Network 'small-world-ness': a quantitative method for determining canonical network equivalence

Background: Many technological, biological, social, and information networks fall into the broad class of 'small-world' networks: they have tightly interconnected clusters of nodes, and a shortest mean path length that is similar to a matched random graph (same number of nodes and edges). This semi-quantitative definition leads to a categorical distinction ('small/not-small') rather than a quantitative, continuous grading of networks, and can lead to uncertainty about a network's small-world status. Moreover, systems described by small-world networks are often studied using an equivalent canonical network model-the Watts-Strogatz (WS) model. However, the process of establishing an equivalent WS model is imprecise and there is a pressing need to discover ways in which this equivalence may be quantified. Methodology/Principal Findings: We defined a precise measure of 'small-world-ness' S based on the trade off between high local clustering and short path length. A network is now deemed a 'small-world' if S. 1-an assertion which may be tested statistically. We then examined the behavior of S on a large data-set of real-world systems. We found that all these systems were linked by a linear relationship between their S values and the network size n. Moreover, we show a method for assigning a unique Watts-Strogatz (WS) model to any real-world network, and show analytically that the WS models associated with our sample of networks also show linearity between S and n. Linearity between S and n is not, however, inevitable, and neither is S maximal for an arbitrary network of given size. Linearity may, however, be explained by a common limiting growth process. Conclusions/Significance: We have shown how the notion of a small-world network may be quantified. Several key properties of the metric are described and the use of WS canonical models is placed on a more secure footing

Experimental signature of the attractive Coulomb force between positive and negative magnetic monopoles in spin ice

A non-Ohmic current that grows exponentially with the square root of applied electric field is well known from thermionic field emission (the Schottky effect)1, electrolytes (the second Wien effect)2 and semiconductors (the Poole–Frenkel effect)3. It is a universal signature of the attractive Coulomb force between positive and negative electrical charges, which is revealed as the charges are driven in opposite directions by the force of an applied electric field. Here we apply thermal quenches4 to spin ice5,6,7,8,9,10,11 to prepare metastable populations of bound pairs of positive and negative emergent magnetic monopoles12,13,14,15,16 at millikelvin temperatures. We find that the application of a magnetic field results in a universal exponential-root field growth of magnetic current, thus confirming the microscopic Coulomb force between the magnetic monopole quasiparticles and establishing a magnetic analogue of the Poole–Frenkel effect. At temperatures above 300 mK, gradual restoration of kinetic monopole equilibria causes the non-Ohmic current to smoothly evolve into the high-field Wien effect2 for magnetic monopoles, as confirmed by comparison to a recent and rigorous theory of the Wien effect in spin ice17,18. Our results extend the universality of the exponential-root field form into magnetism and illustrate the power of emergent particle kinetics to describe far-from-equilibrium response in complex systems

A Comparison of Different Approaches to Unravel the Latent Structure within Metabolic Syndrome

Background: Exploratory factor analysis is a commonly used statistical technique in metabolic syndrome research to uncover latent structure amongst metabolic variables. The application of factor analysis requires methodological decisions that reflect the hypothesis of the metabolic syndrome construct. These decisions often raise the complexity of the interpretation from the output. We propose two alternative techniques developed from cluster analysis which can achieve a clinically relevant structure, whilst maintaining intuitive advantages of clustering methodology. Methods: Two advanced techniques of clustering in the VARCLUS and matroid methods are discussed and implemented on a metabolic syndrome data set to analyze the structure of ten metabolic risk factors. The subjects were selected from the normative aging study based in Boston, Massachusetts. The sample included a total of 847 men aged between 21 and 81 years who provided complete data on selected risk factors during the period 1987 to 1991. Results: Four core components were identified by the clustering methods. These are labelled obesity, lipids, insulin resistance and blood pressure. The exploratory factor analysis with oblique rotation suggested an overlap of the loadings identified on the insulin resistance and obesity factors. The VARCLUS and matroid analyses separated these components and were able to demonstrate associations between individual risk factors. Conclusions: An oblique rotation can be selected to reflect the clinical concept of a single underlying syndrome, howeve

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

Dwelling in Strangeness: accounts of the Kingsley Hall Community, London (1965-1970)

This article explores archival accounts of the experimental community, Kingsley Hall (1965-70), established by R. D. Laing, the radical Scottish psychiatrist. The paper contributes to renewed interest in Kingsley Hall, R. D. Laing's radical psychiatry and UK counterculture. Archival sources enable not only the further exploration of already known figures but also let us hear previously unheard voices. Following a discussion of archival materials, the Hall is analyzed thematically and historically as (i) an inner spaceship; (ii) an embattled middle-class countercultural plantation; (iii) a site of spiritual renewal and development; (iv) a single-building arts colony; and (v) a countercultural experiment. Finally, it is argued that with re-evaluation of 1960s and 1970s counterculture now underway on the Left, the Hall’s experiment in Laingian countercultural psychiatry—as we may fittingly call it—may yet inform future radical projects (in mental health and beyond)

Factor structure of the Hospital Anxiety and Depression Scale in Japanese psychiatric outpatient and student populations

<p>Abstract</p> <p>Background</p> <p>The Hospital Anxiety and Depression Scale (HADS) is a common screening instrument excluding somatic symptoms of depression and anxiety, but previous studies have reported inconsistencies of its factor structure. The construct validity of the Japanese version of the HADS has yet to be reported. To examine the factor structure of the HADS in a Japanese population is needed.</p> <p>Methods</p> <p>Exploratory and confirmatory factor analyses were conducted in the combined data of 408 psychiatric outpatients and 1069 undergraduate students. The data pool was randomly split in half for a cross validation. An exploratory factor analysis was performed on one half of the data, and the fitness of the plausible model was examined in the other half of the data using a confirmatory factor analysis. Simultaneous multi-group analyses between the subgroups (outpatients vs. students, and men vs. women) were subsequently conducted.</p> <p>Results</p> <p>A two-factor model where items 6 and 7 had dual loadings was supported. These factors were interpreted as reflecting anxiety and depression. Item 10 showed low contributions to both of the factors. Simultaneous multi-group analyses indicated a factor pattern stability across the subgroups.</p> <p>Conclusion</p> <p>The Japanese version of HADS indicated good factorial validity in our samples. However, ambiguous wording of item 7 should be clarified in future revisions.</p

Neonatal cerebrovascular autoregulation.

Cerebrovascular pressure autoregulation is the physiologic mechanism that holds cerebral blood flow (CBF) relatively constant across changes in cerebral perfusion pressure (CPP). Cerebral vasoreactivity refers to the vasoconstriction and vasodilation that occur during fluctuations in arterial blood pressure (ABP) to maintain autoregulation. These are vital protective mechanisms of the brain. Impairments in pressure autoregulation increase the risk of brain injury and persistent neurologic disability. Autoregulation may be impaired during various neonatal disease states including prematurity, hypoxic-ischemic encephalopathy (HIE), intraventricular hemorrhage, congenital cardiac disease, and infants requiring extracorporeal membrane oxygenation (ECMO). Because infants are exquisitely sensitive to changes in cerebral blood flow (CBF), both hypoperfusion and hyperperfusion can cause significant neurologic injury. We will review neonatal pressure autoregulation and autoregulation monitoring techniques with a focus on brain protection. Current clinical therapies have failed to fully prevent permanent brain injuries in neonates. Adjuvant treatments that support and optimize autoregulation may improve neurologic outcomes