Collective emotions online and their influence on community life

E-communities, social groups interacting online, have recently become an object of interdisciplinary research. As with face-to-face meetings, Internet exchanges may not only include factual information but also emotional information - how participants feel about the subject discussed or other group members. Emotions are known to be important in affecting interaction partners in offline communication in many ways. Could emotions in Internet exchanges affect others and systematically influence quantitative and qualitative aspects of the trajectory of e-communities? The development of automatic sentiment analysis has made large scale emotion detection and analysis possible using text messages collected from the web. It is not clear if emotions in e-communities primarily derive from individual group members' personalities or if they result from intra-group interactions, and whether they influence group activities. We show the collective character of affective phenomena on a large scale as observed in 4 million posts downloaded from Blogs, Digg and BBC forums. To test whether the emotions of a community member may influence the emotions of others, posts were grouped into clusters of messages with similar emotional valences. The frequency of long clusters was much higher than it would be if emotions occurred at random. Distributions for cluster lengths can be explained by preferential processes because conditional probabilities for consecutive messages grow as a power law with cluster length. For BBC forum threads, average discussion lengths were higher for larger values of absolute average emotional valence in the first ten comments and the average amount of emotion in messages fell during discussions. Our results prove that collective emotional states can be created and modulated via Internet communication and that emotional expressiveness is the fuel that sustains some e-communities.Comment: 23 pages including Supporting Information, accepted to PLoS ON

Handler beliefs affect scent detection dog outcomes

Our aim was to evaluate how human beliefs affect working dog outcomes in an applied environment. We asked whether beliefs of scent detection dog handlers affect team performance and evaluated relative importance of human versus dog influences on handlers’ beliefs. Eighteen drug and/or explosive detection dog/handler teams each completed two sets of four brief search scenarios (conditions). Handlers were falsely told that two conditions contained a paper marking scent location (human influence). Two conditions contained decoy scents (food/toy) to encourage dog interest in a false location (dog influence). Conditions were (1) control; (2) paper marker; (3) decoy scent; and (4) paper marker at decoy scent. No conditions contained drug or explosive scent; any alerting response was incorrect. A repeated measures analysis of variance was used with search condition as the independent variable and number of alerts as the dependent variable. Additional nonparametric tests compared human and dog influence. There were 225 incorrect responses, with no differences in mean responses across conditions. Response patterns differed by condition. There were more correct (no alert responses) searches in conditions without markers. Within marked conditions, handlers reported that dogs alerted more at marked locations than other locations. Handlers’ beliefs that scent was present potentiated handler identification of detection dog alerts. Human more than dog influences affected alert locations. This confirms that handler beliefs affect outcomes of scent detection dog deployments

Intervention in gene regulatory networks via greedy control policies based on long-run behavior

<p>Abstract</p> <p>Background</p> <p>A salient purpose for studying gene regulatory networks is to derive intervention strategies, the goals being to identify potential drug targets and design gene-based therapeutic intervention. Optimal stochastic control based on the transition probability matrix of the underlying Markov chain has been studied extensively for probabilistic Boolean networks. Optimization is based on minimization of a cost function and a key goal of control is to reduce the steady-state probability mass of undesirable network states. Owing to computational complexity, it is difficult to apply optimal control for large networks.</p> <p>Results</p> <p>In this paper, we propose three new greedy stationary control policies by directly investigating the effects on the network long-run behavior. Similar to the recently proposed mean-first-passage-time (MFPT) control policy, these policies do not depend on minimization of a cost function and avoid the computational burden of dynamic programming. They can be used to design stationary control policies that avoid the need for a user-defined cost function because they are based directly on long-run network behavior; they can be used as an alternative to dynamic programming algorithms when the latter are computationally prohibitive; and they can be used to predict the best control gene with reduced computational complexity, even when one is employing dynamic programming to derive the final control policy. We compare the performance of these three greedy control policies and the MFPT policy using randomly generated probabilistic Boolean networks and give a preliminary example for intervening in a mammalian cell cycle network.</p> <p>Conclusion</p> <p>The newly proposed control policies have better performance in general than the MFPT policy and, as indicated by the results on the mammalian cell cycle network, they can potentially serve as future gene therapeutic intervention strategies.</p

Different effects of deep inspirations on central and peripheral airways in healthy and allergen-challenged mice

<p>Abstract</p> <p>Background</p> <p>Deep inspirations (DI) have bronchodilatory and bronchoprotective effects in healthy human subjects, but these effects appear to be absent in asthmatic lungs. We have characterized the effects of DI on lung mechanics during mechanical ventilation in healthy mice and in a murine model of acute and chronic airway inflammation.</p> <p>Methods</p> <p>Balb/c mice were sensitized to ovalbumin (OVA) and exposed to nebulized OVA for 1 week or 12 weeks. Control mice were challenged with PBS. Mice were randomly selected to receive DI, which were given twice during the minute before assessment of lung mechanics.</p> <p>Results</p> <p>DI protected against bronchoconstriction of central airways in healthy mice and in mice with acute airway inflammation, but not when OVA-induced chronic inflammation was present. DI reduced lung resistance induced by methacholine from 3.8 ± 0.3 to 2.8 ± 0.1 cmH₂O·s·mL^-1in healthy mice and 5.1 ± 0.3 to 3.5 ± 0.3 cmH₂O·s·mL^-1in acute airway inflammation (both <it>P </it>< 0.001). In healthy mice, DI reduced the maximum decrease in lung compliance from 15.9 ± 1.5% to 5.6 ± 0.6% (<it>P </it>< 0.0001). This protective effect was even more pronounced in mice with chronic inflammation where DI attenuated maximum decrease in compliance from 44.1 ± 6.6% to 14.3 ± 1.3% (<it>P </it>< 0.001). DI largely prevented increased peripheral tissue damping (G) and tissue elastance (H) in both healthy (G and H both <it>P </it>< 0.0001) and chronic allergen-treated animals (G and H both <it>P </it>< 0.0001).</p> <p>Conclusion</p> <p>We have tested a mouse model of potential value for defining mechanisms and sites of action of DI in healthy and asthmatic human subjects. Our current results point to potent protective effects of DI on peripheral parts of chronically inflamed murine lungs and that the presence of DI may blunt airway hyperreactivity.</p

Current paradigm of the 18-kDa translocator protein (TSPO) as a molecular target for PET imaging in neuroinflammation and neurodegenerative diseases

Neuroinflammation is a process characterised by drastic changes in microglial morphology and by marked upregulation of the 18-kDa translocator protein (TSPO) on the mitochondria. The continual increase in incidence of neuroinflammation and neurodegenerative diseases poses a major health issue in many countries, requiring more innovative diagnostic and monitoring tools. TSPO expression may constitute a biomarker for brain inflammation that could be monitored by using TSPO tracers as neuroimaging agents. From medical imaging perspectives, this review focuses on the current concepts related to the TSPO, and discusses briefly on the status of its PET imaging related to neuroinflammation and neurodegenerative diseases in humans

Response operators for Markov processes in a finite state space: radius of convergence and link to the response theory for Axiom A systems

Using straightforward linear algebra we derive response operators describing the impact of small perturbations to finite state Markov processes. The results can be used for studying empirically constructed—e.g. from observations or through coarse graining of model simulations—finite state approximation of statistical mechanical systems. Recent results concerning the convergence of the statistical properties of finite state Markov approximation of the full asymptotic dynamics on the SRB measure in the limit of finer and finer partitions of the phase space are suggestive of some degree of robustness of the obtained results in the case of Axiom A system. Our findings give closed formulas for the linear and nonlinear response theory at all orders of perturbation and provide matrix expressions that can be directly implemented in any coding language, plus providing bounds on the radius of convergence of the perturbative theory. In particular, we relate the convergence of the response theory to the rate of mixing of the unperturbed system. One can use the formulas derived for finite state Markov processes to recover previous findings obtained on the response of continuous time Axiom A dynamical systems to perturbations, by considering the generator of time evolution for the measure and for the observables. A very basic, low-tech, and computationally cheap analysis of the response of the Lorenz ’63 model to perturbations provides rather encouraging results regarding the possibility of using the approximate representation given by finite state Markov processes to compute the system’s response