PURPOSE NOD2 plays an important role in the recognition of intracellular bacteria through its ability to sense the components of bacterial peptidoglycan (PGN), namely muramyl dipeptide (MDP) and muramyl tripeptide (MTP). Specific mutations in the human NOD2 gene cause Blau syndrome, an autosomal dominant form of uveitis, arthritis, and dermatitis. As a first step toward understanding the role o...

The immunogenicity and efficacy of an inactivated whole SIVmac (32H) preparation adjuvanted with muramyl dipeptide (SIV-MDP) and a gp120-enriched SIVmac (32H) ISCOM preparation (SIV-ISCOM), were compared by immunizing four rhesus macaques (Macaca mulatta) four times with SIV-MDP and four others in the same way with SIV-ISCOM. Two monkeys immunized with whole inactivated measles virus (MV) adjuv...

Muramyl dipeptide (MDP)-Lys (L18), a synthetic MDP analogue derived from bacterial cell walls, has been reported to be a potent immunoadjuvant that enhances protective immunity against pathogens and tumors by stimulating immune-competent cells, such as monocytes and macrophages. However, it is not known whether MDP-Lys modulates the function of dendritic cells (DCs), which are the most potent a...

Maximum distance profile (MDP) convolutional codes have the property that their column distances are as large as possible. It has been shown that, transmitting over an erasure channel, these codes have optimal recovery rate for windows of a certain length. Reverse MDP convolutional codes have the additional advantage that they are suitable for forward and backward decoding algorithms. Beyond th...

Non-stationary domains, that change in unpredicted ways, are a challenge for agents searching for optimal policies in sequential decision-making problems. This paper presents a combination of Markov Decision Processes (MDP) with Answer Set Programming (ASP), named Online ASP for MDP (oASP(MDP)), which is a method capable of constructing the set of domain states while the agent interacts with a ...

Abstract Markov decision process (MDP) offers a general framework for modelling sequential making where outcomes are random. In particular, it serves as mathematical reinforcement learning. This paper introduces an extension of MDP, namely quantum MDP (qMDP), that can serve model about systems. We develop dynamic programming algorithms policy evaluation and finding optimal policies qMDPs in the...

Controlling the network cost by delivering popular content to users, as well improving streaming quality and overall user experience, have been key goals for providers (CP) in recent years. While proposals improve performance, through caching or other mechanisms (DASH, multicasting, etc.) abound, works proposed turn problem on its head complement such efforts. Instead of trying reduce deliver e...