Slightly generalized Generalized Contagion: Unifying simple models of biological and social spreading

We motivate and explore the basic features of generalized contagion, a model mechanism that unifies fundamental models of biological and social contagion. Generalized contagion builds on the elementary observation that spreading and contagion of all kinds involve some form of system memory. We discuss the three main classes of systems that generalized contagion affords, resembling: simple biological contagion; critical mass contagion of social phenomena; and an intermediate, and explosive, vanishing critical mass contagion. We also present a simple explanation of the global spreading condition in the context of a small seed of infected individuals.Comment: 8 pages, 5 figures; chapter to appear in "Spreading Dynamics in Social Systems"; Eds. Sune Lehmann and Yong-Yeol Ahn, Springer Natur

Clustering based active learning for evolving data streams

Data labeling is an expensive and time-consuming task. Choosing which labels to use is increasingly becoming important. In the active learning setting, a classifier is trained by asking for labels for only a small fraction of all instances. While many works exist that deal with this issue in non-streaming scenarios, few works exist in the data stream setting. In this paper we propose a new active learning approach for evolving data streams based on a pre-clustering step, for selecting the most informative instances for labeling. We consider a batch incremental setting: when a new batch arrives, first we cluster the examples, and then, we select the best instances to train the learner. The clustering approach allows to cover the whole data space avoiding to oversample examples from only few areas. We compare our method w.r.t. state of the art active learning strategies over real datasets. The results highlight the improvement in performance of our proposal. Experiments on parameter sensitivity are also reported

Extracellular Matrix Aggregates from Differentiating Embryoid Bodies as a Scaffold to Support ESC Proliferation and Differentiation

Embryonic stem cells (ESCs) have emerged as potential cell sources for tissue engineering and regeneration owing to its virtually unlimited replicative capacity and the potential to differentiate into a variety of cell types. Current differentiation strategies primarily involve various growth factor/inducer/repressor concoctions with less emphasis on the substrate. Developing biomaterials to promote stem cell proliferation and differentiation could aid in the realization of this goal. Extracellular matrix (ECM) components are important physiological regulators, and can provide cues to direct ESC expansion and differentiation. ECM undergoes constant remodeling with surrounding cells to accommodate specific developmental event. In this study, using ESC derived aggregates called embryoid bodies (EB) as a model, we characterized the biological nature of ECM in EB after exposure to different treatments: spontaneously differentiated and retinoic acid treated (denoted as SPT and RA, respectively). Next, we extracted this treatment-specific ECM by detergent decellularization methods (Triton X-100, DOC and SDS are compared). The resulting EB ECM scaffolds were seeded with undifferentiated ESCs using a novel cell seeding strategy, and the behavior of ESCs was studied. Our results showed that the optimized protocol efficiently removes cells while retaining crucial ECM and biochemical components. Decellularized ECM from SPT EB gave rise to a more favorable microenvironment for promoting ESC attachment, proliferation, and early differentiation, compared to native EB and decellularized ECM from RA EB. These findings suggest that various treatment conditions allow the formulation of unique ESC-ECM derived scaffolds to enhance ESC bioactivities, including proliferation and differentiation for tissue regeneration applications. © 2013 Goh et al

A rapid screening tool for psychological distress in children 3--6years old: results of a validation study.

International audienceABSTRACT: BACKGROUND: The mental health needs of young children in humanitarian contexts often remain unaddressed. The lack of a validated, rapid and simple tool for screening combined with few mental health professionals able to accurately diagnose and provide appropriate care mean that young children remain without care. Here, we present the results of the principle cross-cultural validation of the "Psychological Screening for Young Children aged 3 to 6" (PSYCAa3-6). The PSYCa 3--6 is a simple scale for children 3 to 6 years old administered by non-specialists, to screen young children in crises and thereby refer them to care if needed. METHODS: This study was conducted in Maradi, Niger. The scale was translated into Hausa, using corroboration of independent translations. A cross-cultural validation was implemented using quantitative and qualitative methods. A random sample of 580 mothers or caregivers of children 3 to 6 years old were included. The tool was psychometrically examined and diagnostic properties were assessed comparing the PSYCa 3--6 against a clinical interview as the gold standard. RESULTS: The PSYCa 3--6 Hausa version demonstrated good concurrent validity, as scores correlated with the gold standard and the Clinical Global Impression Severity Scale (CGI-S) [rho = 0.41, p-value = 0.00]. A reduction procedure was used to reduce the scale from 40 to 22 items. The test-retest reliability of the PSYCa 3--6 was found to be high (ICC 0.81, CI95% [0.68; 0.89]). In our sample, although not the purpose of this study, approximately 54 of 580 children required subsequent follow-up with a psychologist. CONCLUSIONS: To our knowledge, this is the first validation of a screening scale for children 3 to 6 years old with a cross-cultural validation component, for use in humanitarian contexts. The Hausa version of the PSYCa 3--6 is a reliable and a valuable screening tool for psychological distress. Further studies to replicate our findings and additional validations of the PSYCa 3--6 in other populations may help improve the delivery of mental health care to children

Geckos decouple fore- and hind limb kinematics in response to changes in incline

This work is supported by an NSF grant (NSF IOS-1147043) to TE

A General Model of Dynamics on Networks with Graph Automorphism Lumping

In this paper we introduce a general Markov chain model of dynamical processes on networks. In this model, nodes in the network can adopt a finite number of states and transitions can occur that involve multiple nodes changing state at once. The rules that govern transitions only depend on measures related to the state and structure of the network and not on the particular nodes involved. We prove that symmetries of the network can be used to lump equivalent states in state-space. We illustrate how several examples of well-known dynamical processes on networks correspond to particular cases of our general model. This work connects a wide range of models specified in terms of node-based dynamical rules to their exact continuous-time Markov chain formulation

Inflammasome Sensor Nlrp1b-Dependent Resistance to Anthrax Is Mediated by Caspase-1, IL-1 Signaling and Neutrophil Recruitment

Bacillus anthracis infects hosts as a spore, germinates, and disseminates in its vegetative form. Production of anthrax lethal and edema toxins following bacterial outgrowth results in host death. Macrophages of inbred mouse strains are either sensitive or resistant to lethal toxin depending on whether they express the lethal toxin responsive or non-responsive alleles of the inflammasome sensor Nlrp1b (Nlrp1bS/S or Nlrp1bR/R, respectively). In this study, Nlrp1b was shown to affect mouse susceptibility to infection. Inbred and congenic mice harboring macrophage-sensitizing Nlrp1bS/S alleles (which allow activation of caspase-1 and IL-1β release in response to anthrax lethal toxin challenge) effectively controlled bacterial growth and dissemination when compared to mice having Nlrp1bR/R alleles (which cannot activate caspase-1 in response to toxin). Nlrp1bS-mediated resistance to infection was not dependent on the route of infection and was observed when bacteria were introduced by either subcutaneous or intravenous routes. Resistance did not occur through alterations in spore germination, as vegetative bacteria were also killed in Nlrp1bS/S mice. Resistance to infection required the actions of both caspase-1 and IL-1β as Nlrp1bS/S mice deleted of caspase-1 or the IL-1 receptor, or treated with the Il-1 receptor antagonist anakinra, were sensitized to infection. Comparison of circulating neutrophil levels and IL-1β responses in Nlrp1bS/S,Nlrp1bR/R and IL-1 receptor knockout mice implicated Nlrp1b and IL-1 signaling in control of neutrophil responses to anthrax infection. Neutrophil depletion experiments verified the importance of this cell type in resistance to B. anthracis infection. These data confirm an inverse relationship between murine macrophage sensitivity to lethal toxin and mouse susceptibility to spore infection, and establish roles for Nlrp1bS, caspase-1, and IL-1β in countering anthrax infection

Exact analysis of summary statistics for continuous-time discrete-state Markov processes on networks using graph-automorphism lumping

We propose a unified framework to represent a wide range of continuous-time discrete-state Markov processes on networks, and show how many network dynamics models in the literature can be represented in this unified framework. We show how a particular sub-set of these models, referred to here as single-vertex-transition (SVT) processes, lead to the analysis of quasi-birth-and-death (QBD) processes in the theory of continuous-time Markov chains. We illustrate how to analyse a number of summary statistics for these processes, such as absorption probabilities and first-passage times. We extend the graph-automorphism lumping approach [Kiss, Miller, Simon, Mathematics of Epidemics on Networks, 2017; Simon, Taylor, Kiss, J. Math. Bio. 62(4), 2011], by providing a matrix-oriented representation of this technique, and show how it can be applied to a very wide range of dynamical processes on networks. This approach can be used not only to solve the master equation of the system, but also to analyse the summary statistics of interest. We also show the interplay between the graph-automorphism lumping approach and the QBD structures when dealing with SVT processes. Finally, we illustrate our theoretical results with examples from the areas of opinion dynamics and mathematical epidemiology