412 research outputs found
Hydrogen peroxide is a neuronal alarmin that triggers specific RNAs, local translation of Annexin A2, and cytoskeletal remodeling in Schwann cells
Schwann cells are key players in neuro-regeneration: They sense "alarm" signals released by degenerating nerve terminals and differentiate toward a proregenerative phenotype, with phagocytosis of nerve debris and nerve guidance. At the murine neuromuscular junction, hydrogen peroxide (H2O2) is a key signal of Schwann cells' activation in response to a variety of nerve injuries. Here we report that Schwann cells exposed to low doses of H2O2 rewire the expression of several RNAs at both transcriptional and translational levels. Among the genes positively regulated at both levels, we identified an enriched cluster involved in cytoskeleton remodeling and cell migration, with the Annexin (Anxa) proteins being the most represented family. We show that both Annexin A2 (Anxa2) transcript and protein accumulate at the tips of long pseudopods that Schwann cells extend upon H2O2 exposure. Interestingly, Schwann cells reply to this signal and to nerve injury by locally translating Anxa2 in pseudopods, and undergo an extensive cytoskeleton remodeling. Our results show that, similarly to neurons, Schwann cells take advantage of local protein synthesis to change shape and move toward damaged axonal terminals to facilitate axonal regeneration
Non conservative Abelian sandpile model with BTW toppling rule
A non conservative Abelian sandpile model with BTW toppling rule introduced
in [Tsuchiya and Katori, Phys. Rev. E {\bf 61}, 1183 (2000)] is studied. Using
a scaling analysis of the different energy scales involved in the model and
numerical simulations it is shown that this model belong to a universality
class different from that of previous models considered in the literature.Comment: RevTex, 5 pages, 6 ps figs, Minor change
Reproduction of Twentieth Century Intradecadal to Multidecadal Surface Temperature Variability in Radiatively Forced Coupled Climate Models
[1] Coupled Model Intercomparison Project 3 simulations that included time-varying radiative forcings were ranked according to their ability to consistently reproduce twentieth century intradecadal to multidecadal (IMD) surface temperature variability at the 5° by 5° spatial scale. IMD variability was identified using the running Mann-Whitney Z method. Model rankings were given context by comparing the IMD variability in preindustrial control runs to observations and by contrasting the IMD variability among the ensemble members within each model. These experiments confirmed that the inclusion of time-varying external forcings brought simulations into closer agreement with observations. Additionally, they illustrated that the magnitude of unforced variability differed between models. This led to a supplementary metric that assessed model ability to reproduce observations while accounting for each model\u27s own degree of unforced variability. These two metrics revealed that discernable differences in skill exist between models and that none of the models reproduced observations at their theoretical optimum level. Overall, these results demonstrate a methodology for assessing coupled models relative to each other within a multimodel framework
Universality classes in directed sandpile models
We perform large scale numerical simulations of a directed version of the
two-state stochastic sandpile model. Numerical results show that this
stochastic model defines a new universality class with respect to the Abelian
directed sandpile. The physical origin of the different critical behavior has
to be ascribed to the presence of multiple topplings in the stochastic model.
These results provide new insights onto the long debated question of
universality in abelian and stochastic sandpiles.Comment: 5 pages, RevTex, includes 9 EPS figures. Minor english corrections.
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Simple uncertainty frameworks for selecting weighting schemes and interpreting multimodel ensemble climate change experiments
Future climate change projections are often derived from ensembles of simulations from multiple global circulation models using heuristic weighting schemes. This study provides a more rigorous justification for this by introducing a nested family of three simple analysis of variance frameworks. Statistical frameworks are essential in order to quantify the uncertainty associated with the estimate of the mean climate change response.
The most general framework yields the “one model, one vote” weighting scheme often used in climate projection. However, a simpler additive framework is found to be preferable when the climate change response is not strongly model dependent. In such situations, the weighted multimodel mean may be interpreted as an estimate of the actual climate response, even in the presence of shared model biases.
Statistical significance tests are derived to choose the most appropriate framework for specific multimodel ensemble data. The framework assumptions are explicit and can be checked using simple tests and graphical techniques. The frameworks can be used to test for evidence of nonzero climate response and to construct confidence intervals for the size of the response.
The methodology is illustrated by application to North Atlantic storm track data from the Coupled Model Intercomparison Project phase 5 (CMIP5) multimodel ensemble. Despite large variations in the historical storm tracks, the cyclone frequency climate change response is not found to be model dependent over most of the region. This gives high confidence in the response estimates. Statistically significant decreases in cyclone frequency are found on the flanks of the North Atlantic storm track and in the Mediterranean basin
From waves to avalanches: two different mechanisms of sandpile dynamics
Time series resulting from wave decomposition show the existence of different
correlation patterns for avalanche dynamics. For the d=2 Bak-Tang-Wiesenfeld
model, long range correlations determine a modification of the wave size
distribution under coarse graining in time, and multifractal scaling for
avalanches. In the Manna model, the distribution of avalanches coincides with
that of waves, which are uncorrelated and obey finite size scaling, a result
expected also for the d=3 Bak et al. model.Comment: 5 pages, 4 figure
Chapter 12 - Long-term climate change: Projections, commitments and irreversibility
This chapter assesses long-term projections of climate change for the end of the 21st century and beyond, where the forced signal depends on the scenario and is typically larger than the internal variability of the climate system. Changes are expressed with respect to a baseline period of 1986-2005, unless otherwise stated
Dissipative Abelian Sandpiles and Random Walks
We show that the dissipative Abelian sandpile on a graph L can be related to
a random walk on a graph which consists of L extended with a trapping site.
From this relation it can be shown, using exact results and a scaling
assumption, that the dissipative sandpiles' correlation length exponent \nu
always equals 1/d_w, where d_w is the fractal dimension of the random walker.
This leads to a new understanding of the known results that \nu=1/2 on any
Euclidean lattice. Our result is however more general and as an example we also
present exact data for finite Sierpinski gaskets which fully confirm our
predictions.Comment: 10 pages, 1 figur
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Selecting CMIP5 GCMs for downscaling over multiple regions
The unprecedented availability of 6-hourly data from a multi-model GCM ensemble in the CMIP5 data archive presents the new opportunity to dynamically downscale multiple GCMs to develop high-resolution climate projections relevant to detailed assessment of climate vulnerability and climate change impacts. This enables the development of high resolution projections derived from the same set of models that are used to characterise the range of future climate changes at the global and large-scale, and as assessed in the IPCC AR5. However, the technical and human resource required to dynamically-downscale the full CMIP5 ensemble are significant and not necessary if the aim is to develop scenarios covering a representative range of future climate conditions relevant to a climate change risk assessment. This paper illustrates a methodology for selecting from the available CMIP5 models in order to identify a set of 8–10 GCMs for use in regional climate change assessments. The selection focuses on their suitability across multiple regions—Southeast Asia, Europe and Africa. The selection (a) avoids the inclusion of the least realistic models for each region and (b) simultaneously captures the maximum possible range of changes in surface temperature and precipitation for three continental-scale regions. We find that, of the CMIP5 GCMs with 6-hourly fields available, three simulate the key regional aspects of climate sufficiently poorly that we consider the projections from those models ‘implausible’ (MIROC-ESM, MIROC-ESM-CHEM, and IPSL-CM5B-LR). From the remaining models, we demonstrate a selection methodology which avoids the poorest models by including them in the set only if their exclusion would significantly reduce the range of projections sampled. The result of this process is a set of models suitable for using to generate downscaled climate change information for a consistent multi-regional assessment of climate change impacts and adaptation
Branching Processes and Evolution at the Ends of a Food Chain
In a critically self--organized model of punctuated equilibrium, boundaries
determine peculiar scaling of the size distribution of evolutionary avalanches.
This is derived by an inhomogeneous generalization of standard branching
processes, extending previous mean field descriptions and yielding
together with , as distribution exponent of avalanches starting from
species at the ends of a food chain. For the nearest neighbor chain one obtains
numerically , and for the
first return times of activity, again distinct from bulk exponents.Comment: REVTex file, 12 pages, 2 figures in eps-files uuencoded, psfig.st
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