3,940 research outputs found
Obesity-induced changes in lipid mediators persist after weight loss.
BackgroundObesity induces significant changes in lipid mediators, however, the extent to which these changes persist after weight loss has not been investigated.Subjects/methodsWe fed C57BL6 mice a high-fat diet to generate obesity and then switched the diet to a lower-fat diet to induce weight loss. We performed a comprehensive metabolic profiling of lipid mediators including oxylipins, endocannabinoids, sphingosines and ceramides in key metabolic tissues (including adipose, liver, muscle and hypothalamus) and plasma.ResultsWe found that changes induced by obesity were largely reversible in most metabolic tissues but the adipose tissue retained a persistent obese metabolic signature. Prostaglandin signaling was perturbed in the obese state and lasting increases in PGD2, and downstream metabolites 15-deoxy PGJ2 and delta-12-PGJ2 were observed after weight loss. Furthermore expression of the enzyme responsible for PGD2 synthesis (hematopoietic prostaglandin D synthase, HPGDS) was increased in obese adipose tissues and remained high after weight loss. We found that inhibition of HPGDS over the course of 5 days resulted in decreased food intake in mice. Increased HPGDS expression was also observed in human adipose tissues obtained from obese compared with lean individuals. We then measured circulating levels of PGD2 in obese patients before and after weight loss and found that while elevated relative to lean subjects, levels of this metabolite did not decrease after significant weight loss.ConclusionsThese results suggest that lasting changes in lipid mediators induced by obesity, still present after weight loss, may play a role in the biological drive to regain weight
On Counting Triangles through Edge Sampling in Large Dynamic Graphs
Traditional frameworks for dynamic graphs have relied on processing only the
stream of edges added into or deleted from an evolving graph, but not any
additional related information such as the degrees or neighbor lists of nodes
incident to the edges. In this paper, we propose a new edge sampling framework
for big-graph analytics in dynamic graphs which enhances the traditional model
by enabling the use of additional related information. To demonstrate the
advantages of this framework, we present a new sampling algorithm, called Edge
Sample and Discard (ESD). It generates an unbiased estimate of the total number
of triangles, which can be continuously updated in response to both edge
additions and deletions. We provide a comparative analysis of the performance
of ESD against two current state-of-the-art algorithms in terms of accuracy and
complexity. The results of the experiments performed on real graphs show that,
with the help of the neighborhood information of the sampled edges, the
accuracy achieved by our algorithm is substantially better. We also
characterize the impact of properties of the graph on the performance of our
algorithm by testing on several Barabasi-Albert graphs.Comment: A short version of this article appeared in Proceedings of the 2017
IEEE/ACM International Conference on Advances in Social Networks Analysis and
Mining (ASONAM 2017
Evolution of Cooperation and Coordination in a Dynamically Networked Society
Situations of conflict giving rise to social dilemmas are widespread in
society and game theory is one major way in which they can be investigated.
Starting from the observation that individuals in society interact through
networks of acquaintances, we model the co-evolution of the agents' strategies
and of the social network itself using two prototypical games, the Prisoner's
Dilemma and the Stag Hunt. Allowing agents to dismiss ties and establish new
ones, we find that cooperation and coordination can be achieved through the
self-organization of the social network, a result that is non-trivial,
especially in the Prisoner's Dilemma case. The evolution and stability of
cooperation implies the condensation of agents exploiting particular game
strategies into strong and stable clusters which are more densely connected,
even in the more difficult case of the Prisoner's Dilemma.Comment: 18 pages, 14 figures. to appea
Multiple dynamical time-scales in networks with hierarchically nested modular organization
Many natural and engineered complex networks have intricate mesoscopic
organization, e.g., the clustering of the constituent nodes into several
communities or modules. Often, such modularity is manifested at several
different hierarchical levels, where the clusters defined at one level appear
as elementary entities at the next higher level. Using a simple model of a
hierarchical modular network, we show that such a topological structure gives
rise to characteristic time-scale separation between dynamics occurring at
different levels of the hierarchy. This generalizes our earlier result for
simple modular networks, where fast intra-modular and slow inter-modular
processes were clearly distinguished. Investigating the process of
synchronization of oscillators in a hierarchical modular network, we show the
existence of as many distinct time-scales as there are hierarchical levels in
the system. This suggests a possible functional role of such mesoscopic
organization principle in natural systems, viz., in the dynamical separation of
events occurring at different spatial scales.Comment: 10 pages, 4 figure
The network structure of visited locations according to geotagged social media photos
Businesses, tourism attractions, public transportation hubs and other points
of interest are not isolated but part of a collaborative system. Making such
collaborative network surface is not always an easy task. The existence of
data-rich environments can assist in the reconstruction of collaborative
networks. They shed light into how their members operate and reveal a potential
for value creation via collaborative approaches. Social media data are an
example of a means to accomplish this task. In this paper, we reconstruct a
network of tourist locations using fine-grained data from Flickr, an online
community for photo sharing. We have used a publicly available set of Flickr
data provided by Yahoo! Labs. To analyse the complex structure of tourism
systems, we have reconstructed a network of visited locations in Europe,
resulting in around 180,000 vertices and over 32 million edges. An analysis of
the resulting network properties reveals its complex structure.Comment: 8 pages, 3 figure
Colored Motifs Reveal Computational Building Blocks in the C. elegans Brain
Background: Complex networks can often be decomposed into less complex sub-networks whose structures can give hints about the functional
organization of the network as a whole. However, these structural
motifs can only tell one part of the functional story because in this
analysis each node and edge is treated on an equal footing. In real
networks, two motifs that are topologically identical but whose nodes
perform very different functions will play very different roles in the
network.
Methodology/Principal Findings: Here, we combine structural information
derived from the topology of the neuronal network of the nematode C.
elegans with information about the biological function of these nodes,
thus coloring nodes by function. We discover that particular
colorations of motifs are significantly more abundant in the worm brain
than expected by chance, and have particular computational functions
that emphasize the feed-forward structure of information processing in
the network, while evading feedback loops. Interneurons are strongly
over-represented among the common motifs, supporting the notion that
these motifs process and transduce the information from the sensor
neurons towards the muscles. Some of the most common motifs identified
in the search for significant colored motifs play a crucial role in the
system of neurons controlling the worm's locomotion.
Conclusions/Significance: The analysis of complex networks in terms of
colored motifs combines two independent data sets to generate insight
about these networks that cannot be obtained with either data set
alone. The method is general and should allow a decomposition of any
complex networks into its functional (rather than topological) motifs
as long as both wiring and functional information is available
Generalized Painleve-Gullstrand descriptions of Kerr-Newman black holes
Generalized Painleve-Gullstrand metrics are explicitly constructed for the
Kerr-Newman family of charged rotating black holes. These descriptions are free
of all coordinate singularities; moreover, unlike the Doran and other proposed
metrics, an extra tunable function is introduced to ensure all variables in the
metrics remain real for all values of the mass M, charge Q, angular momentum
aM, and cosmological constant \Lambda > - 3/(a^2). To describe fermions in
Kerr-Newman spacetimes, the stronger requirement of non-singular vierbein
one-forms at the horizon(s) is imposed and coordinate singularities are
eliminated by local Lorentz boosts. Other known vierbein fields of Kerr-Newman
black holes are analysed and discussed; and it is revealed that some of these
descriptions are actually not related by physical Lorentz transformations to
the original Kerr-Newman expression in Boyer-Lindquist coordinates - which is
the reason complex components appear (for certain ranges of the radial
coordinate) in these metrics. As an application of our constructions the
correct effective Hawking temperature for Kerr black holes is derived with the
method of Parikh and Wilczek.Comment: 5 pages; extended to include application to derivation of Hawking
radiation for Kerr black holes with Parikh-Wilczek metho
Mesoscopic organization reveals the constraints governing C. elegans nervous system
One of the biggest challenges in biology is to understand how activity at the
cellular level of neurons, as a result of their mutual interactions, leads to
the observed behavior of an organism responding to a variety of environmental
stimuli. Investigating the intermediate or mesoscopic level of organization in
the nervous system is a vital step towards understanding how the integration of
micro-level dynamics results in macro-level functioning. In this paper, we have
considered the somatic nervous system of the nematode Caenorhabditis elegans,
for which the entire neuronal connectivity diagram is known. We focus on the
organization of the system into modules, i.e., neuronal groups having
relatively higher connection density compared to that of the overall network.
We show that this mesoscopic feature cannot be explained exclusively in terms
of considerations, such as optimizing for resource constraints (viz., total
wiring cost) and communication efficiency (i.e., network path length).
Comparison with other complex networks designed for efficient transport (of
signals or resources) implies that neuronal networks form a distinct class.
This suggests that the principal function of the network, viz., processing of
sensory information resulting in appropriate motor response, may be playing a
vital role in determining the connection topology. Using modular spectral
analysis, we make explicit the intimate relation between function and structure
in the nervous system. This is further brought out by identifying functionally
critical neurons purely on the basis of patterns of intra- and inter-modular
connections. Our study reveals how the design of the nervous system reflects
several constraints, including its key functional role as a processor of
information.Comment: Published version, Minor modifications, 16 pages, 9 figure
Modeling Bacterial DNA: Simulation of Self-avoiding Supercoiled Worm-Like Chains Including Structural Transitions of the Helix
Under supercoiling constraints, naked DNA, such as a large part of bacterial
DNA, folds into braided structures called plectonemes. The double-helix can
also undergo local structural transitions, leading to the formation of
denaturation bubbles and other alternative structures. Various polymer models
have been developed to capture these properties, with Monte-Carlo (MC)
approaches dedicated to the inference of thermodynamic properties. In this
chapter, we explain how to perform such Monte-Carlo simulations, following two
objectives. On one hand, we present the self-avoiding supercoiled Worm-Like
Chain (ssWLC) model, which is known to capture the folding properties of
supercoiled DNA, and provide a detailed explanation of a standard MC simulation
method. On the other hand, we explain how to extend this ssWLC model to include
structural transitions of the helix.Comment: Book chapter to appear in The Bacterial Nucleoid, Methods and
Protocols, Springer serie
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