorithm (ClustRNet: Clustered Random Networks provides the generation of random graphs optimized according to a local or global, and relative or absolute measure of clustering. We compare our algorithm to other similar methods and show that ours more successfully produces desired network characteristics. Finding appropriate null models is crucial in bioinformatics research, and is often difficult, particularly for biological networks. As we demonstrate, the networks generated by ClustRNet can serve as random controls when investigating the impacts of complex network features beyond the byproduct of degree and clustering in empirical networks. Conclusion ClustRNet generates ensembles of graphs of specified edge structure and clustering. These graphs allow for systematic study of the impacts of connectivity and redundancies on network function and dynamics. This process is a key step in

An examination of a reciprocal relationship between network governance and network structure

DEFF Research Database (Denmark)

Bergenholtz, Carsten; Goduscheit, René Chester

2011-01-01

In the present article, we examine the network structure and governance of inter-organisational innovation networks over time. Network governance refers to the issue of how to manage and coordinate the relational activities and processes in the network while research on network structure deals...

Comparison of leaf-on and leaf-off ALS data for mapping riparian tree species

Science.gov (United States)

Laslier, Marianne; Ba, Antoine; Hubert-Moy, Laurence; Dufour, Simon

2017-10-01

Forest species composition is a fundamental indicator of forest study and management. However, describing forest species composition at large scales and of highly diverse populations remains an issue for which remote sensing can provide significant contribution, in particular, Airborne Laser Scanning (ALS) data. Riparian corridors are good examples of highly valuable ecosystems, with high species richness and large surface areas that can be time consuming and expensive to monitor with in situ measurements. Remote sensing could be useful to study them, but few studies have focused on monitoring riparian tree species using ALS data. This study aimed to determine which metrics derived from ALS data are best suited to identify and map riparian tree species. We acquired very high density leaf-on and leaf-off ALS data along the Sélune River (France). In addition, we inventoried eight main riparian deciduous tree species along the study site. After manual segmentation of the inventoried trees, we extracted 68 morphological and structural metrics from both leaf-on and leaf-off ALS point clouds. Some of these metrics were then selected using Sequential Forward Selection (SFS) algorithm. Support Vector Machine (SVM) classification results showed good accuracy with 7 metrics (0.77). Both leaf-on and leafoff metrics were kept as important metrics for distinguishing tree species. Results demonstrate the ability of 3D information derived from high density ALS data to identify riparian tree species using external and internal structural metrics. They also highlight the complementarity of leaf-on and leaf-off Lidar data for distinguishing riparian tree species.

Nest Digging by Leaf-Cutting Ants: Effect of Group Size and Functional Structures

Directory of Open Access Journals (Sweden)

Roberto da Silva Camargo

2012-01-01

Full Text Available Leaf-cutting ant workers dig underground chambers, for housing their symbiotic fungus, interconnected by a vast quantity of tunnels whose function is to permit the entrance of food (leaves, gaseous exchanges, and movement of workers, offspring, and the queen. Digging is a task executed by a group of workers, but little is known about the group effect and group-constructed functional structures. Thus, we analyzed the structures formed by worker groups (5, 10, 20, and 40 individuals of the leaf-cutting ant, Atta sexdens rubropilosa, for 2 days of excavation. The digging arena was the same for the 4 groups, with each group corresponding to a different density. Our results verified a pattern of tunneling by the workers, but no chamber was constructed. The group effect is well known, since the 40-worker group dug significantly more than the groups of 5, 10, and 20. These groups did not differ statistically from each other. Analysis of load/worker verified that workers of the smallest group carried the greatest load. Our paper demonstrates the group effect on the digging of nests, namely, that excavation is proportional to group size, but without emergence of a functional structure such as a chamber.

Optimal neural networks for protein-structure prediction

International Nuclear Information System (INIS)

Head-Gordon, T.; Stillinger, F.H.

1993-01-01

The successful application of neural-network algorithms for prediction of protein structure is stymied by three problem areas: the sparsity of the database of known protein structures, poorly devised network architectures which make the input-output mapping opaque, and a global optimization problem in the multiple-minima space of the network variables. We present a simplified polypeptide model residing in two dimensions with only two amino-acid types, A and B, which allows the determination of the global energy structure for all possible sequences of pentamer, hexamer, and heptamer lengths. This model simplicity allows us to compile a complete structural database and to devise neural networks that reproduce the tertiary structure of all sequences with absolute accuracy and with the smallest number of network variables. These optimal networks reveal that the three problem areas are convoluted, but that thoughtful network designs can actually deconvolute these detrimental traits to provide network algorithms that genuinely impact on the ability of the network to generalize or learn the desired mappings. Furthermore, the two-dimensional polypeptide model shows sufficient chemical complexity so that transfer of neural-network technology to more realistic three-dimensional proteins is evident

The influence of branch order on optimal leaf vein geometries: Murray's law and area preserving branching.

Directory of Open Access Journals (Sweden)

Charles A Price

Full Text Available Models that predict the form of hierarchical branching networks typically invoke optimization based on biomechanical similitude, the minimization of impedance to fluid flow, or construction costs. Unfortunately, due to the small size and high number of vein segments found in real biological networks, complete descriptions of networks needed to evaluate such models are rare. To help address this we report results from the analysis of the branching geometry of 349 leaf vein networks comprising over 1.5 million individual vein segments. In addition to measuring the diameters of individual veins before and after vein bifurcations, we also assign vein orders using the Horton-Strahler ordering algorithm adopted from the study of river networks. Our results demonstrate that across all leaves, both radius tapering and the ratio of daughter to parent branch areas for leaf veins are in strong agreement with the expectation from Murray's law. However, as veins become larger, area ratios shift systematically toward values expected under area-preserving branching. Our work supports the idea that leaf vein networks differentiate roles of leaf support and hydraulic supply between hierarchical orders.

Learning Latent Structure in Complex Networks

DEFF Research Database (Denmark)

Mørup, Morten; Hansen, Lars Kai

such as the Modularity, it has recently been shown that latent structure in complex networks is learnable by Bayesian generative link distribution models (Airoldi et al., 2008, Hofman and Wiggins, 2008). In this paper we propose a new generative model that allows representation of latent community structure......Latent structure in complex networks, e.g., in the form of community structure, can help understand network dynamics, identify heterogeneities in network properties, and predict ‘missing’ links. While most community detection algorithms are based on optimizing heuristic clustering objectives...... as in the previous Bayesian approaches and in addition allows learning of node specific link properties similar to that in the modularity objective. We employ a new relaxation method for efficient inference in these generative models that allows us to learn the behavior of very large networks. We compare the link...

Robustness and structure of complex networks

Science.gov (United States)

Shao, Shuai

This dissertation covers the two major parts of my PhD research on statistical physics and complex networks: i) modeling a new type of attack -- localized attack, and investigating robustness of complex networks under this type of attack; ii) discovering the clustering structure in complex networks and its influence on the robustness of coupled networks. Complex networks appear in every aspect of our daily life and are widely studied in Physics, Mathematics, Biology, and Computer Science. One important property of complex networks is their robustness under attacks, which depends crucially on the nature of attacks and the structure of the networks themselves. Previous studies have focused on two types of attack: random attack and targeted attack, which, however, are insufficient to describe many real-world damages. Here we propose a new type of attack -- localized attack, and study the robustness of complex networks under this type of attack, both analytically and via simulation. On the other hand, we also study the clustering structure in the network, and its influence on the robustness of a complex network system. In the first part, we propose a theoretical framework to study the robustness of complex networks under localized attack based on percolation theory and generating function method. We investigate the percolation properties, including the critical threshold of the phase transition pc and the size of the giant component Pinfinity. We compare localized attack with random attack and find that while random regular (RR) networks are more robust against localized attack, Erdoḧs-Renyi (ER) networks are equally robust under both types of attacks. As for scale-free (SF) networks, their robustness depends crucially on the degree exponent lambda. The simulation results show perfect agreement with theoretical predictions. We also test our model on two real-world networks: a peer-to-peer computer network and an airline network, and find that the real-world networks

STRUCTURE AND COOPTATION IN ORGANIZATION NETWORK

Directory of Open Access Journals (Sweden)

Valéria Riscarolli

2007-10-01

Full Text Available Business executive are rethinking business concept, based on horizontalization principles. As so, most organizational functions are outsourced, leading the enterprise to build business through a network of organizations. Here we study the case of Cia Hering’s network of organizations, a leader in knit apparel segment in Latin America (IEMI, 2004, looking at the network’s structure and levels of cooptation. A theoretical model was used using Quinn et al. (2001 “sun ray” network structure as basis to analyze the case study. Main results indicate higher degree of structural conformity, but incipient degree of coopetation in the network.

An evolving network model with community structure

International Nuclear Information System (INIS)

Li Chunguang; Maini, Philip K

2005-01-01

Many social and biological networks consist of communities-groups of nodes within which connections are dense, but between which connections are sparser. Recently, there has been considerable interest in designing algorithms for detecting community structures in real-world complex networks. In this paper, we propose an evolving network model which exhibits community structure. The network model is based on the inner-community preferential attachment and inter-community preferential attachment mechanisms. The degree distributions of this network model are analysed based on a mean-field method. Theoretical results and numerical simulations indicate that this network model has community structure and scale-free properties

Immunization of networks with community structure

International Nuclear Information System (INIS)

Masuda, Naoki

2009-01-01

In this study, an efficient method to immunize modular networks (i.e. networks with community structure) is proposed. The immunization of networks aims at fragmenting networks into small parts with a small number of removed nodes. Its applications include prevention of epidemic spreading, protection against intentional attacks on networks, and conservation of ecosystems. Although preferential immunization of hubs is efficient, good immunization strategies for modular networks have not been established. On the basis of an immunization strategy based on eigenvector centrality, we develop an analytical framework for immunizing modular networks. To this end, we quantify the contribution of each node to the connectivity in a coarse-grained network among modules. We verify the effectiveness of the proposed method by applying it to model and real networks with modular structure.

The network structure of human personality according to the NEO-PI-R: matching network community structure to factor structure.

Directory of Open Access Journals (Sweden)

Rutger Goekoop

Full Text Available 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.

Patchworking Network Structures

DEFF Research Database (Denmark)

Norus, Jesper

2004-01-01

analyzes fourdifferent managerial strategies of how to create network structures to deal with theinterfaces between industry, university and public institutions. The research-orientedstrategy, the incubator strategy, the industrial-partnering strategy, and the policyorientedstrategy. The research...... groups has been treated as a contingent factor.However, little attention has been given to the managerial efforts that entrepreneurshave make to establish the fit between small firms, university research, and publicpolicies such as regulatory policies and R&D policies through network-type structures.......New biotechnology organizations are perfect objects to study these relationshipsbecause new biotechnologies and techniques predominantly come from the universitysector (Kenney, 1986; Yoxen; 1984; Zucker & Darby, 1997; Robbins-Roth, 2001).From the perspective of the small biotechnology firms (SBFs,) this paper...

Network structure and travel time perception.

Science.gov (United States)

Parthasarathi, Pavithra; Levinson, David; Hochmair, Hartwig

2013-01-01

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

The plant economics spectrum is structured by leaf habits and growth forms across subtropical species.

Science.gov (United States)

Zhao, Yan-Tao; Ali, Arshad; Yan, En-Rong

2017-02-01

The plant economics spectrum that integrates the combination of leaf and wood syndromes provides a useful framework for the examination of species strategies at the whole-plant level. However, it remains unclear how species that differ in leaf habits and growth forms are integrated within the plant economics spectrum in subtropical forests. We measured five leaf and six wood traits across 58 subtropical plant species, which represented two leaf habits (evergreen vs deciduous) and two growth forms (tree vs shrub) in eastern China. Principal component analysis (PCA) was employed separately to construct the leaf (LES), wood (WES) and whole-plant (WPES) economics spectra. Leaf and wood traits are highly intra- and intercorrelated, thus defining not only the LES and WES, but also a WPES. Multi-trait variations in PCAs revealed that the traits which were representative of the acquisitive strategy, i.e., cheap tissue investment and rapid returns on that investment, were clustered at one end, while traits that represented the conservative strategy, i.e., expensive tissue investment and slower returns, were clustered at other end in each of the axes of the leaf and wood syndromes (PC1-axis) and the plant height strategy (PC2-axis). The local WPES, LES and WES were tightly correlated with each other. Evergreens shaped the conservative side, while deciduous species structured the acquisitive side of the WPES and LES. With respect to plant height strategies, trees formulated the acquisitive side and shrub species made up the conservative side of the WPES, LES and WES. In conclusion, our results suggested that the LES and WES were coordinated to a WPES for subtropical species. The finding of this local spectrum of plant form and function would be beneficial for modeling nutrient fluxes and species compositions in the changing climate, but also for understanding species strategies in an evolutionary context. © The Author 2016. Published by Oxford University Press. All rights

Information transfer in community structured multiplex networks

Science.gov (United States)

Solé Ribalta, Albert; Granell, Clara; Gómez, Sergio; Arenas, Alex

2015-08-01

The study of complex networks that account for different types of interactions has become a subject of interest in the last few years, specially because its representational power in the description of users interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.). The mathematical description of these interacting networks has been coined under the name of multilayer networks, where each layer accounts for a type of interaction. It has been shown that diffusive processes on top of these networks present a phenomenology that cannot be explained by the naive superposition of single layer diffusive phenomena but require the whole structure of interconnected layers. Nevertheless, the description of diffusive phenomena on multilayer networks has obviated the fact that social networks have strong mesoscopic structure represented by different communities of individuals driven by common interests, or any other social aspect. In this work, we study the transfer of information in multilayer networks with community structure. The final goal is to understand and quantify, if the existence of well-defined community structure at the level of individual layers, together with the multilayer structure of the whole network, enhances or deteriorates the diffusion of packets of information.

Information transfer in community structured multiplex networks

Directory of Open Access Journals (Sweden)

Albert eSolé Ribalta

2015-08-01

Full Text Available The study of complex networks that account for different types of interactions has become a subject of interest in the last few years, specially because its representational power in the description of users interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.. The mathematical description of these interacting networks has been coined under the name of multilayer networks, where each layer accounts for a type of interaction. It has been shown that diffusive processes on top of these networks present a phenomenology that cannot be explained by the naive superposition of single layer diffusive phenomena but require the whole structure of interconnected layers. Nevertheless, the description of diffusive phenomena on multilayer networks has obviated the fact that social networks have strong mesoscopic structure represented by different communities of individuals driven by common interests, or any other social aspect. In this work, we study the transfer of information in multilayer networks with community structure. The final goal is to understand and quantify, if the existence of well-defined community structure at the level of individual layers, together with the multilayer structure of the whole network, enhances or deteriorates the diffusion of packets of information.

Advanced Polymer Network Structures

Science.gov (United States)

2016-02-01

attractive interaction (n = 2.0) and a neutral interaction (n = 1.0); n is equal to unity for self-interactions among the monomers of first network and...... Network Structures by Robert Lambeth, Joseph Lenhart, and Tim Sirk Weapons and Materials Research Directorate, ARL Yelena Sliozberg TKC Global

Epidemic spreading on complex networks with community structures

NARCIS (Netherlands)

Stegehuis, C.; van der Hofstad, R.W.; van Leeuwaarden, J.S.H.

2016-01-01

Many real-world networks display a community structure. We study two random graph models that create a network with similar community structure as a given network. One model preserves the exact community structure of the original network, while the other model only preserves the set of communities

Biomimetic fabrication and tunable wetting properties of three-dimensional hierarchical ZnO structures by combining soft lithography templated with lotus leaf and hydrothermal treatments

OpenAIRE

Dai, Shuxi; Zhang, Dianbo; Shi, Qing; Han, Xiao; Wang, Shujie; Du, Zuliang

2013-01-01

Three-dimensional hierarchical ZnO films with lotus-leaf-like micro/nano structures were successfully fabricated via a biomimetic route combining sol-gel technique, soft lithography and hydrothermal treatments. PDMS mold replicated from a fresh lotus leaf was used to imprint microscale pillar structures directly into a ZnO sol film. Hierarchical ZnO micro/nano structures were subsequently fabricated by a low-temperature hydrothermal growth of secondary ZnO nanorod arrays on the micro-structur...

Transcriptional analyses of natural leaf senescence in maize.

Directory of Open Access Journals (Sweden)

Wei Yang Zhang

Full Text Available Leaf senescence is an important biological process that contributes to grain yield in crops. To study the molecular mechanisms underlying natural leaf senescence, we harvested three different developmental ear leaves of maize, mature leaves (ML, early senescent leaves (ESL, and later senescent leaves (LSL, and analyzed transcriptional changes using RNA-sequencing. Three sets of data, ESL vs. ML, LSL vs. ML, and LSL vs. ESL, were compared, respectively. In total, 4,552 genes were identified as differentially expressed. Functional classification placed these genes into 18 categories including protein metabolism, transporters, and signal transduction. At the early stage of leaf senescence, genes involved in aromatic amino acids (AAAs biosynthetic process and transport, cellular polysaccharide biosynthetic process, and the cell wall macromolecule catabolic process, were up-regulated. Whereas, genes involved in amino acid metabolism, transport, apoptosis, and response to stimulus were up-regulated at the late stage of leaf senescence. Further analyses reveals that the transport-related genes at the early stage of leaf senescence potentially take part in enzyme and amino acid transport and the genes upregulated at the late stage are involved in sugar transport, indicating nutrient recycling mainly takes place at the late stage of leaf senescence. Comparison between the data of natural leaf senescence in this study and previously reported data for Arabidopsis implies that the mechanisms of leaf senescence in maize are basically similar to those in Arabidopsis. A comparison of natural and induced leaf senescence in maize was performed. Athough many basic biological processes involved in senescence occur in both types of leaf senescence, 78.07% of differentially expressed genes in natural leaf senescence were not identifiable in induced leaf senescence, suggesting that differences in gene regulatory network may exist between these two leaf senescence

Disruption of mycorrhizal extraradical mycelium and changes in leaf water status and soil aggregate stability in rootbox-grown trifoliate orange

Directory of Open Access Journals (Sweden)

Ying-Ning eZou

2015-03-01

Full Text Available Arbuscular mycorrhizas possess well developed extraradical mycelium (ERM network that enlarge the surrounding soil for better acquisition of water and nutrients, besides soil aggregation. Distinction in ERM functioning was studied under a rootbox system, which consisted of root+hyphae and root-free hyphae compartments separated by 37-μm nylon mesh with an air gap. Trifoliate orange (Poncirus trifoliata seedlings were inoculated with Funneliformis mosseae in root+hyphae compartment, and the ERM network was established between the two compartments. The ERM network of air gap was disrupted before 8 h of the harvest (one time disruption or multiple disruptions during seedlings acclimation. Our results showed that mycorrhizal inoculation induced a significant increase in growth (plant height, stem diameter, and leaf, stem, and root biomass and physiological characters (leaf relative water content, leaf water potential, and transpiration rate, irrespective of ERM status. Easily-extractable glomalin-related soil protein (EE-GRSP and total GRSP (T-GRSP concentration and mean weight diameter (MWD, an indicator of soil aggregate stability were significantly higher in mycorrhizosphere of root+hyphae and root-free hyphae compartments than non-mycorrhizosphere. One time disruption of ERM network did not influence plant growth and soil properties but only notably decreased leaf water. Periodical disruption of ERM network at weekly interval markedly inhibited the mycorrhizal roles on plant growth, leaf water, GRSP production, and MWD in root+hyphae and hyphae chambers. EE-GRSP was the most responsive GRSP fraction to changes in leaf water and MWD under root+hyphae and hyphae conditions. It suggests that effect of peridical disruption of ERM network was more impactful than one-time disruption of ERM network with regard to leaf water, plant growth, and aggregate stability responses, thereby, implying ERM network aided in developing the host plant metabolically

Global Electricity Trade Network: Structures and Implications

Science.gov (United States)

Ji, Ling; Jia, Xiaoping; Chiu, Anthony S. F.; Xu, Ming

2016-01-01

Nations increasingly trade electricity, and understanding the structure of the global power grid can help identify nations that are critical for its reliability. This study examines the global grid as a network with nations as nodes and international electricity trade as links. We analyze the structure of the global electricity trade network and find that the network consists of four sub-networks, and provide a detailed analysis of the largest network, Eurasia. Russia, China, Ukraine, and Azerbaijan have high betweenness measures in the Eurasian sub-network, indicating the degrees of centrality of the positions they hold. The analysis reveals that the Eurasian sub-network consists of seven communities based on the network structure. We find that the communities do not fully align with geographical proximity, and that the present international electricity trade in the Eurasian sub-network causes an approximately 11 million additional tons of CO2 emissions. PMID:27504825

Network Ecology and Adolescent Social Structure.

Science.gov (United States)

McFarland, Daniel A; Moody, James; Diehl, David; Smith, Jeffrey A; Thomas, Reuben J

2014-12-01

Adolescent societies-whether arising from weak, short-term classroom friendships or from close, long-term friendships-exhibit various levels of network clustering, segregation, and hierarchy. Some are rank-ordered caste systems and others are flat, cliquish worlds. Explaining the source of such structural variation remains a challenge, however, because global network features are generally treated as the agglomeration of micro-level tie-formation mechanisms, namely balance, homophily, and dominance. How do the same micro-mechanisms generate significant variation in global network structures? To answer this question we propose and test a network ecological theory that specifies the ways features of organizational environments moderate the expression of tie-formation processes, thereby generating variability in global network structures across settings. We develop this argument using longitudinal friendship data on schools (Add Health study) and classrooms (Classroom Engagement study), and by extending exponential random graph models to the study of multiple societies over time.

Communication on the structure of biological networks

Indian Academy of Sciences (India)

Networks are widely used to represent interaction pattern among the components in complex systems. Structures of real networks from different domains may vary quite significantly. As there is an interplay between network architecture and dynamics, structure plays an important role in communication and spreading of ...

The overlapping community structure of structural brain network in young healthy individuals.

Directory of Open Access Journals (Sweden)

Kai Wu

2011-05-01

Full Text Available Community structure is a universal and significant feature of many complex networks in biology, society, and economics. Community structure has also been revealed in human brain structural and functional networks in previous studies. However, communities overlap and share many edges and nodes. Uncovering the overlapping community structure of complex networks remains largely unknown in human brain networks. Here, using regional gray matter volume, we investigated the structural brain network among 90 brain regions (according to a predefined anatomical atlas in 462 young, healthy individuals. Overlapped nodes between communities were defined by assuming that nodes (brain regions can belong to more than one community. We demonstrated that 90 brain regions were organized into 5 overlapping communities associated with several well-known brain systems, such as the auditory/language, visuospatial, emotion, decision-making, social, control of action, memory/learning, and visual systems. The overlapped nodes were mostly involved in an inferior-posterior pattern and were primarily related to auditory and visual perception. The overlapped nodes were mainly attributed to brain regions with higher node degrees and nodal efficiency and played a pivotal role in the flow of information through the structural brain network. Our results revealed fuzzy boundaries between communities by identifying overlapped nodes and provided new insights into the understanding of the relationship between the structure and function of the human brain. This study provides the first report of the overlapping community structure of the structural network of the human brain.

Resistance and Security Index of Networks: Structural Information Perspective of Network Security.

Science.gov (United States)

Li, Angsheng; Hu, Qifu; Liu, Jun; Pan, Yicheng

2016-06-03

Recently, Li and Pan defined the metric of the K-dimensional structure entropy of a structured noisy dataset G to be the information that controls the formation of the K-dimensional structure of G that is evolved by the rules, order and laws of G, excluding the random variations that occur in G. Here, we propose the notion of resistance of networks based on the one- and two-dimensional structural information of graphs. Given a graph G, we define the resistance of G, written , as the greatest overall number of bits required to determine the code of the module that is accessible via random walks with stationary distribution in G, from which the random walks cannot escape. We show that the resistance of networks follows the resistance law of networks, that is, for a network G, the resistance of G is , where and are the one- and two-dimensional structure entropies of G, respectively. Based on the resistance law, we define the security index of a network G to be the normalised resistance of G, that is, . We show that the resistance and security index are both well-defined measures for the security of the networks.

Resistance and Security Index of Networks: Structural Information Perspective of Network Security

Science.gov (United States)

Li, Angsheng; Hu, Qifu; Liu, Jun; Pan, Yicheng

2016-01-01

Recently, Li and Pan defined the metric of the K-dimensional structure entropy of a structured noisy dataset G to be the information that controls the formation of the K-dimensional structure of G that is evolved by the rules, order and laws of G, excluding the random variations that occur in G. Here, we propose the notion of resistance of networks based on the one- and two-dimensional structural information of graphs. Given a graph G, we define the resistance of G, written , as the greatest overall number of bits required to determine the code of the module that is accessible via random walks with stationary distribution in G, from which the random walks cannot escape. We show that the resistance of networks follows the resistance law of networks, that is, for a network G, the resistance of G is , where and are the one- and two-dimensional structure entropies of G, respectively. Based on the resistance law, we define the security index of a network G to be the normalised resistance of G, that is, . We show that the resistance and security index are both well-defined measures for the security of the networks. PMID:27255783

Resistance and Security Index of Networks: Structural Information Perspective of Network Security

Science.gov (United States)

Li, Angsheng; Hu, Qifu; Liu, Jun; Pan, Yicheng

2016-06-01

Recently, Li and Pan defined the metric of the K-dimensional structure entropy of a structured noisy dataset G to be the information that controls the formation of the K-dimensional structure of G that is evolved by the rules, order and laws of G, excluding the random variations that occur in G. Here, we propose the notion of resistance of networks based on the one- and two-dimensional structural information of graphs. Given a graph G, we define the resistance of G, written , as the greatest overall number of bits required to determine the code of the module that is accessible via random walks with stationary distribution in G, from which the random walks cannot escape. We show that the resistance of networks follows the resistance law of networks, that is, for a network G, the resistance of G is , where and are the one- and two-dimensional structure entropies of G, respectively. Based on the resistance law, we define the security index of a network G to be the normalised resistance of G, that is, . We show that the resistance and security index are both well-defined measures for the security of the networks.

Fog inhibition, satellite fauna and unusual leaf structure in a Namib Desert dune plant Trianthema hereroensis

International Nuclear Information System (INIS)

Seely, M.K.; De Vos, M.P.; Louw, G.N.

1977-01-01

The plant Trianthema hereroensis, which is endemic to the Namib Desert, has been shown to absorb tritiated water rapidly through its leaves and translocate the labelled water to the root system. The unusual leaf structure and the associated satellite fauna have been described [af

Evolving production network structures

DEFF Research Database (Denmark)

Grunow, Martin; Gunther, H.O.; Burdenik, H.

2007-01-01

When deciding about future production network configurations, the current structures have to be taken into account. Further, core issues such as the maturity of the products and the capacity requirements for test runs and ramp-ups must be incorporated. Our approach is based on optimization...... modelling and assigns products and capacity expansions to production sites under the above constraints. It also considers the production complexity at the individual sites and the flexibility of the network. Our implementation results for a large manufacturing network reveal substantial possible cost...

Structure of forest ecosystems and leaf area index of wood plants -results of monitoring over the years 1991-1994

International Nuclear Information System (INIS)

Oszlanyi, J.

1995-01-01

Monitored characteristics and their dynamics over the last four vegetation seasons reveal the following conclusions: 1) Changes of monitored parameters (e.g. the structure of tree and shrub layer, the leaf area index) are slow, drab and insignificant at the permanent monitoring representing a major part of forest ecosystems of the area affected by the Hydroelectric power structures Gabcikovo. Despite the absence of floods, the ground water level is at a sufficient height to contact rhisosphere of wood plants and the recorded changes are in accord with growth regularities. 2) An increase of the ground water level in the upper part of the monitored territory and a partial renaturation of hydropedological conditions led to an improvement of production-ecological parameters of the area. Changes of its structure are of positive tendency, the leaf area index is stabilised at high values and somewhere even increased (in 1994 being by 70-80% higher than in 1991). 3) Localities with a permanent decrease of the ground water level (band along the old river-bed of the Danube, a dry triangle among the old river-bed of the Danube, the inlet canal and the river arm supplied by the intake structure at Dobrohost and other places) were afflicted by negative changes, locally indicating destruction of tree and shrub layers, with the leaf area index significantly reduced by 20-30%. (author). 1 tab., 5 refs [sk

Structural Behavioral Study on the General Aviation Network Based on Complex Network

Science.gov (United States)

Zhang, Liang; Lu, Na

2017-12-01

The general aviation system is an open and dissipative system with complex structures and behavioral features. This paper has established the system model and network model for general aviation. We have analyzed integral attributes and individual attributes by applying the complex network theory and concluded that the general aviation network has influential enterprise factors and node relations. We have checked whether the network has small world effect, scale-free property and network centrality property which a complex network should have by applying degree distribution of functions and proved that the general aviation network system is a complex network. Therefore, we propose to achieve the evolution process of the general aviation industrial chain to collaborative innovation cluster of advanced-form industries by strengthening network multiplication effect, stimulating innovation performance and spanning the structural hole path.

The prisoner's dilemma in structured scale-free networks

International Nuclear Information System (INIS)

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

2009-01-01

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

Clustering coefficient and community structure of bipartite networks

Science.gov (United States)

Zhang, Peng; Wang, Jinliang; Li, Xiaojia; Li, Menghui; Di, Zengru; Fan, Ying

2008-12-01

Many real-world networks display natural bipartite structure, where the basic cycle is a square. In this paper, with the similar consideration of standard clustering coefficient in binary networks, a definition of the clustering coefficient for bipartite networks based on the fraction of squares is proposed. In order to detect community structures in bipartite networks, two different edge clustering coefficients LC4 and LC3 of bipartite networks are defined, which are based on squares and triples respectively. With the algorithm of cutting the edge with the least clustering coefficient, communities in artificial and real world networks are identified. The results reveal that investigating bipartite networks based on the original structure can show the detailed properties that is helpful to get deep understanding about the networks.

Structural principles in network glasses

International Nuclear Information System (INIS)

Boolchand, P.

1986-01-01

Substantial progress in decoding the structure of network glasses has taken place in the past few years. Crucial insights into the molecular structure of glasses have emerged by application of Raman bond and Moessbauer site spectroscopy. In this context, the complimentary role of each spectroscopy as a check on the interpretation of the other, is perhaps one of the more significant developments in the field. New advances in the theory of the subject have also taken place. It is thus appropriate to inquire what general principles if any, have emerged on the structure of real glasses. The author reviews some of the principal ideas on the structure of inorganic network glasses with the aid of specific examples. (Auth.)

Dynamics of vacuum-sealed, double-leaf partitions

Science.gov (United States)

Kavanaugh, Joshua Stephen

The goal of this research is to investigate the feasibility and potential effectiveness of using vacuum-sealed, double-leaf partitions for applications in noise control. Substantial work has been done previously on double-leaf partitions where the acoustics of the inner chamber and mechanical vibrations of structural supports are passively and actively controlled. The work presented here is unique in that the proposed system aims to eliminate the need for active acoustic control of transmitted acoustic energy by removing all the air between the two panels of the double partition. Therefore, the only remaining energy paths would be along the boundary and at the points where there are intermediate structural supports connecting the two panels. The eventual goal of the research is to develop a high-loss double-leaf partition that simplifies active control by removing the need for control of the air cavity and channeling all the energy into discrete structural paths. The work presented here is a first step towards the goal of designing a high-loss, actively-controlled double-leaf partition with an air-evacuated inner chamber. One experiment is conducted to investigate the effects of various levels of vacuum on the response of a double-leaf partition whose panels are mechanically coupled only at the boundary. Another experiment is conducted which investigates the effect of changing the stiffness of an intermediate support coupling the two panels of a double-leaf partition in which a vacuum has been applied to the inner cavity. The available equipment was able to maintain a 99% vacuum between the panels. Both experiments are accompanied by analytical models used to investigate the importance of various dynamic parameters. Results show that the vacuum-sealed system shows some potential for increased transmission loss, primarily by the changing the natural frequencies of the double-leaf partition.

Combining neural networks for protein secondary structure prediction

DEFF Research Database (Denmark)

Riis, Søren Kamaric

1995-01-01

In this paper structured neural networks are applied to the problem of predicting the secondary structure of proteins. A hierarchical approach is used where specialized neural networks are designed for each structural class and then combined using another neural network. The submodels are designed...... by using a priori knowledge of the mapping between protein building blocks and the secondary structure and by using weight sharing. Since none of the individual networks have more than 600 adjustable weights over-fitting is avoided. When ensembles of specialized experts are combined the performance...

Implications of network structure on public health collaboratives.

Science.gov (United States)

Retrum, Jessica H; Chapman, Carrie L; Varda, Danielle M

2013-10-01

Interorganizational collaboration is an essential function of public health agencies. These partnerships form social networks that involve diverse types of partners and varying levels of interaction. Such collaborations are widely accepted and encouraged, yet very little comparative research exists on how public health partnerships develop and evolve, specifically in terms of how subsequent network structures are linked to outcomes. A systems science approach, that is, one that considers the interdependencies and nested features of networks, provides the appropriate methods to examine the complex nature of these networks. Applying Mays and Scutchfields's categorization of "structural signatures" (breadth, density, and centralization), this research examines how network structure influences the outcomes of public health collaboratives. Secondary data from the Program to Analyze, Record, and Track Networks to Enhance Relationships (www.partnertool.net) data set are analyzed. This data set consists of dyadic (N = 12,355), organizational (N = 2,486), and whole network (N = 99) data from public health collaborations around the United States. Network data are used to calculate structural signatures and weighted least squares regression is used to examine how network structures can predict selected intermediary outcomes (resource contributions, overall value and trust rankings, and outcomes) in public health collaboratives. Our findings suggest that network structure may have an influence on collaborative-related outcomes. The structural signature that had the most significant relationship to outcomes was density, with higher density indicating more positive outcomes. Also significant was the finding that more breadth creates new challenges such as difficulty in reaching consensus and creating ties with other members. However, assumptions that these structural components lead to improved outcomes for public health collaboratives may be slightly premature. Implications of

NAPS: Network Analysis of Protein Structures

Science.gov (United States)

Chakrabarty, Broto; Parekh, Nita

2016-01-01

Traditionally, protein structures have been analysed by the secondary structure architecture and fold arrangement. An alternative approach that has shown promise is modelling proteins as a network of non-covalent interactions between amino acid residues. The network representation of proteins provide a systems approach to topological analysis of complex three-dimensional structures irrespective of secondary structure and fold type and provide insights into structure-function relationship. We have developed a web server for network based analysis of protein structures, NAPS, that facilitates quantitative and qualitative (visual) analysis of residue–residue interactions in: single chains, protein complex, modelled protein structures and trajectories (e.g. from molecular dynamics simulations). The user can specify atom type for network construction, distance range (in Å) and minimal amino acid separation along the sequence. NAPS provides users selection of node(s) and its neighbourhood based on centrality measures, physicochemical properties of amino acids or cluster of well-connected residues (k-cliques) for further analysis. Visual analysis of interacting domains and protein chains, and shortest path lengths between pair of residues are additional features that aid in functional analysis. NAPS support various analyses and visualization views for identifying functional residues, provide insight into mechanisms of protein folding, domain-domain and protein–protein interactions for understanding communication within and between proteins. URL:http://bioinf.iiit.ac.in/NAPS/. PMID:27151201

Network structure of subway passenger flows

Science.gov (United States)

Xu, Q.; Mao, B. H.; Bai, Y.

2016-03-01

The results of transportation infrastructure network analyses have been used to analyze complex networks in a topological context. However, most modeling approaches, including those based on complex network theory, do not fully account for real-life traffic patterns and may provide an incomplete view of network functions. This study utilizes trip data obtained from the Beijing Subway System to characterize individual passenger movement patterns. A directed weighted passenger flow network was constructed from the subway infrastructure network topology by incorporating trip data. The passenger flow networks exhibit several properties that can be characterized by power-law distributions based on flow size, and log-logistic distributions based on the fraction of boarding and departing passengers. The study also characterizes the temporal patterns of in-transit and waiting passengers and provides a hierarchical clustering structure for passenger flows. This hierarchical flow organization varies in the spatial domain. Ten cluster groups were identified, indicating a hierarchical urban polycentric structure composed of large concentrated flows at urban activity centers. These empirical findings provide insights regarding urban human mobility patterns within a large subway network.

Epidemics in adaptive networks with community structure

Science.gov (United States)

Shaw, Leah; Tunc, Ilker

2010-03-01

Models for epidemic spread on static social networks do not account for changes in individuals' social interactions. Recent studies of adaptive networks have modeled avoidance behavior, as non-infected individuals try to avoid contact with infectives. Such models have not generally included realistic social structure. Here we study epidemic spread on an adaptive network with community structure. We model the effect of heterogeneous communities on infection levels and epidemic extinction. We also show how an epidemic can alter the community structure.

Metagovernance, network structure, and legitimacy

DEFF Research Database (Denmark)

Daugbjerg, Carsten; Fawcett, Paul

2017-01-01

This article develops a heuristic for comparative governance analysis. The heuristic depicts four network types by combining network structure with the state’s capacity to metagovern. It suggests that each network type produces a particular combination of input and output legitimacy. We illustrate...... the heuristic and its utility using a comparative study of agri-food networks (organic farming and land use) in four countries, which each exhibit different combinations of input and output legitimacy respectively. The article concludes by using a fifth case study to illustrate what a network type that produces...... high levels of input and output legitimacy might look like....

A 3-D functional-structural grapevine model that couples the dynamics of water transport with leaf gas exchange.

Science.gov (United States)

Zhu, Junqi; Dai, Zhanwu; Vivin, Philippe; Gambetta, Gregory A; Henke, Michael; Peccoux, Anthony; Ollat, Nathalie; Delrot, Serge

2017-12-23

Predicting both plant water status and leaf gas exchange under various environmental conditions is essential for anticipating the effects of climate change on plant growth and productivity. This study developed a functional-structural grapevine model which combines a mechanistic understanding of stomatal function and photosynthesis at the leaf level (i.e. extended Farqhuhar-von Caemmerer-Berry model) and the dynamics of water transport from soil to individual leaves (i.e. Tardieu-Davies model). The model included novel features that account for the effects of xylem embolism (fPLC) on leaf hydraulic conductance and residual stomatal conductance (g0), variable root and leaf hydraulic conductance, and the microclimate of individual organs. The model was calibrated with detailed datasets of leaf photosynthesis, leaf water potential, xylem sap abscisic acid (ABA) concentration and hourly whole-plant transpiration observed within a soil drying period, and validated with independent datasets of whole-plant transpiration under both well-watered and water-stressed conditions. The model well captured the effects of radiation, temperature, CO2 and vapour pressure deficit on leaf photosynthesis, transpiration, stomatal conductance and leaf water potential, and correctly reproduced the diurnal pattern and decline of water flux within the soil drying period. In silico analyses revealed that decreases in g0 with increasing fPLC were essential to avoid unrealistic drops in leaf water potential under severe water stress. Additionally, by varying the hydraulic conductance along the pathway (e.g. root and leaves) and changing the sensitivity of stomatal conductance to ABA and leaf water potential, the model can produce different water use behaviours (i.e. iso- and anisohydric). The robust performance of this model allows for modelling climate effects from individual plants to fields, and for modelling plants with complex, non-homogenous canopies. In addition, the model provides a

Leaf wetness distribution within a potato crop

Science.gov (United States)

Heusinkveld, B. G.

2010-07-01

The Netherlands has a mild maritime climate and therefore the major interest in leaf wetness is associated with foliar plant diseases. During moist micrometeorological conditions (i.e. dew, fog, rain), foliar fungal diseases may develop quickly and thereby destroy a crop quickly. Potato crop monocultures covering several hectares are especially vulnerable to such diseases. Therefore understanding and predicting leaf wetness in potato crops is crucial in crop disease control strategies. A field experiment was carried out in a large homogeneous potato crop in the Netherlands during the growing season of 2008. Two innovative sensor networks were installed as a 3 by 3 grid at 3 heights covering an area of about 2 hectares within two larger potato crops. One crop was located on a sandy soil and one crop on a sandy peat soil. In most cases leaf wetting starts in the top layer and then progresses downward. Leaf drying takes place in the same order after sunrise. A canopy dew simulation model was applied to simulate spatial leaf wetness distribution. The dew model is based on an energy balance model. The model can be run using information on the above-canopy wind speed, air temperature, humidity, net radiation and within canopy air temperature, humidity and soil moisture content and temperature conditions. Rainfall was accounted for by applying an interception model. The results of the dew model agreed well with the leaf wetness sensors if all local conditions were considered. The measurements show that the spatial correlation of leaf wetness decreases downward.

Network Structure, Collaborative Context, and Individual Creativity

DEFF Research Database (Denmark)

Stea, Diego; Soda, Giuseppe; Pedersen, Torben

2016-01-01

Network research has yet to determine whether bonding ties or bridging ties are more beneficial for individual creativity, but the debate has mostly overlooked the organizational context in which such ties are formed. In particular, the causal chain connecting network structures and individual...... with the network’s organizational context. Thus, actors in dense network structures acquire more knowledge and eventually become more creative in organizational contexts where collaboration is high. Conversely, brokers who arbitrage information across disconnected network contacts acquire more valuable knowledge...

Effects of canopy structural variables on retrieval of leaf dry matter content and specific leaf area from remotely sensed data

NARCIS (Netherlands)

Ali, A.M.; Darvishzadeh, R.; Skidmore, A.K.; van Duren, I.C.

2016-01-01

Leaf dry matter content (LDMC) and specific leaf area (SLA) are two important traits in measuring biodiversity. To use remote sensing for the estimation of these traits, it is essential to understand the underlying factors that influence their relationships with canopy reflectance. The effect of

Leaf non-structural carbohydrate allocation and C:N:P stoichiometry in response to light acclimation in seedlings of two subtropical shade-tolerant tree species.

Science.gov (United States)

Xie, Hongtao; Yu, Mukui; Cheng, Xiangrong

2018-03-01

Light availability greatly affects plant growth and development. In shaded environments, plants must respond to reduced light intensity to ensure a regular rate of photosynthesis to maintain the dynamic balance of nutrients, such as leaf non-structural carbohydrates (NSCs), carbon (C), nitrogen (N) and phosphorus (P). To improve our understanding of the nutrient utilization strategies of understory shade-tolerant plants, we compared the variations in leaf NSCs, C, N and P in response to heterogeneous controlled light conditions between two subtropical evergreen broadleaf shade-tolerant species, Elaeocarpus sylvestris (E. sylvestris) and Illicium henryi (I. henryi). Light intensity treatments were applied at five levels (100%, 52%, 33%, 15% and 6% full sunlight) for 30 weeks to identify the effects of reduced light intensity on leaf NSC allocation patterns and leaf C:N:P stoichiometry characteristics. We found that leaf soluble sugar, starch and NSC concentrations in E. sylvestris showed decreasing trends with reduced light intensity, whereas I. henryi presented slightly increasing trends from 100% to 15% full sunlight and then significant decreases at extremely low light intensity (6% full sunlight). The soluble sugar/starch ratio of E. sylvestris decreased with decreasing light intensity, whereas that of I. henryi remained stable. Moreover, both species exhibited increasing trends in leaf N and P concentrations but limited leaf N:P and C:P ratio fluctuations with decreasing light intensity, revealing their adaptive strategies for poor light environments and their growth strategies under ideal light environments. There were highly significant correlations between leaf NSC variables and C:N:P stoichiometric variables in both species, revealing a trade-off in photosynthesis production between leaf NSC and carbon allocation. Thus, shade-tolerant plants readjusted their allocation of leaf NSCs, C, N and P in response to light acclimation. Redundancy analysis showed

Structural analysis of behavioral networks from the Internet

International Nuclear Information System (INIS)

Meiss, M R; Menczer, F; Vespignani, A

2008-01-01

In spite of the Internet's phenomenal growth and social impact, many aspects of the collective communication behavior of its users are largely unknown. Understanding the structure and dynamics of the behavioral networks that connect users with each other and with services across the Internet is key to modeling the network and designing future applications. We present a characterization of the properties of the behavioral networks generated by several million users of the Abilene (Internet2) network. Structural features of these networks offer new insights into scaling properties of network activity and ways of distinguishing particular patterns of traffic. For example, we find that the structure of the behavioral network associated with Web activity is characterized by such extreme heterogeneity as to challenge any simple attempt to model Web server traffic

Structural analysis of behavioral networks from the Internet

Energy Technology Data Exchange (ETDEWEB)

Meiss, M R; Menczer, F [Department of Computer Science, Indiana University, Bloomington, IN 47405 (United States); Vespignani, A [Department of Informatics, Indiana University, Bloomington, IN 47408 (United States)], E-mail: mmeiss@indiana.edu

2008-06-06

In spite of the Internet's phenomenal growth and social impact, many aspects of the collective communication behavior of its users are largely unknown. Understanding the structure and dynamics of the behavioral networks that connect users with each other and with services across the Internet is key to modeling the network and designing future applications. We present a characterization of the properties of the behavioral networks generated by several million users of the Abilene (Internet2) network. Structural features of these networks offer new insights into scaling properties of network activity and ways of distinguishing particular patterns of traffic. For example, we find that the structure of the behavioral network associated with Web activity is characterized by such extreme heterogeneity as to challenge any simple attempt to model Web server traffic.

AfSIS MODIS Collection: Leaf Area Index - FPAR, 2012 Release

Data.gov (United States)

Center for International Earth Science Information Network, Columbia University — The Africa Soil Information Service (AfSIS) Moderate Resolution Imaging Spectroradiometer (MODIS) Collection Leaf Area Index (LAI) and Photosynthetically Active...

Exploring network structure, dynamics, and function using networkx

Energy Technology Data Exchange (ETDEWEB)

Hagberg, Aric [Los Alamos National Laboratory; Swart, Pieter [Los Alamos National Laboratory; S Chult, Daniel [COLGATE UNIV

2008-01-01

NetworkX is a Python language package for exploration and analysis of networks and network algorithms. The core package provides data structures for representing many types of networks, or graphs, including simple graphs, directed graphs, and graphs with parallel edges and self loops. The nodes in NetworkX graphs can be any (hashable) Python object and edges can contain arbitrary data; this flexibility mades NetworkX ideal for representing networks found in many different scientific fields. In addition to the basic data structures many graph algorithms are implemented for calculating network properties and structure measures: shortest paths, betweenness centrality, clustering, and degree distribution and many more. NetworkX can read and write various graph formats for eash exchange with existing data, and provides generators for many classic graphs and popular graph models, such as the Erdoes-Renyi, Small World, and Barabasi-Albert models, are included. The ease-of-use and flexibility of the Python programming language together with connection to the SciPy tools make NetworkX a powerful tool for scientific computations. We discuss some of our recent work studying synchronization of coupled oscillators to demonstrate how NetworkX enables research in the field of computational networks.

Density-based and transport-based core-periphery structures in networks.

Science.gov (United States)

Lee, Sang Hoon; Cucuringu, Mihai; Porter, Mason A

2014-03-01

Networks often possess mesoscale structures, and studying them can yield insights into both structure and function. It is most common to study community structure, but numerous other types of mesoscale structures also exist. In this paper, we examine core-periphery structures based on both density and transport. In such structures, core network components are well-connected both among themselves and to peripheral components, which are not well-connected to anything. We examine core-periphery structures in a wide range of examples of transportation, social, and financial networks-including road networks in large urban areas, a rabbit warren, a dolphin social network, a European interbank network, and a migration network between counties in the United States. We illustrate that a recently developed transport-based notion of node coreness is very useful for characterizing transportation networks. We also generalize this notion to examine core versus peripheral edges, and we show that the resulting diagnostic is also useful for transportation networks. To examine the properties of transportation networks further, we develop a family of generative models of roadlike networks. We illustrate the effect of the dimensionality of the embedding space on transportation networks, and we demonstrate that the correlations between different measures of coreness can be very different for different types of networks.

Relationships of leaf dark respiration to leaf nitrogen, specific leaf area and leaf life-span: a test across biomes and functional groups.

Science.gov (United States)

Reich, Peter B; Walters, Michael B; Ellsworth, David S; Vose, James M; Volin, John C; Gresham, Charles; Bowman, William D

1998-05-01

Based on prior evidence of coordinated multiple leaf trait scaling, we hypothesized that variation among species in leaf dark respiration rate (R d ) should scale with variation in traits such as leaf nitrogen (N), leaf life-span, specific leaf area (SLA), and net photosynthetic capacity (A max ). However, it is not known whether such scaling, if it exists, is similar among disparate biomes and plant functional types. We tested this idea by examining the interspecific relationships between R d measured at a standard temperature and leaf life-span, N, SLA and A max for 69 species from four functional groups (forbs, broad-leafed trees and shrubs, and needle-leafed conifers) in six biomes traversing the Americas: alpine tundra/subalpine forest, Colorado; cold temperate forest/grassland, Wisconsin; cool temperate forest, North Carolina; desert/shrubland, New Mexico; subtropical forest, South Carolina; and tropical rain forest, Amazonas, Venezuela. Area-based R d was positively related to area-based leaf N within functional groups and for all species pooled, but not when comparing among species within any site. At all sites, mass-based R d (R d-mass ) decreased sharply with increasing leaf life-span and was positively related to SLA and mass-based A max and leaf N (leaf N mass ). These intra-biome relationships were similar in shape and slope among sites, where in each case we compared species belonging to different plant functional groups. Significant R d-mass -N mass relationships were observed in all functional groups (pooled across sites), but the relationships differed, with higher R d at any given leaf N in functional groups (such as forbs) with higher SLA and shorter leaf life-span. Regardless of biome or functional group, R d-mass was well predicted by all combinations of leaf life-span, N mass and/or SLA (r 2 ≥ 0.79, P morphological, chemical and metabolic traits.

Learning and structure of neuronal networks

Indian Academy of Sciences (India)

structures, protein–protein interaction networks, social interactions, the Internet, and so on can be described by complex networks [1–5]. Recent developments in the understanding of complex networks has led to deeper insights about their origin and other properties [1–5]. One common realization that emerges from these ...

A Mapping Between Structural and Functional Brain Networks.

Science.gov (United States)

Meier, Jil; Tewarie, Prejaas; Hillebrand, Arjan; Douw, Linda; van Dijk, Bob W; Stufflebeam, Steven M; Van Mieghem, Piet

2016-05-01

The relationship between structural and functional brain networks is still highly debated. Most previous studies have used a single functional imaging modality to analyze this relationship. In this work, we use multimodal data, from functional MRI, magnetoencephalography, and diffusion tensor imaging, and assume that there exists a mapping between the connectivity matrices of the resting-state functional and structural networks. We investigate this mapping employing group averaged as well as individual data. We indeed find a significantly high goodness of fit level for this structure-function mapping. Our analysis suggests that a functional connection is shaped by all walks up to the diameter in the structural network in both modality cases. When analyzing the inverse mapping, from function to structure, longer walks in the functional network also seem to possess minor influence on the structural connection strengths. Even though similar overall properties for the structure-function mapping are found for different functional modalities, our results indicate that the structure-function relationship is modality dependent.

Nonparametric inference of network structure and dynamics

Science.gov (United States)

Peixoto, Tiago P.

The network structure of complex systems determine their function and serve as evidence for the evolutionary mechanisms that lie behind them. Despite considerable effort in recent years, it remains an open challenge to formulate general descriptions of the large-scale structure of network systems, and how to reliably extract such information from data. Although many approaches have been proposed, few methods attempt to gauge the statistical significance of the uncovered structures, and hence the majority cannot reliably separate actual structure from stochastic fluctuations. Due to the sheer size and high-dimensionality of many networks, this represents a major limitation that prevents meaningful interpretations of the results obtained with such nonstatistical methods. In this talk, I will show how these issues can be tackled in a principled and efficient fashion by formulating appropriate generative models of network structure that can have their parameters inferred from data. By employing a Bayesian description of such models, the inference can be performed in a nonparametric fashion, that does not require any a priori knowledge or ad hoc assumptions about the data. I will show how this approach can be used to perform model comparison, and how hierarchical models yield the most appropriate trade-off between model complexity and quality of fit based on the statistical evidence present in the data. I will also show how this general approach can be elegantly extended to networks with edge attributes, that are embedded in latent spaces, and that change in time. The latter is obtained via a fully dynamic generative network model, based on arbitrary-order Markov chains, that can also be inferred in a nonparametric fashion. Throughout the talk I will illustrate the application of the methods with many empirical networks such as the internet at the autonomous systems level, the global airport network, the network of actors and films, social networks, citations among

Robust Superhydrophobic Carbon Nanotube Film with Lotus Leaf Mimetic Multiscale Hierarchical Structures.

Science.gov (United States)

Wang, Pengwei; Zhao, Tianyi; Bian, Ruixin; Wang, Guangyan; Liu, Huan

2017-12-26

Superhydrophobic carbon nanotube (CNT) films have demonstrated many fascinating performances in versatile applications, especially for those involving solid/liquid interfacial processes, because of their ability to affect the material/energy transfer at interfaces. Thus, developing superhydrophobic CNTs has attracted extensive research interests in the past decades, and it could be achieved either by surface coating of low-free energy materials or by constructing micro/nanohierarchical structures via various complicated processes. So far, developing a simple approach to fabricate stable superhydrophobic CNTs remains a challenge because the capillary force induced coalescence frequently happens when interacting with liquid. Herein, drawing inspirations from the lotus leaf, we proposed a simple one-step chemical vapor deposition approach with programmable controlled gas flow to directly fabricate a CNT film with rather stable superhydrophobicity, which can effectively prevent even small water droplets from permeating into the film. The robust superhydrophobicity was attributable to typical lotus-leaf-like micro/nanoscale hierarchical surface structures of the CNT film, where many microscale clusters composed of entangled nanotubes randomly protrude out of the under-layer aligned nanotubes. Consequently, dual-scale air pockets were trapped within each microscale CNT cluster and between, which could largely reduce the liquid/solid interface, leading to a Cassie state. Moreover, the superhydrophobicity of the CNT film showed excellent durability after long time exposure to air and even to corrosive liquids with a wide range of pH values. We envision that the approach developed is advantageous for versatile physicochemical interfacial processes, such as drag reduction, electrochemical catalysis, anti-icing, and biosensors.

Leaf-IT: An Android application for measuring leaf area.

Science.gov (United States)

Schrader, Julian; Pillar, Giso; Kreft, Holger

2017-11-01

The use of plant functional traits has become increasingly popular in ecological studies because plant functional traits help to understand key ecological processes in plant species and communities. This also includes changes in diversity, inter- and intraspecific interactions, and relationships of species at different spatiotemporal scales. Leaf traits are among the most important traits as they describe key dimensions of a plant's life history strategy. Further, leaf area is a key parameter with relevance for other traits such as specific leaf area, which in turn correlates with leaf chemical composition, photosynthetic rate, leaf longevity, and carbon investment. Measuring leaf area usually involves the use of scanners and commercial software and can be difficult under field conditions. We present Leaf-IT, a new smartphone application for measuring leaf area and other trait-related areas. Leaf-IT is free, designed for scientific purposes, and runs on Android 4 or higher. We tested the precision and accuracy using objects with standardized area and compared the area measurements of real leaves with the well-established, commercial software WinFOLIA using the Altman-Bland method. Area measurements of standardized objects show that Leaf-IT measures area with high accuracy and precision. Area measurements with Leaf-IT of real leaves are comparable to those of WinFOLIA. Leaf-IT is an easy-to-use application running on a wide range of smartphones. That increases the portability and use of Leaf-IT and makes it possible to measure leaf area under field conditions typical for remote locations. Its high accuracy and precision are similar to WinFOLIA. Currently, its main limitation is margin detection of damaged leaves or complex leaf morphologies.

True Nature of Supply Network Communication Structure (P.1-14

Directory of Open Access Journals (Sweden)

Lokhman Hakim bin Osman

2017-02-01

Full Text Available Globalization of world economy has altered the definition of organizational structure. Global supply chain can no longer be viewed as an arm-length structure. It has become more complex. The complexity demands deeper research and understanding. This research analyzed a structure of supply network in an attempt to elucidate the true structure of the supply network. Using the quantitative Social Network Analysis methodology, findings of this study indicated that, the structure of the supply network differs depending on the types of network relations. An important implication of these findings would be a more focus resource management upon network relationship development that is based on firms’ positions in the different network structure. This research also contributes to the various strategies of effective and efficient supply chain management.Keywords: Supply Chain Management, Network Studies, Inter-Organizational Relations, Social Capital

Inferring network structure from cascades

Science.gov (United States)

Ghonge, Sushrut; Vural, Dervis Can

2017-07-01

Many physical, biological, and social phenomena can be described by cascades taking place on a network. Often, the activity can be empirically observed, but not the underlying network of interactions. In this paper we offer three topological methods to infer the structure of any directed network given a set of cascade arrival times. Our formulas hold for a very general class of models where the activation probability of a node is a generic function of its degree and the number of its active neighbors. We report high success rates for synthetic and real networks, for several different cascade models.

Industrial entrepreneurial network: Structural and functional analysis

Science.gov (United States)

Medvedeva, M. A.; Davletbaev, R. H.; Berg, D. B.; Nazarova, J. J.; Parusheva, S. S.

2016-12-01

Structure and functioning of two model industrial entrepreneurial networks are investigated in the present paper. One of these networks is forming when implementing an integrated project and consists of eight agents, which interact with each other and external environment. The other one is obtained from the municipal economy and is based on the set of the 12 real business entities. Analysis of the networks is carried out on the basis of the matrix of mutual payments aggregated over the certain time period. The matrix is created by the methods of experimental economics. Social Network Analysis (SNA) methods and instruments were used in the present research. The set of basic structural characteristics was investigated: set of quantitative parameters such as density, diameter, clustering coefficient, different kinds of centrality, and etc. They were compared with the random Bernoulli graphs of the corresponding size and density. Discovered variations of random and entrepreneurial networks structure are explained by the peculiarities of agents functioning in production network. Separately, were identified the closed exchange circuits (cyclically closed contours of graph) forming an autopoietic (self-replicating) network pattern. The purpose of the functional analysis was to identify the contribution of the autopoietic network pattern in its gross product. It was found that the magnitude of this contribution is more than 20%. Such value allows using of the complementary currency in order to stimulate economic activity of network agents.

Effects of contact network structure on epidemic transmission trees: implications for data required to estimate network structure.

Science.gov (United States)

Carnegie, Nicole Bohme

2018-01-30

Understanding the dynamics of disease spread is key to developing effective interventions to control or prevent an epidemic. The structure of the network of contacts over which the disease spreads has been shown to have a strong influence on the outcome of the epidemic, but an open question remains as to whether it is possible to estimate contact network features from data collected in an epidemic. The approach taken in this paper is to examine the distributions of epidemic outcomes arising from epidemics on networks with particular structural features to assess whether that structure could be measured from epidemic data and what other constraints might be needed to make the problem identifiable. To this end, we vary the network size, mean degree, and transmissibility of the pathogen, as well as the network feature of interest: clustering, degree assortativity, or attribute-based preferential mixing. We record several standard measures of the size and spread of the epidemic, as well as measures that describe the shape of the transmission tree in order to ascertain whether there are detectable signals in the final data from the outbreak. The results suggest that there is potential to estimate contact network features from transmission trees or pure epidemic data, particularly for diseases with high transmissibility or for which the relevant contact network is of low mean degree. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Dynamical community structure of populations evolving on genotype networks

International Nuclear Information System (INIS)

Capitán, José A.; Aguirre, Jacobo; Manrubia, Susanna

2015-01-01

Neutral evolutionary dynamics of replicators occurs on large and heterogeneous networks of genotypes. These networks, formed by all genotypes that yield the same phenotype, have a complex architecture that conditions the molecular composition of populations and their movements on genome spaces. Here we consider as an example the case of populations evolving on RNA secondary structure neutral networks and study the community structure of the network revealed through dynamical properties of the population at equilibrium and during adaptive transients. We unveil a rich hierarchical community structure that, eventually, can be traced back to the non-trivial relationship between RNA secondary structure and sequence composition. We demonstrate that usual measures of modularity that only take into account the static, topological structure of networks, cannot identify the community structure disclosed by population dynamics

Distance metric learning for complex networks: Towards size-independent comparison of network structures

Science.gov (United States)

Aliakbary, Sadegh; Motallebi, Sadegh; Rashidian, Sina; Habibi, Jafar; Movaghar, Ali

2015-02-01

Real networks show nontrivial topological properties such as community structure and long-tail degree distribution. Moreover, many network analysis applications are based on topological comparison of complex networks. Classification and clustering of networks, model selection, and anomaly detection are just some applications of network comparison. In these applications, an effective similarity metric is needed which, given two complex networks of possibly different sizes, evaluates the amount of similarity between the structural features of the two networks. Traditional graph comparison approaches, such as isomorphism-based methods, are not only too time consuming but also inappropriate to compare networks with different sizes. In this paper, we propose an intelligent method based on the genetic algorithms for integrating, selecting, and weighting the network features in order to develop an effective similarity measure for complex networks. The proposed similarity metric outperforms state of the art methods with respect to different evaluation criteria.

Communication on the structure of biological networks

Indian Academy of Sciences (India)

Introduction. Over the past few years, network science has drawn attention from a large number of ... The qualitative properties of biological networks cannot ... Here, we study the underlying undirected structure of empirical biological networks.

Heritability of the Structures and 13C Fractionation in Tomato Leaf Wax Alkanes: A Genetic Model System to Inform Paleoenvironmental Reconstructions

Directory of Open Access Journals (Sweden)

Amanda L. D. Bender

2017-06-01

Full Text Available Leaf wax n-alkanes are broadly used to reconstruct paleoenvironmental information. However, the utility of n-alkanes as a paleoenvironmental proxy may be modulated by the extent to which biological as well as environmental factors influence the structural and isotopic variability of leaf waxes. In paleoclimate applications, there is usually an implicit assumption that most variation of leaf wax traits through a time series can be attributed to environmental change and that biological sources of variability within plant communities are small. For example, changes in hydrology affect the δ2H of waxes via rainwater and the δ13C of leaf waxes by changing plant communities. We measured the degree of genetic control over δ13C variation in leaf waxes within closely related species with an experimental greenhouse growth study. We measured the proportion of variability in structural and isotopic leaf wax traits that is attributable to genetic variation using a set of 76 introgression lines (ILs between two interfertile Solanum (tomato species: S. lycopersicum cv M82 (hereafter cv M82 and S. pennellii. Leaves of S. pennellii, a wild desert tomato relative, produced significantly more iso-alkanes than cv M82, a domesticated tomato cultivar adapted to water-replete conditions. We report a methylation index to summarize the ratio of branched (iso- and anteiso- to total alkanes. Between Solanum pennellii and cv M82, the iso-alkanes were found to be enriched in 13C by 1.2–1.4‰ over n-alkanes. The broad-sense heritability values (H2 of leaf wax traits describe the degree to which genetic variation contributes to variation of these traits. Variation of individual carbon isotopic compositions of alkanes were of low heritability (H2 = 0.13–0.19, suggesting that most variation in δ13C of leaf waxes in this study can be attributed to environmental variance. This supports the interpretation that variation in the δ13C of wax compounds recorded in sediments

Heritability of the structures and 13C fractionation in tomato leaf wax alkanes: a genetic model system to inform paleoenvironmental reconstructions

Science.gov (United States)

Bender, Amanda L. D.; Chitwood, Daniel H.; Bradley, Alexander S.

2017-06-01

Leaf wax n-alkanes are broadly used to reconstruct paleoenvironmental information. However, the utility of n-alkanes as a paleoenvironmental proxy may be modulated by the extent to which biological as well as environmental factors influence the structural and isotopic variability of leaf waxes. In paleoclimate applications, there is usually an implicit assumption that most variation of leaf wax traits through a time series can be attributed to environmental change and that biological sources of variability within plant communities are small. For example, changes in hydrology affect the δ2H of waxes via rainwater and the δ13C of leaf waxes by changing plant communities. We measured the degree of genetic control over δ13C variation in leaf waxes within closely related species with an experimental greenhouse growth study. We measured the proportion of variability in structural and isotopic leaf wax traits that is attributable to genetic variation using a set of 76 introgression lines (ILs) between two interfertile Solanum (tomato) species: S. lycopersicum cv M82 (hereafter cv M82) and S. pennellii. Leaves of S. pennellii, a wild desert tomato relative, produced significantly more iso-alkanes than cv M82, a domesticated tomato cultivar adapted to water-replete conditions. We report a methylation index to summarize the ratio of branched (iso- and anteiso-) to total alkanes. Between S. pennellii and cv M82, the iso-alkanes were found to be enriched in 13C by 1.2-1.4‰ over n-alkanes. The broad-sense heritability values (H2) of leaf wax traits describe the degree to which genetic variation contributes to variation of these traits. Variation of individual carbon isotopic compositions of alkanes were of low heritability (H2 = 0.13-0.19), suggesting that most variation in δ13C of leaf waxes in this study can be attributed to environmental variance. This supports the interpretation that variation in the δ13C of wax compounds recorded in sediments reflects

Developing a network-level structural capacity index for structural evaluation of pavements.

Science.gov (United States)

2013-03-01

The objective of this project was to develop a structural index for use in network-level pavement evaluation to facilitate : the inclusion of the pavements structural condition in pavement management applications. The primary goal of network-level...

Neural Networks for protein Structure Prediction

DEFF Research Database (Denmark)

Bohr, Henrik

1998-01-01

This is a review about neural network applications in bioinformatics. Especially the applications to protein structure prediction, e.g. prediction of secondary structures, prediction of surface structure, fold class recognition and prediction of the 3-dimensional structure of protein backbones...

Validation of network communicability metrics for the analysis of brain structural networks.

Directory of Open Access Journals (Sweden)

Jennifer Andreotti

Full Text Available Computational network analysis provides new methods to analyze the brain's structural organization based on diffusion imaging tractography data. Networks are characterized by global and local metrics that have recently given promising insights into diagnosis and the further understanding of psychiatric and neurologic disorders. Most of these metrics are based on the idea that information in a network flows along the shortest paths. In contrast to this notion, communicability is a broader measure of connectivity which assumes that information could flow along all possible paths between two nodes. In our work, the features of network metrics related to communicability were explored for the first time in the healthy structural brain network. In addition, the sensitivity of such metrics was analysed using simulated lesions to specific nodes and network connections. Results showed advantages of communicability over conventional metrics in detecting densely connected nodes as well as subsets of nodes vulnerable to lesions. In addition, communicability centrality was shown to be widely affected by the lesions and the changes were negatively correlated with the distance from lesion site. In summary, our analysis suggests that communicability metrics that may provide an insight into the integrative properties of the structural brain network and that these metrics may be useful for the analysis of brain networks in the presence of lesions. Nevertheless, the interpretation of communicability is not straightforward; hence these metrics should be used as a supplement to the more standard connectivity network metrics.

Phase synchronization on small-world networks with community structure

International Nuclear Information System (INIS)

Xiao-Hua, Wang; Li-Cheng, Jiao; Jian-She, Wu

2010-01-01

In this paper, we propose a simple model that can generate small-world network with community structure. The network is introduced as a tunable community organization with parameter r, which is directly measured by the ratio of inter- to intra-community connectivity, and a smaller r corresponds to a stronger community structure. The structure properties, including the degree distribution, clustering, the communication efficiency and modularity are also analysed for the network. In addition, by using the Kuramoto model, we investigated the phase synchronization on this network, and found that increasing the fuzziness of community structure will markedly enhance the network synchronizability; however, in an abnormal region (r ≤ 0.001), the network has even worse synchronizability than the case of isolated communities (r = 0). Furthermore, this network exhibits a remarkable synchronization behaviour in topological scales: the oscillators of high densely interconnected communities synchronize more easily, and more rapidly than the whole network. (general)

Population structure of Xylella fastidiosa associated with almond leaf scorch disease in the San Joaquin Valley of California

Science.gov (United States)

Xylella fastidiosa (Xf) causes disease in many commercial crops including almond leaf scorch (ALS) disease in susceptible almond (Prunus dulcis). In this study, genetic diversity and population structure of Xf associated with ALS disease were evaluated. Strains isolated from two almond production si...

The structural and functional brain networks that support human social networks.

Science.gov (United States)

Noonan, M P; Mars, R B; Sallet, J; Dunbar, R I M; Fellows, L K

2018-02-20

Social skills rely on a specific set of cognitive processes, raising the possibility that individual differences in social networks are related to differences in specific brain structural and functional networks. Here, we tested this hypothesis with multimodality neuroimaging. With diffusion MRI (DMRI), we showed that differences in structural integrity of particular white matter (WM) tracts, including cingulum bundle, extreme capsule and arcuate fasciculus were associated with an individual's social network size (SNS). A voxel-based morphology analysis demonstrated correlations between gray matter (GM) volume and SNS in limbic and temporal lobe regions. These structural changes co-occured with functional network differences. As a function of SNS, dorsomedial and dorsolateral prefrontal cortex showed altered resting-state functional connectivity with the default mode network (DMN). Finally, we integrated these three complementary methods, interrogating the relationship between social GM clusters and specific WM and resting-state networks (RSNs). Probabilistic tractography seeded in these GM nodes utilized the SNS-related WM pathways. Further, the spatial and functional overlap between the social GM clusters and the DMN was significantly closer than other control RSNs. These integrative analyses provide convergent evidence of the role of specific circuits in SNS, likely supporting the adaptive behavior necessary for success in extensive social environments. Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.

Reconstructing consensus Bayesian network structures with application to learning molecular interaction networks

NARCIS (Netherlands)

Fröhlich, H.; Klau, G.W.

2013-01-01

Bayesian Networks are an established computational approach for data driven network inference. However, experimental data is limited in its availability and corrupted by noise. This leads to an unavoidable uncertainty about the correct network structure. Thus sampling or bootstrap based strategies

Disentangling bipartite and core-periphery structure in financial networks

International Nuclear Information System (INIS)

Barucca, Paolo; Lillo, Fabrizio

2016-01-01

A growing number of systems are represented as networks whose architecture conveys significant information and determines many of their properties. Examples of network architecture include modular, bipartite, and core-periphery structures. However inferring the network structure is a non trivial task and can depend sometimes on the chosen null model. Here we propose a method for classifying network structures and ranking its nodes in a statistically well-grounded fashion. The method is based on the use of Belief Propagation for learning through Entropy Maximization on both the Stochastic Block Model (SBM) and the degree-corrected Stochastic Block Model (dcSBM). As a specific application we show how the combined use of the two ensembles—SBM and dcSBM—allows to disentangle the bipartite and the core-periphery structure in the case of the e-MID interbank network. Specifically we find that, taking into account the degree, this interbank network is better described by a bipartite structure, while using the SBM the core-periphery structure emerges only when data are aggregated for more than a week.

Structural stability of interaction networks against negative external fields

Science.gov (United States)

Yoon, S.; Goltsev, A. V.; Mendes, J. F. F.

2018-04-01

We explore structural stability of weighted and unweighted networks of positively interacting agents against a negative external field. We study how the agents support the activity of each other to confront the negative field, which suppresses the activity of agents and can lead to collapse of the whole network. The competition between the interactions and the field shape the structure of stable states of the system. In unweighted networks (uniform interactions) the stable states have the structure of k -cores of the interaction network. The interplay between the topology and the distribution of weights (heterogeneous interactions) impacts strongly the structural stability against a negative field, especially in the case of fat-tailed distributions of weights. We show that apart from critical slowing down there is also a critical change in the system structure that precedes the network collapse. The change can serve as an early warning of the critical transition. To characterize changes of network structure we develop a method based on statistical analysis of the k -core organization and so-called "corona" clusters belonging to the k -cores.

Influence of choice of null network on small-world parameters of structural correlation networks.

Directory of Open Access Journals (Sweden)

S M Hadi Hosseini

Full Text Available In recent years, coordinated variations in brain morphology (e.g., volume, thickness have been employed as a measure of structural association between brain regions to infer large-scale structural correlation networks. Recent evidence suggests that brain networks constructed in this manner are inherently more clustered than random networks of the same size and degree. Thus, null networks constructed by randomizing topology are not a good choice for benchmarking small-world parameters of these networks. In the present report, we investigated the influence of choice of null networks on small-world parameters of gray matter correlation networks in healthy individuals and survivors of acute lymphoblastic leukemia. Three types of null networks were studied: 1 networks constructed by topology randomization (TOP, 2 networks matched to the distributional properties of the observed covariance matrix (HQS, and 3 networks generated from correlation of randomized input data (COR. The results revealed that the choice of null network not only influences the estimated small-world parameters, it also influences the results of between-group differences in small-world parameters. In addition, at higher network densities, the choice of null network influences the direction of group differences in network measures. Our data suggest that the choice of null network is quite crucial for interpretation of group differences in small-world parameters of structural correlation networks. We argue that none of the available null models is perfect for estimation of small-world parameters for correlation networks and the relative strengths and weaknesses of the selected model should be carefully considered with respect to obtained network measures.

Influence of Choice of Null Network on Small-World Parameters of Structural Correlation Networks

Science.gov (United States)

Hosseini, S. M. Hadi; Kesler, Shelli R.

2013-01-01

In recent years, coordinated variations in brain morphology (e.g., volume, thickness) have been employed as a measure of structural association between brain regions to infer large-scale structural correlation networks. Recent evidence suggests that brain networks constructed in this manner are inherently more clustered than random networks of the same size and degree. Thus, null networks constructed by randomizing topology are not a good choice for benchmarking small-world parameters of these networks. In the present report, we investigated the influence of choice of null networks on small-world parameters of gray matter correlation networks in healthy individuals and survivors of acute lymphoblastic leukemia. Three types of null networks were studied: 1) networks constructed by topology randomization (TOP), 2) networks matched to the distributional properties of the observed covariance matrix (HQS), and 3) networks generated from correlation of randomized input data (COR). The results revealed that the choice of null network not only influences the estimated small-world parameters, it also influences the results of between-group differences in small-world parameters. In addition, at higher network densities, the choice of null network influences the direction of group differences in network measures. Our data suggest that the choice of null network is quite crucial for interpretation of group differences in small-world parameters of structural correlation networks. We argue that none of the available null models is perfect for estimation of small-world parameters for correlation networks and the relative strengths and weaknesses of the selected model should be carefully considered with respect to obtained network measures. PMID:23840672

The macroecology of phylogenetically structured hummingbird-plant networks

DEFF Research Database (Denmark)

González, Ana M. Martín; Dalsgaard, Bo; Nogues, David Bravo

2015-01-01

Aim To investigate the association between hummingbird–plant network structure and species richness, phylogenetic signal on species' interaction pattern, insularity and historical and current climate. Location Fifty-four communities along a c. 10,000 km latitudinal gradient across the Americas (39...... approach, we examined the influence of species richness, phylogenetic signal, insularity and current and historical climate conditions on network structure (null-model-corrected specialization and modularity). Results Phylogenetically related species, especially plants, showed a tendency to interact...... with a similar array of mutualistic partners. The spatial variation in network structure exhibited a constant association with species phylogeny (R2 = 0.18–0.19); however, network structure showed the strongest association with species richness and environmental factors (R2 = 0.20–0.44 and R2 = 0...

Final report on the safety assessment of AloeAndongensis Extract, Aloe Andongensis Leaf Juice,aloe Arborescens Leaf Extract, Aloe Arborescens Leaf Juice, Aloe Arborescens Leaf Protoplasts, Aloe Barbadensis Flower Extract, Aloe Barbadensis Leaf, Aloe Barbadensis Leaf Extract, Aloe Barbadensis Leaf Juice,aloe Barbadensis Leaf Polysaccharides, Aloe Barbadensis Leaf Water, Aloe Ferox Leaf Extract, Aloe Ferox Leaf Juice, and Aloe Ferox Leaf Juice Extract.

Science.gov (United States)

2007-01-01

Plant materials derived from the Aloe plant are used as cosmetic ingredients, including Aloe Andongensis Extract, Aloe Andongensis Leaf Juice, Aloe Arborescens Leaf Extract, Aloe Arborescens Leaf Juice, Aloe Arborescens Leaf Protoplasts, Aloe Barbadensis Flower Extract, Aloe Barbadensis Leaf, Aloe Barbadensis Leaf Extract, Aloe Barbadensis Leaf Juice, Aloe Barbadensis Leaf Polysaccharides, Aloe Barbadensis Leaf Water, Aloe Ferox Leaf Extract, Aloe Ferox Leaf Juice, and Aloe Ferox Leaf Juice Extract. These ingredients function primarily as skin-conditioning agents and are included in cosmetics only at low concentrations. The Aloe leaf consists of the pericyclic cells, found just below the plant's skin, and the inner central area of the leaf, i.e., the gel, which is used for cosmetic products. The pericyclic cells produce a bitter, yellow latex containing a number of anthraquinones, phototoxic compounds that are also gastrointestinal irritants responsible for cathartic effects. The gel contains polysaccharides, which can be acetylated, partially acetylated, or not acetylated. An industry established limit for anthraquinones in aloe-derived material for nonmedicinal use is 50 ppm or lower. Aloe-derived ingredients are used in a wide variety of cosmetic product types at concentrations of raw material that are 0.1% or less, although can be as high as 20%. The concentration of Aloe in the raw material also may vary from 100% to a low of 0.0005%. Oral administration of various anthraquinone components results in a rise in their blood concentrations, wide systemic distribution, accumulation in the liver and kidneys, and excretion in urine and feces; polysaccharide components are distributed systemically and metabolized into smaller molecules. aloe-derived material has fungicidal, antimicrobial, and antiviral activities, and has been effective in wound healing and infection treatment in animals. Aloe barbadensis (also known as Aloe vera)-derived ingredients were not toxic

Unifying Inference of Meso-Scale Structures in Networks.

Science.gov (United States)

Tunç, Birkan; Verma, Ragini

2015-01-01

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

Unifying Inference of Meso-Scale Structures in Networks.

Directory of Open Access Journals (Sweden)

Birkan Tunç

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

Network structure of multivariate time series.

Science.gov (United States)

Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

2015-10-21

Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

Modeling Insurgent Network Structure and Dynamics

Science.gov (United States)

Gabbay, Michael; Thirkill-Mackelprang, Ashley

2010-03-01

We present a methodology for mapping insurgent network structure based on their public rhetoric. Indicators of cooperative links between insurgent groups at both the leadership and rank-and-file levels are used, such as joint policy statements or joint operations claims. In addition, a targeting policy measure is constructed on the basis of insurgent targeting claims. Network diagrams which integrate these measures of insurgent cooperation and ideology are generated for different periods of the Iraqi and Afghan insurgencies. The network diagrams exhibit meaningful changes which track the evolution of the strategic environment faced by insurgent groups. Correlations between targeting policy and network structure indicate that insurgent targeting claims are aimed at establishing a group identity among the spectrum of rank-and-file insurgency supporters. A dynamical systems model of insurgent alliance formation and factionalism is presented which evolves the relationship between insurgent group dyads as a function of their ideological differences and their current relationships. The ability of the model to qualitatively and quantitatively capture insurgent network dynamics observed in the data is discussed.

Network Properties of the Ensemble of RNA Structures

Science.gov (United States)

Clote, Peter; Bayegan, Amir

2015-01-01

We describe the first dynamic programming algorithm that computes the expected degree for the network, or graph G = (V, E) of all secondary structures of a given RNA sequence a = a 1, …, a n. Here, the nodes V correspond to all secondary structures of a, while an edge exists between nodes s, t if the secondary structure t can be obtained from s by adding, removing or shifting a base pair. Since secondary structure kinetics programs implement the Gillespie algorithm, which simulates a random walk on the network of secondary structures, the expected network degree may provide a better understanding of kinetics of RNA folding when allowing defect diffusion, helix zippering, and related conformation transformations. We determine the correlation between expected network degree, contact order, conformational entropy, and expected number of native contacts for a benchmarking dataset of RNAs. Source code is available at http://bioinformatics.bc.edu/clotelab/RNAexpNumNbors. PMID:26488894

Offspring social network structure predicts fitness in families.

Science.gov (United States)

Royle, Nick J; Pike, Thomas W; Heeb, Philipp; Richner, Heinz; Kölliker, Mathias

2012-12-22

Social structures such as families emerge as outcomes of behavioural interactions among individuals, and can evolve over time if families with particular types of social structures tend to leave more individuals in subsequent generations. The social behaviour of interacting individuals is typically analysed as a series of multiple dyadic (pair-wise) interactions, rather than a network of interactions among multiple individuals. However, in species where parents feed dependant young, interactions within families nearly always involve more than two individuals simultaneously. Such social networks of interactions at least partly reflect conflicts of interest over the provision of costly parental investment. Consequently, variation in family network structure reflects variation in how conflicts of interest are resolved among family members. Despite its importance in understanding the evolution of emergent properties of social organization such as family life and cooperation, nothing is currently known about how selection acts on the structure of social networks. Here, we show that the social network structure of broods of begging nestling great tits Parus major predicts fitness in families. Although selection at the level of the individual favours large nestlings, selection at the level of the kin-group primarily favours families that resolve conflicts most effectively.

Road Network Selection Based on Road Hierarchical Structure Control

Directory of Open Access Journals (Sweden)

HE Haiwei

2015-04-01

Full Text Available A new road network selection method based on hierarchical structure is studied. Firstly, road network is built as strokes which are then classified into hierarchical collections according to the criteria of betweenness centrality value (BC value. Secondly, the hierarchical structure of the strokes is enhanced using structural characteristic identification technique. Thirdly, the importance calculation model was established according to the relationships among the hierarchical structure of the strokes. Finally, the importance values of strokes are got supported with the model's hierarchical calculation, and with which the road network is selected. Tests are done to verify the advantage of this method by comparing it with other common stroke-oriented methods using three kinds of typical road network data. Comparision of the results show that this method had few need to semantic data, and could eliminate the negative influence of edge strokes caused by the criteria of BC value well. So, it is better to maintain the global hierarchical structure of road network, and suitable to meet with the selection of various kinds of road network at the same time.

Uncovering the community structure associated with the diffusion dynamics on networks

International Nuclear Information System (INIS)

Cheng, Xue-Qi; Shen, Hua-Wei

2010-01-01

As two main focuses of the study of complex networks, the community structure and the dynamics on networks have both attracted much attention in various scientific fields. However, it is still an open question how the community structure is associated with the dynamics on complex networks. In this paper, through investigating the diffusion process taking place on networks, we demonstrate that the intrinsic community structure of networks can be revealed by the stable local equilibrium states of the diffusion process. Furthermore, we show that such community structure can be directly identified through the optimization of the conductance of the network, which measures how easily the diffusion among different communities occurs. Tests on benchmark networks indicate that the conductance optimization method significantly outperforms the modularity optimization methods in identifying the community structure of networks. Applications to real world networks also demonstrate the effectiveness of the conductance optimization method. This work provides insights into the multiple topological scales of complex networks, and the community structure obtained can naturally reflect the diffusion capability of the underlying network

Influence of degree correlations on network structure and stability in protein-protein interaction networks

Directory of Open Access Journals (Sweden)

Zimmer Ralf

2007-08-01

Full Text Available Abstract Background The existence of negative correlations between degrees of interacting proteins is being discussed since such negative degree correlations were found for the large-scale yeast protein-protein interaction (PPI network of Ito et al. More recent studies observed no such negative correlations for high-confidence interaction sets. In this article, we analyzed a range of experimentally derived interaction networks to understand the role and prevalence of degree correlations in PPI networks. We investigated how degree correlations influence the structure of networks and their tolerance against perturbations such as the targeted deletion of hubs. Results For each PPI network, we simulated uncorrelated, positively and negatively correlated reference networks. Here, a simple model was developed which can create different types of degree correlations in a network without changing the degree distribution. Differences in static properties associated with degree correlations were compared by analyzing the network characteristics of the original PPI and reference networks. Dynamics were compared by simulating the effect of a selective deletion of hubs in all networks. Conclusion Considerable differences between the network types were found for the number of components in the original networks. Negatively correlated networks are fragmented into significantly less components than observed for positively correlated networks. On the other hand, the selective deletion of hubs showed an increased structural tolerance to these deletions for the positively correlated networks. This results in a lower rate of interaction loss in these networks compared to the negatively correlated networks and a decreased disintegration rate. Interestingly, real PPI networks are most similar to the randomly correlated references with respect to all properties analyzed. Thus, although structural properties of networks can be modified considerably by degree

Identification of Non-Linear Structures using Recurrent Neural Networks

DEFF Research Database (Denmark)

Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.

Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....

Identification of Non-Linear Structures using Recurrent Neural Networks

DEFF Research Database (Denmark)

Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.

1995-01-01

Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....

On Adding Structure to Unstructured Overlay Networks

Science.gov (United States)

Leitão, João; Carvalho, Nuno A.; Pereira, José; Oliveira, Rui; Rodrigues, Luís

Unstructured peer-to-peer overlay networks are very resilient to churn and topology changes, while requiring little maintenance cost. Therefore, they are an infrastructure to build highly scalable large-scale services in dynamic networks. Typically, the overlay topology is defined by a peer sampling service that aims at maintaining, in each process, a random partial view of peers in the system. The resulting random unstructured topology is suboptimal when a specific performance metric is considered. On the other hand, structured approaches (for instance, a spanning tree) may optimize a given target performance metric but are highly fragile. In fact, the cost for maintaining structures with strong constraints may easily become prohibitive in highly dynamic networks. This chapter discusses different techniques that aim at combining the advantages of unstructured and structured networks. Namely we focus on two distinct approaches, one based on optimizing the overlay and another based on optimizing the gossip mechanism itself.

Modelling the structure of complex networks

DEFF Research Database (Denmark)

Herlau, Tue

networks has been independently studied as mathematical objects in their own right. As such, there has been both an increased demand for statistical methods for complex networks as well as a quickly growing mathematical literature on the subject. In this dissertation we explore aspects of modelling complex....... The next chapters will treat some of the various symmetries, representer theorems and probabilistic structures often deployed in the modelling complex networks, the construction of sampling methods and various network models. The introductory chapters will serve to provide context for the included written...

The global structure of knowledge network

NARCIS (Netherlands)

Angelopoulos, Spyros; Lomi, Alessandro

2017-01-01

In this paper, we treat patent citations as knowledge networks connecting pieces of formalized knowledge and people, and focus on how ideas are connected, rather than how they are protected. We focus on the global structural properties of formalized knowledge network, and more specifically on the

Resistance in winter barley against Ramularia leaf spot

DEFF Research Database (Denmark)

Hjortshøj, Rasmus Lund

Ramularia leaf spot is an emerging disease in barley caused by R. collo-cygni. At present little is known about the resistance mechanisms carried out by the host plant to avoid disease development. Nor is the lifecycle of the fungus or its populations structure fully understood. To gain insight....... fulvum-tomato and S. tritici-wheat in order to find modelsystems to enhance interpretation of results from R. collo-cygni-barley interaction. Results from the mapping showed that resistance to Ramularia leaf spot is controlled by a number of QTL’s, some of which co-locate with other physiological traits....... The populations further segregated for physiological leaf spots, a phenomenon related to the leaf damage imposed by Rubellin, although, resistance to physiological leafspots appeared to come from the Ramularia leaf spot susceptible parent. The toxin assay further supported this result as the genotypes susceptible...

Structure of a randomly grown 2-d network

DEFF Research Database (Denmark)

Ajazi, Fioralba; Napolitano, George M.; Turova, Tatyana

2015-01-01

We introduce a growing random network on a plane as a model of a growing neuronal network. The properties of the structure of the induced graph are derived. We compare our results with available data. In particular, it is shown that depending on the parameters of the model the system undergoes in...... in time different phases of the structure. We conclude with a possible explanation of some empirical data on the connections between neurons.......We introduce a growing random network on a plane as a model of a growing neuronal network. The properties of the structure of the induced graph are derived. We compare our results with available data. In particular, it is shown that depending on the parameters of the model the system undergoes...

Social inheritance can explain the structure of animal social networks

Science.gov (United States)

Ilany, Amiyaal; Akçay, Erol

2016-01-01

The social network structure of animal populations has major implications for survival, reproductive success, sexual selection and pathogen transmission of individuals. But as of yet, no general theory of social network structure exists that can explain the diversity of social networks observed in nature, and serve as a null model for detecting species and population-specific factors. Here we propose a simple and generally applicable model of social network structure. We consider the emergence of network structure as a result of social inheritance, in which newborns are likely to bond with maternal contacts, and via forming bonds randomly. We compare model output with data from several species, showing that it can generate networks with properties such as those observed in real social systems. Our model demonstrates that important observed properties of social networks, including heritability of network position or assortative associations, can be understood as consequences of social inheritance. PMID:27352101

Leaf Structure and Taxonomy of Petunia and Calibrachoa (Solanaceae

Directory of Open Access Journals (Sweden)

Claudia dos Reis

2002-03-01

Full Text Available We studied the leaf anatomy of sixteen species of Calibrachoa and eight species of Petunia. In Calibrachoa leaves, the vascular bundles sheath (endodermis was formed by parenchymatous developed cells, different from those of the mesophyll. In Petunia, this sheath did not show a marked morphological differentiation. The Calibrachoa leaves could be separated according to the type of leaf margins, the distribution of the stomata on leaf surfaces, the organization of the mesophyll and the morphology of the trichomes. Based on these results, an indented dichotomous identification key was elaborated for the species of the genus Calibrachoa.Foram estudados, sob o ponto de vista anatômico, os limbos foliares de dezesseis espécies de Calibrachoa Llav. & Lex. e de oito espécies de Petunia Juss. (Solanaceae. Em Calibrachoa, a bainha que envolve os feixes vasculares (endoderme é formada por células desenvolvidas e distintas das do mesofilo. Em Petunia, esta bainha não apresenta diferenciação morfológica marcante. As folhas das espécies de Calibrachoa foram separadas entre si levando-se em conta a distribuição dos estômatos nas faces foliares, a organização do mesofilo, o tipo de bordo e a morfologia dos tricomas. Com base nesses resultados, foi elaborada uma chave dicotômica indentada de identificação para as espécies do gênero Calibrachoa.

Synchronization in complex networks with a modular structure.

Science.gov (United States)

Park, Kwangho; Lai, Ying-Cheng; Gupte, Saurabh; Kim, Jong-Won

2006-03-01

Networks with a community (or modular) structure arise in social and biological sciences. In such a network individuals tend to form local communities, each having dense internal connections. The linkage among the communities is, however, much more sparse. The dynamics on modular networks, for instance synchronization, may be of great social or biological interest. (Here by synchronization we mean some synchronous behavior among the nodes in the network, not, for example, partially synchronous behavior in the network or the synchronizability of the network with some external dynamics.) By using a recent theoretical framework, the master-stability approach originally introduced by Pecora and Carroll in the context of synchronization in coupled nonlinear oscillators, we address synchronization in complex modular networks. We use a prototype model and develop scaling relations for the network synchronizability with respect to variations of some key network structural parameters. Our results indicate that random, long-range links among distant modules is the key to synchronization. As an application we suggest a viable strategy to achieve synchronous behavior in social networks.

A key heterogeneous structure of fractal networks based on inverse renormalization scheme

Science.gov (United States)

Bai, Yanan; Huang, Ning; Sun, Lina

2018-06-01

Self-similarity property of complex networks was found by the application of renormalization group theory. Based on this theory, network topologies can be classified into universality classes in the space of configurations. In return, through inverse renormalization scheme, a given primitive structure can grow into a pure fractal network, then adding different types of shortcuts, it exhibits different characteristics of complex networks. However, the effect of primitive structure on networks structural property has received less attention. In this paper, we introduce a degree variance index to measure the dispersion of nodes degree in the primitive structure, and investigate the effect of the primitive structure on network structural property quantified by network efficiency. Numerical simulations and theoretical analysis show a primitive structure is a key heterogeneous structure of generated networks based on inverse renormalization scheme, whether or not adding shortcuts, and the network efficiency is positively correlated with degree variance of the primitive structure.

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

Science.gov (United States)

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.

Remote sensing of nutrient deficiency in Lactuca sativa using neural networks for terrestrial and advanced life support applications

Science.gov (United States)

Sears, Edie Seldon

2000-12-01

A remote sensing study using reflectance and fluorescence spectra of hydroponically grown Lactuca sativa (lettuce) canopies was conducted. An optical receiver was designed and constructed to interface with a commercial fiber optic spectrometer for data acquisition. Optical parameters were varied to determine effects of field of view and distance to target on vegetation stress assessment over the test plant growth cycle. Feedforward backpropagation neural networks (NN) were implemented to predict the presence of canopy stress. Effects of spatial and spectral resolutions on stress predictions of the neural network were also examined. Visual inspection and fresh mass values failed to differentiate among controls, plants cultivated with 25% of the recommended concentration of phosphorous (P), and those cultivated with 25% nitrogen (N) based on fresh mass and visual inspection. The NN's were trained on input vectors created using reflectance and test day, fluorescence and test day, and reflectance, fluorescence, and test day. Four networks were created representing four levels of spectral resolution: 100-nm NN, 10-nm NN, 1-nm NN, and 0.1-nm NN. The 10-nm resolution was found to be sufficient for classifying extreme nitrogen deficiency in freestanding hydroponic lettuce. As a result of leaf angle and canopy structure broadband scattering intensity in the 700-nm to 1000-nm range was found to be the most useful portion of the spectrum in this study. More subtle effects of "greenness" and fluorescence emission were believed to be obscured by canopy structure and leaf orientation. As field of view was not as found to be as significant as originally believed, systems implementing higher repetitions over more uniformly oriented, i.e. smaller, flatter, target areas would provide for more discernible neural network input vectors. It is believed that this technique holds considerable promise for early detection of extreme nitrogen deficiency. Further research is recommended using

Zinc deficiency in field-grown pecan trees: changes in leaf nutrient concentrations and structure.

Science.gov (United States)

Ojeda-Barrios, Dámaris; Abadía, Javier; Lombardini, Leonardo; Abadía, Anunciación; Vázquez, Saúl

2012-06-01

Zinc (Zn) deficiency is a typical nutritional disorder in pecan trees [Carya illinoinensis (Wangenh.) C. Koch] grown under field conditions in calcareous soils in North America, including northern Mexico and south-western United States. The aim of this study was to assess the morphological and nutritional changes in pecan leaves affected by Zn deficiency as well as the Zn distribution within leaves. Zinc deficiency led to decreases in leaf chlorophyll concentrations, leaf area and trunk cross-sectional area. Zinc deficiency increased significantly the leaf concentrations of K and Ca, and decreased the leaf concentrations of Zn, Fe, Mn and Cu. All nutrient values found in Zn-deficient leaves were within the sufficiency ranges, with the only exception of Zn, which was approximately 44, 11 and 9 µg g(-1) dry weight in Zn-sufficient, moderately and markedly Zn-deficient leaves, respectively. Zinc deficiency led to decreases in leaf thickness, mainly due to a reduction in the thickness of the palisade parenchyma, as well as to increases in stomatal density and size. The localisation of Zn was determined using the fluorophore Zinpyr-1 and ratio-imaging technique. Zinc was mainly localised in the palisade mesophyll area in Zn-sufficient leaves, whereas no signal could be obtained in Zn-deficient leaves. The effects of Zn deficiency on the leaf characteristics of pecan trees include not only decreases in leaf chlorophyll and Zn concentrations, but also a reduction in the thickness of the palisade parenchyma, an increase in stomatal density and pore size and the practical disappearance of Zn leaf pools. These characteristics must be taken into account to design strategies to correct Zn deficiency in pecan tree in the field. Copyright © 2012 Society of Chemical Industry.

Structural Quality of Service in Large-Scale Networks

DEFF Research Database (Denmark)

Pedersen, Jens Myrup

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

Leaf reflectance variation along a vertical crown gradient of two deciduous tree species in a Belgian industrial habitat

International Nuclear Information System (INIS)

Khavaninzadeh, Ali Reza; Veroustraete, Frank; Van Wittenberghe, Shari; Verrelst, Jochem; Samson, Roeland

2015-01-01

The reflectometry of leaf asymmetry is a novel approach in the bio-monitoring of tree health in urban or industrial habitats. Leaf asymmetry responds to the degree of environmental pollution and reflects structural changes in a leaf due to environmental pollution. This paper describes the boundary conditions to scale up from leaf to canopy level reflectance, by describing the variability of adaxial and abaxial leaf reflectance, hence leaf asymmetry, along the crown height gradients of two tree species. Our findings open a research pathway towards bio-monitoring based on the airborne remote sensing of tree canopies and their leaf asymmetric properties. - Highlights: • Reflectometry of leaf asymmetry is a novel approach in tree health bio-monitoring. • Leaf asymmetry reflects degrees of structural changes by environmental pollution. • Conditions to scale up from leaf to canopy level reflectance are described. • A research pathway is opened towards airborne pollution bio-assessment. - Tree leaf asymmetry responds to the degree of environmental pollution and reflects leaf structural changes differentially according to species and height in the crown

Structural parameter identifiability analysis for dynamic reaction networks

DEFF Research Database (Denmark)

Davidescu, Florin Paul; Jørgensen, Sten Bay

2008-01-01

method based on Lie derivatives. The proposed systematic two phase methodology is illustrated on a mass action based model for an enzymatically catalyzed reaction pathway network where only a limited set of variables is measured. The methodology clearly pinpoints the structurally identifiable parameters...... where for a given set of measured variables it is desirable to investigate which parameters may be estimated prior to spending computational effort on the actual estimation. This contribution addresses the structural parameter identifiability problem for the typical case of reaction network models....... The proposed analysis is performed in two phases. The first phase determines the structurally identifiable reaction rates based on reaction network stoichiometry. The second phase assesses the structural parameter identifiability of the specific kinetic rate expressions using a generating series expansion...

Nuclear Structure and Decay Data (NSDD) network

International Nuclear Information System (INIS)

Pronyaev, V.G.

2001-02-01

This report provides a brief description of the Nuclear Structure and Decay Data (NSDD) Network in response to a request from the Advisory Group Meeting on ''Co-ordination of the International Network of Nuclear Structure and Decay Data Evaluators'' (IAEA, Vienna, 14-17 December 1998, report IAEA(NDS)-399 (1999)). This report supersedes the special issue of the Nuclear Data Newsletter No. 20 published in November 1994. (author)

The Effect of Epidermal Structures on Leaf Spectral Signatures of Ice Plants (Aizoaceae

Directory of Open Access Journals (Sweden)

René Hans-Jürgen Heim

2015-12-01

Full Text Available Epidermal structures (ES of leaves are known to affect the functional properties and spectral responses. Spectral studies focused mostly on the effect of hairs or wax layers only. We studied a wider range of different ES and their impact on spectral properties. Additionally, we identified spectral regions that allow distinguishing different ES. We used a field spectrometer to measure ex situ leaf spectral responses from 350 nm–2500 nm. A spectral library for 25 species of the succulent family Aizoaceae was assembled. Five functional types were defined based on ES: flat epidermal cell surface, convex to papillary epidermal cell surface, bladder cells, hairs and wax cover. We tested the separability of ES using partial least squares discriminant analysis (PLS-DA based on the spectral data. Subsequently, variable importance (VIP was calculated to identify spectral regions relevant for discriminating our functional types (classes. Classification performance was high, with a kappa value of 0.9 indicating well-separable spectral classes. VIP calculations identified six spectral regions of increased importance for the classification. We confirmed and extended previous findings regarding the visible-near-infrared spectral region. Our experiments also confirmed that epidermal leaf traits can be classified due to clearly distinguishable spectral signatures across species and genera within the Aizoaceae.

Functional clustering in hippocampal cultures: relating network structure and dynamics

International Nuclear Information System (INIS)

Feldt, S; Dzakpasu, R; Olariu, E; Żochowski, M; Wang, J X; Shtrahman, E

2010-01-01

In this work we investigate the relationship between gross anatomic structural network properties, neuronal dynamics and the resultant functional structure in dissociated rat hippocampal cultures. Specifically, we studied cultures as they developed under two conditions: the first supporting glial cell growth (high glial group), and the second one inhibiting it (low glial group). We then compared structural network properties and the spatio-temporal activity patterns of the neurons. Differences in dynamics between the two groups could be linked to the impact of the glial network on the neuronal network as the cultures developed. We also implemented a recently developed algorithm called the functional clustering algorithm (FCA) to obtain the resulting functional network structure. We show that this new algorithm is useful for capturing changes in functional network structure as the networks evolve over time. The FCA detects changes in functional structure that are consistent with expected dynamical differences due to the impact of the glial network. Cultures in the high glial group show an increase in global synchronization as the cultures age, while those in the low glial group remain locally synchronized. We additionally use the FCA to quantify the amount of synchronization present in the cultures and show that the total level of synchronization in the high glial group is stronger than in the low glial group. These results indicate an interdependence between the glial and neuronal networks present in dissociated cultures

Structural network heterogeneities and network dynamics: a possible dynamical mechanism for hippocampal memory reactivation.

Science.gov (United States)

Jablonski, Piotr; Poe, Gina; Zochowski, Michal

2007-03-01

The hippocampus has the capacity for reactivating recently acquired memories and it is hypothesized that one of the functions of sleep reactivation is the facilitation of consolidation of novel memory traces. The dynamic and network processes underlying such a reactivation remain, however, unknown. We show that such a reactivation characterized by local, self-sustained activity of a network region may be an inherent property of the recurrent excitatory-inhibitory network with a heterogeneous structure. The entry into the reactivation phase is mediated through a physiologically feasible regulation of global excitability and external input sources, while the reactivated component of the network is formed through induced network heterogeneities during learning. We show that structural changes needed for robust reactivation of a given network region are well within known physiological parameters.

Impact of environmental inputs on reverse-engineering approach to network structures.

Science.gov (United States)

Wu, Jianhua; Sinfield, James L; Buchanan-Wollaston, Vicky; Feng, Jianfeng

2009-12-04

Uncovering complex network structures from a biological system is one of the main topic in system biology. The network structures can be inferred by the dynamical Bayesian network or Granger causality, but neither techniques have seriously taken into account the impact of environmental inputs. With considerations of natural rhythmic dynamics of biological data, we propose a system biology approach to reveal the impact of environmental inputs on network structures. We first represent the environmental inputs by a harmonic oscillator and combine them with Granger causality to identify environmental inputs and then uncover the causal network structures. We also generalize it to multiple harmonic oscillators to represent various exogenous influences. This system approach is extensively tested with toy models and successfully applied to a real biological network of microarray data of the flowering genes of the model plant Arabidopsis Thaliana. The aim is to identify those genes that are directly affected by the presence of the sunlight and uncover the interactive network structures associating with flowering metabolism. We demonstrate that environmental inputs are crucial for correctly inferring network structures. Harmonic causal method is proved to be a powerful technique to detect environment inputs and uncover network structures, especially when the biological data exhibit periodic oscillations.

Does investment in leaf defenses drive changes in leaf economic strategy? A focus on whole-plant ontogeny.

Science.gov (United States)

Mason, Chase M; Donovan, Lisa A

2015-04-01

Leaf defenses have long been studied in the context of plant growth rate, resource availability, and optimal investment theory. Likewise, one of the central modern paradigms of plant ecophysiology, the leaf economics spectrum (LES), has been extensively studied in the context of these factors across ecological scales ranging from global species data sets to temporal shifts within individuals. Despite strong physiological links between LES strategy and leaf defenses in structure, function, and resource investment, the relationship between these trait classes has not been well explored. This study investigates the relationship between leaf defenses and LES strategy across whole-plant ontogeny in three diverse Helianthus species known to exhibit dramatic ontogenetic shifts in LES strategy, focusing primarily on physical and quantitative chemical defenses. Plants were grown under controlled environmental conditions and sampled for LES and defense traits at four ontogenetic stages. Defenses were found to shift strongly with ontogeny, and to correlate strongly with LES strategy. More advanced ontogenetic stages with more conservative LES strategy leaves had higher tannin activity and toughness in all species, and higher leaf dry matter content in two of three species. Modeling results in two species support the conclusion that changes in defenses drive changes in LES strategy through ontogeny, and in one species that changes in defenses and LES strategy are likely independently driven by ontogeny. Results of this study support the hypothesis that leaf-level allocation to defenses might be an important determinant of leaf economic traits, where high investment in defenses drives a conservative LES strategy.

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

Science.gov (United States)

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.

WDM Network and Multicasting Protocol Strategies

Directory of Open Access Journals (Sweden)

Pinar Kirci

2014-01-01

Full Text Available Optical technology gains extensive attention and ever increasing improvement because of the huge amount of network traffic caused by the growing number of internet users and their rising demands. However, with wavelength division multiplexing (WDM, it is easier to take the advantage of optical networks and optical burst switching (OBS and to construct WDM networks with low delay rates and better data transparency these technologies are the best choices. Furthermore, multicasting in WDM is an urgent solution for bandwidth-intensive applications. In the paper, a new multicasting protocol with OBS is proposed. The protocol depends on a leaf initiated structure. The network is composed of source, ingress switches, intermediate switches, edge switches, and client nodes. The performance of the protocol is examined with Just Enough Time (JET and Just In Time (JIT reservation protocols. Also, the paper involves most of the recent advances about WDM multicasting in optical networks. WDM multicasting in optical networks is given as three common subtitles: Broadcast and-select networks, wavelength-routed networks, and OBS networks. Also, in the paper, multicast routing protocols are briefly summarized and optical burst switched WDM networks are investigated with the proposed multicast schemes.

Beyond E-business : towards networked structures

NARCIS (Netherlands)

Grefen, P.W.P.J.

2015-01-01

In Beyond E-Business: Towards Networked Structures Paul Grefen returns with his tried and tested BOAT framework for e-business, now fully expanded and updated with the very latest overview of digitally connected business; from business models, organization structures and architecture, to information

Analysis of leaf surfaces using scanning ion conductance microscopy.

Science.gov (United States)

Walker, Shaun C; Allen, Stephanie; Bell, Gordon; Roberts, Clive J

2015-05-01

Leaf surfaces are highly complex functional systems with well defined chemistry and structure dictating the barrier and transport properties of the leaf cuticle. It is a significant imaging challenge to analyse the very thin and often complex wax-like leaf cuticle morphology in their natural state. Scanning electron microscopy (SEM) and to a lesser extent Atomic force microscopy are techniques that have been used to study the leaf surface but their remains information that is difficult to obtain via these approaches. SEM is able to produce highly detailed and high-resolution images needed to study leaf structures at the submicron level. It typically operates in a vacuum or low pressure environment and as a consequence is generally unable to deal with the in situ analysis of dynamic surface events at submicron scales. Atomic force microscopy also possess the high-resolution imaging required and can follow dynamic events in ambient and liquid environments, but can over exaggerate small features and cannot image most leaf surfaces due to their inherent roughness at the micron scale. Scanning ion conductance microscopy (SICM), which operates in a liquid environment, provides a potential complementary analytical approach able to address these issues and which is yet to be explored for studying leaf surfaces. Here we illustrate the potential of SICM on various leaf surfaces and compare the data to SEM and atomic force microscopy images on the same samples. In achieving successful imaging we also show that SICM can be used to study the wetting of hydrophobic surfaces in situ. This has potentially wider implications than the study of leaves alone as surface wetting phenomena are important in a range of fundamental and applied studies. © 2015 The Authors Journal of Microscopy © 2015 Royal Microscopical Society.

Ozone sensitivity of Fagus sylvatica and Fraxinus excelsior young trees in relation to leaf structure and foliar ozone uptake

International Nuclear Information System (INIS)

Gerosa, Giacomo; Marzuoli, Riccardo; Bussotti, Filippo; Pancrazi, Marica; Ballarin-Denti, Antonio

2003-01-01

The difference in ozone sensitivity between Fagus sylvatica and Fraxinus exclesior is explained by their different stomatal ozone uptake and by their different foliar structure. - During the summer of 2001, 2-year-old Fraxinus excelsior and Fagus sylvatica plants were subjected to ozone-rich environmental conditions at the Regional Forest Nursery at Curno (Northern Italy). Atmospheric ozone concentrations and stomatal conductance were measured, in order to calculate the foliar fluxes by means of a one-dimensional model. The foliar structure of both species was examined (thickness of the lamina and of the individual tissues, leaf mass per area, leaf density) and chlorophyll a fluorescence was determined as a response parameter. Stomatal conductance was always greater in Fraxinus excelsior, as was ozone uptake, although the highest absorption peaks did not match the peaks of ozone concentration in the atmosphere. The foliar structure can help explain this phenomenon: Fraxinus excelsior has a thicker mesophyll than Fagus sylvatica (indicating a greater photosynthesis potential) and a reduced foliar density. This last parameter, related to the apoplastic fraction, suggests a greater ability to disseminate the gases within the leaf as well as a greater potential detoxifying capacity. As foliar symptoms spread, the parameters relating to chlorophyll a fluorescence also change. PI (Performance Index, Strasser, A., Srivastava, A., Tsimilli-Michael, M., 2000. The fluorescence transient as a tool to characterize and screen photosynthetic samples. In: Yunus, M., Pathre, U., Mohanty, P., (Eds.) Probing Photosynthesis: Mechanisms, Regulation and Adaptation. Taylor and Francis, London, UK, pp. 445-483.) has proved to be a more suitable index than Fv/Fm (Quantum Yield Efficiency) to record the onset of stress conditions

Mesoscopic structure conditions the emergence of cooperation on social networks.

Directory of Open Access Journals (Sweden)

Sergi Lozano

Full Text Available BACKGROUND: We study the evolutionary Prisoner's Dilemma on two social networks substrates obtained from actual relational data. METHODOLOGY/PRINCIPAL FINDINGS: We find very different cooperation levels on each of them that cannot be easily understood in terms of global statistical properties of both networks. We claim that the result can be understood at the mesoscopic scale, by studying the community structure of the networks. We explain the dependence of the cooperation level on the temptation parameter in terms of the internal structure of the communities and their interconnections. We then test our results on community-structured, specifically designed artificial networks, finding a good agreement with the observations in both real substrates. CONCLUSION: Our results support the conclusion that studies of evolutionary games on model networks and their interpretation in terms of global properties may not be sufficient to study specific, real social systems. Further, the study allows us to define new quantitative parameters that summarize the mesoscopic structure of any network. In addition, the community perspective may be helpful to interpret the origin and behavior of existing networks as well as to design structures that show resilient cooperative behavior.

Mesoscopic structure conditions the emergence of cooperation on social networks

Energy Technology Data Exchange (ETDEWEB)

Lozano, S.; Arenas, A.; Sanchez, A.

2008-12-01

We study the evolutionary Prisoner's Dilemma on two social networks substrates obtained from actual relational data. We find very different cooperation levels on each of them that cannot be easily understood in terms of global statistical properties of both networks. We claim that the result can be understood at the mesoscopic scale, by studying the community structure of the networks. We explain the dependence of the cooperation level on the temptation parameter in terms of the internal structure of the communities and their interconnections. We then test our results on community-structured, specifically designed artificial networks, finding a good agreement with the observations in both real substrates. Our results support the conclusion that studies of evolutionary games on model networks and their interpretation in terms of global properties may not be sufficient to study specific, real social systems. Further, the study allows us to define new quantitative parameters that summarize the mesoscopic structure of any network. In addition, the community perspective may be helpful to interpret the origin and behavior of existing networks as well as to design structures that show resilient cooperative behavior.

Complex modular structure of large-scale brain networks

Science.gov (United States)

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

2009-06-01

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

Topological properties of complex networks in protein structures

Science.gov (United States)

Kim, Kyungsik; Jung, Jae-Won; Min, Seungsik

2014-03-01

We study topological properties of networks in structural classification of proteins. We model the native-state protein structure as a network made of its constituent amino-acids and their interactions. We treat four structural classes of proteins composed predominantly of α helices and β sheets and consider several proteins from each of these classes whose sizes range from amino acids of the Protein Data Bank. Particularly, we simulate and analyze the network metrics such as the mean degree, the probability distribution of degree, the clustering coefficient, the characteristic path length, the local efficiency, and the cost. This work was supported by the KMAR and DP under Grant WISE project (153-3100-3133-302-350).

Community structures and role detection in music networks

Science.gov (United States)

Teitelbaum, T.; Balenzuela, P.; Cano, P.; Buldú, Javier M.

2008-12-01

We analyze the existence of community structures in two different social networks using data obtained from similarity and collaborative features between musical artists. Our analysis reveals some characteristic organizational patterns and provides information about the driving forces behind the growth of the networks. In the similarity network, we find a strong correlation between clusters of artists and musical genres. On the other hand, the collaboration network shows two different kinds of communities: rather small structures related to music bands and geographic zones, and much bigger communities built upon collaborative clusters with a high number of participants related through the period the artists were active. Finally, we detect the leading artists inside their corresponding communities and analyze their roles in the network by looking at a few topological properties of the nodes.

Plumbing the depths: extracellular water storage in specialized leaf structures and its functional expression in a three-domain pressure -volume relationship.

Science.gov (United States)

Nguyen, Hoa T; Meir, Patrick; Wolfe, Joe; Mencuccini, Maurizio; Ball, Marilyn C

2017-07-01

A three-domain pressure-volume relationship (PV curve) was studied in relation to leaf anatomical structure during dehydration in the grey mangrove, Avicennia marina. In domain 1, relative water content (RWC) declined 13% with 0.85 MPa decrease in leaf water potential, reflecting a decrease in extracellular water stored primarily in trichomes and petiolar cisternae. In domain 2, RWC decreased by another 12% with a further reduction in leaf water potential to -5.1 MPa, the turgor loss point. Given the osmotic potential at full turgor (-4.2 MPa) and the effective modulus of elasticity (~40 MPa), domain 2 emphasized the role of cell wall elasticity in conserving cellular hydration during leaf water loss. Domain 3 was dominated by osmotic effects and characterized by plasmolysis in most tissues and cell types without cell wall collapse. Extracellular and cellular water storage could support an evaporation rate of 1 mmol m -2 s -1 for up to 54 and 50 min, respectively, before turgor loss was reached. This study emphasized the importance of leaf anatomy for the interpretation of PV curves, and identified extracellular water storage sites that enable transient water use without substantive turgor loss when other factors, such as high soil salinity, constrain rates of water transport. © 2016 John Wiley & Sons Ltd.

Relationships between soil and leaf mineral composition are element-specific, environment-dependent and geographically structured in the emerging model Arabidopsis halleri.

Science.gov (United States)

Stein, Ricardo J; Höreth, Stephan; de Melo, J Romário F; Syllwasschy, Lara; Lee, Gwonjin; Garbin, Mário L; Clemens, Stephan; Krämer, Ute

2017-02-01

Leaf mineral composition, the leaf ionome, reflects the complex interaction between a plant and its environment including local soil composition, an influential factor that can limit species distribution and plant productivity. Here we addressed within-species variation in plant-soil interactions and edaphic adaptation using Arabidopsis halleri, a well-suited model species as a facultative metallophyte and metal hyperaccumulator. We conducted multi-element analysis of 1972 paired leaf and soil samples from 165 European populations of A. halleri, at individual resolution to accommodate soil heterogeneity. Results were further confirmed under standardized conditions upon cultivation of 105 field-collected genotypes on an artificially metal-contaminated soil in growth chamber experiments. Soil-independent between- and within-population variation set apart leaf accumulation of zinc, cadmium and lead from all other nutrient and nonessential elements, concurring with differential hypothesized ecological roles in either biotic interaction or nutrition. For these metals, soil-leaf relationships were element-specific, differed between metalliferous and nonmetalliferous soils and were geographically structured both in the field and under standardized growth conditions, implicating complex scenarios of recent ecological adaptation. Our study provides an example and a reference for future related work and will serve as a basis for the molecular-genetic dissection and ecological analysis of the observed phenotypic variation. © 2016 The Authors. New Phytologist © 2016 New Phytologist Trust.

Structure of Retail Services in the Brazilian Hosting Network

Directory of Open Access Journals (Sweden)

Claudio Zancan

2015-08-01

Full Text Available this research has identified Brazilian hosting networks through infrastructure services indicators that it was sold to tourists in organizations that form these networks. The theory consulted the discussion of structural techniques present in Social Network Analysis. The study has three stages: documental research, creation of Tourism database and interviews. The results identified three networks with the highest expression in Brazil formed by hotels, lodges, and resorts. Different char-acteristics of infrastructure and services were observed between hosting networks. Future studies suggest a comparative analysis of structural indicators present in other segments of tourism services, as well as the existing international influ-ence on the development of the Brazilian hosting networks.

Foraging on individual leaves by an intracellular feeding insect is not associated with leaf biomechanical properties or leaf orientation.

Directory of Open Access Journals (Sweden)

Justin Fiene

Full Text Available Nearly all herbivorous arthropods make foraging-decisions on individual leaves, yet systematic investigations of the adaptive significance and ecological factors structuring these decisions are rare with most attention given to chewing herbivores. This study investigated why an intracellular feeding herbivore, Western flower thrips (WFT Frankliniella occidentalis Pergande, generally avoids feeding on the adaxial leaf surface of cotton cotyledons. WFT showed a significant aversion to adaxial-feeding even when excised-cotyledons were turned up-side (abaxial-side 'up', suggesting that negative-phototaxis was not a primary cause of thrips foraging patterns. No-choice bioassays in which individual WFT females were confined to either the abaxial or adaxial leaf surface showed that 35% fewer offspring were produced when only adaxial feeding was allowed, which coincided with 32% less plant feeding on that surface. To test the hypothesis that leaf biomechanical properties inhibited thrips feeding on the adaxial surface, we used a penetrometer to measure two variables related to the 'toughness' of each leaf surface. Neither variable negatively co-varied with feeding. Thus, while avoiding the upper leaf surface was an adaptive foraging strategy, the proximate cause remains to be elucidated, but is likely due, in part, to certain leaf properties that inhibit feeding.

Structural Connectivity Networks of Transgender People

NARCIS (Netherlands)

Hahn, Andreas; Kranz, Georg S.; Küblböck, Martin; Kaufmann, Ulrike; Ganger, Sebastian; Hummer, Allan; Seiger, Rene; Spies, Marie; Winkler, Dietmar; Kasper, Siegfried; Windischberger, Christian; Swaab, Dick F.; Lanzenberger, Rupert

2015-01-01

Although previous investigations of transsexual people have focused on regional brain alterations, evaluations on a network level, especially those structural in nature, are largely missing. Therefore, we investigated the structural connectome of 23 female-to-male (FtM) and 21 male-to-female (MtF)

Tree-Based Unrooted Phylogenetic Networks.

Science.gov (United States)

Francis, A; Huber, K T; Moulton, V

2018-02-01

Phylogenetic networks are a generalization of phylogenetic trees that are used to represent non-tree-like evolutionary histories that arise in organisms such as plants and bacteria, or uncertainty in evolutionary histories. An unrooted phylogenetic network on a non-empty, finite set X of taxa, or network, is a connected, simple graph in which every vertex has degree 1 or 3 and whose leaf set is X. It is called a phylogenetic tree if the underlying graph is a tree. In this paper we consider properties of tree-based networks, that is, networks that can be constructed by adding edges into a phylogenetic tree. We show that although they have some properties in common with their rooted analogues which have recently drawn much attention in the literature, they have some striking differences in terms of both their structural and computational properties. We expect that our results could eventually have applications to, for example, detecting horizontal gene transfer or hybridization which are important factors in the evolution of many organisms.

Acquisition and diversification of cladodes: leaf-like organs in the genus Asparagus.

Science.gov (United States)

Nakayama, Hokuto; Yamaguchi, Takahiro; Tsukaya, Hirokazu

2012-03-01

The genus Asparagus is unusual in producing axillary, determinate organs called cladodes, which may take on either a flattened or cylindrical form. Here, we investigated the evolution of cladodes to elucidate the mechanisms at play in the diversification of shoot morphology. Our observations of Asparagus asparagoides, which has leaf-like cladodes, showed that its cladodes are anatomically and developmentally similar to leaves but differ in the adaxial/abaxial polarity of the vasculature. In addition to the expression of an ortholog of KNAT1, orthologous genes that are normally expressed in leaves, asymmetric leaves1 and HD-ZIPIII, were found to be expressed in cladode primordia in a leaf-like manner. The cylindrical cladodes of Asparagus officinalis showed largely similar expression patterns but showed evidence of being genetically abaxialized. These results provide evidence that cladodes are modified axillary shoots, suggest that the co-option of preexisting gene networks involved in leaf development transferred the leaf-like form to axillary shoots, and imply that altered expression of leaf polarity genes led to the evolution of cylindrical cladodes in the A. officinalis clade.

Resolution of Singularities Introduced by Hierarchical Structure in Deep Neural Networks.

Science.gov (United States)

Nitta, Tohru

2017-10-01

We present a theoretical analysis of singular points of artificial deep neural networks, resulting in providing deep neural network models having no critical points introduced by a hierarchical structure. It is considered that such deep neural network models have good nature for gradient-based optimization. First, we show that there exist a large number of critical points introduced by a hierarchical structure in deep neural networks as straight lines, depending on the number of hidden layers and the number of hidden neurons. Second, we derive a sufficient condition for deep neural networks having no critical points introduced by a hierarchical structure, which can be applied to general deep neural networks. It is also shown that the existence of critical points introduced by a hierarchical structure is determined by the rank and the regularity of weight matrices for a specific class of deep neural networks. Finally, two kinds of implementation methods of the sufficient conditions to have no critical points are provided. One is a learning algorithm that can avoid critical points introduced by the hierarchical structure during learning (called avoidant learning algorithm). The other is a neural network that does not have some critical points introduced by the hierarchical structure as an inherent property (called avoidant neural network).

A new hierarchical method to find community structure in networks

Science.gov (United States)

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.

The Structure of Online Consumer Communication Networks

NARCIS (Netherlands)

B.G.C. Dellaert (Benedict); M.J.W. Harmsen-van Hout (Marjolein); P.J.J. Herings (Jean-Jacques)

2006-01-01

textabstractIn this paper we study the structure of the bilateral communication links within Online Consumer Communication Networks (OCCNs), such as virtual communities. Compared to the offline world, consumers in online networks are highly flexible to choose their communication partners and little

Modeling Temporal Evolution and Multiscale Structure in Networks

DEFF Research Database (Denmark)

Herlau, Tue; Mørup, Morten; Schmidt, Mikkel Nørgaard

2013-01-01

Many real-world networks exhibit both temporal evolution and multiscale structure. We propose a model for temporally correlated multifurcating hierarchies in complex networks which jointly capture both effects. We use the Gibbs fragmentation tree as prior over multifurcating trees and a change......-point model to account for the temporal evolution of each vertex. We demonstrate that our model is able to infer time-varying multiscale structure in synthetic as well as three real world time-evolving complex networks. Our modeling of the temporal evolution of hierarchies brings new insights...

The structure of complex networks theory and applications

CERN Document Server

Estrada, Ernesto

2012-01-01

This book deals with the analysis of the structure of complex networks by combining results from graph theory, physics, and pattern recognition. The book is divided into two parts. 11 chapters are dedicated to the development of theoretical tools for the structural analysis of networks, and 7 chapters are illustrating, in a critical way, applications of these tools to real-world scenarios. The first chapters provide detailed coverage of adjacency and metric and topologicalproperties of networks, followed by chapters devoted to the analysis of individual fragments and fragment-based global inva

An automated approach to network features of protein structure ensembles

Science.gov (United States)

Bhattacharyya, Moitrayee; Bhat, Chanda R; Vishveshwara, Saraswathi

2013-01-01

Network theory applied to protein structures provides insights into numerous problems of biological relevance. The explosion in structural data available from PDB and simulations establishes a need to introduce a standalone-efficient program that assembles network concepts/parameters under one hood in an automated manner. Herein, we discuss the development/application of an exhaustive, user-friendly, standalone program package named PSN-Ensemble, which can handle structural ensembles generated through molecular dynamics (MD) simulation/NMR studies or from multiple X-ray structures. The novelty in network construction lies in the explicit consideration of side-chain interactions among amino acids. The program evaluates network parameters dealing with topological organization and long-range allosteric communication. The introduction of a flexible weighing scheme in terms of residue pairwise cross-correlation/interaction energy in PSN-Ensemble brings in dynamical/chemical knowledge into the network representation. Also, the results are mapped on a graphical display of the structure, allowing an easy access of network analysis to a general biological community. The potential of PSN-Ensemble toward examining structural ensemble is exemplified using MD trajectories of an ubiquitin-conjugating enzyme (UbcH5b). Furthermore, insights derived from network parameters evaluated using PSN-Ensemble for single-static structures of active/inactive states of β2-adrenergic receptor and the ternary tRNA complexes of tyrosyl tRNA synthetases (from organisms across kingdoms) are discussed. PSN-Ensemble is freely available from http://vishgraph.mbu.iisc.ernet.in/PSN-Ensemble/psn_index.html. PMID:23934896

Structural constraints in complex networks

International Nuclear Information System (INIS)

Zhou, S; Mondragon, R J

2007-01-01

We present a link rewiring mechanism to produce surrogates of a network where both the degree distribution and the rich-club connectivity are preserved. We consider three real networks, the autonomous system (AS)-Internet, protein interaction and scientific collaboration. We show that for a given degree distribution, the rich-club connectivity is sensitive to the degree-degree correlation, and on the other hand the degree-degree correlation is constrained by the rich-club connectivity. In particular, in the case of the Internet, the assortative coefficient is always negative and a minor change in its value can reverse the network's rich-club structure completely; while fixing the degree distribution and the rich-club connectivity restricts the assortative coefficient to such a narrow range, that a reasonable model of the Internet can be produced by considering mainly the degree distribution and the rich-club connectivity. We also comment on the suitability of using the maximal random network as a null model to assess the rich-club connectivity in real networks

Coevolution of network structure and cooperation in the public goods game

International Nuclear Information System (INIS)

Wang Lei; Xia Chengyi; Wang Juan

2013-01-01

Recently, the emergence of cooperation has become a central topic in the evolutionary game field, and coevolution of game dynamics and network topology structure can give us a fresh viewpoint of how the network evolves and cooperation arises. In this paper, we show in detail a picture of the co-evolutionary behaviors between the microscopic structure of the network and cooperation promotion in the public goods game (PGG). Based on a mechanism named after evolutionary preferential attachment (EPA), in which the growth of the network depends on the outcome of PGG dynamics, we explore the structural properties of networks and cooperative behaviors taking place on the networks created by EPA rules. Extensive simulation results indicate that the structure of the resulting networks displays a transition from homogeneous to heterogeneous properties as the selection strength ϵ increases, and the cooperative behaviors have a non-trivial state in which cooperators and defectors can simultaneously occupy the hub nodes in the network. Current results are of interest for us to further understand the cooperation persistence and structure evolution in many natural, social and economical systems. (paper)

"Breath figures" on leaf surfaces-formation and effects of microscopic leaf wetness.

Science.gov (United States)

Burkhardt, Juergen; Hunsche, Mauricio

2013-01-01

"Microscopic leaf wetness" means minute amounts of persistent liquid water on leaf surfaces which are invisible to the naked eye. The water is mainly maintained by transpired water vapor condensing onto the leaf surface and to attached leaf surface particles. With an estimated average thickness of less than 1 μm, microscopic leaf wetness is about two orders of magnitude thinner than morning dewfall. The most important physical processes which reduce the saturation vapor pressure and promote condensation are cuticular absorption and the deliquescence of hygroscopic leaf surface particles. Deliquescent salts form highly concentrated solutions. Depending on the type and concentration of the dissolved ions, the physicochemical properties of microscopic leaf wetness can be considerably different from those of pure water. Microscopic leaf wetness can form continuous thin layers on hydrophobic leaf surfaces and in specific cases can act similar to surfactants, enabling a strong potential influence on the foliar exchange of ions. Microscopic leaf wetness can also enhance the dissolution, the emission, and the reaction of specific atmospheric trace gases e.g., ammonia, SO2, or ozone, leading to a strong potential role for microscopic leaf wetness in plant/atmosphere interaction. Due to its difficult detection, there is little knowledge about the occurrence and the properties of microscopic leaf wetness. However, based on the existing evidence and on physicochemical reasoning it can be hypothesized that microscopic leaf wetness occurs on almost any plant worldwide and often permanently, and that it significantly influences the exchange processes of the leaf surface with its neighboring compartments, i.e., the plant interior and the atmosphere. The omission of microscopic water in general leaf wetness concepts has caused far-reaching, misleading conclusions in the past.

Robustness and modular structure in networks

DEFF Research Database (Denmark)

Bagrow, James P.; Lehmann, Sune; Ahn, Yong-Yeol

2015-01-01

-12]. Many complex systems, from power grids and the Internet to the brain and society [13-15], can be modeled using modular networks comprised of small, densely connected groups of nodes [16, 17]. These modules often overlap, with network elements belonging to multiple modules [18, 19]. Yet existing work...... on robustness has not considered the role of overlapping, modular structure. Here we study the robustness of these systems to the failure of elements. We show analytically and empirically that it is possible for the modules themselves to become uncoupled or non-overlapping well before the network disintegrates....... If overlapping modular organization plays a role in overall functionality, networks may be far more vulnerable than predicted by conventional percolation theory....

Cognitive and Social Structure of the Elite Collaboration Network of Astrophysics: A Case Study on Shifting Network Structures

Science.gov (United States)

Heidler, Richard

2011-01-01

Scientific collaboration can only be understood along the epistemic and cognitive grounding of scientific disciplines. New scientific discoveries in astrophysics led to a major restructuring of the elite network of astrophysics. To study the interplay of the epistemic grounding and the social network structure of a discipline, a mixed-methods…

Structural controllability and controlling centrality of temporal networks.

Science.gov (United States)

Pan, Yujian; Li, Xiang

2014-01-01

Temporal networks are such networks where nodes and interactions may appear and disappear at various time scales. With the evidence of ubiquity of temporal networks in our economy, nature and society, it's urgent and significant to focus on its structural controllability as well as the corresponding characteristics, which nowadays is still an untouched topic. We develop graphic tools to study the structural controllability as well as its characteristics, identifying the intrinsic mechanism of the ability of individuals in controlling a dynamic and large-scale temporal network. Classifying temporal trees of a temporal network into different types, we give (both upper and lower) analytical bounds of the controlling centrality, which are verified by numerical simulations of both artificial and empirical temporal networks. We find that the positive relationship between aggregated degree and controlling centrality as well as the scale-free distribution of node's controlling centrality are virtually independent of the time scale and types of datasets, meaning the inherent robustness and heterogeneity of the controlling centrality of nodes within temporal networks.

Structural and Infrastructural Underpinnings of International R&D Networks

DEFF Research Database (Denmark)

Niang, Mohamed; Sørensen, Brian Vejrum

2009-01-01

This paper explores the process of globally distributing R&D activities with an emphasis on the effects of network maturity. It discusses emerging configurations by asking how the structure and infrastructure of international R&D networks evolve along with the move from a strong R&D center...... to dispersed development. Drawing from case studies of two international R&D networks, it presents a capability maturity model and argues that understanding the interaction between new structures and infrastructures of the dispersed networks has become a key requirement for developing organizational...

Complex network perspective on structure and function of ...

Indian Academy of Sciences (India)

of community social networks, which are dense node–node links within modules, but have sparser links between ... 3.2 Bow tie structure. The whole metabolic network of S. aureus is then decomposed into four parts based on the 'bow tie' structure (figure 2, table 2). It should be noted that most nodes in S, P and IS parts are ...

Efficient network-matrix architecture for general flow transport inspired by natural pinnate leaves.

Science.gov (United States)

Hu, Liguo; Zhou, Han; Zhu, Hanxing; Fan, Tongxiang; Zhang, Di

2014-11-14

Networks embedded in three dimensional matrices are beneficial to deliver physical flows to the matrices. Leaf architectures, pervasive natural network-matrix architectures, endow leaves with high transpiration rates and low water pressure drops, providing inspiration for efficient network-matrix architectures. In this study, the network-matrix model for general flow transport inspired by natural pinnate leaves is investigated analytically. The results indicate that the optimal network structure inspired by natural pinnate leaves can greatly reduce the maximum potential drop and the total potential drop caused by the flow through the network while maximizing the total flow rate through the matrix. These results can be used to design efficient networks in network-matrix architectures for a variety of practical applications, such as tissue engineering, cell culture, photovoltaic devices and heat transfer.

Effect of Plant Growth Regulators on Leaf Number, Leaf Area and Leaf Dry Matter in Grape

Directory of Open Access Journals (Sweden)

Zahoor Ahmad BHAT

2011-03-01

Full Text Available Influence of phenylureas (CPPU and brassinosteriod (BR along with GA (gibberellic acid were studied on seedless grape vegetative characteristics like leaf number, leaf area and leaf dry matter. Growth regulators were sprayed on the vines either once (7 days after fruit set or 15 days after fruit set or twice (7+15 days after fruit set. CPPU 2 ppm+BR 0.4 ppm+GA 25 ppm produced maximum number of leaves (18.78 while as untreated vines produced least leaf number (16.22 per shoot. Maximum leaf area (129.70 cm2 and dry matter content (26.51% was obtained with higher CPPU (3 ppm and BR (0.4 ppm combination along with GA 25 ppm. Plant growth regulators whether naturally derived or synthetic are used to improve the productivity and quality of grapes. The relatively high value of grapes justifies more expensive inputs. A relatively small improvement in yield or fruit quality can justify the field application of a very costly product. Application of new generation growth regulators like brassinosteroids and phenylureas like CPPU have been reported to increase the leaf number as well as leaf area and dry matter thereby indirectly influencing the fruit yield and quality in grapes.

A Hierarchical Dispatch Structure for Distribution Network Pricing

OpenAIRE

Yuan, Zhao; Hesamzadeh, Mohammad Reza

2015-01-01

This paper presents a hierarchical dispatch structure for efficient distribution network pricing. The dispatch coordination problem in the context of hierarchical network operators are addressed. We formulate decentralized generation dispatch into a bilevel optimization problem in which main network operator and the connected distribution network operator optimize their costs in two levels. By using Karush-Kuhn-Tucker conditions and Fortuny-Amat McCarl linearization, the bilevel optimization ...

Structure-Function Network Mapping and Its Assessment via Persistent Homology

Science.gov (United States)

2017-01-01

Understanding the relationship between brain structure and function is a fundamental problem in network neuroscience. This work deals with the general method of structure-function mapping at the whole-brain level. We formulate the problem as a topological mapping of structure-function connectivity via matrix function, and find a stable solution by exploiting a regularization procedure to cope with large matrices. We introduce a novel measure of network similarity based on persistent homology for assessing the quality of the network mapping, which enables a detailed comparison of network topological changes across all possible thresholds, rather than just at a single, arbitrary threshold that may not be optimal. We demonstrate that our approach can uncover the direct and indirect structural paths for predicting functional connectivity, and our network similarity measure outperforms other currently available methods. We systematically validate our approach with (1) a comparison of regularized vs. non-regularized procedures, (2) a null model of the degree-preserving random rewired structural matrix, (3) different network types (binary vs. weighted matrices), and (4) different brain parcellation schemes (low vs. high resolutions). Finally, we evaluate the scalability of our method with relatively large matrices (2514x2514) of structural and functional connectivity obtained from 12 healthy human subjects measured non-invasively while at rest. Our results reveal a nonlinear structure-function relationship, suggesting that the resting-state functional connectivity depends on direct structural connections, as well as relatively parsimonious indirect connections via polysynaptic pathways. PMID:28046127

Changing organizational structures of jihadist networks in the Netherlands

NARCIS (Netherlands)

de Bie, Jasper L.; de Poot, Christianne J.; Freilich, Joshua D.; Chermak, Steven M.

2017-01-01

This paper uses Social Network Analysis to study and compare the organizational structures and division of roles of three jihadist networks in the Netherlands. It uses unique longitudinal Dutch police data covering the 2000–2013 period. This study demonstrates how the organizational structures

Structure of the human chromosome interaction network.

Directory of Open Access Journals (Sweden)

Sergio Sarnataro

Full Text Available New Hi-C technologies have revealed that chromosomes have a complex network of spatial contacts in the cell nucleus of higher organisms, whose organisation is only partially understood. Here, we investigate the structure of such a network in human GM12878 cells, to derive a large scale picture of nuclear architecture. We find that the intensity of intra-chromosomal interactions is power-law distributed. Inter-chromosomal interactions are two orders of magnitude weaker and exponentially distributed, yet they are not randomly arranged along the genomic sequence. Intra-chromosomal contacts broadly occur between epigenomically homologous regions, whereas inter-chromosomal contacts are especially associated with regions rich in highly expressed genes. Overall, genomic contacts in the nucleus appear to be structured as a network of networks where a set of strongly individual chromosomal units, as envisaged in the 'chromosomal territory' scenario derived from microscopy, interact with each other via on average weaker, yet far from random and functionally important interactions.

How structure determines correlations in neuronal networks.

Directory of Open Access Journals (Sweden)

Volker Pernice

2011-05-01

Full Text Available Networks are becoming a ubiquitous metaphor for the understanding of complex biological systems, spanning the range between molecular signalling pathways, neural networks in the brain, and interacting species in a food web. In many models, we face an intricate interplay between the topology of the network and the dynamics of the system, which is generally very hard to disentangle. A dynamical feature that has been subject of intense research in various fields are correlations between the noisy activity of nodes in a network. We consider a class of systems, where discrete signals are sent along the links of the network. Such systems are of particular relevance in neuroscience, because they provide models for networks of neurons that use action potentials for communication. We study correlations in dynamic networks with arbitrary topology, assuming linear pulse coupling. With our novel approach, we are able to understand in detail how specific structural motifs affect pairwise correlations. Based on a power series decomposition of the covariance matrix, we describe the conditions under which very indirect interactions will have a pronounced effect on correlations and population dynamics. In random networks, we find that indirect interactions may lead to a broad distribution of activation levels with low average but highly variable correlations. This phenomenon is even more pronounced in networks with distance dependent connectivity. In contrast, networks with highly connected hubs or patchy connections often exhibit strong average correlations. Our results are particularly relevant in view of new experimental techniques that enable the parallel recording of spiking activity from a large number of neurons, an appropriate interpretation of which is hampered by the currently limited understanding of structure-dynamics relations in complex networks.

Improving the Reliability of Network Metrics in Structural Brain Networks by Integrating Different Network Weighting Strategies into a Single Graph

Directory of Open Access Journals (Sweden)

Stavros I. Dimitriadis

2017-12-01

Full Text Available Structural brain networks estimated from diffusion MRI (dMRI via tractography have been widely studied in healthy controls and patients with neurological and psychiatric diseases. However, few studies have addressed the reliability of derived network metrics both node-specific and network-wide. Different network weighting strategies (NWS can be adopted to weight the strength of connection between two nodes yielding structural brain networks that are almost fully-weighted. Here, we scanned five healthy participants five times each, using a diffusion-weighted MRI protocol and computed edges between 90 regions of interest (ROI from the Automated Anatomical Labeling (AAL template. The edges were weighted according to nine different methods. We propose a linear combination of these nine NWS into a single graph using an appropriate diffusion distance metric. We refer to the resulting weighted graph as an Integrated Weighted Structural Brain Network (ISWBN. Additionally, we consider a topological filtering scheme that maximizes the information flow in the brain network under the constraint of the overall cost of the surviving connections. We compared each of the nine NWS and the ISWBN based on the improvement of: (a intra-class correlation coefficient (ICC of well-known network metrics, both node-wise and per network level; and (b the recognition accuracy of each subject compared to the remainder of the cohort, as an attempt to access the uniqueness of the structural brain network for each subject, after first applying our proposed topological filtering scheme. Based on a threshold where the network level ICC should be >0.90, our findings revealed that six out of nine NWS lead to unreliable results at the network level, while all nine NWS were unreliable at the node level. In comparison, our proposed ISWBN performed as well as the best performing individual NWS at the network level, and the ICC was higher compared to all individual NWS at the node

Structure and growth of weighted networks

Energy Technology Data Exchange (ETDEWEB)

Riccaboni, Massimo [Department of Computer and Management Sciences, University of Trento, Trento (Italy); Schiavo, Stefano [Department of Economics, University of Trento, Trento (Italy)], E-mail: massimo.riccaboni@unitn.it, E-mail: stefano.schiavo@unitn.it

2010-02-15

We develop a simple theoretical framework for the evolution of weighted networks that is consistent with a number of stylized features of real-world data. In our framework, the Barabasi-Albert model of network evolution is extended by assuming that link weights evolve according to a geometric Brownian motion. Our model is verified by means of simulations and real-world trade data. We show that the model correctly predicts the intensity and growth distribution of links, the size-variance relationship of the growth of link weights, the relationship between the degree and strength of nodes, and the scale-free structure of the network.

Online Social Networks: Essays on Membership, Privacy, and Structure

NARCIS (Netherlands)

Hofstra, B.

2017-01-01

The structure of social networks is crucial for obtaining social support, for meaningful connections to unknown social groups, and to overcome prejudice. Yet, we know little about the structure of social networks beyond those contacts that stand closest to us. This lack of knowledge results from a

Joint Modelling of Structural and Functional Brain Networks

DEFF Research Database (Denmark)

Andersen, Kasper Winther; Herlau, Tue; Mørup, Morten

-parametric Bayesian network model which allows for joint modelling and integration of multiple networks. We demonstrate the model’s ability to detect vertices that share structure across networks jointly in functional MRI (fMRI) and diffusion MRI (dMRI) data. Using two fMRI and dMRI scans per subject, we establish...

Spatial structure of an individual-based plant–pollinator network

DEFF Research Database (Denmark)

Dupont, Yoko Luise; Nielsen, Kristian Trøjelsgaard; Hagen, Melanie

2014-01-01

The influence of space on the structure (e.g. modularity) of complex ecological networks remains largely unknown. Here, we sampled an individual-based plant–pollinator network by following the movements and flower visits of marked bumblebee individuals within a population of thistle plants...... for which the identities and spatial locations of stems were mapped in a 50  50 m study plot. The plant–pollinator network was dominated by parasitic male bumblebees and had a significantly modular structure, with four identified modules being clearly separated in space. This indicated that individual....... This demonstrated that individual-based plant–pollinator networks are influenced by both the spatial structure of plant populations and individual-specific plant traits. Additionally, bumblebee individuals with long observation times were important for both the connectivity between and within modules. The latter...

Biological transportation networks: Modeling and simulation

KAUST Repository

Albi, Giacomo; Artina, Marco; Foransier, Massimo; Markowich, Peter A.

2015-01-01

We present a model for biological network formation originally introduced by Cai and Hu [Adaptation and optimization of biological transport networks, Phys. Rev. Lett. 111 (2013) 138701]. The modeling of fluid transportation (e.g., leaf venation

Community Structure of Leaf-Litter Ants in a Neotropical Dry Forest: A Biogeographic Approach to Explain Betadiversity

Directory of Open Access Journals (Sweden)

Rogério Silvestre

2012-01-01

Full Text Available This paper describes habitat and geographic correlates of ant diversity in Serra da Bodoquena, a poorly surveyed region of central-western Brazil. We discuss leaf-litter ant diversity on a regional scale, with emphasis on the contribution of each of the processes that form the evolutionary basis of contemporary beta diversity. The diversity of leaf-litter ants was assessed from a series of 262 Winkler samples conducted in two microbasins within a deciduous forest domain. A total of 170 litter-dwelling ant species in 45 genera and 11 subfamilies was identified. The data showed that the study areas exhibited different arrangements of ant fauna, with a high turnover in species composition between sites, indicating high beta diversity. Our analysis suggests that the biogeographic history of this tropical dry forest in the centre of South America could explain ant assemblage structure more than competitive dominance. The co-occurrence analysis showed that species co-occur less often than expected by chance in only two of the localities, suggesting that, for most of the species, co-occurrences are random. The assessment of the structure of the diversity of litter-dwelling ants is the first step in understanding the beta diversity patterns in this region of great biogeographic importance.

Why do leaf-tying caterpillars abandon their leaf ties?

Directory of Open Access Journals (Sweden)

Michelle Sliwinski

2013-09-01

Full Text Available Leaf-tying caterpillars act as ecosystem engineers by building shelters between overlapping leaves, which are inhabited by other arthropods. Leaf-tiers have been observed to leave their ties and create new shelters (and thus additional microhabitats, but the ecological factors affecting shelter fidelity are poorly known. For this study, we explored the effects of resource limitation and occupant density on shelter fidelity and assessed the consequences of shelter abandonment. We first quantified the area of leaf material required for a caterpillar to fully develop for two of the most common leaf-tiers that feed on white oak, Quercus alba. On average, Psilocorsis spp. caterpillars consumed 21.65 ± 0.67 cm2 leaf material to complete development. We also measured the area of natural leaf ties found in a Maryland forest, to determine the distribution of resources available to caterpillars in situ. Of 158 natural leaf ties examined, 47% were too small to sustain an average Psilocorsis spp. caterpillar for the entirety of its development. We also manipulated caterpillar densities within experimental ties on potted trees to determine the effects of cohabitants on the likelihood of a caterpillar to leave its tie. We placed 1, 2, or 4 caterpillars in ties of a standard size and monitored the caterpillars twice daily to track their movement. In ties with more than one occupant, caterpillars showed a significantly greater propensity to leave their tie, and left sooner and at a faster rate than those in ties as single occupants. To understand the consequences of leaf tie abandonment, we observed caterpillars searching a tree for a site to build a shelter in the field. This is a risky behavior, as 17% of the caterpillars observed died while searching for a shelter site. Caterpillars that successfully built a shelter traveled 110 ± 20 cm and took 28 ± 7 min to find a suitable site to build a shelter. In conclusion, leaf-tying caterpillars must frequently

Structural covariance networks in the mouse brain.

Science.gov (United States)

Pagani, Marco; Bifone, Angelo; Gozzi, Alessandro

2016-04-01

The presence of networks of correlation between regional gray matter volume as measured across subjects in a group of individuals has been consistently described in several human studies, an approach termed structural covariance MRI (scMRI). Complementary to prevalent brain mapping modalities like functional and diffusion-weighted imaging, the approach can provide precious insights into the mutual influence of trophic and plastic processes in health and pathological states. To investigate whether analogous scMRI networks are present in lower mammal species amenable to genetic and experimental manipulation such as the laboratory mouse, we employed high resolution morphoanatomical MRI in a large cohort of genetically-homogeneous wild-type mice (C57Bl6/J) and mapped scMRI networks using a seed-based approach. We show that the mouse brain exhibits robust homotopic scMRI networks in both primary and associative cortices, a finding corroborated by independent component analyses of cortical volumes. Subcortical structures also showed highly symmetric inter-hemispheric correlations, with evidence of distributed antero-posterior networks in diencephalic regions of the thalamus and hypothalamus. Hierarchical cluster analysis revealed six identifiable clusters of cortical and sub-cortical regions corresponding to previously described neuroanatomical systems. Our work documents the presence of homotopic cortical and subcortical scMRI networks in the mouse brain, thus supporting the use of this species to investigate the elusive biological and neuroanatomical underpinnings of scMRI network development and its derangement in neuropathological states. The identification of scMRI networks in genetically homogeneous inbred mice is consistent with the emerging view of a key role of environmental factors in shaping these correlational networks. Copyright © 2016 Elsevier Inc. All rights reserved.

Socio-Cognitive Phenotypes Differentially Modulate Large-Scale Structural Covariance Networks.

Science.gov (United States)

Valk, Sofie L; Bernhardt, Boris C; Böckler, Anne; Trautwein, Fynn-Mathis; Kanske, Philipp; Singer, Tania

2017-02-01

Functional neuroimaging studies have suggested the existence of 2 largely distinct social cognition networks, one for theory of mind (taking others' cognitive perspective) and another for empathy (sharing others' affective states). To address whether these networks can also be dissociated at the level of brain structure, we combined behavioral phenotyping across multiple socio-cognitive tasks with 3-Tesla MRI cortical thickness and structural covariance analysis in 270 healthy adults, recruited across 2 sites. Regional thickness mapping only provided partial support for divergent substrates, highlighting that individual differences in empathy relate to left insular-opercular thickness while no correlation between thickness and mentalizing scores was found. Conversely, structural covariance analysis showed clearly divergent network modulations by socio-cognitive and -affective phenotypes. Specifically, individual differences in theory of mind related to structural integration between temporo-parietal and dorsomedial prefrontal regions while empathy modulated the strength of dorsal anterior insula networks. Findings were robust across both recruitment sites, suggesting generalizability. At the level of structural network embedding, our study provides a double dissociation between empathy and mentalizing. Moreover, our findings suggest that structural substrates of higher-order social cognition are reflected rather in interregional networks than in the the local anatomical markup of specific regions per se. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Acquisition and Diversification of Cladodes: Leaf-Like Organs in the Genus Asparagus[W

Science.gov (United States)

Nakayama, Hokuto; Yamaguchi, Takahiro; Tsukaya, Hirokazu

2012-01-01

The genus Asparagus is unusual in producing axillary, determinate organs called cladodes, which may take on either a flattened or cylindrical form. Here, we investigated the evolution of cladodes to elucidate the mechanisms at play in the diversification of shoot morphology. Our observations of Asparagus asparagoides, which has leaf-like cladodes, showed that its cladodes are anatomically and developmentally similar to leaves but differ in the adaxial/abaxial polarity of the vasculature. In addition to the expression of an ortholog of KNAT1, orthologous genes that are normally expressed in leaves, ASYMMETRIC LEAVES1 and HD-ZIPIII, were found to be expressed in cladode primordia in a leaf-like manner. The cylindrical cladodes of Asparagus officinalis showed largely similar expression patterns but showed evidence of being genetically abaxialized. These results provide evidence that cladodes are modified axillary shoots, suggest that the co-option of preexisting gene networks involved in leaf development transferred the leaf-like form to axillary shoots, and imply that altered expression of leaf polarity genes led to the evolution of cylindrical cladodes in the A. officinalis clade. PMID:22415273

The effect of glyphosate on import into a sink leaf of sugar beet

International Nuclear Information System (INIS)

Shieh, Wenjang; Geiger, D.R.

1990-01-01

The basis for glyphosate inducted limitation of carbon import into developing leaves was studied in sugar beet. To separate the effects of the herbicide on export from those on import, glyphosate was supplied to a developing leaf from two exporting source leaves which fed the sink leaf. Carbon import into the sink leaf was determined by supplying 14 CO 2 to a third source leaf which also supplies carbon to the monitored sink leaf. Import into the sink leaf decreased within 2 to 3 h after glyphosate application, even though photosynthesis and export in the source leaf supplying 14 C were unaffected. Reduced import into the sink leaf was accompanied by increased import by the tap root. Elongation of the sink leaf was only slightly decreased following arrival of glyphosate. Photosynthesis by the sink leaf was not inhibited. The results to data support the view that import is slowed by the inhibition of synthesis of structural or storage compounds in the developing leaves

Comparison and validation of community structures in complex networks

Science.gov (United States)

Gustafsson, Mika; Hörnquist, Michael; Lombardi, Anna

2006-07-01

The issue of partitioning a network into communities has attracted a great deal of attention recently. Most authors seem to equate this issue with the one of finding the maximum value of the modularity, as defined by Newman. Since the problem formulated this way is believed to be NP-hard, most effort has gone into the construction of search algorithms, and less to the question of other measures of community structures, similarities between various partitionings and the validation with respect to external information. Here we concentrate on a class of computer generated networks and on three well-studied real networks which constitute a bench-mark for network studies; the karate club, the US college football teams and a gene network of yeast. We utilize some standard ways of clustering data (originally not designed for finding community structures in networks) and show that these classical methods sometimes outperform the newer ones. We discuss various measures of the strength of the modular structure, and show by examples features and drawbacks. Further, we compare different partitions by applying some graph-theoretic concepts of distance, which indicate that one of the quality measures of the degree of modularity corresponds quite well with the distance from the true partition. Finally, we introduce a way to validate the partitionings with respect to external data when the nodes are classified but the network structure is unknown. This is here possible since we know everything of the computer generated networks, as well as the historical answer to how the karate club and the football teams are partitioned in reality. The partitioning of the gene network is validated by use of the Gene Ontology database, where we show that a community in general corresponds to a biological process.

Vegetation Structure of Ebony Leaf Monkey (Trachypithecus auratus) Habitat in Kecubung Ulolanang Nature Preservation Central Java-Indonesia

OpenAIRE

Ervina Rahmawati; Jafron Wasiq Hidayat

2018-01-01

Kecubung Ulolanang Nature Preservation is ebony leaf monkey’s habitats in Central Java Indonesia. Continuously degradation of their population is caused by illegal hunting and habitat degradation that made this species being vulnerable. Habitat conservation is one of important aspects to prevent them from extinction. The purpose of this research was to analyze the vegetation’s structure and composition, which was potentially, becomes habitat and food source for the monkeys. Data collected usi...

Linking structure and activity in nonlinear spiking networks.

Directory of Open Access Journals (Sweden)

Gabriel Koch Ocker

2017-06-01

Full Text Available Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of structure-driven activity has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing. Here, we overcome this limitation and present a new relationship between connectivity and activity in networks of nonlinear spiking neurons by developing a diagrammatic fluctuation expansion based on statistical field theory. We explicitly show how recurrent network structure produces pairwise and higher-order correlated activity, and how nonlinearities impact the networks' spiking activity. Our findings open new avenues to investigating how single-neuron nonlinearities-including those of different cell types-combine with connectivity to shape population activity and function.

Linking structure and activity in nonlinear spiking networks.

Science.gov (United States)

Ocker, Gabriel Koch; Josić, Krešimir; Shea-Brown, Eric; Buice, Michael A

2017-06-01

Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of structure-driven activity has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing. Here, we overcome this limitation and present a new relationship between connectivity and activity in networks of nonlinear spiking neurons by developing a diagrammatic fluctuation expansion based on statistical field theory. We explicitly show how recurrent network structure produces pairwise and higher-order correlated activity, and how nonlinearities impact the networks' spiking activity. Our findings open new avenues to investigating how single-neuron nonlinearities-including those of different cell types-combine with connectivity to shape population activity and function.

Community detection for networks with unipartite and bipartite structure

Science.gov (United States)

Chang, Chang; Tang, Chao

2014-09-01

Finding community structures in networks is important in network science, technology, and applications. To date, most algorithms that aim to find community structures only focus either on unipartite or bipartite networks. A unipartite network consists of one set of nodes and a bipartite network consists of two nonoverlapping sets of nodes with only links joining the nodes in different sets. However, a third type of network exists, defined here as the mixture network. Just like a bipartite network, a mixture network also consists of two sets of nodes, but some nodes may simultaneously belong to two sets, which breaks the nonoverlapping restriction of a bipartite network. The mixture network can be considered as a general case, with unipartite and bipartite networks viewed as its limiting cases. A mixture network can represent not only all the unipartite and bipartite networks, but also a wide range of real-world networks that cannot be properly represented as either unipartite or bipartite networks in fields such as biology and social science. Based on this observation, we first propose a probabilistic model that can find modules in unipartite, bipartite, and mixture networks in a unified framework based on the link community model for a unipartite undirected network [B Ball et al (2011 Phys. Rev. E 84 036103)]. We test our algorithm on synthetic networks (both overlapping and nonoverlapping communities) and apply it to two real-world networks: a southern women bipartite network and a human transcriptional regulatory mixture network. The results suggest that our model performs well for all three types of networks, is competitive with other algorithms for unipartite or bipartite networks, and is applicable to real-world networks.

The Deep Structure of Organizational Online Networking

DEFF Research Database (Denmark)

Trier, Matthias; Richter, Alexander

2015-01-01

While research on organizational online networking recently increased significantly, most studies adopt quantitative research designs with a focus on the consequences of social network configurations. Very limited attention is paid to comprehensive theoretical conceptions of the complex phenomenon...... of organizational online networking. We address this gap by adopting a theoretical framework of the deep structure of organizational online networking with a focus on their emerging meaning for the employees. We apply and assess the framework in a qualitative case study of a large-scale implementation...... of a corporate social network site (SNS) in a global organization. We reveal organizational online networking as a multi-dimensional phenomenon with multiplex relationships that are unbalanced, primarily consist of weak ties and are subject to temporal change. Further, we identify discourse drivers...

Locating a tree in a phylogenetic network

NARCIS (Netherlands)

Iersel, van L.J.J.; Semple, C.; Steel, M.A.

2010-01-01

Phylogenetic trees and networks are leaf-labelled graphs that are used to describe evolutionary histories of species. The Tree Containment problem asks whether a given phylogenetic tree is embedded in a given phylogenetic network. Given a phylogenetic network and a cluster of species, the Cluster

Network structure detection and analysis of Shanghai stock market

Directory of Open Access Journals (Sweden)

Sen Wu

2015-04-01

Full Text Available Purpose: In order to investigate community structure of the component stocks of SSE (Shanghai Stock Exchange 180-index, a stock correlation network is built to find the intra-community and inter-community relationship. Design/methodology/approach: The stock correlation network is built taking the vertices as stocks and edges as correlation coefficients of logarithm returns of stock price. It is built as undirected weighted at first. GN algorithm is selected to detect community structure after transferring the network into un-weighted with different thresholds. Findings: The result of the network community structure analysis shows that the stock market has obvious industrial characteristics. Most of the stocks in the same industry or in the same supply chain are assigned to the same community. The correlation of the internal stock prices’ fluctuation is closer than in different communities. The result of community structure detection also reflects correlations among different industries. Originality/value: Based on the analysis of the community structure in Shanghai stock market, the result reflects some industrial characteristics, which has reference value to relationship among industries or sub-sectors of listed companies.

A Decomposition Algorithm for Learning Bayesian Network Structures from Data

DEFF Research Database (Denmark)

Zeng, Yifeng; Cordero Hernandez, Jorge

2008-01-01

It is a challenging task of learning a large Bayesian network from a small data set. Most conventional structural learning approaches run into the computational as well as the statistical problems. We propose a decomposition algorithm for the structure construction without having to learn...... the complete network. The new learning algorithm firstly finds local components from the data, and then recover the complete network by joining the learned components. We show the empirical performance of the decomposition algorithm in several benchmark networks....

Structural health monitoring using wireless sensor networks

Science.gov (United States)

Sreevallabhan, K.; Nikhil Chand, B.; Ramasamy, Sudha

2017-11-01

Monitoring and analysing health of large structures like bridges, dams, buildings and heavy machinery is important for safety, economical, operational, making prior protective measures, and repair and maintenance point of view. In recent years there is growing demand for such larger structures which in turn make people focus more on safety. By using Microelectromechanical Systems (MEMS) Accelerometer we can perform Structural Health Monitoring by studying the dynamic response through measure of ambient vibrations and strong motion of such structures. By using Wireless Sensor Networks (WSN) we can embed these sensors in wireless networks which helps us to transmit data wirelessly thus we can measure the data wirelessly at any remote location. This in turn reduces heavy wiring which is a cost effective as well as time consuming process to lay those wires. In this paper we developed WSN based MEMS-accelerometer for Structural to test the results in the railway bridge near VIT University, Vellore campus.

Impact of constrained rewiring on network structure and node dynamics

Science.gov (United States)

Rattana, P.; Berthouze, L.; Kiss, I. Z.

2014-11-01

In this paper, we study an adaptive spatial network. We consider a susceptible-infected-susceptible (SIS) epidemic on the network, with a link or contact rewiring process constrained by spatial proximity. In particular, we assume that susceptible nodes break links with infected nodes independently of distance and reconnect at random to susceptible nodes available within a given radius. By systematically manipulating this radius we investigate the impact of rewiring on the structure of the network and characteristics of the epidemic. We adopt a step-by-step approach whereby we first study the impact of rewiring on the network structure in the absence of an epidemic, then with nodes assigned a disease status but without disease dynamics, and finally running network and epidemic dynamics simultaneously. In the case of no labeling and no epidemic dynamics, we provide both analytic and semianalytic formulas for the value of clustering achieved in the network. Our results also show that the rewiring radius and the network's initial structure have a pronounced effect on the endemic equilibrium, with increasingly large rewiring radiuses yielding smaller disease prevalence.

The formation of a core-periphery structure in heterogeneous financial networks

NARCIS (Netherlands)

van der Leij, M.; in 't Veld, D.; Hommes, C.

2016-01-01

Recent empirical evidence suggests that financial networks exhibit a core-periphery network structure. This paper aims at giving an explanation for the emergence of such a structure using network formation theory. We propose a simple model of the overnight interbank lending market, in which banks

A Trusted Autonomic Architecture to Safeguard Cyber-Physical Control Leaf Nodes and Protect Process Integrity

OpenAIRE

Chiluvuri, Nayana Teja

2015-01-01

Cyber-physical systems are networked through IT infrastructure and susceptible to malware. Threats targeting process control are much more safety-critical than traditional computing systems since they jeopardize the integrity of physical infrastructure. Existing defence mechanisms address security at the network nodes but do not protect the physical infrastructure if network integrity is compromised. An interface guardian architecture is implemented on cyber-physical control leaf nodes to mai...

A leaf gas exchange model that accounts for intra-canopy variability by considering leaf nitrogen content and local acclimation to radiation in grapevine (Vitis vinifera L.).

Science.gov (United States)

Prieto, Jorge A; Louarn, Gaëtan; Perez Peña, Jorge; Ojeda, Hernán; Simonneau, Thierry; Lebon, Eric

2012-07-01

Understanding the distribution of gas exchange within a plant is a prerequisite for scaling up from leaves to canopies. We evaluated whether leaf traits were reliable predictors of the effects of leaf ageing and leaf irradiance on leaf photosynthetic capacity (V(cmax) , J(max) ) in field-grown vines (Vitis vinifera L). Simultaneously, we measured gas exchange, leaf mass per area (LMA) and nitrogen content (N(m) ) of leaves at different positions within the canopy and at different phenological stages. Daily mean leaf irradiance cumulated over 10 d (PPFD(10) ) was obtained by 3D modelling of the canopy structure. N(m) decreased over the season in parallel to leaf ageing while LMA was mainly affected by leaf position. PPFD(10) explained 66, 28 and 73% of the variation of LMA, N(m) and nitrogen content per area (N(a) ), respectively. Nitrogen content per unit area (N(a) = LMA × N(m) ) was the best predictor of the intra-canopy variability of leaf photosynthetic capacity. Finally, we developed a classical photosynthesis-stomatal conductance submodel and by introducing N(a) as an input, the model accurately simulated the daily pattern of gas exchange for leaves at different positions in the canopy and at different phenological stages during the season. © 2012 Blackwell Publishing Ltd.

Effects of Neuromodulation on Excitatory-Inhibitory Neural Network Dynamics Depend on Network Connectivity Structure

Science.gov (United States)

Rich, Scott; Zochowski, Michal; Booth, Victoria

2018-01-01

Acetylcholine (ACh), one of the brain's most potent neuromodulators, can affect intrinsic neuron properties through blockade of an M-type potassium current. The effect of ACh on excitatory and inhibitory cells with this potassium channel modulates their membrane excitability, which in turn affects their tendency to synchronize in networks. Here, we study the resulting changes in dynamics in networks with inter-connected excitatory and inhibitory populations (E-I networks), which are ubiquitous in the brain. Utilizing biophysical models of E-I networks, we analyze how the network connectivity structure in terms of synaptic connectivity alters the influence of ACh on the generation of synchronous excitatory bursting. We investigate networks containing all combinations of excitatory and inhibitory cells with high (Type I properties) or low (Type II properties) modulatory tone. To vary network connectivity structure, we focus on the effects of the strengths of inter-connections between excitatory and inhibitory cells (E-I synapses and I-E synapses), and the strengths of intra-connections among excitatory cells (E-E synapses) and among inhibitory cells (I-I synapses). We show that the presence of ACh may or may not affect the generation of network synchrony depending on the network connectivity. Specifically, strong network inter-connectivity induces synchronous excitatory bursting regardless of the cellular propensity for synchronization, which aligns with predictions of the PING model. However, when a network's intra-connectivity dominates its inter-connectivity, the propensity for synchrony of either inhibitory or excitatory cells can determine the generation of network-wide bursting.

Epidemic outbreaks in growing scale-free networks with local structure

Science.gov (United States)

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.

Structural Analysis of Complex Networks

CERN Document Server

Dehmer, Matthias

2011-01-01

Filling a gap in literature, this self-contained book presents theoretical and application-oriented results that allow for a structural exploration of complex networks. The work focuses not only on classical graph-theoretic methods, but also demonstrates the usefulness of structural graph theory as a tool for solving interdisciplinary problems. Applications to biology, chemistry, linguistics, and data analysis are emphasized. The book is suitable for a broad, interdisciplinary readership of researchers, practitioners, and graduate students in discrete mathematics, statistics, computer science,

Analysis of the structure of complex networks at different resolution levels

Energy Technology Data Exchange (ETDEWEB)

Arenas, A.; Fernandez, A.; Gomez, S.

2008-02-28

Modular structure is ubiquitous in real-world complex networks, and its detection is important because it gives insights in the structure-functionality relationship. The standard approach is based on the optimization of a quality function, modularity, which is a relative quality measure for a partition of a network into modules. Recently some authors 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 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 to find the exact splits reported in the literature, as well as the substructure beyond the actual split.

Adapting Bayes Network Structures to Non-stationary Domains

DEFF Research Database (Denmark)

Nielsen, Søren Holbech; Nielsen, Thomas Dyhre

2008-01-01

When an incremental structural learning method gradually modifies a Bayesian network (BN) structure to fit a sequential stream of observations, we call the process structural adaptation. Structural adaptation is useful when the learner is set to work in an unknown environment, where a BN is gradu...

Association of structural global brain network properties with intelligence in normal aging.

Directory of Open Access Journals (Sweden)

Florian U Fischer

Full Text Available Higher general intelligence attenuates age-associated cognitive decline and the risk of dementia. Thus, intelligence has been associated with cognitive reserve or resilience in normal aging. Neurophysiologically, intelligence is considered as a complex capacity that is dependent on a global cognitive network rather than isolated brain areas. An association of structural as well as functional brain network characteristics with intelligence has already been reported in young adults. We investigated the relationship between global structural brain network properties, general intelligence and age in a group of 43 cognitively healthy elderly, age 60-85 years. Individuals were assessed cross-sectionally using Wechsler Adult Intelligence Scale-Revised (WAIS-R and diffusion-tensor imaging. Structural brain networks were reconstructed individually using deterministic tractography, global network properties (global efficiency, mean shortest path length, and clustering coefficient were determined by graph theory and correlated to intelligence scores within both age groups. Network properties were significantly correlated to age, whereas no significant correlation to WAIS-R was observed. However, in a subgroup of 15 individuals aged 75 and above, the network properties were significantly correlated to WAIS-R. Our findings suggest that general intelligence and global properties of structural brain networks may not be generally associated in cognitively healthy elderly. However, we provide first evidence of an association between global structural brain network properties and general intelligence in advanced elderly. Intelligence might be affected by age-associated network deterioration only if a certain threshold of structural degeneration is exceeded. Thus, age-associated brain structural changes seem to be partially compensated by the network and the range of this compensation might be a surrogate of cognitive reserve or brain resilience.

Association of Structural Global Brain Network Properties with Intelligence in Normal Aging

Science.gov (United States)

Fischer, Florian U.; Wolf, Dominik; Scheurich, Armin; Fellgiebel, Andreas

2014-01-01

Higher general intelligence attenuates age-associated cognitive decline and the risk of dementia. Thus, intelligence has been associated with cognitive reserve or resilience in normal aging. Neurophysiologically, intelligence is considered as a complex capacity that is dependent on a global cognitive network rather than isolated brain areas. An association of structural as well as functional brain network characteristics with intelligence has already been reported in young adults. We investigated the relationship between global structural brain network properties, general intelligence and age in a group of 43 cognitively healthy elderly, age 60–85 years. Individuals were assessed cross-sectionally using Wechsler Adult Intelligence Scale-Revised (WAIS-R) and diffusion-tensor imaging. Structural brain networks were reconstructed individually using deterministic tractography, global network properties (global efficiency, mean shortest path length, and clustering coefficient) were determined by graph theory and correlated to intelligence scores within both age groups. Network properties were significantly correlated to age, whereas no significant correlation to WAIS-R was observed. However, in a subgroup of 15 individuals aged 75 and above, the network properties were significantly correlated to WAIS-R. Our findings suggest that general intelligence and global properties of structural brain networks may not be generally associated in cognitively healthy elderly. However, we provide first evidence of an association between global structural brain network properties and general intelligence in advanced elderly. Intelligence might be affected by age-associated network deterioration only if a certain threshold of structural degeneration is exceeded. Thus, age-associated brain structural changes seem to be partially compensated by the network and the range of this compensation might be a surrogate of cognitive reserve or brain resilience. PMID:24465994

A user exposure based approach for non-structural road network vulnerability analysis.

Directory of Open Access Journals (Sweden)

Lei Jin

Full Text Available Aiming at the dense urban road network vulnerability without structural negative consequences, this paper proposes a novel non-structural road network vulnerability analysis framework. Three aspects of the framework are mainly described: (i the rationality of non-structural road network vulnerability, (ii the metrics for negative consequences accounting for variant road conditions, and (iii the introduction of a new vulnerability index based on user exposure. Based on the proposed methodology, a case study in the Sioux Falls network which was usually threatened by regular heavy snow during wintertime is detailedly discussed. The vulnerability ranking of links of Sioux Falls network with respect to heavy snow scenario is identified. As a result of non-structural consequences accompanied by conceivable degeneration of network, there are significant increases in generalized travel time costs which are measurements for "emotionally hurt" of topological road network.

Analysis of structure-function network decoupling in the brain systems of spastic diplegic cerebral palsy.

Science.gov (United States)

Lee, Dongha; Pae, Chongwon; Lee, Jong Doo; Park, Eun Sook; Cho, Sung-Rae; Um, Min-Hee; Lee, Seung-Koo; Oh, Maeng-Keun; Park, Hae-Jeong

2017-10-01

Manifestation of the functionalities from the structural brain network is becoming increasingly important to understand a brain disease. With the aim of investigating the differential structure-function couplings according to network systems, we investigated the structural and functional brain networks of patients with spastic diplegic cerebral palsy with periventricular leukomalacia compared to healthy controls. The structural and functional networks of the whole brain and motor system, constructed using deterministic and probabilistic tractography of diffusion tensor magnetic resonance images and Pearson and partial correlation analyses of resting-state functional magnetic resonance images, showed differential embedding of functional networks in the structural networks in patients. In the whole-brain network of patients, significantly reduced global network efficiency compared to healthy controls were found in the structural networks but not in the functional networks, resulting in reduced structural-functional coupling. On the contrary, the motor network of patients had a significantly lower functional network efficiency over the intact structural network and a lower structure-function coupling than the control group. This reduced coupling but reverse directionality in the whole-brain and motor networks of patients was prominent particularly between the probabilistic structural and partial correlation-based functional networks. Intact (or less deficient) functional network over impaired structural networks of the whole brain and highly impaired functional network topology over the intact structural motor network might subserve relatively preserved cognitions and impaired motor functions in cerebral palsy. This study suggests that the structure-function relationship, evaluated specifically using sparse functional connectivity, may reveal important clues to functional reorganization in cerebral palsy. Hum Brain Mapp 38:5292-5306, 2017. © 2017 Wiley Periodicals

Sensitivity analysis of human brain structural network construction

Directory of Open Access Journals (Sweden)

Kuang Wei

2017-12-01

Full Text Available Network neuroscience leverages diffusion-weighted magnetic resonance imaging and tractography to quantify structural connectivity of the human brain. However, scientists and practitioners lack a clear understanding of the effects of varying tractography parameters on the constructed structural networks. With diffusion images from the Human Connectome Project (HCP, we characterize how structural networks are impacted by the spatial resolution of brain atlases, total number of tractography streamlines, and grey matter dilation with various graph metrics. We demonstrate how injudicious combinations of highly refined brain parcellations and low numbers of streamlines may inadvertently lead to disconnected network models with isolated nodes. Furthermore, we provide solutions to significantly reduce the likelihood of generating disconnected networks. In addition, for different tractography parameters, we investigate the distributions of values taken by various graph metrics across the population of HCP subjects. Analyzing the ranks of individual subjects within the graph metric distributions, we find that the ranks of individuals are affected differently by atlas scale changes. Our work serves as a guideline for researchers to optimize the selection of tractography parameters and illustrates how biological characteristics of the brain derived in network neuroscience studies can be affected by the choice of atlas parcellation schemes. Diffusion tractography has been proven to be a promising noninvasive technique to study the network properties of the human brain. However, how various tractography and network construction parameters affect network properties has not been studied using a large cohort of high-quality data. We utilize data provided by the Human Connectome Project to characterize the changes to network properties induced by varying the brain parcellation atlas scales, the number of reconstructed tractography tracks, and the degree of grey

Leaf size and leaf display of thirty-eight tropical tree species

NARCIS (Netherlands)

Poorter, L.; Rozendaal, D.M.A.

2008-01-01

Trees forage for light through optimal leaf display. Effective leaf display is determined by metamer traits (i.e., the internode, petiole, and corresponding leaf), and thus these traits strongly co-determine carbon gain and as a result competitive advantage in a light-limited environment. We

Structuring evolution: biochemical networks and metabolic diversification in birds.

Science.gov (United States)

Morrison, Erin S; Badyaev, Alexander V

2016-08-25

Recurrence and predictability of evolution are thought to reflect the correspondence between genomic and phenotypic dimensions of organisms, and the connectivity in deterministic networks within these dimensions. Direct examination of the correspondence between opportunities for diversification imbedded in such networks and realized diversity is illuminating, but is empirically challenging because both the deterministic networks and phenotypic diversity are modified in the course of evolution. Here we overcome this problem by directly comparing the structure of a "global" carotenoid network - comprising of all known enzymatic reactions among naturally occurring carotenoids - with the patterns of evolutionary diversification in carotenoid-producing metabolic networks utilized by birds. We found that phenotypic diversification in carotenoid networks across 250 species was closely associated with enzymatic connectivity of the underlying biochemical network - compounds with greater connectivity occurred the most frequently across species and were the hotspots of metabolic pathway diversification. In contrast, we found no evidence for diversification along the metabolic pathways, corroborating findings that the utilization of the global carotenoid network was not strongly influenced by history in avian evolution. The finding that the diversification in species-specific carotenoid networks is qualitatively predictable from the connectivity of the underlying enzymatic network points to significant structural determinism in phenotypic evolution.

Ames and other European networks in integrity of ageing structures

International Nuclear Information System (INIS)

Davies, L.M.; Von Estorff, U.; Crutzen, S.

1996-01-01

Several European institutions and organisations and the Joint Research Centre have developed co-operative programmes now organised into Networks for mutual benefit. They include utilities, engineering companies, Research and Development laboratories and regulatory bodies. Networks are organised and managed like the successful Programme for the Inspection of Steel Components (PISC). The JRC's Institute for Advanced Materials of the European Commission plays the role of Operating Agent and manager of these Networks: ENIQ. AMES, NESC, each of them dealing with specific aspect of fitness for purpose of materials in structural components. This paper describes the structure and the objectives of these networks. Particular emphasis is given to the network AMES

Structure-function relationship in complex brain networks expressed by hierarchical synchronization

International Nuclear Information System (INIS)

Zhou Changsong; Zemanova, Lucia; Zamora-Lopez, Gorka; Hilgetag, Claus C; Kurths, Juergen

2007-01-01

The brain is one of the most complex systems in nature, with a structured complex connectivity. Recently, large-scale corticocortical connectivities, both structural and functional, have received a great deal of research attention, especially using the approach of complex network analysis. Understanding the relationship between structural and functional connectivity is of crucial importance in neuroscience. Here we try to illuminate this relationship by studying synchronization dynamics in a realistic anatomical network of cat cortical connectivity. We model the nodes (cortical areas) by a neural mass model (population model) or by a subnetwork of interacting excitable neurons (multilevel model). We show that if the dynamics is characterized by well-defined oscillations (neural mass model and subnetworks with strong couplings), the synchronization patterns are mainly determined by the node intensity (total input strengths of a node) and the detailed network topology is rather irrelevant. On the other hand, the multilevel model with weak couplings displays more irregular, biologically plausible dynamics, and the synchronization patterns reveal a hierarchical cluster organization in the network structure. The relationship between structural and functional connectivity at different levels of synchronization is explored. Thus, the study of synchronization in a multilevel complex network model of cortex can provide insights into the relationship between network topology and functional organization of complex brain networks

Structure-function relationship in complex brain networks expressed by hierarchical synchronization

Energy Technology Data Exchange (ETDEWEB)

Zhou Changsong [Institute of Physics, University of Potsdam, PF 601553, 14415 Potsdam (Germany); Zemanova, Lucia [Institute of Physics, University of Potsdam, PF 601553, 14415 Potsdam (Germany); Zamora-Lopez, Gorka [Institute of Physics, University of Potsdam, PF 601553, 14415 Potsdam (Germany); Hilgetag, Claus C [Jacobs University Bremen, Campus Ring 6, Rm 116, D-28759 Bremen (Germany); Kurths, Juergen [Institute of Physics, University of Potsdam, PF 601553, 14415 Potsdam (Germany)

2007-06-15

The brain is one of the most complex systems in nature, with a structured complex connectivity. Recently, large-scale corticocortical connectivities, both structural and functional, have received a great deal of research attention, especially using the approach of complex network analysis. Understanding the relationship between structural and functional connectivity is of crucial importance in neuroscience. Here we try to illuminate this relationship by studying synchronization dynamics in a realistic anatomical network of cat cortical connectivity. We model the nodes (cortical areas) by a neural mass model (population model) or by a subnetwork of interacting excitable neurons (multilevel model). We show that if the dynamics is characterized by well-defined oscillations (neural mass model and subnetworks with strong couplings), the synchronization patterns are mainly determined by the node intensity (total input strengths of a node) and the detailed network topology is rather irrelevant. On the other hand, the multilevel model with weak couplings displays more irregular, biologically plausible dynamics, and the synchronization patterns reveal a hierarchical cluster organization in the network structure. The relationship between structural and functional connectivity at different levels of synchronization is explored. Thus, the study of synchronization in a multilevel complex network model of cortex can provide insights into the relationship between network topology and functional organization of complex brain networks.

Effects of network structure on the synchronizability of nonlinearly coupled Hindmarsh–Rose neurons

International Nuclear Information System (INIS)

Li, Chun-Hsien; Yang, Suh-Yuh

2015-01-01

This work is devoted to investigate the effects of network structure on the synchronizability of nonlinearly coupled dynamical network of Hindmarsh–Rose neurons with a sigmoidal coupling function. We mainly focus on the networks that exhibit the small-world character or scale-free property. By checking the first nonzero eigenvalue of the outer-coupling matrix, which is closely related to the synchronization threshold, the synchronizabilities of three specific network ensembles with prescribed network structures are compared. Interestingly, we find that networks with more connections will not necessarily result in better synchronizability. - Highlights: • We investigate the effects of network structure on the synchronizability of nonlinearly coupled Hindmarsh–Rose neurons. • We mainly consider the networks that exhibit the small-world character or scale-free property. • The synchronizability of three specific network ensembles with prescribed network structures are compared. • Networks with more connections will not necessarily result in better synchronizability

Effects of network structure on the synchronizability of nonlinearly coupled Hindmarsh–Rose neurons

Energy Technology Data Exchange (ETDEWEB)

Li, Chun-Hsien, E-mail: chli@nknucc.nknu.edu.tw [Department of Mathematics, National Kaohsiung Normal University, Yanchao District, Kaohsiung City 82444, Taiwan (China); Yang, Suh-Yuh, E-mail: syyang@math.ncu.edu.tw [Department of Mathematics, National Central University, Jhongli District, Taoyuan City 32001, Taiwan (China)

2015-10-23

This work is devoted to investigate the effects of network structure on the synchronizability of nonlinearly coupled dynamical network of Hindmarsh–Rose neurons with a sigmoidal coupling function. We mainly focus on the networks that exhibit the small-world character or scale-free property. By checking the first nonzero eigenvalue of the outer-coupling matrix, which is closely related to the synchronization threshold, the synchronizabilities of three specific network ensembles with prescribed network structures are compared. Interestingly, we find that networks with more connections will not necessarily result in better synchronizability. - Highlights: • We investigate the effects of network structure on the synchronizability of nonlinearly coupled Hindmarsh–Rose neurons. • We mainly consider the networks that exhibit the small-world character or scale-free property. • The synchronizability of three specific network ensembles with prescribed network structures are compared. • Networks with more connections will not necessarily result in better synchronizability.

TreeNetViz: revealing patterns of networks over tree structures.

Science.gov (United States)

Gou, Liang; Zhang, Xiaolong Luke

2011-12-01

Network data often contain important attributes from various dimensions such as social affiliations and areas of expertise in a social network. If such attributes exhibit a tree structure, visualizing a compound graph consisting of tree and network structures becomes complicated. How to visually reveal patterns of a network over a tree has not been fully studied. In this paper, we propose a compound graph model, TreeNet, to support visualization and analysis of a network at multiple levels of aggregation over a tree. We also present a visualization design, TreeNetViz, to offer the multiscale and cross-scale exploration and interaction of a TreeNet graph. TreeNetViz uses a Radial, Space-Filling (RSF) visualization to represent the tree structure, a circle layout with novel optimization to show aggregated networks derived from TreeNet, and an edge bundling technique to reduce visual complexity. Our circular layout algorithm reduces both total edge-crossings and edge length and also considers hierarchical structure constraints and edge weight in a TreeNet graph. These experiments illustrate that the algorithm can reduce visual cluttering in TreeNet graphs. Our case study also shows that TreeNetViz has the potential to support the analysis of a compound graph by revealing multiscale and cross-scale network patterns. © 2011 IEEE

Effect of sunlight shielding on leaf structure and amino acids ...

African Journals Online (AJOL)

Light sensitive albino tea cultivar 'Jinguang' (Camellia sinensis) which grows albinism leaf in yellow colour, results to high level of amino acids but low levels of photosynthetic pigments including chlorophylls, neoxanthin, violaxanthin, phytoxanthin and β-carotene when it is exposed to high sunlight illumination in the ...

Brain networks, structural realism, and local approaches to the scientific realism debate.

Science.gov (United States)

Yan, Karen; Hricko, Jonathon

2017-08-01

We examine recent work in cognitive neuroscience that investigates brain networks. Brain networks are characterized by the ways in which brain regions are functionally and anatomically connected to one another. Cognitive neuroscientists use various noninvasive techniques (e.g., fMRI) to investigate these networks. They represent them formally as graphs. And they use various graph theoretic techniques to analyze them further. We distinguish between knowledge of the graph theoretic structure of such networks (structural knowledge) and knowledge of what instantiates that structure (nonstructural knowledge). And we argue that this work provides structural knowledge of brain networks. We explore the significance of this conclusion for the scientific realism debate. We argue that our conclusion should not be understood as an instance of a global structural realist claim regarding the structure of the unobservable part of the world, but instead, as a local structural realist attitude towards brain networks in particular. And we argue that various local approaches to the realism debate, i.e., approaches that restrict realist commitments to particular theories and/or entities, are problematic insofar as they don't allow for the possibility of such a local structural realist attitude. Copyright © 2017 Elsevier Ltd. All rights reserved.

Learning Orthographic Structure With Sequential Generative Neural Networks.

Science.gov (United States)

Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco

2016-04-01

Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine (RBM), a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual information in the form of internal, distributed representations. We assessed whether this type of network can extract the orthographic structure of English monosyllables by learning a generative model of the letter sequences forming a word training corpus. We show that the network learned an accurate probabilistic model of English graphotactics, which can be used to make predictions about the letter following a given context as well as to autonomously generate high-quality pseudowords. The model was compared to an extended version of simple recurrent networks, augmented with a stochastic process that allows autonomous generation of sequences, and to non-connectionist probabilistic models (n-grams and hidden Markov models). We conclude that sequential RBMs and stochastic simple recurrent networks are promising candidates for modeling cognition in the temporal domain. Copyright © 2015 Cognitive Science Society, Inc.

Structural and functional networks in complex systems with delay.

Science.gov (United States)

Eguíluz, Víctor M; Pérez, Toni; Borge-Holthoefer, Javier; Arenas, Alex

2011-05-01

Functional networks of complex systems are obtained from the analysis of the temporal activity of their components, and are often used to infer their unknown underlying connectivity. We obtain the equations relating topology and function in a system of diffusively delay-coupled elements in complex networks. We solve exactly the resulting equations in motifs (directed structures of three nodes) and in directed networks. The mean-field solution for directed uncorrelated networks shows that the clusterization of the activity is dominated by the in-degree of the nodes, and that the locking frequency decreases with increasing average degree. We find that the exponent of a power law degree distribution of the structural topology γ is related to the exponent of the associated functional network as α=(2-γ)(-1) for γ<2. © 2011 American Physical Society

Community Structure in Online Collegiate Social Networks

Science.gov (United States)

Traud, Amanda; Kelsic, Eric; Mucha, Peter; Porter, Mason

2009-03-01

Online social networking sites have become increasingly popular with college students. The networks we studied are defined through ``friendships'' indicated by Facebook users from UNC, Oklahoma, Caltech, Georgetown, and Princeton. We apply the tools of network science to study the Facebook networks from these five different universities at a single point in time. We investigate each single-institution network's community structure, which we obtain through partitioning the graph using an eigenvector method. We use both graphical and quantitative tools, including pair-counting methods, which we interpret through statistical analysis and permutation tests to measure the correlations between the network communities and a set of characteristics given by each user (residence, class year, major, and high school). We also analyze the single gender subsets of these networks, and the impact of missing demographical data. Our study allows us to compare the online social networks for the five schools as well as infer differences in offline social interactions. At the schools studied, we were able to define which characteristics of the Facebook users correlate best with friendships.

The leaf angle distribution of natural plant populations: assessing the canopy with a novel software tool.

Science.gov (United States)

Müller-Linow, Mark; Pinto-Espinosa, Francisco; Scharr, Hanno; Rascher, Uwe

2015-01-01

Three-dimensional canopies form complex architectures with temporally and spatially changing leaf orientations. Variations in canopy structure are linked to canopy function and they occur within the scope of genetic variability as well as a reaction to environmental factors like light, water and nutrient supply, and stress. An important key measure to characterize these structural properties is the leaf angle distribution, which in turn requires knowledge on the 3-dimensional single leaf surface. Despite a large number of 3-d sensors and methods only a few systems are applicable for fast and routine measurements in plants and natural canopies. A suitable approach is stereo imaging, which combines depth and color information that allows for easy segmentation of green leaf material and the extraction of plant traits, such as leaf angle distribution. We developed a software package, which provides tools for the quantification of leaf surface properties within natural canopies via 3-d reconstruction from stereo images. Our approach includes a semi-automatic selection process of single leaves and different modes of surface characterization via polygon smoothing or surface model fitting. Based on the resulting surface meshes leaf angle statistics are computed on the whole-leaf level or from local derivations. We include a case study to demonstrate the functionality of our software. 48 images of small sugar beet populations (4 varieties) have been analyzed on the base of their leaf angle distribution in order to investigate seasonal, genotypic and fertilization effects on leaf angle distributions. We could show that leaf angle distributions change during the course of the season with all varieties having a comparable development. Additionally, different varieties had different leaf angle orientation that could be separated in principle component analysis. In contrast nitrogen treatment had no effect on leaf angles. We show that a stereo imaging setup together with the

‘Breath figures’ on leaf surfaces – formation and effects of microscopic leaf wetness

Directory of Open Access Journals (Sweden)

Jürgen eBurkhardt

2013-10-01

Full Text Available ‘Microscopic leaf wetness’ means minute amounts of persistent liquid water on leaf surfaces which are invisible to the naked eye. The water is mainly maintained by transpired water vapor condensing onto the leaf surface and to attached leaf surface particles. With an estimated average thickness of less than 1 µm, microscopic leaf wetness it is about 2 orders of magnitude thinner than morning dewfall. The most important physical processes which reduce the saturation vapor pressure and promote condensation are cuticular absorption and the deliquescence of hygroscopic leaf surface particles. Deliquescent salts form highly concentrated solutions. Depending on the amount and concentration of the dissolved ions, the physicochemical properties of microscopic leaf wetness can be considerably different from those of pure water. Microscopic leaf wetness can form continuous thin layers on hydrophobic leaf surfaces and in specific cases can act similar to surfactants, enabling a strong potential influence on the foliar exchange of ions. Microscopic leaf wetness can also enhance the dissolution, the emission, and the reaction of specific atmospheric trace gases e.g. ammonia, SO2, or ozone, leading to a strong potential role for microscopic leaf wetness in plant/atmosphere interaction. Due to its difficult detection, there is little knowledge about the occurrence and the properties of microscopic leaf wetness. However, based on the existing evidence and on physicochemical reasoning it can be hypothesized that microscopic leaf wetness occurs on almost any plant worldwide and often permanently, and that it significantly influences the exchange processes of the leaf surface with its neighboring compartments, i.e., the plant interior and the atmosphere. The omission of microscopic water in general leaf wetness concepts has caused far-reaching, misleading conclusions in the past.

Network Structure, Collaborative Context, and Individual Creativity

DEFF Research Database (Denmark)

Soda, Giuseppe; Stea, Diego; Pedersen, Torben

2017-01-01

The debate on whether bonding or bridging ties are more beneficial for acquiring knowledge that is conducive to individual creativity has mostly overlooked the context in which such ties are formed. We challenge the widespread assumption that closed, heavily bonded networks imply a collaborative...... attitude on the part of the embedded actors and propose that the level of collaboration in a network can be independent from that network’s structural characteristics, such that it moderates the effects of closed and brokering network positions on the acquisition of knowledge that supports creativity....... Individuals embedded in closed networks acquire more knowledge and become more creative when the level of collaboration in their network is high. Brokers who arbitrage information across disconnected contacts acquire more knowledge and become more creative when collaboration is low. An analysis of employee...

Effect of synapse dilution on the memory retrieval in structured attractor neural networks

Science.gov (United States)

Brunel, N.

1993-08-01

We investigate a simple model of structured attractor neural network (ANN). In this network a module codes for the category of the stored information, while another group of neurons codes for the remaining information. The probability distribution of stabilities of the patterns and the prototypes of the categories are calculated, for two different synaptic structures. The stability of the prototypes is shown to increase when the fraction of neurons coding for the category goes down. Then the effect of synapse destruction on the retrieval is studied in two opposite situations : first analytically in sparsely connected networks, then numerically in completely connected ones. In both cases the behaviour of the structured network and that of the usual homogeneous networks are compared. When lesions increase, two transitions are shown to appear in the behaviour of the structured network when one of the patterns is presented to the network. After the first transition the network recognizes the category of the pattern but not the individual pattern. After the second transition the network recognizes nothing. These effects are similar to syndromes caused by lesions in the central visual system, namely prosopagnosia and agnosia. In both types of networks (structured or homogeneous) the stability of the prototype is greater than the stability of individual patterns, however the first transition, for completely connected networks, occurs only when the network is structured.

Romanian network for structural integrity assessment of nuclear components

International Nuclear Information System (INIS)

Roth, Maria; Constantinescu, Dan Mihai; Brad, Sebastian; Ducu, Catalin

2008-01-01

Full text: Based of the Romanian option to develop and operate nuclear facilities, using as model the networks created at European level and taking into account the international importance of the structural integrity assessments for lifetime extension of the nuclear components, a national Project started since 2005 in the framework of the National Program 'Research of Excellence', Modulus I 2006-2008, managed by the Ministry of Education and Research. Entitled 'Integrated Network for Structural Integrity Monitoring of Critical Components in Nuclear Facilities', with the acronym RIMIS, the Project had two main objectives: - to elaborate a procedure applicable to the structural integrity assessment of the critical components used in Romanian nuclear facilities; - to integrate the national networking in a similar one, at European level, to enhance the scientific significance of Romanian R and D organizations as well as to increase the contribution to solving one of the major issue of the nuclear field. The paper aimed to present the activities performed in the Romanian institutes, involved in the Project, the final results obtained as part of the R and D activities, including experimental, theoretical and modeling ones regarding structural integrity assessment of nuclear components employed in CANDU type reactors. Also the activity carried out in the framework of the NULIFE network, created at European level of the FP6 Program and sustained by the RIMIS network will be described. (authors)

NCI National Clinical Trials Network Structure

Science.gov (United States)

Learn about how the National Clinical Trials Network (NCTN) is structured. The NCTN is a program of the National Cancer Institute that gives funds and other support to cancer research organizations to conduct cancer clinical trials.

Research on Community Structure in Bus Transport Networks

International Nuclear Information System (INIS)

Yang Xuhua; Wang Bo; Sun Youxian

2009-01-01

We abstract the bus transport networks (BTNs) to two kinds of complex networks with space L and space P methods respectively. Using improved community detecting algorithm (PKM agglomerative algorithm), we analyze the community property of two kinds of BTNs graphs. The results show that the BTNs graph described with space L method have obvious community property, but the other kind of BTNs graph described with space P method have not. The reason is that the BTNs graph described with space P method have the intense overlapping community property and general community division algorithms can not identify this kind of community structure. To overcome this problem, we propose a novel community structure called N-depth community and present a corresponding community detecting algorithm, which can detect overlapping community. Applying the novel community structure and detecting algorithm to a BTN evolution model described with space P, whose network property agrees well with real BTNs', we get obvious community property. (general)

Active influence in dynamical models of structural balance in social networks

Science.gov (United States)

Summers, Tyler H.; Shames, Iman

2013-07-01

We consider a nonlinear dynamical system on a signed graph, which can be interpreted as a mathematical model of social networks in which the links can have both positive and negative connotations. In accordance with a concept from social psychology called structural balance, the negative links play a key role in both the structure and dynamics of the network. Recent research has shown that in a nonlinear dynamical system modeling the time evolution of “friendliness levels” in the network, two opposing factions emerge from almost any initial condition. Here we study active external influence in this dynamical model and show that any agent in the network can achieve any desired structurally balanced state from any initial condition by perturbing its own local friendliness levels. Based on this result, we also introduce a new network centrality measure for signed networks. The results are illustrated in an international-relations network using United Nations voting record data from 1946 to 2008 to estimate friendliness levels amongst various countries.

Structure identification and adaptive synchronization of uncertain general complex dynamical networks

International Nuclear Information System (INIS)

Xu Yuhua; Zhou Wuneng; Fang Jian'an; Lu Hongqian

2009-01-01

This Letter proposes an approach to identify the topological structure and unknown parameters for uncertain general complex networks simultaneously. By designing effective adaptive controllers, we achieve synchronization between two complex networks. The unknown network topological structure and system parameters of uncertain general complex dynamical networks are identified simultaneously in the process of synchronization. Several useful criteria for synchronization are given. Finally, an illustrative example is presented to demonstrate the application of the theoretical results.

Structure identification and adaptive synchronization of uncertain general complex dynamical networks

Energy Technology Data Exchange (ETDEWEB)

Xu Yuhua, E-mail: yuhuaxu2004@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China) and Department of Maths, Yunyang Teacher' s College, Hubei 442000 (China); Zhou Wuneng, E-mail: wnzhou@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Fang Jian' an [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Lu Hongqian [Shandong Institute of Light Industry, Shandong Jinan 250353 (China)

2009-12-28

This Letter proposes an approach to identify the topological structure and unknown parameters for uncertain general complex networks simultaneously. By designing effective adaptive controllers, we achieve synchronization between two complex networks. The unknown network topological structure and system parameters of uncertain general complex dynamical networks are identified simultaneously in the process of synchronization. Several useful criteria for synchronization are given. Finally, an illustrative example is presented to demonstrate the application of the theoretical results.

The Design and Implementation of the Leaf Area Index Sensor

Directory of Open Access Journals (Sweden)

Xiuhong Li

2015-03-01

Full Text Available The quick and accurate acquisition of crop growth parameters on a large scale is important for agricultural management and food security. The combination of photographic and wireless sensor network (WSN techniques can be used to collect agricultural information, such as leaf area index (LAI, over long distances and in real time. Such acquisition not only provides farmers with photographs of crops and suggestions for farmland management, but also the collected quantitative parameters, such as LAI, can be used to support large scale research in ecology, hydrology, remote sensing, etc. The present research developed a Leaf Area Index Sensor (LAIS to continuously monitor the growth of crops in several sampling points, and applied 3G/WIFI communication technology to remotely collect (and remotely setup and upgrade crop photos in real-time. Then the crop photos are automatically processed and LAI is estimated based on the improved leaf area index of Lang and Xiang (LAILX algorithm in LAIS. The research also constructed a database of images and other information relating to crop management. The leaf length and width method (LAILLW can accurately measure LAI through direct field harvest. The LAIS has been tested in several exemplary applications, and validation with LAI from LAILLW. The LAI acquired by LAIS had been proved reliable.

Displacement and deformation measurement for large structures by camera network

Science.gov (United States)

Shang, Yang; Yu, Qifeng; Yang, Zhen; Xu, Zhiqiang; Zhang, Xiaohu

2014-03-01

A displacement and deformation measurement method for large structures by a series-parallel connection camera network is presented. By taking the dynamic monitoring of a large-scale crane in lifting operation as an example, a series-parallel connection camera network is designed, and the displacement and deformation measurement method by using this series-parallel connection camera network is studied. The movement range of the crane body is small, and that of the crane arm is large. The displacement of the crane body, the displacement of the crane arm relative to the body and the deformation of the arm are measured. Compared with a pure series or parallel connection camera network, the designed series-parallel connection camera network can be used to measure not only the movement and displacement of a large structure but also the relative movement and deformation of some interesting parts of the large structure by a relatively simple optical measurement system.

Autonomous smart sensor network for full-scale structural health monitoring

Science.gov (United States)

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

2010-04-01

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

Structural dimensions of knowledge-action networks for sustainability

Science.gov (United States)

Tischa A. Munoz; B.B. Cutts

2016-01-01

Research on the influence of social network structure over flows of knowledge in support of sustainability governance and action has recently flourished. These studies highlight three challenges to evaluating knowledge-action networks: first, defining boundaries; second, characterizing power distributions; and third, identifying obstacles to knowledge sharing and...

Epidemic spreading in weighted scale-free networks with community structure

International Nuclear Information System (INIS)

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

2009-01-01

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

Detecting the overlapping and hierarchical community structure in complex networks

International Nuclear Information System (INIS)

Lancichinetti, Andrea; Fortunato, Santo; Kertesz, Janos

2009-01-01

Many networks in nature, society and technology are characterized by a mesoscopic level of organization, with groups of nodes forming tightly connected units, called communities or modules, that are only weakly linked to each other. Uncovering this community structure is one of the most important problems in the field of complex networks. Networks often show a hierarchical organization, with communities embedded within other communities; moreover, nodes can be shared between different communities. Here, we present the first algorithm that finds both overlapping communities and the hierarchical structure. The method is based on the local optimization of a fitness function. Community structure is revealed by peaks in the fitness histogram. The resolution can be tuned by a parameter enabling different hierarchical levels of organization to be investigated. Tests on real and artificial networks give excellent results.

Modeling structure and resilience of the dark network.

Science.gov (United States)

De Domenico, Manlio; Arenas, Alex

2017-02-01

While the statistical and resilience properties of the Internet are no longer changing significantly across time, the Darknet, a network devoted to keep anonymous its traffic, still experiences rapid changes to improve the security of its users. Here we study the structure of the Darknet and find that its topology is rather peculiar, being characterized by a nonhomogeneous distribution of connections, typical of scale-free networks; very short path lengths and high clustering, typical of small-world networks; and lack of a core of highly connected nodes. We propose a model to reproduce such features, demonstrating that the mechanisms used to improve cybersecurity are responsible for the observed topology. Unexpectedly, we reveal that its peculiar structure makes the Darknet much more resilient than the Internet (used as a benchmark for comparison at a descriptive level) to random failures, targeted attacks, and cascade failures, as a result of adaptive changes in response to the attempts of dismantling the network across time.

Exponential random graph models for networks with community structure.

Science.gov (United States)

Fronczak, Piotr; Fronczak, Agata; Bujok, Maksymilian

2013-09-01

Although the community structure organization is an important characteristic of real-world networks, most of the traditional network models fail to reproduce the feature. Therefore, the models are useless as benchmark graphs for testing community detection algorithms. They are also inadequate to predict various properties of real networks. With this paper we intend to fill the gap. We develop an exponential random graph approach to networks with community structure. To this end we mainly built upon the idea of blockmodels. We consider both the classical blockmodel and its degree-corrected counterpart and study many of their properties analytically. We show that in the degree-corrected blockmodel, node degrees display an interesting scaling property, which is reminiscent of what is observed in real-world fractal networks. A short description of Monte Carlo simulations of the models is also given in the hope of being useful to others working in the field.

The Meso-level Structure of F/OSS Collaboration Network

DEFF Research Database (Denmark)

Conald, Guido; Rullani, Francesco

2010-01-01

Social networks in Free/Open Source Software (F/OSS) have been usually analyzed at the level of the single project e.g., [6], or at the level of a whole ecology of projects, e.g., [33]. In this paper, we also investigate the social network generated by developers who collaborate to one or multiple...... F/OSS projects, but we focus on the less-studied meso-level structure emerging when applying to this network a community-detection technique. The network of ‘communities’ emerging from this analysis links sub-groups of densely connected developers, sub-groups that are smaller than the components...... of the network but larger than the teams working on single projects. Our results reveal the complexity of this meso-level structure, where several dense sub-groups of developers are connected by sparse collaboration among different sub-groups. We discuss the theoretical implications of our findings...

Imaging structural and functional brain networks in temporal lobe epilepsy

Science.gov (United States)

Bernhardt, Boris C.; Hong, SeokJun; Bernasconi, Andrea; Bernasconi, Neda

2013-01-01

Early imaging studies in temporal lobe epilepsy (TLE) focused on the search for mesial temporal sclerosis, as its surgical removal results in clinically meaningful improvement in about 70% of patients. Nevertheless, a considerable subgroup of patients continues to suffer from post-operative seizures. Although the reasons for surgical failure are not fully understood, electrophysiological and imaging data suggest that anomalies extending beyond the temporal lobe may have negative impact on outcome. This hypothesis has revived the concept of human epilepsy as a disorder of distributed brain networks. Recent methodological advances in non-invasive neuroimaging have led to quantify structural and functional networks in vivo. While structural networks can be inferred from diffusion MRI tractography and inter-regional covariance patterns of structural measures such as cortical thickness, functional connectivity is generally computed based on statistical dependencies of neurophysiological time-series, measured through functional MRI or electroencephalographic techniques. This review considers the application of advanced analytical methods in structural and functional connectivity analyses in TLE. We will specifically highlight findings from graph-theoretical analysis that allow assessing the topological organization of brain networks. These studies have provided compelling evidence that TLE is a system disorder with profound alterations in local and distributed networks. In addition, there is emerging evidence for the utility of network properties as clinical diagnostic markers. Nowadays, a network perspective is considered to be essential to the understanding of the development, progression, and management of epilepsy. PMID:24098281

Imaging structural and functional brain networks in temporal lobe epilepsy.

Science.gov (United States)

Bernhardt, Boris C; Hong, Seokjun; Bernasconi, Andrea; Bernasconi, Neda

2013-10-01

Early imaging studies in temporal lobe epilepsy (TLE) focused on the search for mesial temporal sclerosis, as its surgical removal results in clinically meaningful improvement in about 70% of patients. Nevertheless, a considerable subgroup of patients continues to suffer from post-operative seizures. Although the reasons for surgical failure are not fully understood, electrophysiological and imaging data suggest that anomalies extending beyond the temporal lobe may have negative impact on outcome. This hypothesis has revived the concept of human epilepsy as a disorder of distributed brain networks. Recent methodological advances in non-invasive neuroimaging have led to quantify structural and functional networks in vivo. While structural networks can be inferred from diffusion MRI tractography and inter-regional covariance patterns of structural measures such as cortical thickness, functional connectivity is generally computed based on statistical dependencies of neurophysiological time-series, measured through functional MRI or electroencephalographic techniques. This review considers the application of advanced analytical methods in structural and functional connectivity analyses in TLE. We will specifically highlight findings from graph-theoretical analysis that allow assessing the topological organization of brain networks. These studies have provided compelling evidence that TLE is a system disorder with profound alterations in local and distributed networks. In addition, there is emerging evidence for the utility of network properties as clinical diagnostic markers. Nowadays, a network perspective is considered to be essential to the understanding of the development, progression, and management of epilepsy.

Imaging structural and functional brain networks in temporal lobe epilepsy

Directory of Open Access Journals (Sweden)

Boris eBernhardt

2013-10-01

Full Text Available Early imaging studies in temporal lobe epilepsy (TLE focused on the search for mesial temporal sclerosis, as its surgical removal results in clinically meaningful improvement in about 70% of patients. Nevertheless, a considerable subgroup of patients continues to suffer from post-operative seizures. Although the reasons for surgical failure are not fully understood, electrophysiological and imaging data suggest that anomalies extending beyond the temporal lobe may have negative impact on outcome. This hypothesis has revived the concept of human epilepsy as a disorder of distributed brain networks. Recent methodological advances in non-invasive neuroimaging have led to quantify structural and functional networks in vivo. While structural networks can be inferred from diffusion MRI tractography and inter-regional covariance patterns of structural measures such as cortical thickness, functional connectivity is generally computed based on statistical dependencies of neurophysiological time-series, measured through functional MRI or electroencephalographic techniques. This review considers the application of advanced analytical methods in structural and functional connectivity analyses in TLE. We will specifically highlight findings from graph-theoretical analysis that allow assessing topological organization of brain networks. These studies have provided compelling evidence that TLE is a system disorder with profound alterations in local and distributed networks. In addition, there is emerging evidence for the utility of network properties as clinical diagnostic markers. Nowadays, a network perspective is considered to be essential to the understanding of the development, progression, and management of epilepsy.

Measuring structural similarity in large online networks.

Science.gov (United States)

Shi, Yongren; Macy, Michael

2016-09-01

Structural similarity based on bipartite graphs can be used to detect meaningful communities, but the networks have been tiny compared to massive online networks. Scalability is important in applications involving tens of millions of individuals with highly skewed degree distributions. Simulation analysis holding underlying similarity constant shows that two widely used measures - Jaccard index and cosine similarity - are biased by the distribution of out-degree in web-scale networks. However, an alternative measure, the Standardized Co-incident Ratio (SCR), is unbiased. We apply SCR to members of Congress, musical artists, and professional sports teams to show how massive co-following on Twitter can be used to map meaningful affiliations among cultural entities, even in the absence of direct connections to one another. Our results show how structural similarity can be used to map cultural alignments and demonstrate the potential usefulness of social media data in the study of culture, politics, and organizations across the social and behavioral sciences. Copyright © 2016 Elsevier Inc. All rights reserved.

Multi-frequency complex network from time series for uncovering oil-water flow structure.

Science.gov (United States)

Gao, Zhong-Ke; Yang, Yu-Xuan; Fang, Peng-Cheng; Jin, Ning-De; Xia, Cheng-Yi; Hu, Li-Dan

2015-02-04

Uncovering complex oil-water flow structure represents a challenge in diverse scientific disciplines. This challenge stimulates us to develop a new distributed conductance sensor for measuring local flow signals at different positions and then propose a novel approach based on multi-frequency complex network to uncover the flow structures from experimental multivariate measurements. In particular, based on the Fast Fourier transform, we demonstrate how to derive multi-frequency complex network from multivariate time series. We construct complex networks at different frequencies and then detect community structures. Our results indicate that the community structures faithfully represent the structural features of oil-water flow patterns. Furthermore, we investigate the network statistic at different frequencies for each derived network and find that the frequency clustering coefficient enables to uncover the evolution of flow patterns and yield deep insights into the formation of flow structures. Current results present a first step towards a network visualization of complex flow patterns from a community structure perspective.

Vegetation Structure of Ebony Leaf Monkey (Trachypithecus auratus) Habitat in Kecubung Ulolanang Nature Preservation Central Java-Indonesia

Science.gov (United States)

Ervina, Rahmawati; Wasiq, Hidayat Jafron

2018-02-01

Kecubung Ulolanang Nature Preservation is ebony leaf monkey's habitats in Central Java Indonesia. Continuously degradation of their population is caused by illegal hunting and habitat degradation that made this species being vulnerable. Habitat conservation is one of important aspects to prevent them from extinction. The purpose of this research was to analyze the vegetation's structure and composition, which was potentially, becomes habitat and food source for the monkeys. Data collected using purposive sampling with line transect method of four different level of vegetation. Data analysis used Important Value Index and Diversity Index. There were 43 species of vegetation at seedling stage, 18 species at sapling stage, 8 species at poles stage and 27 species at trees stage. Species that had the highest important value index at seedling was Stenochlaena palustri , at the sapling was Gnetum gnemon, at pole was Swietenia mahagoni and at tree was Tectona grandis . Species of trees those were potentially to become habitat (food source) for ebony leaf monkey were T. grandis, Dipterocarpus gracilis, Quercus sundaica and Ficus superba. The highest diversity index was at seedling gwoth stage.

Coevolution of game and network structure with adjustable linking

Science.gov (United States)

Qin, Shao-Meng; Zhang, Guo-Yong; Chen, Yong

2009-12-01

Most papers about the evolutionary game on graph assume the statistic network structure. However, in the real world, social interaction could change the relationship among people. And the change of social structure will also affect people’s strategies. We build a coevolution model of prisoner’s dilemma game and network structure to study the dynamic interaction in the real world. Differing from other coevolution models, players rewire their network connections according to the density of cooperation and other players’ payoffs. We use a parameter α to control the effect of payoff in the process of rewiring. Based on the asynchronous update rule and Monte Carlo simulation, we find that, when players prefer to rewire their links to those who are richer, the temptation can increase the cooperation density.

Leaf habit and woodiness regulate different leaf economy traits at a given nutrient supply.

Science.gov (United States)

Ordoñez, Jenny C; van Bodegom, Peter M; Witte, Jan-Philip M; Bartholomeus, Ruud P; van Dobben, Han F; Aerts, Rien

2010-11-01

The large variation in the relationships between environmental factors and plant traits observed in natural communities exemplifies the alternative solutions that plants have developed in response to the same environmental limitations. Qualitative attributes, such as growth form, woodiness, and leaf habit can be used to approximate these alternative solutions. Here, we quantified the extent to which these attributes affect leaf trait values at a given resource supply level, using measured plant traits from 105 different species (254 observations) distributed across 50 sites in mesic to wet plant communities in The Netherlands. For each site, soil total N, soil total P, and water supply estimates were obtained by field measurements and modeling. Effects of growth forms, woodiness, and leaf habit on relations between leaf traits (SLA, specific leaf area; LNC, leaf nitrogen concentration; and LPC, leaf phosphorus concentration) vs. nutrient and water supply were quantified using maximum-likelihood methods and Bonferroni post hoc tests. The qualitative attributes explained 8-23% of the variance within sites in leaf traits vs. soil fertility relationships, and therefore they can potentially be used to make better predictions of global patterns of leaf traits in relation to nutrient supply. However, at a given soil fertility, the strength of the effect of each qualitative attribute was not the same for all leaf traits. These differences may imply a differential regulation of the leaf economy traits at a given nutrient supply, in which SLA and LPC seem to be regulated in accordance to changes in plant size and architecture while LNC seems to be primarily regulated at the leaf level by factors related to leaf longevity.

Information Propagation in Complex Networks : Structures and Dynamics

NARCIS (Netherlands)

Märtens, M.

2018-01-01

This thesis is a contribution to a deeper understanding of how information propagates and what this process entails. At its very core is the concept of the network: a collection of nodes and links, which describes the structure of the systems under investigation. The network is a mathematical model

Polarized DIS Structure Functions from Neural Networks

International Nuclear Information System (INIS)

Del Debbio, L.; Guffanti, A.; Piccione, A.

2007-01-01

We present a parametrization of polarized Deep-Inelastic-Scattering (DIS) structure functions based on Neural Networks. The parametrization provides a bias-free determination of the probability measure in the space of structure functions, which retains information on experimental errors and correlations. As an example we discuss the application of this method to the study of the structure function g 1 p (x,Q 2 )

Network structure shapes spontaneous functional connectivity dynamics.

Science.gov (United States)

Shen, Kelly; Hutchison, R Matthew; Bezgin, Gleb; Everling, Stefan; McIntosh, Anthony R

2015-04-08

The structural organization of the brain constrains the range of interactions between different regions and shapes ongoing information processing. Therefore, it is expected that large-scale dynamic functional connectivity (FC) patterns, a surrogate measure of coordination between brain regions, will be closely tied to the fiber pathways that form the underlying structural network. Here, we empirically examined the influence of network structure on FC dynamics by comparing resting-state FC (rsFC) obtained using BOLD-fMRI in macaques (Macaca fascicularis) to structural connectivity derived from macaque axonal tract tracing studies. Consistent with predictions from simulation studies, the correspondence between rsFC and structural connectivity increased as the sample duration increased. Regions with reciprocal structural connections showed the most stable rsFC across time. The data suggest that the transient nature of FC is in part dependent on direct underlying structural connections, but also that dynamic coordination can occur via polysynaptic pathways. Temporal stability was found to be dependent on structural topology, with functional connections within the rich-club core exhibiting the greatest stability over time. We discuss these findings in light of highly variable functional hubs. The results further elucidate how large-scale dynamic functional coordination exists within a fixed structural architecture. Copyright © 2015 the authors 0270-6474/15/355579-10$15.00/0.

Effect of Addition of Moringa Leaf By-Product (Leaf-Waste) on ...

African Journals Online (AJOL)

The effects of incorporation of Moringa leaf fibre (a by-product of leaf processing which contains 24% Crude Fibre by dry weight at 0, 5 and 10 % substitution of wheat flour in cookies was investigated. Three products containing wheat flour: Moringa leaf fibre ratios of 100:0, 95:5, and 90:10 respectively were prepared, and a ...

Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene.

Science.gov (United States)

Li, Jun; Mei, Xue; Prokhorov, Danil; Tao, Dacheng

2017-03-01

Hierarchical neural networks have been shown to be effective in learning representative image features and recognizing object classes. However, most existing networks combine the low/middle level cues for classification without accounting for any spatial structures. For applications such as understanding a scene, how the visual cues are spatially distributed in an image becomes essential for successful analysis. This paper extends the framework of deep neural networks by accounting for the structural cues in the visual signals. In particular, two kinds of neural networks have been proposed. First, we develop a multitask deep convolutional network, which simultaneously detects the presence of the target and the geometric attributes (location and orientation) of the target with respect to the region of interest. Second, a recurrent neuron layer is adopted for structured visual detection. The recurrent neurons can deal with the spatial distribution of visible cues belonging to an object whose shape or structure is difficult to explicitly define. Both the networks are demonstrated by the practical task of detecting lane boundaries in traffic scenes. The multitask convolutional neural network provides auxiliary geometric information to help the subsequent modeling of the given lane structures. The recurrent neural network automatically detects lane boundaries, including those areas containing no marks, without any explicit prior knowledge or secondary modeling.

Estimating leaf photosynthetic pigments information by stepwise multiple linear regression analysis and a leaf optical model

Science.gov (United States)

Liu, Pudong; Shi, Runhe; Wang, Hong; Bai, Kaixu; Gao, Wei

2014-10-01

Leaf pigments are key elements for plant photosynthesis and growth. Traditional manual sampling of these pigments is labor-intensive and costly, which also has the difficulty in capturing their temporal and spatial characteristics. The aim of this work is to estimate photosynthetic pigments at large scale by remote sensing. For this purpose, inverse model were proposed with the aid of stepwise multiple linear regression (SMLR) analysis. Furthermore, a leaf radiative transfer model (i.e. PROSPECT model) was employed to simulate the leaf reflectance where wavelength varies from 400 to 780 nm at 1 nm interval, and then these values were treated as the data from remote sensing observations. Meanwhile, simulated chlorophyll concentration (Cab), carotenoid concentration (Car) and their ratio (Cab/Car) were taken as target to build the regression model respectively. In this study, a total of 4000 samples were simulated via PROSPECT with different Cab, Car and leaf mesophyll structures as 70% of these samples were applied for training while the last 30% for model validation. Reflectance (r) and its mathematic transformations (1/r and log (1/r)) were all employed to build regression model respectively. Results showed fair agreements between pigments and simulated reflectance with all adjusted coefficients of determination (R2) larger than 0.8 as 6 wavebands were selected to build the SMLR model. The largest value of R2 for Cab, Car and Cab/Car are 0.8845, 0.876 and 0.8765, respectively. Meanwhile, mathematic transformations of reflectance showed little influence on regression accuracy. We concluded that it was feasible to estimate the chlorophyll and carotenoids and their ratio based on statistical model with leaf reflectance data.

Structural Approaches to Sequence Evolution Molecules, Networks, Populations

CERN Document Server

Bastolla, Ugo; Roman, H. Eduardo; Vendruscolo, Michele

2007-01-01

Structural requirements constrain the evolution of biological entities at all levels, from macromolecules to their networks, right up to populations of biological organisms. Classical models of molecular evolution, however, are focused at the level of the symbols - the biological sequence - rather than that of their resulting structure. Now recent advances in understanding the thermodynamics of macromolecules, the topological properties of gene networks, the organization and mutation capabilities of genomes, and the structure of populations make it possible to incorporate these key elements into a broader and deeply interdisciplinary view of molecular evolution. This book gives an account of such a new approach, through clear tutorial contributions by leading scientists specializing in the different fields involved.

Automated analysis of Physarum network structure and dynamics

Science.gov (United States)

Fricker, Mark D.; Akita, Dai; Heaton, Luke LM; Jones, Nick; Obara, Boguslaw; Nakagaki, Toshiyuki

2017-06-01

We evaluate different ridge-enhancement and segmentation methods to automatically extract the network architecture from time-series of Physarum plasmodia withdrawing from an arena via a single exit. Whilst all methods gave reasonable results, judged by precision-recall analysis against a ground-truth skeleton, the mean phase angle (Feature Type) from intensity-independent, phase-congruency edge enhancement and watershed segmentation was the most robust to variation in threshold parameters. The resultant single pixel-wide segmented skeleton was converted to a graph representation as a set of weighted adjacency matrices containing the physical dimensions of each vein, and the inter-vein regions. We encapsulate the complete image processing and network analysis pipeline in a downloadable software package, and provide an extensive set of metrics that characterise the network structure, including hierarchical loop decomposition to analyse the nested structure of the developing network. In addition, the change in volume for each vein and intervening plasmodial sheet was used to predict the net flow across the network. The scaling relationships between predicted current, speed and shear force with vein radius were consistent with predictions from Murray’s law. This work was presented at PhysNet 2015.

Automated analysis of Physarum network structure and dynamics

International Nuclear Information System (INIS)

Fricker, Mark D; Heaton, Luke LM; Akita, Dai; Jones, Nick; Obara, Boguslaw; Nakagaki, Toshiyuki

2017-01-01

We evaluate different ridge-enhancement and segmentation methods to automatically extract the network architecture from time-series of Physarum plasmodia withdrawing from an arena via a single exit. Whilst all methods gave reasonable results, judged by precision-recall analysis against a ground-truth skeleton, the mean phase angle (Feature Type) from intensity-independent, phase-congruency edge enhancement and watershed segmentation was the most robust to variation in threshold parameters. The resultant single pixel-wide segmented skeleton was converted to a graph representation as a set of weighted adjacency matrices containing the physical dimensions of each vein, and the inter-vein regions. We encapsulate the complete image processing and network analysis pipeline in a downloadable software package, and provide an extensive set of metrics that characterise the network structure, including hierarchical loop decomposition to analyse the nested structure of the developing network. In addition, the change in volume for each vein and intervening plasmodial sheet was used to predict the net flow across the network. The scaling relationships between predicted current, speed and shear force with vein radius were consistent with predictions from Murray’s law. This work was presented at PhysNet 2015. (paper)

The use of plant models in deep learning: an application to leaf counting in rosette plants.

Science.gov (United States)

Ubbens, Jordan; Cieslak, Mikolaj; Prusinkiewicz, Przemyslaw; Stavness, Ian

2018-01-01

Deep learning presents many opportunities for image-based plant phenotyping. Here we consider the capability of deep convolutional neural networks to perform the leaf counting task. Deep learning techniques typically require large and diverse datasets to learn generalizable models without providing a priori an engineered algorithm for performing the task. This requirement is challenging, however, for applications in the plant phenotyping field, where available datasets are often small and the costs associated with generating new data are high. In this work we propose a new method for augmenting plant phenotyping datasets using rendered images of synthetic plants. We demonstrate that the use of high-quality 3D synthetic plants to augment a dataset can improve performance on the leaf counting task. We also show that the ability of the model to generate an arbitrary distribution of phenotypes mitigates the problem of dataset shift when training and testing on different datasets. Finally, we show that real and synthetic plants are significantly interchangeable when training a neural network on the leaf counting task.

Structure of Small World Innovation Network and Learning Performance

Directory of Open Access Journals (Sweden)

Shuang Song

2014-01-01

Full Text Available This paper examines the differences of learning performance of 5 MNCs (multinational corporations that filed the largest number of patents in China. We establish the innovation network with the patent coauthorship data by these 5 MNCs and classify the networks by the tail of distribution curve of connections. To make a comparison of the learning performance of these 5 MNCs with differing network structures, we develop an organization learning model by regarding the reality as having m dimensions, which denotes the heterogeneous knowledge about the reality. We further set n innovative individuals that are mutually interactive and own unique knowledge about the reality. A longer (shorter distance between the knowledge of the individual and the reality denotes a lower (higher knowledge level of that individual. Individuals interact with and learn from each other within the small-world network. By making 1,000 numerical simulations and averaging the simulated results, we find that the differing structure of the small-world network leads to the differences of learning performance between these 5 MNCs. The network monopolization negatively impacts and network connectivity positively impacts learning performance. Policy implications in the conclusion section suggest that to improve firm learning performance, it is necessary to establish a flat and connective network.

Resolving structural variability in network models and the brain.

Directory of Open Access Journals (Sweden)

Florian Klimm

2014-03-01

Full Text Available Large-scale white matter pathways crisscrossing the cortex create a complex pattern of connectivity that underlies human cognitive function. Generative mechanisms for this architecture have been difficult to identify in part because little is known in general about mechanistic drivers of structured networks. Here we contrast network properties derived from diffusion spectrum imaging data of the human brain with 13 synthetic network models chosen to probe the roles of physical network embedding and temporal network growth. We characterize both the empirical and synthetic networks using familiar graph metrics, but presented here in a more complete statistical form, as scatter plots and distributions, to reveal the full range of variability of each measure across scales in the network. We focus specifically on the degree distribution, degree assortativity, hierarchy, topological Rentian scaling, and topological fractal scaling--in addition to several summary statistics, including the mean clustering coefficient, the shortest path-length, and the network diameter. The models are investigated in a progressive, branching sequence, aimed at capturing different elements thought to be important in the brain, and range from simple random and regular networks, to models that incorporate specific growth rules and constraints. We find that synthetic models that constrain the network nodes to be physically embedded in anatomical brain regions tend to produce distributions that are most similar to the corresponding measurements for the brain. We also find that network models hardcoded to display one network property (e.g., assortativity do not in general simultaneously display a second (e.g., hierarchy. This relative independence of network properties suggests that multiple neurobiological mechanisms might be at play in the development of human brain network architecture. Together, the network models that we develop and employ provide a potentially useful

Molecular Structure-Affinity Relationship of Flavonoids in Lotus Leaf (Nelumbo nucifera Gaertn.) on Binding to Human Serum Albumin and Bovine Serum Albumin by Spectroscopic Method.

Science.gov (United States)

Tang, Xiaosheng; Tang, Ping; Liu, Liangliang

2017-06-23

Lotus leaf has gained growing popularity as an ingredient in herbal formulations due to its various activities. As main functional components of lotus leaf, the difference in structure of flavonoids affected their binding properties and activities. In this paper, the existence of 11 flavonoids in lotus leaf extract was confirmed by High Performance Liquid Chromatography (HPLC) analysis and 11 flavonoids showed various contents in lotus leaf. The interactions between lotus leaf extract and two kinds of serum albumins (human serum albumin (HSA) and bovine serum albumin (BSA)) were investigated by spectroscopic methods. Based on the fluorescence quenching, the interactions between these flavonoids and serum albumins were further checked in detail. The relationship between the molecular properties of flavonoids and their affinities for serum albumins were analyzed and compared. The hydroxylation on 3 and 3' position increased the affinities for serum albumins. Moreover, both of the methylation on 3' position of quercetin and the C₂=C₃ double bond of apigenin and quercetin decreased the affinities for HSA and BSA. The glycosylation lowered the affinities for HSA and BSA depending on the type of sugar moiety. It revealed that the hydrogen bond force played an important role in binding flavonoids to HSA and BSA.

Structural and robustness properties of smart-city transportation networks

International Nuclear Information System (INIS)

Zhang Zhen-Gang; Ding Zhuo; Fan Jing-Fang; Chen Xiao-Song; Meng Jun; Ye Fang-Fu; Ding Yi-Min

2015-01-01

The concept of smart city gives an excellent resolution to construct and develop modern cities, and also demands infrastructure construction. How to build a safe, stable, and highly efficient public transportation system becomes an important topic in the process of city construction. In this work, we study the structural and robustness properties of transportation networks and their sub-networks. We introduce a complementary network model to study the relevance and complementarity between bus network and subway network. Our numerical results show that the mutual supplement of networks can improve the network robustness. This conclusion provides a theoretical basis for the construction of public traffic networks, and it also supports reasonable operation of managing smart cities. (rapid communication)

The evolving network structure of US airline system during 1990-2010

Science.gov (United States)

Lin, Jingyi; Ban, Yifang

2014-09-01

This paper analyzes the growth and evolution of topological features of the US airline network over a 20-year period. It captures the change in the network system from different dimensions of complex networks such as centrality distribution and various structural properties of the network over time. We first illustrate the results of a set of measures, including degree, strength, betweenness centrality, and clustering structure. The geographic features of airport systems, spatial distance and network efficiency are also discussed in this section. In order to further capture the dynamics of the system, this paper also explores the correlation between different measures, and investigates various interactions inside the network. Overall this study offers a novel approach to understanding the growth and evolution of real physical networks.

Structure constrained by metadata in networks of chess players.

Science.gov (United States)

Almeira, Nahuel; Schaigorodsky, Ana L; Perotti, Juan I; Billoni, Orlando V

2017-11-09

Chess is an emblematic sport that stands out because of its age, popularity and complexity. It has served to study human behavior from the perspective of a wide number of disciplines, from cognitive skills such as memory and learning, to aspects like innovation and decision-making. Given that an extensive documentation of chess games played throughout history is available, it is possible to perform detailed and statistically significant studies about this sport. Here we use one of the most extensive chess databases in the world to construct two networks of chess players. One of the networks includes games that were played over-the-board and the other contains games played on the Internet. We study the main topological characteristics of the networks, such as degree distribution and correlations, transitivity and community structure. We complement the structural analysis by incorporating players' level of play as node metadata. Although both networks are topologically different, we show that in both cases players gather in communities according to their expertise and that an emergent rich-club structure, composed by the top-rated players, is also present.

Approximating spectral impact of structural perturbations in large networks

CERN Document Server

Milanese, A; Nishikawa, Takashi; Sun, Jie

2010-01-01

Determining the effect of structural perturbations on the eigenvalue spectra of networks is an important problem because the spectra characterize not only their topological structures, but also their dynamical behavior, such as synchronization and cascading processes on networks. Here we develop a theory for estimating the change of the largest eigenvalue of the adjacency matrix or the extreme eigenvalues of the graph Laplacian when small but arbitrary set of links are added or removed from the network. We demonstrate the effectiveness of our approximation schemes using both real and artificial networks, showing in particular that we can accurately obtain the spectral ranking of small subgraphs. We also propose a local iterative scheme which computes the relative ranking of a subgraph using only the connectivity information of its neighbors within a few links. Our results may not only contribute to our theoretical understanding of dynamical processes on networks, but also lead to practical applications in ran...

Leaf trait response to nutrients and herbivore exclusion across a globally replicated grassland experiment

Science.gov (United States)

Firn, Jennifer

2017-04-01

Leaf trait response to nutrients and herbivore exclusion across a globally replicated grassland experiment Jennifer Firn1, James McGree2, Eric Lind3, Elizabeth Borer3, Eric Seabloom3, Lauren Sullivan3, Kimberly Lapierre4 and the Nutrient Network 1Queensland University of Technology (QUT), School of Earth, Environmental and Biological Sciences, Science and Engineering Faculty, Brisbane, QLD, 4001 Australia 2Queensland University of Technology (QUT), School of Mathematical Sciences, Science and Engineering Faculty, Brisbane, QLD, 4001 Australia 3Universtiy of Minnesota, Department of Ecology, Evolution, and Behavior, 1479 Gortner Avenue, 140 Gortner Laboratory, St. Paul, MN 55108 USA 4Department of integrative Biology, University of California, Berkeley, CA 94720 USA Functional trait research has developed with the aim of finding general patterns in how the function of plant assemblages changes with respect to different land-uses. Most studies have compared sites within and across regions with variations in land-use history, but not necessarily with standardized treatments in an experimental framework. The trends that have emerged from this research is that characteristics of leaf traits such as specific leaf area (SLA) correlate with carbon acquisition strategies known to influence ecosystem functioning. SLA has been found to represent a plant's investment in growing light-capturing area per dry mass content. Species with a relatively high SLA tend to have a higher rate of return on the resources invested into making tissue (cheaper leaves in terms of energy and resources needed to produce them) when compared to species with a lower SLA (more expensive leaves to produce). Few studies have examined quantitatively measured traits in an experimental framework. The Nutrient Network experiment, globally distributed experiment, presents a unique opportunity to examine the response of functional traits across grassland ecosystems characterised by a diverse range of

Global variability in leaf respiration in relation to climate and leaf traits

Science.gov (United States)

Atkin, Owen K.

2015-04-01

Leaf respiration plays a vital role in regulating ecosystem functioning and the Earth's climate. Because of this, it is imperative that that Earth-system, climate and ecosystem-level models be able to accurately predict variations in rates of leaf respiration. In the field of photosynthesis research, the F/vC/B model has enabled modellers to accurately predict variations in photosynthesis through time and space. By contrast, we lack an equivalent biochemical model to predict variations in leaf respiration. Consequently, we need to rely on phenomenological approaches to model variations in respiration across the Earth's surface. Such approaches require that we develop a thorough understanding of how rates of respiration vary among species and whether global environmental gradients play a role in determining variations in leaf respiration. Dealing with these issues requires that data sets be assembled on rates of leaf respiration in biomes across the Earth's surface. In this talk, I will use a newly-assembled global database on leaf respiration and associated traits (including photosynthesis) to highlight variation in leaf respiration (and the balance between respiration and photosynthesis) across global gradients in growth temperature and aridity.

HAAR TRANSFORM BASED ESTIMATION OF CHLOROPHYLL AND STRUCTURE OF THE LEAF

OpenAIRE

Abhinav Arora; R. Menaka; Shivangi Gupta; Archit Mishra

2013-01-01

In this paper, the health of a plant is estimated using various non-destructive Image Processing Techniques. Chlorophyll content was detected based on colour Image Processing. The Haar transform is applied to get size of leaf and the parameters.

A wireless sensor network design and evaluation for large structural strain field monitoring

International Nuclear Information System (INIS)

Qiu, Zixue; Wu, Jian; Yuan, Shenfang

2011-01-01

Structural strain changes under external environmental or mechanical loads are the main monitoring parameters in structural health monitoring or mechanical property tests. This paper presents a wireless sensor network designed for monitoring large structural strain field variation. First of all, a precision strain sensor node is designed for multi-channel strain gauge signal conditioning and wireless monitoring. In order to establish a synchronous strain data acquisition network, the cluster-star network synchronization method is designed in detail. To verify the functionality of the designed wireless network for strain field monitoring capability, a multi-point network evaluation system is developed for an experimental aluminum plate structure for load variation monitoring. Based on the precision wireless strain nodes, the wireless data acquisition network is deployed to synchronously gather, process and transmit strain gauge signals and monitor results under concentrated loads. This paper shows the efficiency of the wireless sensor network for large structural strain field monitoring

Effect of nitrogen supply on leaf appearance, leaf growth, leaf nitrogen economy and photosynthetic capacity in maize (Zea mays L.)

NARCIS (Netherlands)

Vos, J.; Putten, van der P.E.L.; Birch, C.J.

2005-01-01

Leaf area growth and nitrogen concentration per unit leaf area, Na (g m-2 N) are two options plants can use to adapt to nitrogen limitation. Previous work indicated that potato (Solanum tuberosum L.) adapts the size of leaves to maintain Na and photosynthetic capacity per unit leaf area. This paper

Fragmented Romanian sociology: growth and structure of the collaboration network.

Science.gov (United States)

Hâncean, Marian-Gabriel; Perc, Matjaž; Vlăsceanu, Lazăr

2014-01-01

Structural patterns in collaboration networks are essential for understanding how new ideas, research practices, innovation or cooperation circulate and develop within academic communities and between and within university departments. In our research, we explore and investigate the structure of the collaboration network formed by the academics working full-time within all the 17 sociology departments across Romania. We show that the collaboration network is sparse and fragmented, and that it constitutes an environment that does not promote the circulation of new ideas and innovation within the field. Although recent years have witnessed an increase in the productivity of Romanian sociologists, there is still ample room for improvement in terms of the interaction infrastructure that ought to link individuals together so that they could maximize their potentials. We also fail to discern evidence in favor of the Matthew effect governing the growth of the network, which suggests scientific success and productivity are not rewarded. Instead, the structural properties of the collaboration network are partly those of a core-periphery network, where the spread of innovation and change can be explained by structural equivalence rather than by interpersonal influence models. We also provide support for the idea that, within the observed network, collaboration is the product of homophily rather than prestige effects. Further research on the subject based on data from other countries in the region is needed to place our results in a comparative framework, in particular to discern whether the behavior of the Romanian sociologist community is unique or rather common.

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

Science.gov (United States)

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.

Structural covariance networks across the life span, from 6 to 94 years of age

Directory of Open Access Journals (Sweden)

Elizabeth DuPre

2017-10-01

Full Text Available Structural covariance examines covariation of gray matter morphology between brain regions and across individuals. Despite significant interest in the influence of age on structural covariance patterns, no study to date has provided a complete life span perspective—bridging childhood with early, middle, and late adulthood—on the development of structural covariance networks. Here, we investigate the life span trajectories of structural covariance in six canonical neurocognitive networks: default, dorsal attention, frontoparietal control, somatomotor, ventral attention, and visual. By combining data from five open-access data sources, we examine the structural covariance trajectories of these networks from 6 to 94 years of age in a sample of 1,580 participants. Using partial least squares, we show that structural covariance patterns across the life span exhibit two significant, age-dependent trends. The first trend is a stable pattern whose integrity declines over the life span. The second trend is an inverted-U that differentiates young adulthood from other age groups. Hub regions, including posterior cingulate cortex and anterior insula, appear particularly influential in the expression of this second age-dependent trend. Overall, our results suggest that structural covariance provides a reliable definition of neurocognitive networks across the life span and reveal both shared and network-specific trajectories. The importance of life span perspectives is increasingly apparent in understanding normative interactions of large-scale neurocognitive networks. Although recent work has made significant strides in understanding the functional and structural connectivity of these networks, there has been comparatively little attention to life span trajectories of structural covariance networks. In this study we examine patterns of structural covariance across the life span for six neurocognitive networks. Our results suggest that networks exhibit

Analysis of the structure of complex networks at different resolution levels

International Nuclear Information System (INIS)

Arenas, A; Fernandez, A; Gomez, S

2008-01-01

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 Barthelemy 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

Structure versus time in the evolutionary diversification of avian carotenoid metabolic networks.

Science.gov (United States)

Morrison, Erin S; Badyaev, Alexander V

2018-05-01

Historical associations of genes and proteins are thought to delineate pathways available to subsequent evolution; however, the effects of past functional involvements on contemporary evolution are rarely quantified. Here, we examined the extent to which the structure of a carotenoid enzymatic network persists in avian evolution. Specifically, we tested whether the evolution of carotenoid networks was most concordant with phylogenetically structured expansion from core reactions of common ancestors or with subsampling of biochemical pathway modules from an ancestral network. We compared structural and historical associations in 467 carotenoid networks of extant and ancestral species and uncovered the overwhelming effect of pre-existing metabolic network structure on carotenoid diversification over the last 50 million years of avian evolution. Over evolutionary time, birds repeatedly subsampled and recombined conserved biochemical modules, which likely maintained the overall structure of the carotenoid metabolic network during avian evolution. These findings explain the recurrent convergence of evolutionary distant species in carotenoid metabolism and weak phylogenetic signal in avian carotenoid evolution. Remarkable retention of an ancient metabolic structure throughout extensive and prolonged ecological diversification in avian carotenoid metabolism illustrates a fundamental requirement of organismal evolution - historical continuity of a deterministic network that links past and present functional associations of its components. © 2018 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2018 European Society For Evolutionary Biology.

SU-F-T-350: Continuous Leaf Optimization (CLO) for IMRT Leaf Sequencing

Energy Technology Data Exchange (ETDEWEB)

Long, T; Chen, M; Jiang, S; Lu, W [UT Southwestern Medical Center, Dallas, TX (United States)

2016-06-15

Purpose: To study a new step-and-shoot IMRT leaf sequencing model that avoids the two main pitfalls of conventional leaf sequencing: (1) target fluence being stratified into a fixed number of discrete levels and/or (2) aperture leaf positions being restricted to a discrete set of locations. These assumptions induce error into the sequence or reduce the feasible region of potential plans, respectively. Methods: We develop a one-dimensional (single leaf pair) methodology that does not make assumptions (1) or (2) that can be easily extended to a multi-row model. The proposed continuous leaf optimization (CLO) methodology takes in an existing set of apertures and associated intensities, or solution “seed,” and improves the plan without the restrictiveness of 1or (2). It then uses a first-order descent algorithm to converge onto a locally optimal solution. A seed solution can come from models that assume (1) and (2), thus allowing the CLO model to improve upon existing leaf sequencing methodologies. Results: The CLO model was applied to 208 generated target fluence maps in one dimension. In all cases for all tested sequencing strategies, the CLO model made improvements on the starting seed objective function. The CLO model also was able to keep MUs low. Conclusion: The CLO model can improve upon existing leaf sequencing methods by avoiding the restrictions of (1) and (2). By allowing for more flexible leaf positioning, error can be reduced when matching some target fluence. This study lays the foundation for future models and solution methodologies that can incorporate continuous leaf positions explicitly into the IMRT treatment planning model. Supported by Cancer Prevention & Research Institute of Texas (CPRIT) - ID RP150485.

Structural covariance networks across healthy young adults and their consistency.

Science.gov (United States)

Guo, Xiaojuan; Wang, Yan; Guo, Taomei; Chen, Kewei; Zhang, Jiacai; Li, Ke; Jin, Zhen; Yao, Li

2015-08-01

To investigate structural covariance networks (SCNs) as measured by regional gray matter volumes with structural magnetic resonance imaging (MRI) from healthy young adults, and to examine their consistency and stability. Two independent cohorts were included in this study: Group 1 (82 healthy subjects aged 18-28 years) and Group 2 (109 healthy subjects aged 20-28 years). Structural MRI data were acquired at 3.0T and 1.5T using a magnetization prepared rapid-acquisition gradient echo sequence for these two groups, respectively. We applied independent component analysis (ICA) to construct SCNs and further applied the spatial overlap ratio and correlation coefficient to evaluate the spatial consistency of the SCNs between these two datasets. Seven and six independent components were identified for Group 1 and Group 2, respectively. Moreover, six SCNs including the posterior default mode network, the visual and auditory networks consistently existed across the two datasets. The overlap ratios and correlation coefficients of the visual network reached the maximums of 72% and 0.71. This study demonstrates the existence of consistent SCNs corresponding to general functional networks. These structural covariance findings may provide insight into the underlying organizational principles of brain anatomy. © 2014 Wiley Periodicals, Inc.

Development of Human Brain Structural Networks Through Infancy and Childhood

Science.gov (United States)

Huang, Hao; Shu, Ni; Mishra, Virendra; Jeon, Tina; Chalak, Lina; Wang, Zhiyue J.; Rollins, Nancy; Gong, Gaolang; Cheng, Hua; Peng, Yun; Dong, Qi; He, Yong

2015-01-01

During human brain development through infancy and childhood, microstructural and macrostructural changes take place to reshape the brain's structural networks and better adapt them to sophisticated functional and cognitive requirements. However, structural topological configuration of the human brain during this specific development period is not well understood. In this study, diffusion magnetic resonance image (dMRI) of 25 neonates, 13 toddlers, and 25 preadolescents were acquired to characterize network dynamics at these 3 landmark cross-sectional ages during early childhood. dMRI tractography was used to construct human brain structural networks, and the underlying topological properties were quantified by graph-theory approaches. Modular organization and small-world attributes are evident at birth with several important topological metrics increasing monotonically during development. Most significant increases of regional nodes occur in the posterior cingulate cortex, which plays a pivotal role in the functional default mode network. Positive correlations exist between nodal efficiencies and fractional anisotropy of the white matter traced from these nodes, while correlation slopes vary among the brain regions. These results reveal substantial topological reorganization of human brain structural networks through infancy and childhood, which is likely to be the outcome of both heterogeneous strengthening of the major white matter tracts and pruning of other axonal fibers. PMID:24335033

Effects of elevated ozone on CO2 uptake and leaf structure in sugar maple under two light environments

International Nuclear Information System (INIS)

Bäck, J.; Vanderklein, D.W.; Topa, M.A.

1999-01-01

The interactive effects of ozone and light on leaf structure, carbon dioxide uptake and short-term carbon allocation of sugar maple (Acer saccharum Marsh.) seedlings were examined using gas exchange measurements and 14 C-macroautoradiographic techniques. Two-year-old sugar maple seedlings were fumigated from budbreak for 5 months with ambient or 3 × ambient ozone in open-top chambers, receiving either 35% (high light) or 15% (low light) of full sunlight. Ozone accelerated leaf senescence, and reduced net photosynthesis, 14 CO 2 uptake and stomatal conductance, with the effects being most pronounced under low light. The proportion of intercellular space increased in leaves of seedlings grown under elevated ozone and low light, possibly enhancing the susceptibility of mesophyll cells to ozone by increasing the cumulative dose per mesophyll cell. Indeed, damage to spongy mesophyll cells in the elevated ozone × low light treatment was especially frequent. 14 C macroautoradioraphy revealed heterogeneous uptake of 14 CO 2 in well defined areole regions, suggesting patchy stomatal behaviour in all treatments. However, in seedlings grown under elevated ozone and low light, the highest 14 CO 2 uptake occurred along larger veins, while interveinal regions exhibited little or no uptake. Although visible symptoms of ozone injury were not apparent in these seedlings, the cellular damage, reduced photosynthetic rates and reduced whole-leaf chlorophyll levels corroborate the visual scaling of whole-plant senescence, suggesting that the ozone × low light treatment accelerated senescence or senescence-like injury in sugar maple. (author)

Robust Learning of Fixed-Structure Bayesian Networks

OpenAIRE

Diakonikolas, Ilias; Kane, Daniel; Stewart, Alistair

2016-01-01

We investigate the problem of learning Bayesian networks in an agnostic model where an $\\epsilon$-fraction of the samples are adversarially corrupted. Our agnostic learning model is similar to -- in fact, stronger than -- Huber's contamination model in robust statistics. In this work, we study the fully observable Bernoulli case where the structure of the network is given. Even in this basic setting, previous learning algorithms either run in exponential time or lose dimension-dependent facto...

Structure-based control of complex networks with nonlinear dynamics.

Science.gov (United States)

Zañudo, Jorge Gomez Tejeda; Yang, Gang; Albert, Réka

2017-07-11

What can we learn about controlling a system solely from its underlying network structure? Here we adapt a recently developed framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system toward any of its natural long-term dynamic behaviors, regardless of the specific functional forms and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of structural controllability in control theory. Finally, we demonstrate this framework's applicability in dynamic models of gene regulatory networks and identify nodes whose override is necessary for control in the general case but not in specific model instances.

Association between structural brain network efficiency and intelligence increases during adolescence

NARCIS (Netherlands)

Koenis, Marinka M G; Brouwer, Rachel M; Swagerman, Suzanne C; van Soelen, Inge L C; Boomsma, Dorret I; Hulshoff Pol, Hilleke E

2018-01-01

Adolescence represents an important period during which considerable changes in the brain take place, including increases in integrity of white matter bundles, and increasing efficiency of the structural brain network. A more efficient structural brain network has been associated with higher

Unraveling the disease consequences and mechanisms of modular structure in animal social networks

Science.gov (United States)

Sah, Pratha; Leu, Stephan T.; Cross, Paul C.; Hudson, Peter J.; Bansal, Shweta

2017-01-01

Disease risk is a potential cost of group living. Although modular organization is thought to reduce this cost in animal societies, empirical evidence toward this hypothesis has been conflicting. We analyzed empirical social networks from 43 animal species to motivate our study of the epidemiological consequences of modular structure in animal societies. From these empirical studies, we identified the features of interaction patterns associated with network modularity and developed a theoretical network model to investigate when and how subdivisions in social networks influence disease dynamics. Contrary to prior work, we found that disease risk is largely unaffected by modular structure, although social networks beyond a modular threshold experience smaller disease burden and longer disease duration. Our results illustrate that the lowering of disease burden in highly modular social networks is driven by two mechanisms of modular organization: network fragmentation and subgroup cohesion. Highly fragmented social networks with cohesive subgroups are able to structurally trap infections within a few subgroups and also cause a structural delay to the spread of disease outbreaks. Finally, we show that network models incorporating modular structure are necessary only when prior knowledge suggests that interactions within the population are highly subdivided. Otherwise, null networks based on basic knowledge about group size and local contact heterogeneity may be sufficient when data-limited estimates of epidemic consequences are necessary. Overall, our work does not support the hypothesis that modular structure universally mitigates the disease impact of group living.

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

Science.gov (United States)

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.

Social Network Structures among Groundnut Farmers

Science.gov (United States)

Thuo, Mary; Bell, Alexandra A.; Bravo-Ureta, Boris E.; Okello, David K.; Okoko, Evelyn Nasambu; Kidula, Nelson L.; Deom, C. Michael; Puppala, Naveen

2013-01-01

Purpose: Groundnut farmers in East Africa have experienced declines in production despite research and extension efforts to increase productivity. This study examined how social network structures related to acquisition of information about new seed varieties and productivity among groundnut farmers in Uganda and Kenya.…

Decentralized Networked Control of Building Structures

Czech Academy of Sciences Publication Activity Database

Bakule, Lubomír; Rehák, Branislav; Papík, Martin

2016-01-01

Roč. 31, č. 11 (2016), s. 871-886 ISSN 1093-9687 R&D Projects: GA ČR GA13-02149S Institutional support: RVO:67985556 Keywords : decentralized control * networked control * building structures Subject RIV: BC - Control Systems Theory Impact factor: 5.786, year: 2016

Information diffusion in structured online social networks

Science.gov (United States)

Li, Pei; Zhang, Yini; Qiao, Fengcai; Wang, Hui

2015-05-01

Nowadays, due to the word-of-mouth effect, online social networks have been considered to be efficient approaches to conduct viral marketing, which makes it of great importance to understand the diffusion dynamics in online social networks. However, most research on diffusion dynamics in epidemiology and existing social networks cannot be applied directly to characterize online social networks. In this paper, we propose models to characterize the information diffusion in structured online social networks with push-based forwarding mechanism. We introduce the term user influence to characterize the average number of times that messages are browsed which is incurred by a given type user generating a message, and study the diffusion threshold, above which the user influence of generating a message will approach infinity. We conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of use in understanding the diffusion dynamics in online social networks and also critical for advertisers in viral marketing who want to estimate the user influence before posting an advertisement.

Novel method for equivalent stiffness and Coulomb's damping ratio analyses of leaf spring

International Nuclear Information System (INIS)

Wen Jun, Wu; Yu, Xiang; Le Mei, Zhu; Li Jun, He

2012-01-01

The leaf spring is a representative type of laminated structure. Based on the linear theories of curve beams, the first derivatives of the leave's status vector of the leaf spring are provided. The first derivatives of the combination status-vector are obtained by properly dealing with the nonlinear interacted forces between adjacent leaves. Moreover, the precise integration technology and the transform matrix method are introduced to solve the equations. The force displacement curve of a leaf spring is then calculated separately by using the present method and the finite element software ANSYS. From the results, the precision and advantages of the present methods for analyzing the leaf spring are revealed. The Coulomb's damping ratio of the leaf spring is studied by using the present method

A review of structural and functional brain networks: small world and atlas.

Science.gov (United States)

Yao, Zhijun; Hu, Bin; Xie, Yuanwei; Moore, Philip; Zheng, Jiaxiang

2015-03-01

Brain networks can be divided into two categories: structural and functional networks. Many studies of neuroscience have reported that the complex brain networks are characterized by small-world or scale-free properties. The identification of nodes is the key factor in studying the properties of networks on the macro-, micro- or mesoscale in both structural and functional networks. In the study of brain networks, nodes are always determined by atlases. Therefore, the selection of atlases is critical, and appropriate atlases are helpful to combine the analyses of structural and functional networks. Currently, some problems still exist in the establishment or usage of atlases, which are often caused by the segmentation or the parcellation of the brain. We suggest that quantification of brain networks might be affected by the selection of atlases to a large extent. In the process of building atlases, the influences of single subjects and groups should be balanced. In this article, we focused on the effects of atlases on the analysis of brain networks and the improved divisions based on the tractography or connectivity in the parcellation of atlases.

PROTEOMIC PROFILE REVEALS THE DIVERSITY AND COMPLEXITY OF LEAF PROTEINS IN SPINACH (BETA VULGARIS VAR. ALL GREEN

Directory of Open Access Journals (Sweden)

Sudip Ghosh

2016-06-01

Full Text Available Leaf is a source organ that serves dual function in photosynthesis and transpiration. As a primary interface between plant and ecosystem, it performs a range of biological processes from carbon assimilation and metabolite partitioning to plant productivity. Basic features of the leaf functionality are conserved in angiosperms exhibiting common and unique characteristics. Spinach has been the model crop for studying leaf function, primarily photosynthesis. It is a reservoir of several hundreds of primary and secondary biomolecules. To better understand the molecular basis for photochemical reaction and metabolic partitioning, we developed leaf proteome of Indian spinach (Beta vulgaris var. all green. LC-ESI-MS/MS analysis identified 639 proteins exhibiting discrete molecular features and functions, including photosynthesis, transpiration, gaseous exchange, transport, redox status, cell defense, and floral induction besides the presence of proteins with unknown function. This represents the first comprehensive foliage proteome of green leafy vegetable. Together, this work provides important insights into the molecular networks underlying spinach leaf biological processes.

Complex Network Structure Influences Processing in Long-Term and Short-Term Memory

Science.gov (United States)

Vitevitch, Michael S.; Chan, Kit Ying; Roodenrys, Steven

2012-01-01

Complex networks describe how entities in systems interact; the structure of such networks is argued to influence processing. One measure of network structure, clustering coefficient, C, measures the extent to which neighbors of a node are also neighbors of each other. Previous psycholinguistic experiments found that the C of phonological…

Genomic analysis of the hierarchical structure of regulatory networks

Science.gov (United States)

Yu, Haiyuan; Gerstein, Mark

2006-01-01

A fundamental question in biology is how the cell uses transcription factors (TFs) to coordinate the expression of thousands of genes in response to various stimuli. The relationships between TFs and their target genes can be modeled in terms of directed regulatory networks. These relationships, in turn, can be readily compared with commonplace “chain-of-command” structures in social networks, which have characteristic hierarchical layouts. Here, we develop algorithms for identifying generalized hierarchies (allowing for various loop structures) and use these approaches to illuminate extensive pyramid-shaped hierarchical structures existing in the regulatory networks of representative prokaryotes (Escherichia coli) and eukaryotes (Saccharomyces cerevisiae), with most TFs at the bottom levels and only a few master TFs on top. These masters are situated near the center of the protein–protein interaction network, a different type of network from the regulatory one, and they receive most of the input for the whole regulatory hierarchy through protein interactions. Moreover, they have maximal influence over other genes, in terms of affecting expression-level changes. Surprisingly, however, TFs at the bottom of the regulatory hierarchy are more essential to the viability of the cell. Finally, one might think master TFs achieve their wide influence through directly regulating many targets, but TFs with most direct targets are in the middle of the hierarchy. We find, in fact, that these midlevel TFs are “control bottlenecks” in the hierarchy, and this great degree of control for “middle managers” has parallels in efficient social structures in various corporate and governmental settings. PMID:17003135

On the temporal variation of leaf magnetic parameters: seasonal accumulation of leaf-deposited and leaf-encapsulated particles of a roadside tree crown.

Science.gov (United States)

Hofman, Jelle; Wuyts, Karen; Van Wittenberghe, Shari; Samson, Roeland

2014-09-15

Understanding the accumulation behaviour of atmospheric particles inside tree leaves is of great importance for the interpretation of biomagnetic monitoring results. In this study, we evaluated the temporal variation of the saturation isothermal remanent magnetisation (SIRM) of leaves of a roadside urban Platanus × acerifolia Willd. tree in Antwerp, Belgium. We hereby examined the seasonal development of the total leaf SIRM signal as well as the leaf-encapsulated fraction of the deposited dust, by washing the leaves before biomagnetic analysis. On average 38% of the leaf SIRM signal was exhibited by the leaf-encapsulated particles. Significant correlations were found between the SIRM and the cumulative daily average atmospheric PM10 and PM2.5 measurements. Moreover, a steady increase of the SIRM throughout the in-leaf season was observed endorsing the applicability of biomagnetic monitoring as a proxy for the time-integrated PM exposure of urban tree leaves. Strongest correlations were obtained for the SIRM of the leaf-encapsulated particles which confirms the dynamic nature of the leaf surface-accumulated particles. Copyright © 2014 Elsevier B.V. All rights reserved.

Optimization and Static Stress Analysis of Hybrid Fiber Reinforced Composite Leaf Spring

Directory of Open Access Journals (Sweden)

Luay Muhammed Ali Ismaeel

2015-01-01

Full Text Available A monofiber reinforced composite leaf spring is proposed as an alternative to the typical steel one as it is characterized by high strength-to-weight ratio. Different reinforcing schemes are suggested to fabricate the leaf spring. The composite and the typical steel leaf springs are subjected to the same working conditions. A weight saving of about more than 60% can be achieved while maintaining the strength for the structures under consideration. The objective of the present study was to replace material for leaf spring. This study suggests various materials of hybrid fiber reinforced plastics (HFRP. Also the effects of shear moduli of the fibers, matrices, and the composites on the composites performance and responses are discussed. The results and behaviors of each are compared with each other and verified by comparison with analytical solution; a good convergence is found between them. The elastic properties of the hybrid composites are calculated using rules of mixtures and Halpin-Tsi equation through the software of MATLAB v-7. The problem is also analyzed by the technique of finite element analysis (FEA through the software of ANSYS v-14. An element modeling was done for every leaf with eight-node 3D brick element (SOLID185 3D 8-Node Structural Solid.

Effect of Wind on the Relation of Leaf N, P Stoichiometry with Leaf Morphology in Quercus Species

Directory of Open Access Journals (Sweden)

Peng Zhang

2018-02-01

Full Text Available Leaf nitrogen (N and phosphorus (P stoichiometry correlates closely to leaf morphology, which is strongly impacted by wind at multiple scales. However, it is not clear how leaf N, P stoichiometry and its relationship to leaf morphology changes with wind load. We determined the leaf N and P concentrations and leaf morphology—including specific leaf area (SLA and leaf dissection index (LDI—for eight Quercus species under a simulated wind load for seven months. Leaf N and P concentrations increased significantly under these conditions for Quercus acutissima, Quercus rubra, Quercus texana, and Quercus palustris—which have elliptic leaves—due to their higher N, P requirements and a resultant leaf biomass decrease, which is a tolerance strategy for Quercus species under a wind load. Leaf N:P was relatively stable under wind for all species, which supports stoichiometric homeostasis. Leaf N concentrations showed a positive correlation to SLA, leaf N and P concentrations showed positive correlations to LDI under each wind treatment, and the slope of correlations was not affected by wind, which indicates synchronous variations between leaf stoichiometry and leaf morphology under wind. However, the intercept of correlations was affected by wind, and leaf N and P use efficiency decreased under the wind load, which suggests that the Quercus species changes from “fast investment-return” in the control to “slow investment-return” under windy conditions. These results will be valuable to understanding functional strategies for plants under varying wind loads, especially synchronous variations in leaf traits along a wind gradient.

Are leaf physiological traits related to leaf water isotopic enrichment in restinga woody species?

Directory of Open Access Journals (Sweden)

BRUNO H.P. ROSADO

2013-09-01

Full Text Available During plant-transpiration, water molecules having the lighter stable isotopes of oxygen and hydrogen evaporate and diffuse at a faster rate through the stomata than molecules having the heavier isotopes, which cause isotopic enrichment of leaf water. Although previous models have assumed that leaf water is well-mixed and isotopically uniform, non-uniform stomatal closure, promoting different enrichments between cells, and different pools of water within leaves, due to morpho-physiological traits, might lead to inaccuracies in isotopic models predicting leaf water enrichment. We evaluate the role of leaf morpho-physiological traits on leaf water isotopic enrichment in woody species occurring in a coastal vegetation of Brazil known as restinga. Hydrogen and oxygen stable isotope values of soil, plant stem and leaf water and leaf traits were measured in six species from restinga vegetation during a drought and a wet period. Leaf water isotopic enrichment relative to stem water was more homogeneous among species during the drought in contrast to the wet period suggesting convergent responses to deal to temporal heterogeneity in water availability. Average leaf water isotopic enrichment relative to stem water during the drought period was highly correlated with relative apoplastic water content. We discuss this observation in the context of current models of leaf water isotopic enrichment as a function of the Péclet effect. We suggest that future studies should include relative apoplastic water content in isotopic models.

Are leaf physiological traits related to leaf water isotopic enrichment in restinga woody species?

Science.gov (United States)

Rosado, Bruno H P; De Mattos, Eduardo A; Sternberg, Leonel Da S L

2013-09-01

During plant-transpiration, water molecules having the lighter stable isotopes of oxygen and hydrogen evaporate and diffuse at a faster rate through the stomata than molecules having the heavier isotopes, which cause isotopic enrichment of leaf water. Although previous models have assumed that leaf water is well-mixed and isotopically uniform, non-uniform stomatal closure, promoting different enrichments between cells, and different pools of water within leaves, due to morpho-physiological traits, might lead to inaccuracies in isotopic models predicting leaf water enrichment. We evaluate the role of leaf morpho-physiological traits on leaf water isotopic enrichment in woody species occurring in a coastal vegetation of Brazil known as restinga. Hydrogen and oxygen stable isotope values of soil, plant stem and leaf water and leaf traits were measured in six species from restinga vegetation during a drought and a wet period. Leaf water isotopic enrichment relative to stem water was more homogeneous among species during the drought in contrast to the wet period suggesting convergent responses to deal to temporal heterogeneity in water availability. Average leaf water isotopic enrichment relative to stem water during the drought period was highly correlated with relative apoplastic water content. We discuss this observation in the context of current models of leaf water isotopic enrichment as a function of the Péclet effect. We suggest that future studies should include relative apoplastic water content in isotopic models.

Estimates of Leaf Relative Water Content from Optical Polarization Measurements

Science.gov (United States)

Dahlgren, R. P.; Vanderbilt, V. C.; Daughtry, C. S. T.

2017-12-01

Remotely sensing the water status of plant canopies remains a long term goal of remote sensing research. Existing approaches to remotely sensing canopy water status, such as the Crop Water Stress Index (CWSI) and the Equivalent Water Thickness (EWT), have limitations. The CWSI, based upon remotely sensing canopy radiant temperature in the thermal infrared spectral region, does not work well in humid regions, requires estimates of the vapor pressure deficit near the canopy during the remote sensing over-flight and, once stomata close, provides little information regarding the canopy water status. The EWT is based upon the physics of water-light interaction in the 900-2000nm spectral region, not plant physiology. Our goal, development of a remote sensing technique for estimating plant water status based upon measurements in the VIS/NIR spectral region, would potentially provide remote sensing access to plant dehydration physiology - to the cellular photochemistry and structural changes associated with water deficits in leaves. In this research, we used optical, crossed polarization filters to measure the VIS/NIR light reflected from the leaf interior, R, as well as the leaf transmittance, T, for 78 corn (Zea mays) and soybean (Glycine max) leaves having relative water contents (RWC) between 0.60 and 0.98. Our results show that as RWC decreases R increases while T decreases. Our results tie R and T changes in the VIS/NIR to leaf physiological changes - linking the light scattered out of the drying leaf interior to its relative water content and to changes in leaf cellular structure and pigments. Our results suggest remotely sensing the physiological water status of a single leaf - and perhaps of a plant canopy - might be possible in the future.

Network Centric Information Structure - Crisis Information Management

National Research Council Canada - National Science Library

Aarholt, Eldar; Berg, Olav

2004-01-01

This paper presents a generic Network Centric Information Structure (NCIS) that can be used by civilian, military and public sectors, and that supports information handling applied to crises management and emergency response...

Combining structure, governance and context : A configurational approach to network effectiveness

NARCIS (Netherlands)

Raab, J.; Mannak, R.S.; Cambré, B.

2015-01-01

This study explores the way in which network structure (network integration), network context (resource munificence and stability), and network governance mode relate to net -work effectiveness. The model by Provan and Milward (Provan, Keith G., and H. Brinton Milward. 1995. A preliminary theory of

Radiation synthesis and characterization of network structure of natural/synthetic double-network superabsorbent polymers

International Nuclear Information System (INIS)

Sen, M.; Hayrabolulu, H.

2011-01-01

Complete text of publication follows. Superabsorbent polymers (SAPs) are moderately cross linked, 3-D, hydrophilic network polymers that can absorb and conserve considerable amounts of aqueous fluids even under certain heat or pressure. Because of the unique properties superior to conventional absorbents, SAPs have found potential application in many fields such as hygienic products, disposable diapers, horticulture, gel actuators, drug-delivery systems, as well as water-blocking tapes coal dewatering, water managing materials for the renewal of arid and desert environment, etc. In recent years, naturally available resources, such as polysaccharides have drawn considerable attention for the preparation of SAPs. Since the mechanical properties of polysaccharide based natural polymers are low, researchers have mostly focused on natural/synthetic polymer/monomer mixtures to obtain novel SAPs. The aim of this study is to synthesize and characterization of network structure of novel double-network (DN) hydrogels as a SAP. Hydrogels with high mechanical strength have been prepared by radiation induced polymerization and crosslink of acrylic acid sodium salt in the presence of natural polymer locust bean gum. Liquid retention capacities and absorbency under load (AUL) analysis of synthesized SAPs was performed at different temperatures in water and synthetic urine solution, in order to determine their SAP character. For the characterization of network structure of the semi-IPN hydrogels, the average molecular weight between cross links (M c ) were evaluated by using uniaxial compression and oscillatory dynamical mechanical analyses and the advantage and disadvantage of these two technique for the characterization of network structures were compared.

Early grey matter changes in structural covariance networks in Huntington's disease.

Science.gov (United States)

Coppen, Emma M; van der Grond, Jeroen; Hafkemeijer, Anne; Rombouts, Serge A R B; Roos, Raymund A C

2016-01-01

Progressive subcortical changes are known to occur in Huntington's disease (HD), a hereditary neurodegenerative disorder. Less is known about the occurrence and cohesion of whole brain grey matter changes in HD. We aimed to detect network integrity changes in grey matter structural covariance networks and examined relationships with clinical assessments. Structural magnetic resonance imaging data of premanifest HD ( n  = 30), HD patients (n = 30) and controls (n = 30) was used to identify ten structural covariance networks based on a novel technique using the co-variation of grey matter with independent component analysis in FSL. Group differences were studied controlling for age and gender. To explore whether our approach is effective in examining grey matter changes, regional voxel-based analysis was additionally performed. Premanifest HD and HD patients showed decreased network integrity in two networks compared to controls. One network included the caudate nucleus, precuneous and anterior cingulate cortex (in HD p  covariance might be a sensitive approach to reveal early grey matter changes, especially for premanifest HD.

Hemispheric lateralization of topological organization in structural brain networks.

Science.gov (United States)

Caeyenberghs, Karen; Leemans, Alexander

2014-09-01

The study on structural brain asymmetries in healthy individuals plays an important role in our understanding of the factors that modulate cognitive specialization in the brain. Here, we used fiber tractography to reconstruct the left and right hemispheric networks of a large cohort of 346 healthy participants (20-86 years) and performed a graph theoretical analysis to investigate this brain laterality from a network perspective. Findings revealed that the left hemisphere is significantly more "efficient" than the right hemisphere, whereas the right hemisphere showed higher values of "betweenness centrality" and "small-worldness." In particular, left-hemispheric networks displayed increased nodal efficiency in brain regions related to language and motor actions, whereas the right hemisphere showed an increase in nodal efficiency in brain regions involved in memory and visuospatial attention. In addition, we found that hemispheric networks decrease in efficiency with age. Finally, we observed significant gender differences in measures of global connectivity. By analyzing the structural hemispheric brain networks, we have provided new insights into understanding the neuroanatomical basis of lateralized brain functions. Copyright © 2014 Wiley Periodicals, Inc.

Applying 4-regular grid structures in large-scale access networks

DEFF Research Database (Denmark)

Pedersen, Jens Myrup; Knudsen, Thomas P.; Patel, Ahmed

2006-01-01

4-Regular grid structures have been used in multiprocessor systems for decades due to a number of nice properties with regard to routing, protection, and restoration, together with a straightforward planar layout. These qualities are to an increasing extent demanded also in largescale access...... networks, but concerning protection and restoration these demands have been met only to a limited extent by the commonly used ring and tree structures. To deal with the fact that classical 4-regular grid structures are not directly applicable in such networks, this paper proposes a number of extensions...... concerning restoration, protection, scalability, embeddability, flexibility, and cost. The extensions are presented as a tool case, which can be used for implementing semi-automatic and in the longer term full automatic network planning tools....

Structural and robustness properties of smart-city transportation networks

Science.gov (United States)

Zhang, Zhen-Gang; Ding, Zhuo; Fan, Jing-Fang; Meng, Jun; Ding, Yi-Min; Ye, Fang-Fu; Chen, Xiao-Song

2015-09-01

The concept of smart city gives an excellent resolution to construct and develop modern cities, and also demands infrastructure construction. How to build a safe, stable, and highly efficient public transportation system becomes an important topic in the process of city construction. In this work, we study the structural and robustness properties of transportation networks and their sub-networks. We introduce a complementary network model to study the relevance and complementarity between bus network and subway network. Our numerical results show that the mutual supplement of networks can improve the network robustness. This conclusion provides a theoretical basis for the construction of public traffic networks, and it also supports reasonable operation of managing smart cities. Project supported by the Major Projects of the China National Social Science Fund (Grant No. 11 & ZD154).

Research on energy stock market associated network structure based on financial indicators

Science.gov (United States)

Xi, Xian; An, Haizhong

2018-01-01

A financial market is a complex system consisting of many interacting units. In general, due to the various types of information exchange within the industry, there is a relationship between the stocks that can reveal their clear structural characteristics. Complex network methods are powerful tools for studying the internal structure and function of the stock market, which allows us to better understand the stock market. Applying complex network methodology, a stock associated network model based on financial indicators is created. Accordingly, we set threshold value and use modularity to detect the community network, and we analyze the network structure and community cluster characteristics of different threshold situations. The study finds that the threshold value of 0.7 is the abrupt change point of the network. At the same time, as the threshold value increases, the independence of the community strengthens. This study provides a method of researching stock market based on the financial indicators, exploring the structural similarity of financial indicators of stocks. Also, it provides guidance for investment and corporate financial management.

Development of human brain structural networks through infancy and childhood.

Science.gov (United States)

Huang, Hao; Shu, Ni; Mishra, Virendra; Jeon, Tina; Chalak, Lina; Wang, Zhiyue J; Rollins, Nancy; Gong, Gaolang; Cheng, Hua; Peng, Yun; Dong, Qi; He, Yong

2015-05-01

During human brain development through infancy and childhood, microstructural and macrostructural changes take place to reshape the brain's structural networks and better adapt them to sophisticated functional and cognitive requirements. However, structural topological configuration of the human brain during this specific development period is not well understood. In this study, diffusion magnetic resonance image (dMRI) of 25 neonates, 13 toddlers, and 25 preadolescents were acquired to characterize network dynamics at these 3 landmark cross-sectional ages during early childhood. dMRI tractography was used to construct human brain structural networks, and the underlying topological properties were quantified by graph-theory approaches. Modular organization and small-world attributes are evident at birth with several important topological metrics increasing monotonically during development. Most significant increases of regional nodes occur in the posterior cingulate cortex, which plays a pivotal role in the functional default mode network. Positive correlations exist between nodal efficiencies and fractional anisotropy of the white matter traced from these nodes, while correlation slopes vary among the brain regions. These results reveal substantial topological reorganization of human brain structural networks through infancy and childhood, which is likely to be the outcome of both heterogeneous strengthening of the major white matter tracts and pruning of other axonal fibers. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Hierarchical modular structure enhances the robustness of self-organized criticality in neural networks