5,314 research outputs found
Extremism propagation in social networks with hubs
One aspect of opinion change that has been of academic interest is the impact of people with extreme opinions (extremists) on opinion dynamics. An agent-based model has been used to study the role of small-world social network topologies on general opinion change in the presence of extremists. It has been found that opinion convergence to a single extreme occurs only when the average number of network connections for each individual is extremely high. Here, we extend the model to examine the effect of positively skewed degree distributions, in addition to small-world structures, on the types of opinion convergence that occur in the presence of extremists. We also examine what happens when extremist opinions are located on the well-connected nodes (hubs) created by the positively skewed distribution. We find that a positively skewed network topology encourages opinion convergence on a single extreme under a wider range of conditions than topologies whose degree distributions were not skewed. The importance of social position for social influence is highlighted by the result that, when positive extremists are placed on hubs, all population convergence is to the positive extreme even when there are twice as many negative extremists. Thus, our results have shown the importance of considering a positively skewed degree distribution, and in particular network hubs and social position, when examining extremist transmission
From time-series to complex networks: Application to the cerebrovascular flow patterns in atrial fibrillation
A network-based approach is presented to investigate the cerebrovascular flow
patterns during atrial fibrillation (AF) with respect to normal sinus rhythm
(NSR). AF, the most common cardiac arrhythmia with faster and irregular
beating, has been recently and independently associated with the increased risk
of dementia. However, the underlying hemodynamic mechanisms relating the two
pathologies remain mainly undetermined so far; thus the contribution of
modeling and refined statistical tools is valuable. Pressure and flow rate
temporal series in NSR and AF are here evaluated along representative cerebral
sites (from carotid arteries to capillary brain circulation), exploiting
reliable artificially built signals recently obtained from an in silico
approach. The complex network analysis evidences, in a synthetic and original
way, a dramatic signal variation towards the distal/capillary cerebral regions
during AF, which has no counterpart in NSR conditions. At the large artery
level, networks obtained from both AF and NSR hemodynamic signals exhibit
elongated and chained features, which are typical of pseudo-periodic series.
These aspects are almost completely lost towards the microcirculation during
AF, where the networks are topologically more circular and present random-like
characteristics. As a consequence, all the physiological phenomena at
microcerebral level ruled by periodicity - such as regular perfusion, mean
pressure per beat, and average nutrient supply at cellular level - can be
strongly compromised, since the AF hemodynamic signals assume irregular
behaviour and random-like features. Through a powerful approach which is
complementary to the classical statistical tools, the present findings further
strengthen the potential link between AF hemodynamic and cognitive decline.Comment: 12 pages, 10 figure
Caracterização e classificação dos solos do Campo Experimental do Cerrado da Embrapa Amapá, Estado do Amapá.
bitstream/item/63154/1/Oriental-Doc43.pd
A Dynamic Model of Cascades on Random Networks with a Threshold Rule
Cascades on random networks are typically analyzed by assuming they map onto
percolation processes and then are solved using generating function
formulations. This approach assumes that the network is infinite and weakly
connected, yet furthermore approximates a dynamic cascading process as a static
percolation event. In this paper we propose a dynamic Markov model formulation
that assumes a finite network with arbitrary average nodal degree. We apply it
to the case where cascades follow a threshold rule, that is, that a node will
change state ("flip") only if a fraction, exceeding a given threshold, of its
neighbors has changed state previously. The corresponding state transition
matrix, recalculated after each step, records the probability that a node of
degree k has i flipped neighbors after j steps in the cascade's evolution. This
theoretical model reproduces a number of behaviors observed in simulations but
not yet reported in the literature. These include the ability to predict
cascades in a domain previously predicted to forbid cascades without assuming
that the network is locally tree-like, and, due to the dynamic nature of the
model, a "near death" behavior in which cascades initially appear about to die
but later explode. Cascades in the "no cascades" region require a sufficiently
large seed of initially flipped nodes whose size scales with the size of the
network or else the cascade will die out. Our theory also predicts the well
known properties of cascades, for instance that a single node seed can start a
global cascade in the appropriate regime regardless of the (finite) size of the
network. The theory and simulations developed here are compared with a
foundational paper by Watts which used generating function theory.Comment: Rev 1: Added citation to prior work by Gleeson and Cahalane. Revised
abstract to sui
Modularity produces small-world networks with dynamical time-scale separation
The functional consequences of local and global dynamics can be very
different in natural systems. Many such systems have a network description that
exhibits strong local clustering as well as high communication efficiency,
often termed as small-world networks (SWN). We show that modular organization
in otherwise random networks generically give rise to SWN, with a
characteristic time-scale separation between fast intra-modular and slow
inter-modular processes. The universality of this dynamical signature, that
distinguishes modular networks from earlier models of SWN, is demonstrated by
processes as different as spin-ordering, synchronization and diffusion.Comment: 6 pages, 7 figures. Published version, Results and figures of
additional dynamics have been include
Collective Decision Dynamics in the Presence of External Drivers
We develop a sequence of models describing information transmission and
decision dynamics for a network of individual agents subject to multiple
sources of influence. Our general framework is set in the context of an
impending natural disaster, where individuals, represented by nodes on the
network, must decide whether or not to evacuate. Sources of influence include a
one-to-many externally driven global broadcast as well as pairwise
interactions, across links in the network, in which agents transmit either
continuous opinions or binary actions. We consider both uniform and variable
threshold rules on the individual opinion as baseline models for
decision-making. Our results indicate that 1) social networks lead to
clustering and cohesive action among individuals, 2) binary information
introduces high temporal variability and stagnation, and 3) information
transmission over the network can either facilitate or hinder action adoption,
depending on the influence of the global broadcast relative to the social
network. Our framework highlights the essential role of local interactions
between agents in predicting collective behavior of the population as a whole.Comment: 14 pages, 7 figure
Competition and Selection Among Conventions
In many domains, a latent competition among different conventions determines
which one will come to dominate. One sees such effects in the success of
community jargon, of competing frames in political rhetoric, or of terminology
in technical contexts. These effects have become widespread in the online
domain, where the data offers the potential to study competition among
conventions at a fine-grained level.
In analyzing the dynamics of conventions over time, however, even with
detailed on-line data, one encounters two significant challenges. First, as
conventions evolve, the underlying substance of their meaning tends to change
as well; and such substantive changes confound investigations of social
effects. Second, the selection of a convention takes place through the complex
interactions of individuals within a community, and contention between the
users of competing conventions plays a key role in the convention's evolution.
Any analysis must take place in the presence of these two issues.
In this work we study a setting in which we can cleanly track the competition
among conventions. Our analysis is based on the spread of low-level authoring
conventions in the eprint arXiv over 24 years: by tracking the spread of macros
and other author-defined conventions, we are able to study conventions that
vary even as the underlying meaning remains constant. We find that the
interaction among co-authors over time plays a crucial role in the selection of
them; the distinction between more and less experienced members of the
community, and the distinction between conventions with visible versus
invisible effects, are both central to the underlying processes. Through our
analysis we make predictions at the population level about the ultimate success
of different synonymous conventions over time--and at the individual level
about the outcome of "fights" between people over convention choices.Comment: To appear in Proceedings of WWW 2017, data at
https://github.com/CornellNLP/Macro
Proteome and phosphoproteome analysis of brown adipocytes reveals that RICTOR loss dampens global insulin/AKT signaling
Stimulating brown adipose tissue (BAT) activity represents a promising therapy for overcoming metabolic diseases. mTORC2 is important for regulating BAT metabolism, but its downstream targets have not been fully characterized. In this study, we apply proteomics and phosphoproteomics to investigate the downstream effectors of mTORC2 in brown adipocytes. We compare wild-type controls to isogenic cells with an induced knockout of the mTORC2 subunit RICTOR (Rictor-iKO) by stimulating each with insulin for a 30-minute time course. In Rictor-iKO cells, we identify decreases to the abundance of glycolytic and de novo lipogenesis enzymes, and increases to mitochondrial proteins as well as a set of proteins known to increase upon interferon stimulation. We also observe significant differences to basal phosphorylation due to chronic RICTOR loss including decreased phosphorylation of the lipid droplet protein perilipin-1 in Rictor-iKO cells, suggesting that RICTOR could be involved with regulating basal lipolysis or droplet dynamics. Finally, we observe mild dampening of acute insulin signaling response in Rictor-iKO cells, and a subset of AKT substrates exhibiting statistically significant dependence on RICTOR.Fil: Entwisle, Samuel W.. University of Washington; Estados UnidosFil: Martinez Calejman, Camila. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Centro de Estudios Farmacológicos y Botánicos. Universidad de Buenos Aires. Facultad de Medicina. Centro de Estudios Farmacológicos y Botánicos; ArgentinaFil: Valente, Anthony S.. University of Washington; Estados UnidosFil: Lawrence, Robert T.. University of Washington; Estados UnidosFil: Hung, Chien Min. University Of Massachussets. Medical School; Estados UnidosFil: Guertin, David A.. University Of Massachussets. Medical School; Estados UnidosFil: Villen, Judit. University of Washington; Estados Unido
Identificação em mistura de piperamidas em folhas, caule e raízes de Piper ottonoides por CG-EM e RMN 1D e 2D.
Interpreting syndepositional sediment remobilization and deformation beneath submarine gravity flows; a kinematic boundary layer approach
Turbidite sandstones and related deposits commonly contain deformation structures and remobilized sediment that might have resulted from post-depositional modification such as downslope creep (e.g. slumping) or density-driven loading by overlying deposits. However, we consider that deformation can occur during the passage of turbidity currents that exerted shear stress on their substrates (whether entirely pre-existing strata, sediment deposited by earlier parts of the flow itself or some combination of these). Criteria are outlined here, to avoid confusion with products of other mechanisms (e.g. slumping or later tectonics), which establish the synchronicity between the passage of overriding flows and deformation of their substrates. This underpins a new analytical framework for tracking the relationship between deformation, deposition and the transit of the causal turbidity current, through the concept of kinematic boundary layers. Case study examples are drawn from outcrop (Miocene of New Zealand, and Apennines of Italy) and subsurface examples (Britannia Sandstone, Cretaceous, UK Continental Shelf). Example structures include asymmetric flame structures, convolute lamination, some debritic units and injection complexes, together with slurry and mixed slurry facies. These structures may provide insight into the rheology and dynamics of submarine flows and their substrates, and have implications for the development of subsurface turbidite reservoirs
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