Asynchronous spiking neural P systems

We consider here spiking neural P systems with a non-synchronized (i.e., asynchronous) use of rules: in any step, a neuron can apply or not apply its rules which are enabled by the number of spikes it contains (further spikes can come, thus changing the rules enabled in the next step). Because the time between two firings of the output neuron is now irrelevant, the result of a computation is the number of spikes sent out by the system, not the distance between certain spikes leaving the system. The additional non-determinism introduced in the functioning of the system by the non-synchronization is proved not to decrease the computing power in the case of using extended rules (several spikes can be produced by a rule). That is, we obtain again the equivalence with Turing machines (interpreted as generators of sets of (vectors of) numbers). However, this problem remains open for the case of standard spiking neural P systems, whose rules can only produce one spike. On the other hand we prove that asynchronous systems, with extended rules, and where each neuron is either bounded or unbounded, are not computationally complete. For these systems, the configuration reachability, membership (in terms of generated vectors), emptiness, infiniteness, and disjointness problems are shown to be decidable. However, containment and equivalence are undecidable. © 2009 Elsevier B.V. All rights reserved

The CCR4-NOT Complex Physically and Functionally Interacts with TRAMP and the Nuclear Exosome

BACKGROUND: Ccr4-Not is a highly conserved multi-protein complex consisting in yeast of 9 subunits, including Not5 and the major yeast deadenylase Ccr4. It has been connected functionally in the nucleus to transcription by RNA polymerase II and in the cytoplasm to mRNA degradation. However, there has been no evidence so far that this complex is important for RNA degradation in the nucleus. METHODOLOGY/PRINCIPAL FINDINGS: In this work we point to a new role for the Ccr4-Not complex in nuclear RNA metabolism. We determine the importance of the Ccr4-Not complex for the levels of non-coding nuclear RNAs, such as mis-processed and polyadenylated snoRNAs, whose turnover depends upon the nuclear exosome and TRAMP. Consistently, mutation of both the Ccr4-Not complex and the nuclear exosome results in synthetic slow growth phenotypes. We demonstrate physical interactions between the Ccr4-Not complex and the exosome. First, Not5 co-purifies with the exosome. Second, several exosome subunits co-purify with the Ccr4-Not complex. Third, the Ccr4-Not complex is important for the integrity of large exosome-containing complexes. Finally, we reveal a connection between the Ccr4-Not complex and TRAMP through the association of the Mtr4 helicase with the Ccr4-Not complex and the importance of specific subunits of Ccr4-Not for the association of Mtr4 with the nuclear exosome subunit Rrp6. CONCLUSIONS/SIGNIFICANCE: We propose a model in which the Ccr4-Not complex may provide a platform contributing to dynamic interactions between the nuclear exosome and its co-factor TRAMP. Our findings connect for the first time the different players involved in nuclear and cytoplasmic RNA degradation

Efficient nonparametric density estimation on the sphere with applications in fluid mechanics

The application of nonparametric probability density function estimation for the purpose of data analysis is well established. More recently, such methods have been applied to fluid flow calculations since the density of the fluid plays a crucial role in determining the ow. Furthermore, when the calculations involve directional or axial data, the domain of interest falls on the surface of the sphere. Accurate and fast estimation of probability density functions is crucial for these calculations since the density estimation is performed at each iteration during the computation. In particular the values fn(X-1), f(n)(X-2),..., f(n)(X-n) of the density estimate at the sampled points X-i are needed to evolve the system. Usual nonparametric estimators make use of kernel functions to construct f(n). We propose a special sequence of weight functions for nonparametric density estimation that is especially suitable for such applications. The resulting method has a computational advantage over kernel methods in certain situations and also parallelizes easily. Conditions for convergence turn out to be similar to those required for kernel-based methods. We also discuss experiments on different distributions and compare the computational efficiency of our method with kernel based estimators