Inverse Agonist Action of Leu-Enkephalin at ␦ 2 -Opioid Receptors Mediates Spinal Antianalgesia

ABSTRACT Dynorphin A(1-17) given intrathecally releases spinal cholecystokinin to produce an antianalgesic action against spinal morphine in the tail-flick test in CD-1 mice. The present study showed that following the cholecystokinin step, a ␦ 2 -opioid inverse agonist action of Leu-enkephalin (LE), was involved. Pretreatment with intrathecal LE antiserum eliminated dynorphin and cholecystokinin-8s antianalgesia. A small dose of LE intrathecally produced antianalgesia that like that from dynorphin A(1-17) and cholecystokinin was eliminated by naltriben but not 7-benzylidenenaltrexone (␦ 2 -and ␦ 1 -opioid receptor antagonist, respectively). This ]-Leu-enkephalin-Thr analgesia was not attenuated by LE; thus, this ␦ 2 -analgesic agonist and LE inverse agonist action did not occur through competition at the same ␦ 2 -receptor in CD-1 mice. In CD-1 mice, a linear sequence of dynorphin A(1-17) 3 cholecystokinin 3 LE 3 NMDA receptors was indicated: cholecystokinin antiserum inhibited cholecystokinin but not LE; naltriben inhibited LE but not NMDA. The uniqueness of LE in linking dynorphin A(1-17), cholecystokinin, ␦ 2 -opioid, and NMDA receptor activation may unify the separate known mechanisms involved in the antiopioid actions of these components against morphine

Can we identify non-stationary dynamics of trial-to-trial variability?"

Identifying sources of the apparent variability in non-stationary scenarios is a fundamental problem in many biological data analysis settings. For instance, neurophysiological responses to the same task often vary from each repetition of the same experiment (trial) to the next. The origin and functional role of this observed variability is one of the fundamental questions in neuroscience. The nature of such trial-to-trial dynamics however remains largely elusive to current data analysis approaches. A range of strategies have been proposed in modalities such as electro-encephalography but gaining a fundamental insight into latent sources of trial-to-trial variability in neural recordings is still a major challenge. In this paper, we present a proof-of-concept study to the analysis of trial-to-trial variability dynamics founded on non-autonomous dynamical systems. At this initial stage, we evaluate the capacity of a simple statistic based on the behaviour of trajectories in classification settings, the trajectory coherence, in order to identify trial-to-trial dynamics. First, we derive the conditions leading to observable changes in datasets generated by a compact dynamical system (the Duffing equation). This canonical system plays the role of a ubiquitous model of non-stationary supervised classification problems. Second, we estimate the coherence of class-trajectories in empirically reconstructed space of system states. We show how this analysis can discern variations attributable to non-autonomous deterministic processes from stochastic fluctuations. The analyses are benchmarked using simulated and two different real datasets which have been shown to exhibit attractor dynamics. As an illustrative example, we focused on the analysis of the rat's frontal cortex ensemble dynamics during a decision-making task. Results suggest that, in line with recent hypotheses, rather than internal noise, it is the deterministic trend which most likely underlies the observed trial-to-trial variability. Thus, the empirical tool developed within this study potentially allows us to infer the source of variability in in-vivo neural recordings

Ecological Invasion, Roughened Fronts, and a Competitor's Extreme Advance: Integrating Stochastic Spatial-Growth Models

Both community ecology and conservation biology seek further understanding of factors governing the advance of an invasive species. We model biological invasion as an individual-based, stochastic process on a two-dimensional landscape. An ecologically superior invader and a resident species compete for space preemptively. Our general model includes the basic contact process and a variant of the Eden model as special cases. We employ the concept of a "roughened" front to quantify effects of discreteness and stochasticity on invasion; we emphasize the probability distribution of the front-runner's relative position. That is, we analyze the location of the most advanced invader as the extreme deviation about the front's mean position. We find that a class of models with different assumptions about neighborhood interactions exhibit universal characteristics. That is, key features of the invasion dynamics span a class of models, independently of locally detailed demographic rules. Our results integrate theories of invasive spatial growth and generate novel hypotheses linking habitat or landscape size (length of the invading front) to invasion velocity, and to the relative position of the most advanced invader.Comment: The original publication is available at www.springerlink.com/content/8528v8563r7u2742

The changing landscape of disaster volunteering: opportunities, responses and gaps in Australia

There is a growing expectation that volunteers will have a greater role in disaster management in the future compared to the past. This is driven largely by a growing focus on building resilience to disasters. At the same time, the wider landscape of volunteering is fundamentally changing in the twenty-first century. This paper considers implications of this changing landscape for the resilience agenda in disaster management, with a focus on Australia. It first reviews major forces and trends impacting on disaster volunteering, highlighting four key developments: the growth of more diverse and episodic volunteering styles, the impact of new communications technology, greater private sector involvement and growing government expectations of and intervention in the voluntary sector. It then examines opportunities in this changing landscape for the Australian emergency management sector across five key strategic areas and provides examples of Australian responses to these opportunities to date. The five areas of focus are: developing more flexible volunteering strategies, harnessing spontaneous volunteering, building capacity to engage digital (and digitally enabled) volunteers, tapping into the growth of employee and skills-based volunteering and co-producing community-based disaster risk reduction. Although there have been considerable steps taken in Australia in some of these areas, overall there is still a long way to go before the sector can take full advantage of emerging opportunities. The paper thus concludes by identifying important research and practice gaps in this area

Dynamic Models of Language Evolution: The Linguistic Perspective

Language is probably the key defining characteristic of humanity, an immensely powerful tool which provides its users with an infinitely expressive means of representing their complex thoughts and reflections, and of successfully communicating them to others. It is the foundation on which human societies have been built and the means through which humanity’s unparalleled intellectual and technological achievements have been realized. Although we have a natural intuitive understanding of what a language is, the specification of a particular language is nevertheless remarkably difficult, if not impossible, to pin down precisely. All languages contain many separate yet integral systems which work interdependently to allow the expression of our thoughts and the interpretation of others’ expressions: each has, for instance, a set of basic meaningless sounds (e.g. [e], [l], [s]) which can be combined to make different meaningful words and parts of words (e.g. else, less, sell, -less ); these meaningful units can be combined to make complex words (e.g. spinelessness, selling ), and the words themselves can then be combined in very many complex ways into phrases, clauses and an infinite number of meaningful sentences; finally each of these sentences can be interpreted in dramatically different ways, depending on the contexts in which it is uttered and on who is doing the interpretation. Languages can be analysed at any of these different levels, which make up many of the sub-fields of linguistics, and the primary job of linguistic theorists is to try to explain the rules which best explain these complex combinations