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Whole-proteome tree of life suggests a deep burst of organism diversity.
An organism tree of life (organism ToL) is a conceptual and metaphorical tree to capture a simplified narrative of the evolutionary course and kinship among the extant organisms. Such a tree cannot be experimentally validated but may be reconstructed based on characteristics associated with the organisms. Since the whole-genome sequence of an organism is, at present, the most comprehensive descriptor of the organism, a whole-genome sequence-based ToL can be an empirically derivable surrogate for the organism ToL. However, experimentally determining the whole-genome sequences of many diverse organisms was practically impossible until recently. We have constructed three types of ToLs for diversely sampled organisms using the sequences of whole genome, of whole transcriptome, and of whole proteome. Of the three, whole-proteome sequence-based ToL (whole-proteome ToL), constructed by applying information theory-based feature frequency profile method, an "alignment-free" method, gave the most topologically stable ToL. Here, we describe the main features of a whole-proteome ToL for 4,023 species with known complete or almost complete genome sequences on grouping and kinship among the groups at deep evolutionary levels. The ToL reveals 1) all extant organisms of this study can be grouped into 2 "Supergroups," 6 "Major Groups," or 35+ "Groups"; 2) the order of emergence of the "founders" of all of the groups may be assigned on an evolutionary progression scale; 3) all of the founders of the groups have emerged in a "deep burst" at the very beginning period near the root of the ToL-an explosive birth of life's diversity
Flavour Physics in the Soft Wall Model
We extend the description of flavour that exists in the Randall-Sundrum (RS)
model to the soft wall (SW) model in which the IR brane is removed and the
Higgs is free to propagate in the bulk. It is demonstrated that, like the RS
model, one can generate the hierarchy of fermion masses by localising the
fermions at different locations throughout the space. However, there are two
significant differences. Firstly the possible fermion masses scale down, from
the electroweak scale, less steeply than in the RS model and secondly there now
exists a minimum fermion mass for fermions sitting towards the UV brane. With a
quadratic Higgs VEV, this minimum mass is about fifteen orders of magnitude
lower than the electroweak scale. We derive the gauge propagator and despite
the KK masses scaling as , it is demonstrated that the
coefficients of four fermion operators are not divergent at tree level. FCNC's
amongst kaons and leptons are considered and compared to calculations in the RS
model, with a brane localised Higgs and equivalent levels of tuning. It is
found that since the gauge fermion couplings are slightly more universal and
the SM fermions typically sit slightly further towards the UV brane, the
contributions to observables such as and , from the
exchange of KK gauge fields, are significantly reduced.Comment: 33 pages, 15 figures, 5 tables; v2: references added; v3:
modifications to figures 4,5 and 6. version to appear in JHE
Shortest paths and load scaling in scale-free trees
The average node-to-node distance of scale-free graphs depends
logarithmically on N, the number of nodes, while the probability distribution
function (pdf) of the distances may take various forms. Here we analyze these
by considering mean-field arguments and by mapping the m=1 case of the
Barabasi-Albert model into a tree with a depth-dependent branching ratio. This
shows the origins of the average distance scaling and allows a demonstration of
why the distribution approaches a Gaussian in the limit of N large. The load
(betweenness), the number of shortest distance paths passing through any node,
is discussed in the tree presentation.Comment: 8 pages, 8 figures; v2: load calculations extende
Hierarchy measure for complex networks
Nature, technology and society are full of complexity arising from the
intricate web of the interactions among the units of the related systems (e.g.,
proteins, computers, people). Consequently, one of the most successful recent
approaches to capturing the fundamental features of the structure and dynamics
of complex systems has been the investigation of the networks associated with
the above units (nodes) together with their relations (edges). Most complex
systems have an inherently hierarchical organization and, correspondingly, the
networks behind them also exhibit hierarchical features. Indeed, several papers
have been devoted to describing this essential aspect of networks, however,
without resulting in a widely accepted, converging concept concerning the
quantitative characterization of the level of their hierarchy. Here we develop
an approach and propose a quantity (measure) which is simple enough to be
widely applicable, reveals a number of universal features of the organization
of real-world networks and, as we demonstrate, is capable of capturing the
essential features of the structure and the degree of hierarchy in a complex
network. The measure we introduce is based on a generalization of the m-reach
centrality, which we first extend to directed/partially directed graphs. Then,
we define the global reaching centrality (GRC), which is the difference between
the maximum and the average value of the generalized reach centralities over
the network. We investigate the behavior of the GRC considering both a
synthetic model with an adjustable level of hierarchy and real networks.
Results for real networks show that our hierarchy measure is related to the
controllability of the given system. We also propose a visualization procedure
for large complex networks that can be used to obtain an overall qualitative
picture about the nature of their hierarchical structure.Comment: 29 pages, 9 figures, 4 table
Identification of criticality in neuronal avalanches: II. A theoretical and empirical investigation of the Driven case
The observation of apparent power laws in neuronal systems has led to the suggestion that the brain is at, or close to, a critical state and may be a self-organised critical system. Within the framework of self-organised criticality a separation of timescales is thought to be crucial for the observation of power-law dynamics and computational models are often constructed with this property. However, this is not necessarily a characteristic of physiological neural networks—external input does not only occur when the network is at rest/a steady state. In this paper we study a simple neuronal network model driven by a continuous external input (i.e. the model does not have an explicit separation of timescales from seeding the system only when in the quiescent state) and analytically tuned to operate in the region of a critical state (it reaches the critical regime exactly in the absence of input—the case studied in the companion paper to this article). The system displays avalanche dynamics in the form of cascades of neuronal firing separated by periods of silence. We observe partial scale-free behaviour in the distribution of avalanche size for low levels of external input. We analytically derive the distributions of waiting times and investigate their temporal behaviour in relation to different levels of external input, showing that the system’s dynamics can exhibit partial long-range temporal correlations. We further show that as the system approaches the critical state by two alternative ‘routes’, different markers of criticality (partial scale-free behaviour and long-range temporal correlations) are displayed. This suggests that signatures of criticality exhibited by a particular system in close proximity to a critical state are dependent on the region in parameter space at which the system (currently) resides
Traveling and pinned fronts in bistable reaction-diffusion systems on network
Traveling fronts and stationary localized patterns in bistable
reaction-diffusion systems have been broadly studied for classical continuous
media and regular lattices. Analogs of such non-equilibrium patterns are also
possible in networks. Here, we consider traveling and stationary patterns in
bistable one-component systems on random Erd\"os-R\'enyi, scale-free and
hierarchical tree networks. As revealed through numerical simulations,
traveling fronts exist in network-organized systems. They represent waves of
transition from one stable state into another, spreading over the entire
network. The fronts can furthermore be pinned, thus forming stationary
structures. While pinning of fronts has previously been considered for chains
of diffusively coupled bistable elements, the network architecture brings about
significant differences. An important role is played by the degree (the number
of connections) of a node. For regular trees with a fixed branching factor, the
pinning conditions are analytically determined. For large Erd\"os-R\'enyi and
scale-free networks, the mean-field theory for stationary patterns is
constructed
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