204 research outputs found
Self-organized Emergence of Navigability on Small-World Networks
This paper mainly investigates why small-world networks are navigable and how
to navigate small-world networks. We find that the navigability can naturally
emerge from self-organization in the absence of prior knowledge about
underlying reference frames of networks. Through a process of information
exchange and accumulation on networks, a hidden metric space for navigation on
networks is constructed. Navigation based on distances between vertices in the
hidden metric space can efficiently deliver messages on small-world networks,
in which long range connections play an important role. Numerical simulations
further suggest that high cluster coefficient and low diameter are both
necessary for navigability. These interesting results provide profound insights
into scalable routing on the Internet due to its distributed and localized
requirements.Comment: 3 figure
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
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
Flour nutritional profile, and soxhlet-extracted oil physicochemical breakdown-storage performance of white melon (Cucumeropsis mannii Naudin) seed varieties from Southeast Nigeria
White melon (Cucumeropsis mannii Naudin), is among common and yet underutilized oil seed crop within the West African region, does not have sufficient information specific to its nutrient composition for foreign consumers. To supplement existing information, therefore, we investigated the nutritional profile of defatted and full-fat flour, alongside physicochemical breakdown and storage performance of soxhlet-extracted oil from two white melon (C. mannii) seed varieties found in Southeast Nigeria. Nutritional profile involved the determinations of proximate composition, minerals, vitamins, functional properties as well as amino acid profile. Physicochemical breakdown involved the determinations of fatty acid profile, lipid breakdown parameters, as well as associated physical attributes. Results showed defatting of flours increased the protein (69.04%), carbohydrates (16.26%), crude fiber (2.68%), ash (11.9%), mineral (Na ranging from 223.92-246.99 mg/100g), and vitamin contents (Vit B1 ranging from 0.453-0.712 mg/100g). Total amino acid differed slightly when comparing miniature (30.36 g/100g) and large (22.36 g/100g) seeds. Soxhlet-extracted oil possessed low thiobarbituric acid, acid, and peroxide values (0.030 and 0.038 mg MDA/kg, 1.08 and 1.27 mg KOH/g, and 2.95 and 3,94 mEqO2/kg, for large and miniature seeds respectively), and peak linoleic acid (5 and 6.45 mg/ml, for miniature and large seeds respectively). During storage, the thiobarbituric acid and peroxide values of soxhlet-extracted oil increased yet within acceptable limits. © 2023 Nwoke et al
Local Difference Measures between Complex Networks for Dynamical System Model Evaluation
Acknowledgments We thank Reik V. Donner for inspiring suggestions that initialized the work presented herein. Jan H. Feldhoff is credited for providing us with the STARS simulation data and for his contributions to fruitful discussions. Comments by the anonymous reviewers are gratefully acknowledged as they led to substantial improvements of the manuscript.Peer reviewedPublisher PD
Topological Cluster Analysis Reveals the Systemic Organization of the Caenorhabditis elegans Connectome
The modular organization of networks of individual neurons interwoven through synapses has not been fully explored due to the incredible complexity of the connectivity architecture. Here we use the modularity-based community detection method for directed, weighted networks to examine hierarchically organized modules in the complete wiring diagram (connectome) of Caenorhabditis elegans (C. elegans) and to investigate their topological properties. Incorporating bilateral symmetry of the network as an important cue for proper cluster assignment, we identified anatomical clusters in the C. elegans connectome, including a body-spanning cluster, which correspond to experimentally identified functional circuits. Moreover, the hierarchical organization of the five clusters explains the systemic cooperation (e.g., mechanosensation, chemosensation, and navigation) that occurs among the structurally segregated biological circuits to produce higher-order complex behaviors
Modulated Martensite: Why it forms and why it deforms easily
Diffusionless phase transitions are at the core of the multifunctionality of
(magnetic) shape memory alloys, ferroelectrics and multiferroics. Giant strain
effects under external fields are obtained in low symmetric modulated
martensitic phases. We outline the origin of modulated phases, their connection
with tetragonal martensite and consequences for their functional properties by
analysing the martensitic microstructure of epitaxial Ni-Mn-Ga films from the
atomic to macroscale. Geometrical constraints at an austenite-martensite phase
boundary act down to the atomic scale. Hence a martensitic microstructure of
nanotwinned tetragonal martensite can form. Coarsening of twin variants can
reduce twin boundary energy, a process we could follow from the atomic to the
millimetre scale. Coarsening is a fractal process, proceeding in discrete steps
by doubling twin periodicity. The collective defect energy results in a
substantial hysteresis, which allows retaining modulated martensite as a
metastable phase at room temperature. In this metastable state elastic energy
is released by the formation of a 'twins within twins' microstructure which can
be observed from the nanometre to millimetre scale. This hierarchical twinning
results in mesoscopic twin boundaries which are diffuse, in contrast to the
common atomically sharp twin boundaries of tetragonal martensite. We suggest
that observed extraordinarily high mobility of such mesoscopic twin boundaries
originates from their diffuse nature which renders pinning by atomistic point
defects ineffective.Comment: 34 pages, 8 figure
Symbiotic relationship between brain structure and dynamics
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens
Network Structure Implied by Initial Axon Outgrowth in Rodent Cortex: Empirical Measurement and Models
The developmental mechanisms by which the network organization of the adult cortex is established are incompletely understood. Here we report on empirical data on the development of connections in hamster isocortex and use these data to parameterize a network model of early cortical connectivity. Using anterograde tracers at a series of postnatal ages, we investigate the growth of connections in the early cortical sheet and systematically map initial axon extension from sites in anterior (motor), middle (somatosensory) and posterior (visual) cortex. As a general rule, developing axons extend from all sites to cover relatively large portions of the cortical field that include multiple cortical areas. From all sites, outgrowth is anisotropic, covering a greater distance along the medial/lateral axis than along the anterior/posterior axis. These observations are summarized as 2-dimensional probability distributions of axon terminal sites over the cortical sheet. Our network model consists of nodes, representing parcels of cortex, embedded in 2-dimensional space. Network nodes are connected via directed edges, representing axons, drawn according to the empirically derived anisotropic probability distribution. The networks generated are described by a number of graph theoretic measurements including graph efficiency, node betweenness centrality and average shortest path length. To determine if connectional anisotropy helps reduce the total volume occupied by axons, we define and measure a simple metric for the extra volume required by axons crossing. We investigate the impact of different levels of anisotropy on network structure and volume. The empirically observed level of anisotropy suggests a good trade-off between volume reduction and maintenance of both network efficiency and robustness. Future work will test the model's predictions for connectivity in larger cortices to gain insight into how the regulation of axonal outgrowth may have evolved to achieve efficient and economical connectivity in larger brains
Driving and Driven Architectures of Directed Small-World Human Brain Functional Networks
Recently, increasing attention has been focused on the investigation of the human brain connectome that describes the patterns of structural and functional connectivity networks of the human brain. Many studies of the human connectome have demonstrated that the brain network follows a small-world topology with an intrinsically cohesive modular structure and includes several network hubs in the medial parietal regions. However, most of these studies have only focused on undirected connections between regions in which the directions of information flow are not taken into account. How the brain regions causally influence each other and how the directed network of human brain is topologically organized remain largely unknown. Here, we applied linear multivariate Granger causality analysis (GCA) and graph theoretical approaches to a resting-state functional MRI dataset with a large cohort of young healthy participants (n = 86) to explore connectivity patterns of the population-based whole-brain functional directed network. This directed brain network exhibited prominent small-world properties, which obviously improved previous results of functional MRI studies showing weak small-world properties in the directed brain networks in terms of a kernel-based GCA and individual analysis. This brain network also showed significant modular structures associated with 5 well known subsystems: fronto-parietal, visual, paralimbic/limbic, subcortical and primary systems. Importantly, we identified several driving hubs predominantly located in the components of the attentional network (e.g., the inferior frontal gyrus, supplementary motor area, insula and fusiform gyrus) and several driven hubs predominantly located in the components of the default mode network (e.g., the precuneus, posterior cingulate gyrus, medial prefrontal cortex and inferior parietal lobule). Further split-half analyses indicated that our results were highly reproducible between two independent subgroups. The current study demonstrated the directions of spontaneous information flow and causal influences in the directed brain networks, thus providing new insights into our understanding of human brain functional connectome
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