NLRP3 inflammasome activation: The convergence of multiple signalling pathways on ROS production?

The NLR family, pyrin domain-containing 3 (NLRP3) inflammasome is a multiprotein complex that activates caspase 1, leading to the processing and secretion of the pro-inflammatory cytokines interleukin-1beta (IL-1beta) and IL-18. The NLRP3 inflammasome is activated by a wide range of danger signals that derive not only from microorganisms but also from metabolic dysregulation. It is unclear how these highly varied stress signals can be detected by a single inflammasome. In this Opinion article, we review the different signalling pathways that have been proposed to engage the NLRP3 inflammasome and suggest a model in which one of the crucial elements for NLRP3 activation is the generation of reactive oxygen species (ROS).

Basis set and electron correlation effects on initial convergence for vibrational nonlinear optical properties of conjugated organic molecules

The level of ab initio theory which is necessary to compute reliable values for the static and dynamic (hyper)polarizabilities of three medium size π-conjugated organic nonlinear optical (NLO) molecules is investigated. With the employment of field-induced coordinates in combination with a finite field procedure, the calculations were made possible. It is stated that to obtain reasonable values for the various individual contributions to the (hyper)polarizability, it is necessary to include electron correlation. Based on the results, the convergence of the usual perturbation treatment for vibrational anharmonicity was examined

Initial convergence of the perturbation series expansion for vibrational nonlinear optical properties

Initial convergence of the perturbation series expansion for vibrational nonlinear optical (NLO) properties was analyzed. The zero-point vibrational average (ZPVA) was obtained through first-order in mechanical plus electrical anharmonicity. Results indicated that higher-order terms in electrical and mechanical anharmonicity can make substantial contributions to the pure vibrational polarizibility of typical NLO molecules

Algorithmic aspects of bio-inspired string operations

We present building blocks for algorithms for the efficient reduction of square factor, i.e. direct repetitions in strings. So the basic problem is this: given a string, compute all strings that can be obtained by reducing factors of the form zz to z. Two types of algorithms are treated: an offline algorithm is one that can compute a data structure on the given string in advance before the actual search for the square begins; in contrast, online algorithms receive all input only at the time when a request is made. For offline algorithms we treat the following problem: Let u and w be two strings such that w is obtained from u by reducing a square factor zz to only z. If we further are given the suffix table of u, how can we derive the suffix table for w without computing it from scratch? As the suffix table plays a key role in online algorithms for the detection of squares in a string, this derivation can make the iterated reduction of squares more efficient. On the other hand, we also show how a suffix array, used for the offline detection of squares, can be adapted to the new string resulting from the deletion of a square. Because the deletion is a very local change, this adaption is more eficient than the computation of the new suffix array from scratch.

Global Nash convergence of Foster and Young's regret testing

We construct an uncoupled randomized strategy of repeated play such that, if every player follows such a strategy, then the joint mixed strategy profiles converge, almost surely, to a Nash equilibrium of the one-shot game. The procedure requires very little in terms of players' information about the game. In fact, players' actions are based only on their own past payoffs and, in a variant of the strategy, players need not even know that their payoffs are determined through other players' actions. The procedure works for general finite games and is based on appropriate modifications of a simple stochastic learningrule introduced by Foster and Young.

Two-sided learning in New Keynesian models: Dynamics, (lack of) convergence and the value of information

This paper investigates the role of learning by private agents and the central bank(two-sided learning) in a New Keynesian framework in which both sides of the economyhave asymmetric and imperfect knowledge about the true data generating process. Weassume that all agents employ the data that they observe (which may be distinct fordifferent sets of agents) to form beliefs about unknown aspects of the true model ofthe economy, use their beliefs to decide on actions, and revise these beliefs througha statistical learning algorithm as new information becomes available. We study theshort-run dynamics of our model and derive its policy recommendations, particularlywith respect to central bank communications. We demonstrate that two-sided learningcan generate substantial increases in volatility and persistence, and alter the behaviorof the variables in the model in a significant way. Our simulations do not convergeto a symmetric rational expectations equilibrium and we highlight one source thatinvalidates the convergence results of Marcet and Sargent (1989). Finally, we identifya novel aspect of central bank communication in models of learning: communicationcan be harmful if the central bank's model is substantially mis-specified.