Uplink precoding optimization for NOMA cellular-connected UAV networks

Unmanned aerial vehicles (UAVs) are playing an important role in wireless networks, due to their cost effectiveness and flexible deployment. Particularly, integrating UAVs into existing cellular networks has great potential to provide high-rate and ultra-reliable communications. In this paper, we investigate the uplink transmission in a cellular network from a UAV using non-orthogonal multiple access (NOMA) and from ground users to base stations (BSs). Specifically, we aim to maximize the sum rate of uplink from UAV to BSs in a specific band as well as from the UAV’s co-channel users to their associated BSs via optimizing the precoding vectors at the multi-antenna UAV. To mitigate the interference, we apply successive interference cancellation (SIC) not only to the UAV-connected BSs, but also to the BSs associated with ground users in the same band. The precoding optimization problem with constraints on the SIC decoding and the transmission rate requirements is formulated, which is non-convex. Thus, we introduce auxiliary variables and apply approximations based on the first-order Taylor expansion to convert it into a second-order cone programming. Accordingly, an iterative algorithm is designed to obtain the solution to the problem with low complexity. Numerical results are presented to demonstrate the effectiveness of our proposed scheme

Developing integrated crop knowledge networks to advance candidate gene discovery

AbstractThe chances of raising crop productivity to enhance global food security would be greatly improved if we had a complete understanding of all the biological mechanisms that underpinned traits such as crop yield, disease resistance or nutrient and water use efficiency. With more crop genomes emerging all the time, we are nearer having the basic information, at the gene-level, to begin assembling crop gene catalogues and using data from other plant species to understand how the genes function and how their interactions govern crop development and physiology. Unfortunately, the task of creating such a complete knowledge base of gene functions, interaction networks and trait biology is technically challenging because the relevant data are dispersed in myriad databases in a variety of data formats with variable quality and coverage. In this paper we present a general approach for building genome-scale knowledge networks that provide a unified representation of heterogeneous but interconnected datasets to enable effective knowledge mining and gene discovery. We describe the datasets and outline the methods, workflows and tools that we have developed for creating and visualising these networks for the major crop species, wheat and barley. We present the global characteristics of such knowledge networks and with an example linking a seed size phenotype to a barley WRKY transcription factor orthologous to TTG2 from Arabidopsis, we illustrate the value of integrated data in biological knowledge discovery. The software we have developed (www.ondex.org) and the knowledge resources (http://knetminer.rothamsted.ac.uk) we have created are all open-source and provide a first step towards systematic and evidence-based gene discovery in order to facilitate crop improvement

MorphoCol: an ontology-based knowledgebase for the characterisation of clinically significant bacterial colony morphologies

Background One of the major concerns of the biomedical community is the increasing prevalence of antimicrobial resistant microorganisms. Recent findings show that the diversification of colony morphology may be indicative of the expression of virulence factors and increased resistance to antibiotic therapeutics. To transform these findings, and upcoming results, into a valuable clinical decision making tool, colony morphology characterisation should be standardised. Notably, it is important to establish the minimum experimental information necessary to contextualise the environment that originated the colony morphology, and describe the main morphological features associated unambiguously. Results This paper presents MorphoCol, a new ontology-based tool for the standardised, consistent and machine-interpretable description of the morphology of colonies formed by human pathogenic bacteria. The Colony Morphology Ontology (CMO) is the first controlled vocabulary addressing the specificities of the morphology of clinically significant bacteria, whereas the MorphoCol publicly Web-accessible knowledgebase is an end-user means to search and compare CMO annotated colony morphotypes. Its ultimate aim is to help correlate the morphological alterations manifested by colony-forming bacteria during infection with their response to the antimicrobial treatments administered. Conclusions MorphoCol is the first tool to address bacterial colony morphotyping systematically and deliver a free of charge resource to the community. Hopefully, it may introduce interesting features of analysis on pathogenic behaviour and play a significant role in clinical decision making.The authors thank the project PTDC/SAU-ESA/646091/2006/FCOMP-01-0124-FEDER-007480FCT, the Strategic Project PEst-OE/EQB/LA0023/2013, the Project "BioHealth - Biotechnology and Bioengineering approaches to improve health quality", Ref. NORTE-07-0124-FEDER-000027, co-funded by the Programa Operacional Regional do Norte (ON.2 - O Novo Norte), QREN, FEDER, the project "RECI/BBB-EBI/0179/2012 - Consolidating Research Expertise and Resources on Cellular and Molecular Biotechnology at CEB/IBB", Ref. FCOMP-01-0124-FEDER-027462, FEDER, and the Agrupamento INBIOMED from DXPCTSUG-FEDER unha maneira de facer Europa (2012/273). The research leading to these results has received funding from the European Union's Seventh Framework Programme FP7/REGPOT-2012-2013.1 under grant agreement no 316265, BIOCAPS. This document reflects only the author's views and the European Union is not liable for any use that may be made of the information contained herein. The authors also acknowledge PhD Grant of Ana Margarida Sousa SFRH/BD/72551/2010