Mytilus galloprovincialis Myticin C: A Chemotactic Molecule with Antiviral Activity and Immunoregulatory Properties

Previous research has shown that an antimicrobial peptide (AMP) of the myticin class C (Myt C) is the most abundantly expressed gene in cDNA and suppressive subtractive hybridization (SSH) libraries after immune stimulation of mussel Mytilus galloprovincialis. However, to date, the expression pattern, the antimicrobial activities and the immunomodulatory properties of the Myt C peptide have not been determined. In contrast, it is known that Myt C mRNA presents an unusual and high level of polymorphism of unidentified biological significance. Therefore, to provide a better understanding of the features of this interesting molecule, we have investigated its function using four different cloned and expressed variants of Myt C cDNA and polyclonal anti-Myt C sera. The in vivo results suggest that this AMP, mainly present in hemocytes, could be acting as an immune system modulator molecule because its overexpression was able to alter the expression of mussel immune-related genes (as the antimicrobial peptides Myticin B and Mytilin B, the C1q domain-containing protein MgC1q, and lysozyme). Moreover, the in vitro results indicate that Myt C peptides have antimicrobial and chemotactic properties. Their recombinant expression in a fish cell line conferred protection against two different fish viruses (enveloped and non-enveloped). Cell extracts from Myt C expressing fish cells were also able to attract hemocytes. All together, these results suggest that Myt C should be considered not only as an AMP but also as the first chemokine/cytokine-like molecule identified in bivalves and one of the few examples in all of the invertebrates

Reinforcement learning method for ad networks ordering in real-time bidding

High turnover of online advertising and especially real time bidding makes this ad market very attractive to beneficiary stakeholders. For publishers, it is as easy as placing some slots in their webpages and sell these slots in the available online auctions. It is important to determine which online auction market to send their slots to. Based on the traditional Waterfall Strategy, publishers have a fixed ordering of preferred online auction markets, and sell the ad slots by trying these markets sequentially. This fixed-order strategy replies heavily on the experience of publishers, and often it does not provide highest revenue. In this paper, we propose a method for dynamically deciding on the ordering of auction markets for each available ad slot. This method is based on reinforcement learning (RL) and learns the state-action through a tabular method. Since the state-action space is sparse, a prediction model is used to solve this sparsity. We analyze a real-time bidding dataset, and then show that the proposed RL method on this dataset leads to higher revenues. In addition, a sensitivity analysis is performed on the parameters of the method