1,728 research outputs found
In situ imaging of vortices in Bose-Einstein condensates
Laboratory observations of vortex dynamics in Bose-Einstein condensates
(BECs) are essential for determination of many aspects of superfluid dynamics
in these systems. We present a novel application of dark-field imaging that
enables \texttt{\it in situ} detection of two-dimensional vortex distributions
in single-component BECs, a step towards real-time measurements of complex
two-dimensional vortex dynamics within a single BEC. By rotating a Rb
BEC in a magnetic trap, we generate a triangular lattice of vortex cores in the
BEC, with core diameters on the order of 400 nm and cores separated by
approximately 9 m. We have experimentally confirmed that the positions of
the vortex cores can be determined without the need for ballistic expansion of
the BEC.Comment: 5 pages, 4 figure
Identifying communities by influence dynamics in social networks
Communities are not static; they evolve, split and merge, appear and
disappear, i.e. they are product of dynamical processes that govern the
evolution of the network. A good algorithm for community detection should not
only quantify the topology of the network, but incorporate the dynamical
processes that take place on the network. We present a novel algorithm for
community detection that combines network structure with processes that support
creation and/or evolution of communities. The algorithm does not embrace the
universal approach but instead tries to focus on social networks and model
dynamic social interactions that occur on those networks. It identifies
leaders, and communities that form around those leaders. It naturally supports
overlapping communities by associating each node with a membership vector that
describes node's involvement in each community. This way, in addition to
overlapping communities, we can identify nodes that are good followers to their
leader, and also nodes with no clear community involvement that serve as a
proxy between several communities and are equally as important. We run the
algorithm for several real social networks which we believe represent a good
fraction of the wide body of social networks and discuss the results including
other possible applications.Comment: 10 pages, 6 figure
The entropy of randomized network ensembles
Randomized network ensembles are the null models of real networks and are
extensivelly used to compare a real system to a null hypothesis. In this paper
we study network ensembles with the same degree distribution, the same
degree-correlations or the same community structure of any given real network.
We characterize these randomized network ensembles by their entropy, i.e. the
normalized logarithm of the total number of networks which are part of these
ensembles.
We estimate the entropy of randomized ensembles starting from a large set of
real directed and undirected networks. We propose entropy as an indicator to
assess the role of each structural feature in a given real network.We observe
that the ensembles with fixed scale-free degree distribution have smaller
entropy than the ensembles with homogeneous degree distribution indicating a
higher level of order in scale-free networks.Comment: (6 pages,1 figure,2 tables
Stochastic blockmodels and community structure in networks
Stochastic blockmodels have been proposed as a tool for detecting community
structure in networks as well as for generating synthetic networks for use as
benchmarks. Most blockmodels, however, ignore variation in vertex degree,
making them unsuitable for applications to real-world networks, which typically
display broad degree distributions that can significantly distort the results.
Here we demonstrate how the generalization of blockmodels to incorporate this
missing element leads to an improved objective function for community detection
in complex networks. We also propose a heuristic algorithm for community
detection using this objective function or its non-degree-corrected counterpart
and show that the degree-corrected version dramatically outperforms the
uncorrected one in both real-world and synthetic networks.Comment: 11 pages, 3 figure
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Photoactivatable genetically encoded calcium indicators for targeted neuronal imaging.
Circuit mapping requires knowledge of both structural and functional connectivity between cells. Although optical tools have been made to assess either the morphology and projections of neurons or their activity and functional connections, few probes integrate this information. We have generated a family of photoactivatable genetically encoded Ca(2+) indicators that combines attributes of high-contrast photolabeling with high-sensitivity Ca(2+) detection in a single-color protein sensor. We demonstrated in cultured neurons and in fruit fly and zebrafish larvae how single cells could be selected out of dense populations for visualization of morphology and high signal-to-noise measurements of activity, synaptic transmission and connectivity. Our design strategy is transferrable to other sensors based on circularly permutated GFP (cpGFP)
Finding and evaluating community structure in networks
We propose and study a set of algorithms for discovering community structure
in networks -- natural divisions of network nodes into densely connected
subgroups. Our algorithms all share two definitive features: first, they
involve iterative removal of edges from the network to split it into
communities, the edges removed being identified using one of a number of
possible "betweenness" measures, and second, these measures are, crucially,
recalculated after each removal. We also propose a measure for the strength of
the community structure found by our algorithms, which gives us an objective
metric for choosing the number of communities into which a network should be
divided. We demonstrate that our algorithms are highly effective at discovering
community structure in both computer-generated and real-world network data, and
show how they can be used to shed light on the sometimes dauntingly complex
structure of networked systems.Comment: 16 pages, 13 figure
Mapping spatial dimensions of Wilderness recreation outcomes: a study of overnight users
Grand Teton National Park (GRTE) is a popular mountain recreation destination which, like many National Park Service (NPS) units, has experienced a significant increase in visitation in recent years, with total visits increasing by 27% between 2014 and 2017 (NPS 2020). Particularly popular within GRTE is the String and Leigh Lakes (SLL) area, which is a favoured alpine destination for numerous day-use recreation activities and also an important starting point for backcountry and overnight recreational users within GRTE’s Recommended Wilderness. To better understand the visitor experience of overnight backcountry recreationists in the SLL area, data were collected using novel public participatory geographic information systems (PPGIS) during the summer of 2018. PPGIS data were used to identify the locations in which overnight recreationists experienced positive and negative recreation outcomes. Results indicate that they experience more positive outcomes within the Recommended Wilderness, away from high-density, trailhead-proximate areas outside the Recommended Wilderness. Findings also indicate that overnight users experience crowding and conflict more outside of the Recommended Wilderness than elsewhere on their backcountry trip. While this may seem intuitive, these are some of the first empirical results spatially contextualizing backcountry visitor outcomes in a popular national park. The findings thus provide managers with a visitor experience baseline that can be monitored and adaptively managed in the future
Anthrax Lethal Factor Cleavage of Nlrp1 Is Required for Activation of the Inflammasome
NOD-like receptor (NLR) proteins (Nlrps) are cytosolic sensors responsible for detection of pathogen and danger-associated molecular patterns through unknown mechanisms. Their activation in response to a wide range of intracellular danger signals leads to formation of the inflammasome, caspase-1 activation, rapid programmed cell death (pyroptosis) and maturation of IL-1β and IL-18. Anthrax lethal toxin (LT) induces the caspase-1-dependent pyroptosis of mouse and rat macrophages isolated from certain inbred rodent strains through activation of the NOD-like receptor (NLR) Nlrp1 inflammasome. Here we show that LT cleaves rat Nlrp1 and this cleavage is required for toxin-induced inflammasome activation, IL-1 β release, and macrophage pyroptosis. These results identify both a previously unrecognized mechanism of activation of an NLR and a new, physiologically relevant protein substrate of LT
Deciphering Network Community Structure by Surprise
The analysis of complex networks permeates all sciences, from biology to
sociology. A fundamental, unsolved problem is how to characterize the community
structure of a network. Here, using both standard and novel benchmarks, we show
that maximization of a simple global parameter, which we call Surprise (S),
leads to a very efficient characterization of the community structure of
complex synthetic networks. Particularly, S qualitatively outperforms the most
commonly used criterion to define communities, Newman and Girvan's modularity
(Q). Applying S maximization to real networks often provides natural,
well-supported partitions, but also sometimes counterintuitive solutions that
expose the limitations of our previous knowledge. These results indicate that
it is possible to define an effective global criterion for community structure
and open new routes for the understanding of complex networks.Comment: 7 pages, 5 figure
An efficient and principled method for detecting communities in networks
A fundamental problem in the analysis of network data is the detection of
network communities, groups of densely interconnected nodes, which may be
overlapping or disjoint. Here we describe a method for finding overlapping
communities based on a principled statistical approach using generative network
models. We show how the method can be implemented using a fast, closed-form
expectation-maximization algorithm that allows us to analyze networks of
millions of nodes in reasonable running times. We test the method both on
real-world networks and on synthetic benchmarks and find that it gives results
competitive with previous methods. We also show that the same approach can be
used to extract nonoverlapping community divisions via a relaxation method, and
demonstrate that the algorithm is competitively fast and accurate for the
nonoverlapping problem.Comment: 14 pages, 5 figures, 1 tabl
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