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

The developmental dynamics of terrorist organizations

We identify robust statistical patterns in the frequency and severity of violent attacks by terrorist organizations as they grow and age. Using group-level static and dynamic analyses of terrorist events worldwide from 1968-2008 and a simulation model of organizational dynamics, we show that the production of violent events tends to accelerate with increasing size and experience. This coupling of frequency, experience and size arises from a fundamental positive feedback loop in which attacks lead to growth which leads to increased production of new attacks. In contrast, event severity is independent of both size and experience. Thus larger, more experienced organizations are more deadly because they attack more frequently, not because their attacks are more deadly, and large events are equally likely to come from large and small organizations. These results hold across political ideologies and time, suggesting that the frequency and severity of terrorism may be constrained by fundamental processes.Comment: 28 pages, 8 figures, 4 tables, supplementary materia

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

Emergence of scale-free close-knit friendship structure in online social networks

Despite the structural properties of online social networks have attracted much attention, the properties of the close-knit friendship structures remain an important question. Here, we mainly focus on how these mesoscale structures are affected by the local and global structural properties. Analyzing the data of four large-scale online social networks reveals several common structural properties. It is found that not only the local structures given by the indegree, outdegree, and reciprocal degree distributions follow a similar scaling behavior, the mesoscale structures represented by the distributions of close-knit friendship structures also exhibit a similar scaling law. The degree correlation is very weak over a wide range of the degrees. We propose a simple directed network model that captures the observed properties. The model incorporates two mechanisms: reciprocation and preferential attachment. Through rate equation analysis of our model, the local-scale and mesoscale structural properties are derived. In the local-scale, the same scaling behavior of indegree and outdegree distributions stems from indegree and outdegree of nodes both growing as the same function of the introduction time, and the reciprocal degree distribution also shows the same power-law due to the linear relationship between the reciprocal degree and in/outdegree of nodes. In the mesoscale, the distributions of four closed triples representing close-knit friendship structures are found to exhibit identical power-laws, a behavior attributed to the negligible degree correlations. Intriguingly, all the power-law exponents of the distributions in the local-scale and mesoscale depend only on one global parameter -- the mean in/outdegree, while both the mean in/outdegree and the reciprocity together determine the ratio of the reciprocal degree of a node to its in/outdegree.Comment: 48 pages, 34 figure

A Rapid Assessment of the Quality of Neonatal Healthcare in Kilimanjaro Region, Northeast Tanzania.

While child mortality is declining in Africa there has been no evidence of a comparable reduction in neonatal mortality. The quality of inpatient neonatal care is likely a contributing factor but data from resource limited settings are few. The objective of this study was to assess the quality of neonatal care in the district hospitals of the Kilimanjaro region of Tanzania. Clinical records were reviewed for ill or premature neonates admitted to 13 inpatient health facilities in the Kilimanjaro region; staffing and equipment levels were also assessed. Among the 82 neonates reviewed, key health information was missing from a substantial proportion of records: on maternal antenatal cards, blood group was recorded for 52 (63.4%) mothers, Rhesus (Rh) factor for 39 (47.6%), VDRL for 59 (71.9%) and HIV status for 77 (93.1%). From neonatal clinical records, heart rate was recorded for3 (3.7%) neonates, respiratory rate in 14, (17.1%) and temperature in 33 (40.2%). None of 13 facilities had a functioning premature unit despite calculated gestational age <36 weeks in 45.6% of evaluated neonates. Intravenous fluids and oxygen were available in 9 out of 13 of facilities, while antibiotics and essential basic equipment were available in more than two thirds. Medication dosing errors were common; under-dosage for ampicillin, gentamicin and cloxacillin was found in 44.0%, 37.9% and 50% of cases, respectively, while over-dosage was found in 20.0%, 24.2% and 19.9%, respectively. Physician or assistant physician staffing levels by the WHO indicator levels (WISN) were generally low. Key aspects of neonatal care were found to be poorly documented or incorrectly implemented in this appraisal of neonatal care in Kilimanjaro. Efforts towards quality assurance and enhanced motivation of staff may improve outcomes for this vulnerable group

Barriers in phase I cancer clinical trials referrals and enrollment: five-year experience at the Princess Margaret Hospital

BACKGROUND: There is a paucity of literature on the referral outcome of patients seen in phase I trial clinics in academic oncology centres. This study aims to provide information on the accrual rate and to identify obstacles in the recruitment process. METHODS: A retrospective chart review was performed for all new patients referred and seen in the phase I clinic at the Princess Margaret Hospital between January 2000 and June 2005. Data on their demographics, medical history, and details of trial participation or non-entry were recorded. RESULTS: A total of 667 new phase I referrals were seen during the stated period. Of these patients, 197 (29.5%) patients were enrolled into a phase I trial, and 64.5% of them started trial within 1 month of the initial visit. About a quarter (165 of 667) of the patients referred were deemed ineligible at their first visit, with the most frequent reasons for ineligibility being poor performance status, unacceptable bloodwork, too many prior treatments and rapid disease progression. The remaining 305 patients (45.7%) were potentially eligible at their initial visit, but never entered a phase I trial. The main reasons for their non-entry were patient refusal, other treatment recommended first, and lack of available trials or trial spots. CONCLUSION: This study provides information on the clinical realities underlying a referral to a phase I clinic and eventual trial enrollment. Better selection of patients, appropriate education of referring physicians, and opening phase I trials with fewer restrictions on some criteria such as prior therapy may enhance their recruitment rates

General preparation for Pt-based alloy nanoporous nanoparticles as potential nanocatalysts

Although Raney nickel made by dealloying has been used as a heterogeneous catalyst in a variety of organic syntheses for more than 80 years, only recently scientists have begun to realize that dealloying can generate nanoporous alloys with extraordinary structural characteristics. Herein, we achieved successful synthesis of a variety of monodisperse alloy nanoporous nanoparticles via a facile chemical dealloying process using nanocrystalline alloys as precursors. The as-prepared alloy nanoporous nanoparticles with large surface area and small pores show superior catalytic properties compared with alloyed nanoparticles. It is believed that these novel alloy nanoporous nanoparticles would open up new opportunities for catalytic applications

Efficient and accurate greedy search methods for mining functional modules in protein interaction networks

<p>Abstract</p> <p>Background</p> <p>Most computational algorithms mainly focus on detecting highly connected subgraphs in PPI networks as protein complexes but ignore their inherent organization. Furthermore, many of these algorithms are computationally expensive. However, recent analysis indicates that experimentally detected protein complexes generally contain Core/attachment structures.</p> <p>Methods</p> <p>In this paper, a Greedy Search Method based on Core-Attachment structure (GSM-CA) is proposed. The GSM-CA method detects densely connected regions in large protein-protein interaction networks based on the edge weight and two criteria for determining core nodes and attachment nodes. The GSM-CA method improves the prediction accuracy compared to other similar module detection approaches, however it is computationally expensive. Many module detection approaches are based on the traditional hierarchical methods, which is also computationally inefficient because the hierarchical tree structure produced by these approaches cannot provide adequate information to identify whether a network belongs to a module structure or not. In order to speed up the computational process, the Greedy Search Method based on Fast Clustering (GSM-FC) is proposed in this work. The edge weight based GSM-FC method uses a greedy procedure to traverse all edges just once to separate the network into the suitable set of modules.</p> <p>Results</p> <p>The proposed methods are applied to the protein interaction network of S. cerevisiae. Experimental results indicate that many significant functional modules are detected, most of which match the known complexes. Results also demonstrate that the GSM-FC algorithm is faster and more accurate as compared to other competing algorithms.</p> <p>Conclusions</p> <p>Based on the new edge weight definition, the proposed algorithm takes advantages of the greedy search procedure to separate the network into the suitable set of modules. Experimental analysis shows that the identified modules are statistically significant. The algorithm can reduce the computational time significantly while keeping high prediction accuracy.</p