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Kazunori Iwata

Publications and source records attributed to Kazunori Iwata.

5 recordsLinked to original sources
PubMed2006

N-(3-oxo-acyl)homoserine lactones signal cell activation through a mechanism distinct from the canonical pathogen-associated molecular pattern recognition receptor pathways.

Innate immune system receptors function as sensors of infection and trigger the immune responses through ligand-specific signaling pathways. These ligands are pathogen-associated products, such as components of bacterial walls and viral nuclear acids. A common response to such ligands is the activation of mitogen-activated protein kinase p38, whereas double-stranded viral RNA additionally induces the phosphorylation of eukaryotic translation initiation factor 2alpha (eIF2alpha). Here we have shown that p38 and eIF2alpha phosphorylation represent two biochemical markers of the effects induced by N-(3-oxo-acyl)homoserine lactones, the secreted products of a number of Gram-negative bacteria, including the human opportunistic pathogen Pseudomonas aeruginosa. Furthermore, N-(3-oxo-dodecanoyl)homoserine lactone induced distension of mitochondria and the endoplasmic reticulum as well as c-jun gene transcription. These effects occurred in a wide variety of cell types including alveolar macrophages and bronchial epithelial cells, requiring the structural integrity of the lactone ring motif and its natural stereochemistry. These findings suggest that N-(3-oxo-acyl)homoserine lactones might be recognized by receptors of the innate immune system. However, we provide evidence that N-(3-oxo-dodecanoyl)homoserine lactone-mediated signaling does not require the presence of the canonical innate immune system receptors, Toll-like receptors, or two members of the NLR/Nod/Caterpillar family, Nod1 and Nod2. These data offer a new understanding of the effects of N-(3-oxo-dodecanoyl)homoserine lactone on host cells and its role in persistent airway infections caused by P. aeruginosa.

4-Butyrolactone↗
PubMed2006

A statistical property of multiagent learning based on Markov decision process.

We exhibit an important property called the asymptotic equipartition property (AEP) on empirical sequences in an ergodic multiagent Markov decision process (MDP). Using the AEP which facilitates the analysis of multiagent learning, we give a statistical property of multiagent learning, such as reinforcement learning (RL), near the end of the learning process. We examine the effect of the conditions among the agents on the achievement of a cooperative policy in three different cases: blind, visible, and communicable. Also, we derive a bound on the speed with which the empirical sequence converges to the best sequence in probability, so that the multiagent learning yields the best cooperative result.

Learning↗
PubMed2005

The asymptotic equipartition property in reinforcement learning and its relation to return maximization.

We discuss an important property called the asymptotic equipartition property on empirical sequences in reinforcement learning. This states that the typical set of empirical sequences has probability nearly one, that all elements in the typical set are nearly equi-probable, and that the number of elements in the typical set is an exponential function of the sum of conditional entropies if the number of time steps is sufficiently large. The sum is referred to as stochastic complexity. Using the property we elucidate the fact that the return maximization depends on two factors, the stochastic complexity and a quantity depending on the parameters of environment. Here, the return maximization means that the best sequences in terms of expected return have probability one. We also examine the sensitivity of stochastic complexity, which is a qualitative guide in tuning the parameters of action-selection strategy, and show a sufficient condition for return maximization in probability.

Algorithms↗
PubMed2004

A new criterion using information gain for action selection strategy in reinforcement learning.

In this paper, we regard the sequence of returns as outputs from a parametric compound source. Utilizing the fact that the coding rate of the source shows the amount of information about the return, we describe l-learning algorithms based on the predictive coding idea for estimating an expected information gain concerning future information and give a convergence proof of the information gain. Using the information gain, we propose the ratio w of return loss to information gain as a new criterion to be used in probabilistic action-selection strategies. In experimental results, we found that our w-based strategy performs well compared with the conventional Q-based strategy.

Algorithms↗