Timing interactions in social simulations: The voter model

The recent availability of huge high resolution datasets on human activities has revealed the heavy-tailed nature of the interevent time distributions. In social simulations of interacting agents the standard approach has been to use Poisson processes to update the state of the agents, which gives rise to very homogeneous activity patterns with a well defined characteristic interevent time. As a paradigmatic opinion model we investigate the voter model and review the standard update rules and propose two new update rules which are able to account for heterogeneous activity patterns. For the new update rules each node gets updated with a probability that depends on the time since the last event of the node, where an event can be an update attempt (exogenous update) or a change of state (endogenous update). We find that both update rules can give rise to power law interevent time distributions, although the endogenous one more robustly. Apart from that for the exogenous update rule and the standard update rules the voter model does not reach consensus in the infinite size limit, while for the endogenous update there exist a coarsening process that drives the system toward consensus configurations.Comment: Book Chapter, 23 pages, 9 figures, 5 table

Probabilistic Graphs for Sensor Data-driven Modelling of Power Systems at Scale

The growing complexity of the power grid, driven by increasing share of distributed energy resources and by massive deployment of intelligent internet-connected devices, requires new modelling tools for planning and operation. Physics-based state estimation models currently used for data filtering, prediction and anomaly detection are hard to maintain and adapt to the ever-changing complex dynamics of the power system. A data-driven approach based on probabilistic graphs is proposed, where custom non-linear, localised models of the joint density of subset of system variables can be combined to model arbitrarily large and complex systems. The graphical model allows to naturally embed domain knowledge in the form of variables dependency structure or local quantitative relationships. A specific instance where neural-network models are used to represent the local joint densities is proposed, although the methodology generalises to other model classes. Accuracy and scalability are evaluated on a large-scale data set representative of the European transmission grid

Factors affecting the intention of providers to deliver more effective continuing medical education to general practitioners: a pilot study

BACKGROUND: Despite the importance of continuing medical education (CME) for GPs, there has been little research into how providers decide what types of CME to deliver to GPs. This study aimed to identify factors affecting the intention of providers to provide more effective types of CME; and to design a survey instrument which can be used to test the applicability of Triandis' model of social behaviour to the provision of CME to general practitioners. METHODS: This was a cross-sectional study on a convenience sample of 11 Australian providers of CME for interviews and a random sample of 25 providers for the pilot test. Open-ended interviews structured on Triandis' theory were performed with key informants who provide CME to GPs. These were used to develop a pilot survey instrument to measure the factors affecting intention, resulting in a revised instrument for use in further research. RESULTS: There was a broad range of factors affecting providers' intention to deliver more effective forms of CME identified, and these were classifiable in a manner which was consistent with Triandis' model. Key factors affecting providers' intention were the attitude toward CME within organisations and the time and extra work involved. CONCLUSIONS: We identified a range of potential factors influencing the intention of providers to provide more effective forms of CME, in all categories of Triandis model. Those interested in increasing the choice of more effective CME activities available to GPs may need to broaden the methods used in working with providers to influence them to use more effective CME techniques. The interview material and questionnaire analysis of the pilot survey support the use of Triandis model. Further research is needed to validate Triandis'model for the intention to deliver more effective forms of CME. Such research will inform future strategies aimed at increasing the amount and choice of effective CME activities available for GPs

Professional Learning Through Everyday Work: How Finance Professionals Self-Regulate Their Learning

Professional learning is a critical component of ongoing improvement and innovation and the adoption of new practices in the workplace. Professional learning is often achieved through learning embedded in everyday work tasks. However, little is known about how professionals self-regulate their learning through regular work activities. This paper explores how professionals in the finance sector (n-30) self-regulate their learning through day-to-day work. Analysis focuses on three sub-processes of self-regulated learning that have been identified as significant predictors of good self-regulated learning at work. A key characteristic of good self-regulation is viewing learning as a form of long-term, personalised self-improvement. This study provides a foundation for future policy and planning in organisations aiming to encourage self-regulated learning

Detection of viral respiratory pathogens in mild and severe acute respiratory infections in Singapore.

To investigate the performance of laboratory methods and clinical case definitions in detecting the viral pathogens for acute respiratory infections (ARIs) from a prospective community cohort and hospital inpatients, nasopharyngeal swabs from cohort members reporting ARIs (community-ARI) and inpatients admitted with ARIs (inpatient-ARI) were tested by Singleplex Real Time-Polymerase Chain Reaction (SRT-PCR), multiplex RT-PCR (MRT-PCR) and pathogen-chip system (PathChip) between April 2012 and December 2013. Community-ARI and inpatient-ARI was also combined with mild and severe cases of influenza from a historical prospective study as mild-ARI and severe-ARI respectively to evaluate the performance of clinical case definitions. We analysed 130 community-ARI and 140 inpatient-ARI episodes (5 inpatient-ARI excluded because multiple pathogens were detected), involving 138 and 207 samples respectively. Detection by PCR declined with days post-onset for influenza virus; decrease was faster for community-ARI than for inpatient-ARI. No such patterns were observed for non-influenza respiratory virus infections. PathChip added substantially to viruses detected for community-ARI only. Clinical case definitions discriminated influenza from other mild-ARI but performed poorly for severe-ARI and for older participants. Rational strategies for diagnosis and surveillance of influenza and other respiratory virus must acknowledge the differences between ARIs presenting in community and hospital settings

Mutations in pericentrin cause Seckel syndrome with defective ATR-dependent DNA damage signaling

Large brain size is one of the defining characteristics of modern humans. Seckel syndrome (MIM 210600), a disorder of markedly reduced brain and body size, is associated with defective ATR-dependent DNA damage signaling. Only a single hypomorphic mutation of ATR has been identified in this genetically heterogeneous condition. We now report that mutations in the gene encoding pericentrin (PCNT)--resulting in the loss of pericentrin from the centrosome, where it has key functions anchoring both structural and regulatory proteins--also cause Seckel syndrome. Furthermore, we find that cells of individuals with Seckel syndrome due to mutations in PCNT (PCNT-Seckel) have defects in ATR-dependent checkpoint signaling, providing the first evidence linking a structural centrosomal protein with DNA damage signaling. These findings also suggest that other known microcephaly genes implicated in either DNA repair responses or centrosomal function may act in common developmental pathways determining human brain and body size

Human Mas-related G protein-coupled receptors-X1 induce chemokine receptor 2 expression in rat dorsal root ganglia neurons and release of chemokine ligand 2 from the human LAD-2 mast cell line

Primate-specific Mas-related G protein-coupled receptors-X1 (MRGPR-X1) are highly enriched in dorsal root ganglia (DRG) neurons and induce acute pain. Herein, we analyzed effects of MRGPR-X1 on serum response factors (SRF) or nuclear factors of activated T cells (NFAT), which control expression of various markers of chronic pain. Using HEK293, DRG neuron-derived F11 cells and cultured rat DRG neurons recombinantly expressing human MRGPR-X1, we found activation of a SRF reporter gene construct and induction of the early growth response protein-1 via extracellular signal-regulated kinases-1/2 known to play a significant role in the development of inflammatory pain. Furthermore, we observed MRGPR-X1-induced up-regulation of the chemokine receptor 2 (CCR2) via NFAT, which is considered as a key event in the onset of neuropathic pain and, so far, has not yet been described for any endogenous neuropeptide. Up-regulation of CCR2 is often associated with increased release of its endogenous agonist chemokine ligand 2 (CCL2). We also found MRGPR-X1-promoted release of CCL2 in a human connective tissue mast cell line endogenously expressing MRGPR-X1. Thus, we provide first evidence to suggest that MRGPR-X1 induce expression of chronic pain markers in DRG neurons and propose a so far unidentified signaling circuit that enhances chemokine signaling by acting on two distinct yet functionally co-operating cell types. Given the important role of chemokine signaling in pain chronification, we propose that interruption of this signaling circuit might be a promising new strategy to alleviate chemokine-promoted pain