Infectious Diseases Society of America.

The increasing resistance of microorganisms to conventional chemicals and drugs is a serious and evident worldwide problem that has prompted research into the identification of new biocides with broad activity. Plants and their derivatives, such as essential oils, are often used in folk medicine. In nature, essential oils play an important role in the protection of plants. Essential oils contain a wide variety of secondary metabolites that are capable of inhibiting or slowing the growth of bacteria, yeasts and moulds. Essential oils and their components have activity against a variety of targets, particularly the membrane and cytoplasm, and in some cases, they completely change the morphology of the cells. This brief review describes the activity of essential oils against pathogenic bacteria.

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Effect of Essential Oils on Pathogenic Bacteria

International study of the prevalence and outcomes of infection in intensive care units

Formation control analysis and design problems for unmanned aerial vehicle (UAV) swarm systems to achieve time-varying formations are investigated. To achieve predefined time-varying formations, formation protocols are presented for UAV swarm systems first, where the velocities of UAVs can be different when achieving formations. Then, consensus-based approaches are applied to deal with the time-varying formation control problems for UAV swarm systems. Necessary and sufficient conditions for UAV swarm systems to achieve time-varying formations are proposed. An explicit expression of the time-varying formation center function is derived. In addition, a procedure to design the protocol for UAV swarm systems to achieve time-varying formations is given. Finally, a quadrotor formation platform, which consists of five quadrotors is introduced. Theoretical results obtained in this brief are validated on the quardrotor formation platform, and outdoor experimental results are presented.

Time-Varying Formation Control for Unmanned Aerial Vehicles: Theories and Applications

Recently, unmanned aerial vehicles (UAVs), or drones, have attracted a lot of attention, since they represent a new potential market. Along with the maturity of the technology and relevant regulations, a worldwide deployment of these UAVs is expected. Thanks to the high mobility of drones, they can be used to provide a lot of applications, such as service delivery, pollution mitigation, farming, and in the rescue operations. Due to its ubiquitous usability, the UAV will play an important role in the Internet of Things (IoT) vision, and it may become the main key enabler of this vision. While these UAVs would be deployed for specific objectives (e.g., service delivery), they can be, at the same time, used to offer new IoT value-added services when they are equipped with suitable and remotely controllable machine type communications (MTCs) devices (i.e., sensors, cameras, and actuators). However, deploying UAVs for the envisioned purposes cannot be done before overcoming the relevant challenging issues. These challenges comprise not only technical issues, such as physical collision, but also regulation issues as this nascent technology could be associated with problems like breaking the privacy of people or even use it for illegal operations like drug smuggling. Providing the communication to UAVs is another challenging issue facing the deployment of this technology. In this paper, a comprehensive survey on the UAVs and the related issues will be introduced. In addition, our envisioned UAV-based architecture for the delivery of UAV-based value-added IoT services from the sky will be introduced, and the relevant key challenges and requirements will be presented.

Low-Altitude Unmanned Aerial Vehicles-Based Internet of Things Services: Comprehensive Survey and Future Perspectives

A majority of the routing algorithms for unmanned aerial or ground vehicles rely on Global Positioning System (GPS) information for localization. However, disruption of GPS signals, by intention or otherwise, can render these algorithms ineffective. This article provides a way to address this issue by utilizing landmarks to aid localization in GPS-denied environments. Specifically, given a number of vehicles and a set of targets, we formulate a joint routing and landmark placement problem as a combinatorial optimization problem: to compute paths for the vehicles that traverse every target at least once, and to place landmarks to aid the vehicles in localization while each of them traverses its route, such that the sum of the traveling cost and the landmark placement cost is minimized. A mixed-integer linear program is presented, and a set of algorithms and heuristics are proposed for different approaches to address certain issues not covered by the linear program. The performance of each proposed algorithm is evaluated and compared through extensive computational and simulation results.

Algorithms for Localization and Routing of Unmanned Vehicles in GPS-Denied Environments

Learning-based Detection of Stealthy False Data Injection Attack Applied to Cooperative Localization Problem

Transcriptomic Mapping of Neurotoxicity Pathways in the Rat Brain in Response to Intraventricular Polymyxin B

The security of cyber-physical systems, such as vehicle platoons, is critical to ensuring their proper operation and acceptance to society. In platooning, vehicles follow one another according to an agreed upon control law that determines vehicle separation. It has been shown that a vehicle within a platoon and under the control of a malicious actor could cause collisions involving, or decrease the efficiency of, surrounding vehicles. In this paper we focus on detecting, identifying and mitigating so called destabilizing attacks that could cause vehicle collisions. Our approach is decentralized and requires only local sensor information for each vehicle to identify the vehicle responsible for the attack and then deploy an appropriate mitigating controller that prevents collisions. A Deep Learning approach (Convolutional Neural Network) with various data preprocessing techniques are used to detect and identify the malicious vehicle. Results indicate that with noise upto 30% in range/relative speed data we achieve an accuracy upto 96.3%. Also, once the adversarial vehicle is localized, we derive conditions for controller gains using Routh Hurwitz criterion to mitigate the attack and ensure stability of the platoon. Realistic simulator CARLA and MATLAB simulation results validate the effectiveness of our proposed approaches

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Learning Based Vehicle Platooning Threat Detection, Identification and Mitigation

Carbapenemase‐resistant Klebsiella pneumoniae (KP) resistant to multiple antibiotic classes necessitates optimized combination therapy. Our objective is to build a workflow leveraging omics and bacterial count data to identify antibiotic mechanisms that can be used to design and optimize combination regimens. For pharmacodynamic (PD) analysis, previously published static time‐kill studies (J Antimicrob Chemother 70, 2015, 2589) were used with polymyxin B (PMB) and chloramphenicol (CHL) mono and combination therapy against three KP clinical isolates over 24 h. A mechanism‐based model (MBM) was developed using time‐kill data in S‐ADAPT describing PMB‐CHL PD activity against each isolate. Previously published results of PMB (1 mg/L continuous infusion) and CHL (Cmax: 8 mg/L; bolus q6h) mono and combination regimens were evaluated using an in vitro one‐compartment dynamic infection model against a KP clinical isolate (108 CFU/ml inoculum) over 24 h to obtain bacterial samples for multi‐omics analyses. The differentially expressed genes and metabolites in these bacterial samples served as input to develop a partial least squares regression (PLSR) in R that links PD responses with the multi‐omics responses via a multi‐omics pathway analysis. PMB efficacy was increased when combined with CHL, and the MBM described the observed PD well for all strains. The PLSR consisted of 29 omics inputs and predicted MBM PD response (R2 = 0.946). Our analysis found that CHL downregulated metabolites and genes pertinent to lipid A, hence limiting the emergence of PMB resistance. Our workflow linked insights from analysis of multi‐omics data with MBM to identify biological mechanisms explaining observed PD activity in combination therapy.

Proof‐of‐concept for incorporating mechanistic insights from multi‐omics analyses of polymyxin B in combination with chloramphenicol against Klebsiella pneumoniae

Rajnikant Sharma

Papers

Algorithms for Localization and Routing of Unmanned Vehicles in GPS-Denied Environments

Learning-based Detection of Stealthy False Data Injection Attack Applied to Cooperative Localization Problem

Transcriptomic Mapping of Neurotoxicity Pathways in the Rat Brain in Response to Intraventricular Polymyxin B

Learning Based Vehicle Platooning Threat Detection, Identification and Mitigation

Proof‐of‐concept for incorporating mechanistic insights from multi‐omics analyses of polymyxin B in combination with chloramphenicol against Klebsiella pneumoniae