Why Do Cascade Sizes Follow a Power-Law?

We introduce random directed acyclic graph and use it to model the information diffusion network. Subsequently, we analyze the cascade generation model (CGM) introduced by Leskovec et al. [19]. Until now only empirical studies of this model were done. In this paper, we present the first theoretical proof that the sizes of cascades generated by the CGM follow the power-law distribution, which is consistent with multiple empirical analysis of the large social networks. We compared the assumptions of our model with the Twitter social network and tested the goodness of approximation.Comment: 8 pages, 7 figures, accepted to WWW 201

Partnerships: survey respondents' perceptions of inter-professional collaboration to address alcohol-related harms in England

Tackling alcohol-related harms crosses agency and professional boundaries, requiring collaboration between health, criminal justice, education and social welfare institutions. It is a key component of most multicomponent programmes in the United States, Australia and Europe. Partnership working, already embedded in service delivery structures, is a core mechanism for delivery of the new UK Government Alcohol Strategy. This article reports findings from a study of alcohol partnerships across England. The findings are based on a mix of open discussion interviews with key informants and on semi-structured telephone interviews with 90 professionals with roles in local alcohol partnerships. Interviewees reported the challenges of working within a complex network of interlinked partnerships, often within hierarchies under an umbrella partnership, some of them having a formal duty of partnership. The new alcohol strategy has emerged at a time of extensive reorganisation within health, social care and criminal justice structures. Further development of a partnership model for policy implementation would benefit from consideration of the incompatibility arising from required collaboration and from tensions between institutional and professional cultures. A clearer analysis of which aspects of partnership working provide ‘added value’ is needed

High Voltage Apparatus for Nuclear Physics

The design and performance of a transformer-rectifier voltage quadrupling installation for potentials up to 600 KV will be described

Analysis of a microscopic stochastic model of microtubule dynamic instability

A novel theoretical model of dynamic instability of a system of linear (1D) microtubules (MTs) in a bounded domain is introduced for studying the role of a cell edge in vivo and analyzing the effect of competition for a limited amount of tubulin. The model differs from earlier models in that the evolution of MTs is based on the rates of single unit (e.g., a heterodimer per protofilament) transformations, in contrast to postulating effective rates/frequencies of larger-scale changes, extracted, e.g., from the length history plots of MTs. Spontaneous GTP hydrolysis with finite rate after polymerization is assumed, and theoretical estimates of an effective catastrophe frequency as well as other parameters characterizing MT length distributions and cap size are derived. We implement a simple cap model which does not include vectorial hydrolysis. We demonstrate that our theoretical predictions, such as steady state concentration of free tubulin, and parameters of MT length distributions, are in agreement with the numerical simulations. The present model establishes a quantitative link between microscopic parameters governing the dynamics of MTs and macroscopic characteristics of MTs in a closed system. Lastly, we use a computational Monte Carlo model to provide an explanation for non-exponential MT length distributions observed in experiments. In particular, we show that appearance of such non-exponential distributions in the experiments can occur because the true steady state has not been reached, and/or due to the presence of a cell edge.Comment: 14 pages, 7 figure

A framework for different levels of integration of computational models into web-based virtual patients

BACKGROUND: Virtual patients are increasingly common tools used in health care education to foster learning of clinical reasoning skills. One potential way to expand their functionality is to augment virtual patients’ interactivity by enriching them with computational models of physiological and pathological processes. OBJECTIVE: The primary goal of this paper was to propose a conceptual framework for the integration of computational models within virtual patients, with particular focus on (1) characteristics to be addressed while preparing the integration, (2) the extent of the integration, (3) strategies to achieve integration, and (4) methods for evaluating the feasibility of integration. An additional goal was to pilot the first investigation of changing framework variables on altering perceptions of integration. METHODS: The framework was constructed using an iterative process informed by Soft System Methodology. The Virtual Physiological Human (VPH) initiative has been used as a source of new computational models. The technical challenges associated with development of virtual patients enhanced by computational models are discussed from the perspectives of a number of different stakeholders. Concrete design and evaluation steps are discussed in the context of an exemplar virtual patient employing the results of the VPH ARCH project, as well as improvements for future iterations. RESULTS: The proposed framework consists of four main elements. The first element is a list of feasibility features characterizing the integration process from three perspectives: the computational modelling researcher, the health care educationalist, and the virtual patient system developer. The second element included three integration levels: basic, where a single set of simulation outcomes is generated for specific nodes in the activity graph; intermediate, involving pre-generation of simulation datasets over a range of input parameters; advanced, including dynamic solution of the model. The third element is the description of four integration strategies, and the last element consisted of evaluation profiles specifying the relevant feasibility features and acceptance thresholds for specific purposes. The group of experts who evaluated the virtual patient exemplar found higher integration more interesting, but at the same time they were more concerned with the validity of the result. The observed differences were not statistically significant. CONCLUSIONS: This paper outlines a framework for the integration of computational models into virtual patients. The opportunities and challenges of model exploitation are discussed from a number of user perspectives, considering different levels of model integration. The long-term aim for future research is to isolate the most crucial factors in the framework and to determine their influence on the integration outcome

Educational outcomes in extremely preterm children : neuropsychological correlates and predictors of attainment

This study assessed the impact of extremely preterm birth on academic attainment at 11 years of age, investigated neuropsychological antecedents of attainment in reading and mathematics, and examined early predictors of educational outcomes. Children born extremely preterm had significantly poorer academic attainment and a higher prevalence of learning difficulties than their term peers. General cognitive ability and specific deficits in visuospatial skills or phoneme deletion at 6 years were predictive of mathematics and reading attainment at 11 years in both extremely preterm and term children. Phonological processing, attention, and executive functions at 6 years were also associated with academic attainment in children born extremely preterm. Furthermore, social factors, neonatal factors (necrotizing enterocolitis, breech delivery, abnormal cerebral ultrasound, early breast milk provision), and developmental factors at 30 months (head circumference, cognitive development), were independent predictors of educational outcomes at 11 years. Neonatal complications combined with assessments of early cognitive function provide moderate prediction for educational outcomes in children born extremely preterm

Emotional engagements predict and enhance social cognition in young chimpanzees

Social cognition in infancy is evident in coordinated triadic engagements, that is, infants attending jointly with social partners and objects. Current evolutionary theories of primate social cognition tend to highlight species differences in cognition based on human-unique cooperative motives. We consider a developmental model in which engagement experiences produce differential outcomes. We conducted a 10-year-long study in which two groups of laboratory-raised chimpanzee infants were given quantifiably different engagement experiences. Joint attention, cooperativeness, affect, and different levels of cognition were measured in 5- to 12-month-old chimpanzees, and compared to outcomes derived from a normative human database. We found that joint attention skills significantly improved across development for all infants, but by 12 months, the humans significantly surpassed the chimpanzees. We found that cooperativeness was stable in the humans, but by 12 months, the chimpanzee group given enriched engagement experiences significantly surpassed the humans. Past engagement experiences and concurrent affect were significant unique predictors of both joint attention and cooperativeness in 5- to 12-month-old chimpanzees. When engagement experiences and concurrent affect were statistically controlled, joint attention and cooperation were not associated. We explain differential social cognition outcomes in terms of the significant influences of previous engagement experiences and affect, in addition to cognition. Our study highlights developmental processes that underpin the emergence of social cognition in support of evolutionary continuity

Controlled packing and single-droplet resolution of 3D-printed functional synthetic tissues

3D-printing networks of droplets connected by interface bilayers are a powerful platform to build synthetic tissues in which functionality relies on precisely ordered structures. However, the structural precision and consistency in assembling these structures is currently limited, which restricts intricate designs and the complexity of functions performed by synthetic tissues. Here, we report that the equilibrium contact angle (θDIB) between a pair of droplets is a key parameter that dictates the tessellation and precise positioning of hundreds of picolitre-sized droplets within 3D-printed, multi-layer networks. When θDIB approximates the geometrically-derived critical angle (θc) of 35.3°, the resulting networks of droplets arrange in regular hexagonal close-packed (hcp) lattices with the least fraction of defects. With this improved control over droplet packing, we can 3D-print functional synthetic tissues with single-droplet-wide conductive pathways. Our new insights into 3D droplet packing permit the fabrication of complex synthetic tissues, where precisely positioned compartments perform coordinated tasks