Pseudomonas aeruginosa is an important bacterial pathogen, particularly as a cause of infections in hospitalised patients, immunocompromised hosts and patients with cystic fibrosis. Surveillance of nosocomial P. aeruginosa infections has revealed trends of increasing antimicrobial resistance, including carbapenem resistance and multidrug resistance. Mechanisms of antimicrobial resistance include multidrug efflux pumps, β-lactamases and downregulation of outer membrane porins. Mechanisms of virulence include secreted toxins and the ability to form biofilms. The effective treatment of infections caused by P. aeruginosa includes prevention when possible, source control measures as necessary and prompt administration of appropriate antibacterial agents. Antibacterial de-escalation should be pursued in patients with an appropriate clinical response, especially when antibacterial susceptibilities are known. Multidrug-resistant P. aeruginosa may require treatment with less commonly used antibacterials (e.g. colistin), but newer anti-pseudomonal antibacterials are expected to be available in the near future.

/pdf/the-epidemiology-pathogenesis-and-treatment-of-pseudomonas-6s99rwxlx7.pdf

The Epidemiology, Pathogenesis and Treatment of Pseudomonas aeruginosa Infections

Vehicular networks enable vehicles to generate and broadcast messages in order to improve traffic safety and efficiency. However, due to the nontrusted environments, it is difficult for vehicles to evaluate the credibilities of received messages. In this paper, we propose a decentralized trust management system in vehicular networks based on blockchain techniques. In this system, vehicles can validate the received messages from neighboring vehicles using Bayesian Inference Model. Based on the validation result, the vehicle will generate a rating for each message source vehicle. With the ratings uploaded from vehicles, roadside units (RSUs) calculate the trust value offsets of involved vehicles and pack these data into a “block.” Then, each RSU will try to add their “blocks” to the trust blockchain which is maintained by all the RSUs. By employing the joint proof-of-work (PoW) and proof-of-stake consensus mechanism, the more total value of offsets (stake) is in the block, the easier RSU can find the nonce for the hash function (PoW). In this way, all RSUs collaboratively maintain an updated, reliable, and consistent trust blockchain. Simulation results reveal that the proposed system is effective and feasible in collecting, calculating, and storing trust values in vehicular networks.

Blockchain-Based Decentralized Trust Management in Vehicular Networks

Summary Plasmodium vivax is geographically the most widely distributed cause of malaria in people, with up to 2·5 billion people at risk and an estimated 80 million to 300 million clinical cases every year—including severe disease and death. Despite this large burden of disease, P vivax is overlooked and left in the shadow of the enormous problem caused by Plasmodium falciparum in sub-Saharan Africa. The technological advances enabling the sequencing of the P vivax genome and a recent call for worldwide malaria eradication have together placed new emphasis on the importance of addressing P vivax as a major public health problem. However, because of this parasite's biology, it is especially difficult to interrupt the transmission of P vivax , and experts agree that the available methods for preventing and treating infections with P vivax are inadequate. It is thus imperative that the development of new methods and strategies become a priority. Advancing the development of such methods needs renewed emphasis on understanding the biology, pathogenesis, and epidemiology of P vivax . This Review critically examines what is known about P vivax , focusing on identifying the crucial gaps that create obstacles to the elimination of this parasite in human populations.

https://www.thelancet.com/pdfs/journals/laninf/PIIS1473-3099(09)70177-X.pdf

Key gaps in the knowledge of Plasmodium vivax, a neglected human malaria parasite

Antibiotic resistance continues to hamper antimicrobial chemotherapy of infectious disease, and while biocide resistance outside of the laboratory is as yet unrealized, in vitro and in vivo episodes of reduced biocide susceptibility are not uncommon. Efflux mechanisms, both drug-specific and multidrug, are important determinants of intrinsic and/or acquired resistance to these antimicrobials in important human pathogens. Multidrug efflux mechanisms are generally chromosome-encoded, with their expression typically resultant from mutations in regulatory genes, while drug-specific efflux mechanisms are encoded by mobile genetic elements whose acquisition is sufficient for resistance. While it has been suggested that drug-specific efflux systems originated from efflux determinants of self-protection in antibiotic-producing Actinomycetes, chromosomal multidrug efflux determinants, at least in Gram-negative bacteria, are appreciated as having an intended housekeeping function unrelated to drug export and resistance. Thus, it will be important to elucidate the intended natural function of these efflux mechanisms in order, for example, to anticipate environmental conditions or circumstances that might promote their expression and, so, compromise antimicrobial chemotherapy. Given the clinical significance of antimicrobial exporters, it is clear that efflux must be considered in formulating strategies for treatment of drug-resistant infections, both in the development of new agents, for example, less impacted by efflux or in targeting efflux directly with efflux inhibitors.

https://www.tandfonline.com/doi/pdf/10.1080/07853890701195262

Efflux pumps as antimicrobial resistance mechanisms

Purpose of reviewUnlike Plasmodium falciparum, Plasmodium vivax rarely causes severe disease in healthy travellers or in temperate endemic regions and has been regarded as readily treatable with chloroquine. However, in tropical areas, recent reports have highlighted severe and fatal disease associa

New developments in Plasmodium vivax malaria: severe disease and the rise of chloroquine resistance.

Crowdsensing offers an efficient approach to meet the demand in large scale sensing applications In crowdsensing, it is of great interest to find the optimal task allocation, which is challenging since sensing tasks with different requirements of quality of sensing are typically associated with specific locations and mobile users are constrained by time budgets We show that the allocation problem is NP hard We then focus on approximation algorithms, and devise an efficient local ratio based algorithm (LRBA) Our analysis shows that the approximation ratio of the aggregate rewards obtained by the optimal allocation to those by LRBA is 5 This reveals that LRBA is efficient, since a lower (but not tight) bound on the approximation ratio is 4 We also discuss about how to decide the fair prices of sensing tasks to provide incentives since mobile users tend to decline the tasks with low incentives We design a pricing mechanism based on bargaining theory, in which the price of each task is determined by the performing cost and market demand (ie, the number of mobile users who intend to perform the task) Extensive simulation results are provided to demonstrate the advantages of our proposed scheme

http://informationnet.asu.edu/pub/infocom14shibo.pdf

Toward Optimal Allocation of Location Dependent Tasks in Crowdsensing

We study the problem of minimum-number full-view area coverage in camera sensor networks, i.e., how to select the minimum number of camera sensors to guarantee the full-view coverage of a given region. Full-view area coverage is challenging because the full-view coverage of a 2-D continuous domain has to be considered. To tackle this challenge, we first study the intrinsic geometric relationship between the full-view area coverage and the full-view point coverage and prove that the full-view area coverage can be guaranteed, as long as a selected full-view ensuring set of points is full-view covered. This leads to a significant dimension reduction for the full-view area coverage problem. Next, we prove that the minimum-number full-view point coverage is NP-hard and propose two approximation algorithms to solve it from two different perspectives, respectively: 1) By introducing a full-view coverage ratio function, we quantify the “contribution” of each camera sensor to the full-view coverage through which we transform the full-view point coverage into a submodular set cover problem and propose a greedy algorithm (GA); and 2) by studying the geometric relationship between the full-view coverage and the traditional coverage, we propose a set-cover-based algorithm (SCA). We analyze the performance of these two approximation algorithms and characterize their approximation ratios. Furthermore, we devise two distributed algorithms that obtain the same approximation ratios as GA and SCA, respectively. Finally, we provide extensive simulation results to validate our analysis.

Full-View Area Coverage in Camera Sensor Networks: Dimension Reduction and Near-Optimal Solutions

Microbiologic aspects of predominant bacteria isolated from the burn patients in Korea

Pricing and task allocation are vital to improving the efficiency in mobile crowdsensing , an emerging human-in-the-loop application paradigm. Previous studies focused on incentive mechanism design for specific sensing applications where one party (either task initiators or platform) can dominate the pricing and task allocation process. These results, however, are not applicable to a free crowdsensing market where multiple task initiators and task participants (mobile users), as peers, are engaged to maximize their own interests. New incentive mechanisms are pressingly needed to produce a solution, so that the interests of all participating parties can be considered. In this paper, appealing to exchange economy theory, we employ the notion of “Walrasian Equilibrium” as a comprehensive metric, at which there exists a price vector for mobile users and an allocation for task initiators such that the allocation is Pareto optimal and the market gets cleared (i.e., all sensing tasks are performed). We consider a standard model where the utility function for sensing quality is monotonically increasing, differentiable, and concave, and the payoff function for a mobile user is linear. To address the problem, we first characterize the supply–demand pattern for a given price vector, which is the subset of mobile users selected by each task initiator to perform the task. We then devise methods for validating the existence of a Walrasian Equilibrium within each supply–demand pattern. One key step is to divide the space of prices into a collection of appropriate cells, based on the hyperplane arrangement, so that each cell has a unique supply–demand pattern. We devise an algorithm that can find a Walrasian Equilibrium in polynomial time, for a case of practical interest where the classes of mobile devices are bounded. Based on the insight, we further consider the general case and design an efficient pattern search (EPS) algorithm to reduce the search space, thus accelerating the search process accordingly. This is realized by choosing the supply–demand pattern which is closer to the “Walrasian Equilibrium” than the pattern in previous iteration in the search process. Our results show that EPS can find an $\epsilon $ -approximation Walrasian Equilibrium in polynomial time for the general case, given a constant $\epsilon $ .

An Exchange Market Approach to Mobile Crowdsensing: Pricing, Task Allocation, and Walrasian Equilibrium

Wireless mesh networks (WMN) are finding increasing usage in city-wide deployments for providing network connectivity. Mesh routers in WMNs typically use multiple wireless channels to enhance the spatial-reuse of frequency bands, often with multiple radios per node. Due to the cooperative nature of WMNs, they are susceptible to many attacks that cannot be defeated by using traditional cryptographic mechanisms of authentication or encryption alone. A solution approach commonly used for defending against such attacks is behavior-based detection in which some nodes overhear communication in their neighborhood to determine if the behavior by a neighbor is legitimate. It has been proposed to use specialized monitoring nodes deployed strategically throughout the network for performing such detection. The problem that arises is where to deploy these monitoring nodes, how to minimize their number, and which channels to tune their radios to, such that the maximum part of the network can be covered. This problem has been solved for single channel networks by a greedy approximation algorithm since the exact solution is NP-hard. The greedy algorithm achieves the best performance, in terms of the worst case, possible among all polynomial-time algorithms provided that P!=NP. In this paper, we solve the problem for multi-channel multi-radio WMNs. The intuitive extension of the greedy algorithm destroys the property of best performance. Instead, we formulate the problem as an integer linear program, solve its linear program relaxation, and then use two rounding techniques that we develop by adapting existing rounding schemes. We thereby present two approximation algorithms. The first, computationally-light algorithm, called probabilistic rounding algorithm gives an expected best performance in the worst case. The second, called deterministic rounding algorithm achieves the best worst-case performance in a deterministic manner. To evaluate how the three algorithms perform in practice, we simulate them in random networks and scale-free networks.

/pdf/optimal-monitoring-in-multi-channel-multi-radio-wireless-4dq7mt7iqu.pdf

Dong-Hoon Shin

Papers

Toward Optimal Allocation of Location Dependent Tasks in Crowdsensing

Full-View Area Coverage in Camera Sensor Networks: Dimension Reduction and Near-Optimal Solutions

Microbiologic aspects of predominant bacteria isolated from the burn patients in Korea

An Exchange Market Approach to Mobile Crowdsensing: Pricing, Task Allocation, and Walrasian Equilibrium

Optimal monitoring in multi-channel multi-radio wireless mesh networks