A Semi-supervised Sensing Rate Learning based CMAB scheme to combat COVID-19 by trustful data collection in the crowd.

The recruitment of trustworthy and high-quality workers is an important research issue for MCS. Previous studies either assume that the qualities of workers are known in advance, or assume that the platform knows the qualities of workers once it receives their collected data. In reality, to reduce costs and thus maximize revenue, many strategic workers do not perform their sensing tasks honestly and report fake data to the platform, which is called False data attacks. And it is very hard for the platform to evaluate the authenticity of the received data In this paper, an incentive mechanism named Semi-supervision based Combinatorial Multi-Armed Bandit reverse Auction (SCMABA) is proposed to solve the recruitment problem of multiple unknown and strategic workers in MCS. First, we model the worker recruitment as a multi-armed bandit reverse auction problem and design an UCB-based algorithm to separate the exploration and exploitation, regarding the Sensing Rates (SRs) of recruited workers as the gain of the bandit Next, a Semi-supervised Sensing Rate Learning (SSRL) approach is proposed to quickly and accurately obtain the workers' SRs, which consists of two phases, supervision and self-supervision. Last, SCMABA is designed organically combining the SRs acquisition mechanism with multi-armed bandit reverse auction, where supervised SR learning is used in the exploration, and the self-supervised one is used in the exploitation. We theoretically prove that our SCMABA achieves truthfulness and individual rationality and exhibits outstanding performances of the SCMABA mechanism through in-depth simulations of real-world data traces.

External lysis of Escherichia coli by a bacteriophage endolysin modified with hydrophobic amino acids.

Drug-resistant bacteria are a serious threat to global public health. Gram-positive bacterial endolysin preparations have been successfully used to fight Gram-positive bacteria as a novel antimicrobial replacement strategy. However, Gram-negative bacterial phage endolysins cannot be applied directly to destroy Gram-negative strains due to the externally inaccessible peptidoglycan layer of the cell wall; this has seriously hampered the development of endolysin-like antibiotics against Gram-negative bacteria. In this study, 3-12 hydrophobic amino acids were successively added to the C-terminus of Escherichia coli phage endolysin Lysep3 to create five different hydrophobic-modified endolysins. Compared with endogenous Lysep3, endolysins modified with hydrophobic amino acids surprisingly could kill E. coli from outside of the cell at the appropriate pH and endolysin concentration. The lysis ability of modified endolysins were enhanced with increasing numbers of hydrophobic amino acids at the C-terminus of endolysin. Thus, these findings demonstrate that the enhancement of hydrophobicity at the C-terminus enables the endolysin to act upon E. coli from the outside, representing a novel method of lysing Gram-negative antibiotic-resistant bacteria.