ライフサイエンスコーパス: scale-free

1 g activity fluctuations that are essentially scale free.

2 any particular relaxation times and are thus scale-free.

3 l properties-including the property of being scale-free.

4 In neural systems, scale-free activity is often neglected in electrophysiol

5 ing accelerated speech also relates to their scale-free amplitude modulation as indexed by the streng

6 Our findings show that scale-free amplitude modulation of cortical oscillations

7 currently known space of protein families is scale free and discuss the implications of this distribu

8 rk, we systematically alter the structure of scale-free and clique networks and show, through a stoch

9 ties of biological networks, including their scale-free and modular nature.

10 These findings lead to a new hypothesis that scale-free and oscillatory neural processes account for

11 We find that for scale-free and rich-club networks there exist specific n

12 teractions, (3) infer aberrant networks with scale-free and small-world properties, and (4) group mol

13 Results show that the network exhibited scale-free and small-world topologies, indicating the po

14 alyses, biochemical networks are found to be scale-free and small-world, indicating that these networ

15 large model-organism expression datasets are scale-free and that the average clustering coefficient o

16 equal in duration, predicts that results are scale-free and unaffected by the presentation order of t

17 as highlighted by hallmarks of small world, scale free, and hierarchical modular topologies.

18 nalysis of this network shows that it is not scale-free, and is best approximated by the Watts-Stroga

19 This scale-free approach can easily be extended to any multic

20 The architecture of this network is scale-free, as frequently seen in biological networks, a

21 number of real networks are referred to as 'scale-free' because they show a power-law distribution o

22 ted crystals are a model system for studying scale-free behavior as observed in many macroscopic syst

23 the weighted evolving hypergraph exhibits a scale-free behavior for both hyperdegree and hyperstreng

24 certain parameter regimes, recapitulate the scale-free behavior observed in similar graphs of real p

25 We find striking scale-free behavior of the transaction volume after each

26 Moreover, deviations from scale-free behavior were exclusively connected to brief

27 important statistical properties (so-called scale-free behavior) with real molecular networks.

28 iscuss ensembles of random Boolean networks, scale free Boolean networks, "medusa" Boolean networks,

29 al structures and behavioral significance of scale-free brain activity and should motivate future stu

30 The functional significance of scale-free brain activity is indicated by task performan

31 These scale-free brain dynamics contain complex spatiotemporal

32 Although gigahertz-scale free-carrier modulators have been demonstrated in

33 ellar stream in Andromeda II illustrates the scale-free character of the formation of galaxies, down

34 hile some states with higher symmetries have scale-free coherence with respect to n.

35 Scale-free community size spectra (i.e., size distributi

36 f the hub nodes that are responsible for the scale-free connectivity of the PDUG.

37 ng in magnets, and the correct prediction of scale-free correlations arises because the parameters--c

38 Interestingly, we find the same scale-free correlations in economic activities associate

39 the dynamics of other complex systems where scale-free cross-correlations have been observed, includ

40 motivated explanation for the occurrence of scale-free cross-correlations.

41 the hallmarks of complex systems such as the scale-free degree distribution of small-worldness.

42 iversal properties of real complex networks (scale-free degree distribution, small-world and communit

43 similarity among F1 recombinants exhibits a scale-free degree distribution.

44 For scale-free degree distributions, however, this is not th

45 on what the most common properties are, but scale-free degree distributions, strong clustering, and

46 the biological networks show a power law or scale free distribution of connectivities.

47 genously driven extinction events can have a scale-free distribution in simple spatially structured h

48 the radiation hybrid network did not show a scale-free distribution of connectivity but was Gaussian

49 The scale-free distribution of hubs in the protein universe

50 Because of the scale-free distribution of protein interaction networks,

51 Can we model the scale-free distribution of Web hypertext degree under re

52 tabolic networks is organized according to a scale-free distribution, in which hubs with large number

53 gredients reproduces the observed stationary scale-free distributions, which indicates that the devel

54 ion of synchronous cell assemblies and their scale-free dynamics adjusts to the temporal properties o

55 In the model, scale-free dynamics emerge only when the model operates

56 Here, we investigate scale-free dynamics in human brain and show that it cont

57 d frequencies in the brain differ from other scale-free dynamics in nature, such as earth seismic wav

58 that speech comprehension is related to the scale-free dynamics of delta and theta bands, whereas th

59 nd experiment have shown that such critical, scale-free dynamics optimize information processing.

60 As adaptation tuned the cortex toward scale-free dynamics, stimulus discrimination was enhance

61 Scale-free dynamics, with a power spectrum following P p

62 rain electrical field potentials consists of scale-free dynamics.

63 asks are not essential for the expression of scale-free dynamics.

64 rate that the broadband power fluctuation of scale-free electrophysiology is globally synchronized an

65 ong molecular dynamics simulations and large-scale free energy calculations complemented by in vitro

66 Based on these results, we conjecture that scale-free extinction processes and critical phase trans

67 the number of events and their magnitude, or scale-free flow.

68 we demonstrate the hyphenation of production-scale free-flow electrophoresis (FFE) and sheathless ele

69 orn rats in vitro, and show that while these scale-free fluctuations are independent of temporal inpu

70 Scale-free fluctuations are ubiquitous in behavioral per

71 e functional roles as hubs in a hierarchical scale-free fractal protein-protein interaction network.

72 Levy flights are scale-free (fractal) search patterns found in a wide ran

73 t fluctuations in brain activity may exhibit scale-free ("fractal") dynamics.

74 Given origami's scale-free geometric character, this framework for metam

75 Adding cycles to the acyclic scale-free graph increases cooperation when multiple gam

76 We also confirm the negative effect of the scale-free graph on cooperation when effective payoffs a

77 ks with null percolation thresholds, such as scale-free graphs with diverging second moment of the de

78 The RNA-Seq coexpression network displayed scale-free, hierarchical network structure.

79 een recently described as "small-world" and "scale-free." However, studies disagree whether ecologica

80 The power fluctuations of scale-free human electroencephalography (EEG) were coupl

81 omogeneous networks while for d > 2 they are scale-free i.e. they are characterized by large inhomoge

82 signal recorded from the human brain is also scale free; its power-law exponent differentiates betwee

83 tible with the adoption of an optimal biased scale-free (Levy-flight) searching strategy.

84 lieved that random search processes based on scale-free, Levy stable jump length distributions (Levy

85 specificity is an additional pressure for a scale-free-like PPIN.

86 of their prevalence revealed by a pattern of scale-free long-range correlations.

87 fluctuations of broadband and arrhythmic, or scale-free, macaque electrocorticography and human magne

88 twork of the US economy and find that purely scale-free models have trouble matching key attributes o

89 rgic phenotypes, including cluster analysis, scale-free models, candidate biomarkers, and IgE microar

90 food, many organisms adopt a superdiffusive, scale-free movement pattern called a Levy walk, which is

91 lights, a popular but controversial model of scale-free movement patterns.

92 ticular organisms, these subgraphs exhibit a scale-free nature as well.

93 that this model is able to recapitulate the scale-free nature observed in graphs of real protein str

94 Thus, inferences about the scale-free nature of a network may have to be treated wi

95 e fundamental properties help to explain the scale-free nature of complex networks and suggest a comm

96 downstream genes (regulons), indicating the scale-free nature of host gene co-expression in W12.

97 This model reproduces not only the scale-free nature of such graphs but also a number of hi

98 tems, and explain how this is related to the scale-free nature of the extinction process.

99 e rest of that decade would go by before the scale-free nature of the IM space was uncovered.

100 The scale-free nature of this and other systems has previous

101 The scale-free nature of this graph, termed the protein doma

102 ds and 224 chemical reactions, has a typical scale-free nature.

103 patial Prisoner's Dilemma on a lattice and a scale-free network (1,229 subjects).

104 l that surprisingly it self-organizes into a scale-free network exhibiting also a power-law in the di

105 In contrast to the prediction of scale-free network models, however, we find that the mos

106 opic, physics-based evolutionary model for a scale-free network of biological importance and as such

107 were performed using a Barabasi-Albert (BA) scale-free network seeded with a single piece of informa

108 y significant structural data are available, scale-free network statistics based solely on the distri

109 roteins have few partners, consistent with a scale-free network topology.

110 s in the polyprotein can be organized into a scale-free network whose degree of connections between s

111 ls-in three complex social networks-a simple scale-free network, an empirical Venezuelan college stud

112 a random regular graph, a random graph and a scale-free network, and we examine the features of the g

113 s between protein families has the form of a scale-free network, meaning that most protein families o

114 The results are suggestive a hierarchical, scale-free network, where a few highly interconnected ge

115 experimentally derived RNA interactome is a scale-free network, which is not expected from currently

116 Our network is a scale-free network, which means that a small number of d

117 ociations and displayed characteristics of a scale-free network.

118 tructures is organized hierarchically into a scale-free network.

119 This type of pattern can be described by a scale-free network.

120 This type of pattern can be described as a scale-free network.

121 a web of control systems, reminiscent of a 'scale-free' network, whose untangling requires integrate

122 o specific approaches focused on here (i.e., scale-free networks and highly optimized tolerance netwo

123 Deep connections are known to exist between scale-free networks and non-Gibbsian statistics.

124 Both biological and non-biological scale-free networks are particularly resistant to random

125 erturbed lattices, small-world networks, and scale-free networks behave similarly.

126 ologically observed exponents for functional scale-free networks fall in a range corresponding to the

127 Scale-free networks have had a profound impact in Biolog

128 Scale-free networks have lower throughput than their ran

129 Considering the ubiquity of scale-free networks in nature, we hypothesize that this

130 Here we propose that the emergence of many scale-free networks is tied to the efficiency of transpo

131 rganization, reminiscent of 'small-world' or scale-free networks observed in other complex systems.

132 this hidden distributed architecture behind scale-free networks protects the overall transcriptional

133 The decade-old discovery of scale-free networks was one of those events that had hel

134 In contrast, in scale-free networks we predict analytically the emergenc

135 namical rules of these networks can generate scale-free networks with clustering and communities, in

136 sign principles of robust and error-tolerant scale-free networks, and may represent a common blueprin

137 e the efficiency of each method on synthetic scale-free networks, as well as real complex networks.

138 f three classes of small-world networks: (a) scale-free networks, characterized by a vertex connectiv

139 ugh food webs are generally not small-world, scale-free networks, food-web topology is consistent wit

140 r than would be expected for similarly sized scale-free networks, suggesting an inherent hierarchical

141 ual neurons, but here, we investigate ISR in scale-free networks, where the average spiking rate is c

142 ss of inhomogeneously wired networks, called scale-free networks, which include the World-Wide Web, t

143 work has focused on mechanisms that produce scale-free networks.

144 -Reny, random scale-free, or Barabasi-Albert scale-free networks.

145 anding not only PPIs but also other types of scale-free networks.

146 We also explore evolution on random and scale-free networks.

147 ems, and which are typically associated with scale-free networks.

148 e networks are more resilient than simulated scale-free networks.

149 ence is responsible for the emergence of the scale-free networks.

150 size of the minimum dominating set (MDS) in scale-free networks.

151 We find that networks having a power-law ("scale-free") node degree distribution readily generate e

152 cortical state was uniquely characterized by scale-free ongoing population dynamics and moderate corr

153 for biological networks is that they have a scale-free or power-law architecture.

154 of cancer is driven by two factors: (i) the scale-free (or near scale-free) topology of the interact

155 ric random graphs than by Erdos-Reny, random scale-free, or Barabasi-Albert scale-free networks.

156 ) are on a particular extreme: they are also scale free (order free).

157 suggesting that that brain networks have a "scale-free" organization.

158 us pathway interactions may exhibit the same scale-free phenomenon that has been documented for prote

159 lace burst suppression in the broad class of scale-free physical processes termed crackling noise and

160 nspecific fluctuation and synchronization in scale-free population activity also varied across and de

161 tivity is the broadband power fluctuation in scale-free population activity observable with macroscop

162 , we show that genetic distinctiveness has a scale-free power law distribution.

163 e episodes during the sleep period exhibit a scale-free power-law behavior with an exponent alpha tha

164 at in both cases it is described by the same scale-free power-law distribution with an additional pea

165 ribution, whereas those in type-II display a scale-free power-law distribution with exponent approxim

166 s is that the vertex connectivities follow a scale-free power-law distribution.

167 laypeople and professional musicians exhibit scale-free (power law) cross-correlations.

168 pology and their chemistry on an equivalent 'scale-free' power-law foundation.

169 sal curve, suggesting that displacement is a scale-free process.

170 A network with scale-free properties appeared when the Ca2+ genes were

171 e observed effects on long-range dependence, scale-free properties of alpha oscillations themselves,

172 The scale-free properties of the fMRI signal and brain elect

173 ults demonstrate the functional relevance of scale-free properties of the fMRI signal and impose cons

174 We examine the origin of these scale-free properties of the graph of protein domain str

175 --patterns of complex bursting activity with scale-free properties--is examined in leaky Markovian ne

176 orks generated with high MI values displayed scale-free properties.

177 erse, which has been found to poses peculiar scale-free properties.

178 all-world, single-scale, and to some degree, scale-free properties.

179 In apparently scale-free protein-protein interaction networks, or 'int

180 adds to the evolutionary pressure to develop scale-free protein-protein interaction networks.

181 curs when the continuous scale symmetry of a scale-free quantum system is broken into a discrete scal

182 e living animals use a theoretically optimal scale-free random search for sparse resources known as a

183 walk paradigm because they are discrete and scale-free rather than continuous and scale-finite.

184 d the CSK matrix, which was followed by slow scale-free recovery of rheological properties (aging).

185 oss a remarkable 5 orders of magnitude and a scale-free relationship between burst sizes and duration

186 ft glasses, but not all materials expressing scale-free rheology are glassy (see plastics, wood, conc

187 Scale-free rheology is often found in a class of materia

188 We investigate how scale-free (SF) and Erdos-Renyi (ER) topologies affect t

189 in the Hurst exponent (H), which quantifies scale-free signal, was related to three different source

190 Scale-free signals follow a spectral-power curve of the

191 he worldwide air transportation network is a scale-free small-world network.

192 conditions exhibited topological features of scale free, small world and modularity, which were consi

193 method that uses topological constraints of scale-free, small-world biological networks to reconstru

194 ation dynamics towards a special regime with scale-free spatiotemporal activity, after an initial lar

195 x displays critical dynamics, giving rise to scale-free spatiotemporal cascades of activity, termed n

196 This process generates centimeter-scale free-standing structures composed of paper support

197 On a molecular scale, free-standing capsules with an internal volume su

198 hen the input itself possesses natural-like, scale-free statistics.

199 s of Internet traffic and, in some contexts, scale-free structure in the network's interconnection to

200 although some food webs have small-world and scale-free structure, most do not if they exceed a relat

201 n natural and artificial complex systems, of scale-free structures and to their connections with none

202 dients of a scalar distributed on the nodes, scale-free structures will ensure efficient processing,

203 processing, whereas structures that are not scale-free, such as random graphs, will become congested

204 It gives rise to a scale-free tail of the time-aggregated population distri

205 ut path flow allocations are more unequal in scale-free than in random regular networks.

206 A large number of complex networks are scale-free--that is, they follow a power-law degree dist

207 Here we analyze whether the scale-free topologies of the partial networks obtained f

208 ng of patterns of behavior, we show that the scale-free topologies often found in nature enable more

209 The gene network showed small-world and scale-free topologies, suggesting efficiency in genetic

210 These partial networks display scale-free topologies--most proteins participate in only

211 ved protein-protein interaction networks had scale-free topologies.

212 co-conservation based networks to exhibit a scale free topology, as expected for biological networks

213 lized damage, the function of a network with scale-free topology can be significantly restored by a l

214 urrent limited coverage levels, the observed scale-free topology of existing interactome maps cannot

215 g of random phenotypic variation through the scale-free topology of gene regulatory, metabolic, and p

216 A link between the potential scale-free topology of interactome networks and genetic

217 n this study, an algorithm that explores the scale-free topology of networks was proposed based on th

218 ng crucial as we try to explain the emergent scale-free topology of the World Wide Web and use link a

219 tworks whose degree distributions follow the scale-free topology.

220 d similar network structures approximating a scale-free topology.

221 stems, when represented as graphs, exhibit a scale-free topology.

222 ex networks is a direct consequence of their scale-free topology.

223 vealed a common network architecture, termed scale-free topology.

224 ties in small-world networks that followed a scale-free topology.

225 ted that many biological networks exhibit a "scale-free" topology, for which the probability of obser

226 by two factors: (i) the scale-free (or near scale-free) topology of the interaction network, and (ii

227 a hidden distributed architecture behind the scale-free transcriptional regulatory network of yeast b

228 rotein-protein binding that is approximately scale-free (varies as a power law) even though their evo

229 ring numerous methods to tailor the Angstrom-scale free volume properties by judicious selection of t

230 However, the locations graph is scale-free, which allows highly efficient outbreak detec

231 particular, show that these fluctuations are scale-free, with effective correlation lengths proportio