Performance Analysis of UAV Enabled Disaster Recovery Network: A Stochastic Geometric Framework based on Matern Cluster Processes

Drones will be employed by Facebook and Google for capacity off-loading in front/back hauling scenarios utilizing drone-empowered autonomous heterogeneous networks. But in another application, drone-based, post-disaster recovery of communication networks will also be of crucial importance in the design of future smart cities. So, in order to address the design issues of these latter networks, we present (from a stochastic geometric perspective) a comprehensive statistical framework for the spatial distribution of these hybrid user-centric drone/micro cellular networks. We introduce the novel idea of using a Stenien’s cell (with variable radius) to model the region over which the drones will be distributed and the drones will effectively form a Matern cluster process (MCP) across the original network space. We then employ this newly developed framework to investigate the impact of changing several parameters on the performance of the new drone small-cell clustered networks (DSCCNs) and we develop appropriate closed-form expressions that model the performance (later validated via Monte Carlo simulations)

Wireless Power Transfer System for Battery-Less Sensor Nodes

For the first time, the design and implementation of a fully-integrated wireless information and power transfer system, operating at 24 GHz and enabling battery-less sensor nodes, is presented in this paper. The system consists of an RF power source, a receiver antenna array, a rectifier, and a battery-less sensor node which communicates via backscatter modulation at 868 MHz. The rectifier circuits use commercially available Schottky diodes to convert the RF power to DC with a measured efficiency of up to 35%, an improvement of ten percentage points compared with previously reported results. The rectifiers and the receive antenna arrays were jointly designed and optimised, thereby reducing the overall circuit size. The battery-less sensor transmitted data to a base station realised as a GNU Radio flow running on a bladeRF Software Defined Radio module. The whole system was tested in free-space in laboratory conditions and was capable of providing sufficient energy to the sensor node in order to enable operation and wireless communication at a distance of 0.15 metres

Optimal Coverage and Rate in Downlink Cellular Networks: A SIR Meta-Distribution Based Approach

In this paper, we present a detailed analysis of the coverage and spectral efficiency of a downlink cellular network. Rather than relying on the first order statistics of received signal-to- interference-ratio (SIR) such as coverage probability, we focus on characterizing its meta- distribution. Our analysis is based on the alpha- beta-gamma (ABG) path-loss model which provides us with the flexibility to analyze urban macro (UMa) and urban micro (UMi) deployments. With the help of an analytical framework, we demonstrate that selection of underlying degrees-of-freedom such as BS height for optimization of first order statistics such as coverage probability is not optimal in the network-wide sense. Consequently, the SIR meta-distribution must be employed to select appropriate operational points which will ensure consistent user experiences across the network. Our design framework reveals that the traditional results which advocate lowering of BS heights or even optimal selection of BS height do not yield consistent service experience across users. By employing the developed framework we also demonstrate how available spectral resources in terms of time slots/channel partitions can be optimized by considering the meta-distribution of the SIR

Channel State Information based Device Free Wireless Sensing for IoT Devices Employing TinyML

The channel state information (CSI) of the sub-carriers employed in orthogonal frequency division multiplexing (OFDM) systems has been employed traditionally for channel equalisation. However, the CSI intrinsically is a signature of the operational RF environment and can serve as a proxy for certain activities in the operational environment. For instance, the CSI gets influenced by scatterers and therefore can be an indicator of how many scatterers or if there are mobile scatterers etc. The mapping between the activities whose signature CSI encodes and the raw data is not deterministic. Nevertheless, machine learning (ML) based approaches can provide a reliable classification for patterns of life. Most of these approaches have only been implemented in lab environments. This is mainly because the hardware requirements for capturing CSI, processing it and performing signal-processing algorithms are too complex to be implemented in commercial devices. The increased proliferation of IoT sensors and the development of edge-based ML capabilities using the TinyML framework opens up possibilities for the implementation of these techniques at scale on commercial devices. Using RF signature instead of more invasive methods e.g. cameras or wearable devices provide ease of deployment, intrinsic privacy and better usability. The design space of device-free wireless sensing (DFWS) is complex and involves device, firmware and ML considerations. In this article, we present a comprehensive overview and key considerations for the implementation of such solutions. We also demonstrate the viability of these approaches using a simple case study

Leptospira interrogans Stably Infects Zebrafish Embryos, Altering Phagocyte Behavior and Homing to Specific Tissues

Leptospirosis is an extremely widespread zoonotic infection with outcomes ranging from subclinical infection to fatal Weil's syndrome. Despite the global impact of the disease, key aspects of its pathogenesis remain unclear. To examine in detail the earliest steps in the host response to leptospires, we used fluorescently labelled Leptospira interrogans serovar Copenhageni to infect 30 hour post fertilization zebrafish embryos by either the caudal vein or hindbrain ventricle. These embryos have functional innate immunity but have not yet developed an adaptive immune system. Furthermore, they are optically transparent, allowing direct visualization of host–pathogen interactions from the moment of infection. We observed rapid uptake of leptospires by phagocytes, followed by persistent, intracellular infection over the first 48 hours. Phagocytosis of leptospires occasionally resulted in formation of large cellular vesicles consistent with apoptotic bodies. By 24 hours, clusters of infected phagocytes were accumulating lateral to the dorsal artery, presumably in early hematopoietic tissue. Our observations suggest that phagocytosis may be a key defense mechanism in the early stages of leptospirosis, and that phagocytic cells play roles in immunopathogenesis and likely in the dissemination of leptospires to specific target tissues

A Novel Extracytoplasmic Function (ECF) Sigma Factor Regulates Virulence in Pseudomonas aeruginosa

Next to the two-component and quorum sensing systems, cell-surface signaling (CSS) has been recently identified as an important regulatory system in Pseudomonas aeruginosa. CSS systems sense signals from outside the cell and transmit them into the cytoplasm. They generally consist of a TonB-dependent outer membrane receptor, a sigma factor regulator (or anti-sigma factor) in the cytoplasmic membrane, and an extracytoplasmic function (ECF) sigma factor. Upon perception of the extracellular signal by the receptor the ECF sigma factor is activated and promotes the transcription of a specific set of gene(s). Although most P. aeruginosa CSS systems are involved in the regulation of iron uptake, we have identified a novel system involved in the regulation of virulence. This CSS system, which has been designated PUMA3, has a number of unusual characteristics. The most obvious difference is the receptor component which is considerably smaller than that of other CSS outer membrane receptors and lacks a β-barrel domain. Homology modeling of PA0674 shows that this receptor is predicted to be a bilobal protein, with an N-terminal domain that resembles the N-terminal periplasmic signaling domain of CSS receptors, and a C-terminal domain that resembles the periplasmic C-terminal domains of the TolA/TonB proteins. Furthermore, the sigma factor regulator both inhibits the function of the ECF sigma factor and is required for its activity. By microarray analysis we show that PUMA3 regulates the expression of a number of genes encoding potential virulence factors, including a two-partner secretion (TPS) system. Using zebrafish (Danio rerio) embryos as a host we have demonstrated that the P. aeruginosa PUMA3-induced strain is more virulent than the wild-type. PUMA3 represents the first CSS system dedicated to the transcriptional activation of virulence functions in a human pathogen

The Role of Friends’ Disruptive Behavior in the Development of Children’s Tobacco Experimentation: Results from a Preventive Intervention Study

Having friends who engage in disruptive behavior in childhood may be a risk factor for childhood tobacco experimentation. This study tested the role of friends’ disruptive behavior as a mediator of the effects of a classroom based intervention on children’s tobacco experimentation. 433 Children (52% males) were randomly assigned to the Good Behavior Game (GBG) intervention, a universal preventive intervention targeting disruptive behavior, and facilitating positive prosocial peer interactions. Friends’ disruptive behavior was assessed from age 7–10 years. Participants’ experimentation with tobacco was assessed annually from age 10–13. Reduced rates in tobacco experimentation and friends’ disruptive behavior were found among GBG children, as compared to controls. Support for friends’ disruptive behavior as a mediator in the link between intervention status and tobacco experimentation was found. These results remained after controlling for friends’ and parental smoking status, and child ADHD symptoms. The results support the role of friends’ disruptive behavior in preadolescents’ tobacco experimentation

Zebrafish as a new model to study effects of periodontal pathogens on cardiovascular diseases.

Porphyromonas gingivalis (Pg) is a keystone pathogen in the aetiology of chronic periodontitis. However, recent evidence suggests that the bacterium is also able to enter the bloodstream, interact with host cells and tissues, and ultimately contribute to the pathogenesis of cardiovascular disease (CVD). Here we established a novel zebrafish larvae systemic infection model showing that Pg rapidly adheres to and penetrates the zebrafish vascular endothelium causing a dose- and time-dependent mortality with associated development of pericardial oedemas and cardiac damage. The in vivo model was then used to probe the role of Pg expressed gingipain proteases using systemically delivered gingipain-deficient Pg mutants, which displayed significantly reduced zebrafish morbidity and mortality compared to wild-type bacteria. In addition, we used the zebrafish model to show efficacy of a gingipain inhibitor (KYT) on Pg-mediated systemic disease, suggesting its potential use therapeutically. Our data reveal the first real-time in vivo evidence of intracellular Pg within the endothelium of an infection model and establishes that gingipains are crucially linked to systemic disease and potentially contribute to CVD

A fluorescent hormone biosensor reveals the dynamics of jasmonate signalling in plants

Activated forms of jasmonic acid (JA) are central signals coordinating plant responses to stresses, yet tools to analyse their spatial and temporal distribution are lacking. Here we describe a JA perception biosensor termed Jas9-VENUS that allows the quantification of dynamic changes in JA distribution in response to stress with high spatiotemporal sensitivity. We show that Jas9-VENUS abundance is dependent on bioactive JA isoforms, the COI1 co-receptor, a functional Jas motif and proteasome activity. We demonstrate the utility of Jas9-VENUS to analyse responses to JA in planta at a cellular scale, both quantitatively and dynamically. This included using Jas9-VENUS to determine the cotyledon-to-root JA signal velocities on wounding, revealing two distinct phases of JA activity in the root. Our results demonstrate the value of developing quantitative sensors such as Jas9-VENUS to provide high-resolution spatiotemporal data about hormone distribution in response to plant abiotic and biotic stresses