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This contribution describes a generator of stochastic time series of daily precipitation for the interior of Israel from c. 90 to 900 mm mean annual precipitation (MAP) as a tool for studies of daily rain variability. The probability of rainfall on a given day of the year is described by a regular Gaussian peak curve function. The amount of rain is drawn randomly from an exponential distribution whose mean is the daily mean rain amount (averaged across years for each day of the year) described by a flattened Gaussian peak curve. Parameters for the curves have been calculated from monthly aggregated, long-term rain records from seven meteorological stations. Parameters for arbitrary points on the MAP gradient are calculated from a regression equation with MAP as the only independent variable. The simple structure of the generator allows it to produce time series with daily rain patterns that are projected under climate change scenarios and simultaneously control MAP. Increasing within-year variability of daily precipitation amounts also increases among-year variability of MAP as predicted by global circulation models. Thus, the time series incorporate important characteristics for climate change research and represent a flexible tool for simulations of daily vegetation or surface hydrology dynamics.
Calpain 1-gamma filamin interaction in muscle cells : a possible in situ regulation by PKC-alpha
(2006)
Calpains are a family of calcium-dependant cysteine-proteases involved in cytoskeleton remodelling and muscle differentiation. In a recent study, we observed the presence of calpain I in the muscle contractile apparatus and specifically in the N1- and N2-fines. This calpain isoform was found to be involved in the degradation of muscle fibres via proteolysis of key proteins in Z-disk and costameric junctions. The goal of this study was to determine whether gamma-filamin - a specific muscle isoform of the filamin family - is a calpain, I substrate and to characterise this interaction. gamma-Filamin is a major muscle architectural protein located in the Z-fine and under the sarcolemmal membrane. This protein is a component of the chain binding the sarcolemma to the sarcomeric structure. In this study, we found that gamma-filamin formed a stable complex in vitro and in cells with calpain I in the absence of calcium stimulation. We also located the binding domains in the C-terminus of gamma-filamin with a cleavage site between serine 2626 and serine 2627 in the hinge 2 region. The catalytic (80 kDa) and regulatory (28 kDa) subunits of calpain I are both involved in high affinity binding at gamma-filamin. Moreover, we showed that phosphorylation of the filamin C- terminus domain by PKC alpha protected gamma-filamin against proteolysis by calpain I in COS cells. Stimulation of PKC activity in myotubes, prevented gamma-filamin proteolysis by calpain and resulted in an increase in myotube adhesion.
Many real-world networks are characterized by adaptive changes in their topology depending on the state of their nodes. Here we study epidemic dynamics on an adaptive network, where the susceptibles are able to avoid contact with the infected by rewiring their network connections. This gives rise to assortative degree correlation, oscillations, hysteresis, and first order transitions. We propose a low-dimensional model to describe the system and present a full local bifurcation analysis. Our results indicate that the interplay between dynamics and topology can have important consequences for the spreading of infectious diseases and related applications
An Extended Query language for action languages (and its application to aggregates and preferences)
(2006)
In this article, we consider high-dimensional data which contains a low-dimensional non-Gaussian structure contaminated with Gaussian noise and propose a new linear method to identify the non-Gaussian subspace. Our method NGCA (Non-Gaussian Component Analysis) is based on a very general semi-parametric framework and has a theoretical guarantee that the estimation error of finding the non-Gaussian components tends to zero at a parametric rate. NGCA can be used not only as preprocessing for ICA, but also for extracting and visualizing more general structures like clusters. A numerical study demonstrates the usefulness of our method
We report a noise-memory induced phase transition in an array of oscillatory neural systems, which leads to the suppression of synchronous oscillations and restoration of excitable dynamics. This phenomenon is caused by the systematic contributions of temporally correlated parametric noise, i.e., possessing a memory, which stabilizes a deterministically unstable fixed point. Changing the noise correlation time, a reentrant phase transition to noise- induced excitability is observed in a globally coupled array. Since noise-induced excitability implies the restoration of the ability to transmit information, associated spatiotemporal patterns are observed afterwards. Furthermore, an analytic approach to predict the systematic effects of exponentially correlated noise is presented and its results are compared with the simulations