27,466 research outputs found
Dwelling Quietly in the Rich Club: Brain Network Determinants of Slow Cortical Fluctuations
For more than a century, cerebral cartography has been driven by
investigations of structural and morphological properties of the brain across
spatial scales and the temporal/functional phenomena that emerge from these
underlying features. The next era of brain mapping will be driven by studies
that consider both of these components of brain organization simultaneously --
elucidating their interactions and dependencies. Using this guiding principle,
we explored the origin of slowly fluctuating patterns of synchronization within
the topological core of brain regions known as the rich club, implicated in the
regulation of mood and introspection. We find that a constellation of densely
interconnected regions that constitute the rich club (including the anterior
insula, amygdala, and precuneus) play a central role in promoting a stable,
dynamical core of spontaneous activity in the primate cortex. The slow time
scales are well matched to the regulation of internal visceral states,
corresponding to the somatic correlates of mood and anxiety. In contrast, the
topology of the surrounding "feeder" cortical regions show unstable, rapidly
fluctuating dynamics likely crucial for fast perceptual processes. We discuss
these findings in relation to psychiatric disorders and the future of
connectomics.Comment: 35 pages, 6 figure
Apex Peptide Elution Chain Selection: A New Strategy for Selecting Precursors in 2D-LC-MALDI-TOF/TOF Experiments on Complex Biological Samples
LC-MALDI provides an often overlooked opportunity to exploit the separation between LC-MS and MS/MS stages of a 2D-LC-MS-based proteomics experiment, that is, by making a smarter selection for precursor fragmentation. Apex Peptide Elution Chain Selection (APECS) is a simple and powerful method for intensity-based peptide selection in a complex sample separated by 2D-LC, using a MALDI-TOF/TOF instrument. It removes the peptide redundancy present in the adjacent first-dimension (typically strong cation exchange, SCX) fractions by constructing peptide elution profiles that link the precursor ions of the same peptide across SCX fractions. Subsequently, the precursor ion most likely to fragment successfully in a given profile is selected for fragmentation analysis, selecting on precursor intensity and absence of adjacent ions that may cofragment. To make the method independent of experiment-specific tolerance criteria, we introduce the concept of the branching factor, which measures the likelihood of false clustering of precursor ions based on past experiments. By validation with a complex proteome sample of Arabidopsis thaliana, APECS identified an equivalent number of peptides as a conventional data-dependent acquisition method but with a 35% smaller work load. Consequently, reduced sample depletion allowed further selection of lower signal-to-noise ratio precursor ions, leading to a larger number of identified unique peptides.
Curvature of Co-Links Uncovers Hidden Thematic Layers in the World Wide Web
Beyond the information stored in pages of the World Wide Web, novel types of
``meta-information'' are created when they connect to each other. This
information is a collective effect of independent users writing and linking
pages, hidden from the casual user. Accessing it and understanding the
inter-relation of connectivity and content in the WWW is a challenging problem.
We demonstrate here how thematic relationships can be located precisely by
looking only at the graph of hyperlinks, gleaning content and context from the
Web without having to read what is in the pages. We begin by noting that
reciprocal links (co-links) between pages signal a mutual recognition of
authors, and then focus on triangles containing such links, since triangles
indicate a transitive relation. The importance of triangles is quantified by
the clustering coefficient (Watts) which we interpret as a curvature
(Gromov,Bridson-Haefliger). This defines a Web-landscape whose connected
regions of high curvature characterize a common topic. We show experimentally
that reciprocity and curvature, when combined, accurately capture this
meta-information for a wide variety of topics. As an example of future
directions we analyze the neural network of C. elegans (White, Wood), using the
same methods.Comment: 8 pages, 5 figures, expanded version of earlier submission with more
example
Discovering universal statistical laws of complex networks
Different network models have been suggested for the topology underlying
complex interactions in natural systems. These models are aimed at replicating
specific statistical features encountered in real-world networks. However, it
is rarely considered to which degree the results obtained for one particular
network class can be extrapolated to real-world networks. We address this issue
by comparing different classical and more recently developed network models
with respect to their generalisation power, which we identify with large
structural variability and absence of constraints imposed by the construction
scheme. After having identified the most variable networks, we address the
issue of which constraints are common to all network classes and are thus
suitable candidates for being generic statistical laws of complex networks. In
fact, we find that generic, not model-related dependencies between different
network characteristics do exist. This allows, for instance, to infer global
features from local ones using regression models trained on networks with high
generalisation power. Our results confirm and extend previous findings
regarding the synchronisation properties of neural networks. Our method seems
especially relevant for large networks, which are difficult to map completely,
like the neural networks in the brain. The structure of such large networks
cannot be fully sampled with the present technology. Our approach provides a
method to estimate global properties of under-sampled networks with good
approximation. Finally, we demonstrate on three different data sets (C.
elegans' neuronal network, R. prowazekii's metabolic network, and a network of
synonyms extracted from Roget's Thesaurus) that real-world networks have
statistical relations compatible with those obtained using regression models
Connectivity in Dense Networks Confined within Right Prisms
We consider the probability that a dense wireless network confined within a
given convex geometry is fully connected. We exploit a recently reported theory
to develop a systematic methodology for analytically characterizing the
connectivity probability when the network resides within a convex right prism,
a polyhedron that accurately models many geometries that can be found in
practice. To maximize practicality and applicability, we adopt a general
point-to-point link model based on outage probability, and present example
analytical and numerical results for a network employing
multiple-input multiple-output (MIMO) maximum ratio combining (MRC) link level
transmission confined within particular bounding geometries. Furthermore, we
provide suggestions for extending the approach detailed herein to more general
convex geometries.Comment: 8 pages, 6 figures. arXiv admin note: text overlap with
arXiv:1201.401
Situation awareness based automatic basestation detection and coverage reconfiguration in 3G systems
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