Experimental Evaluation of Air-to-Ground VHF Band Communication for UAV Relays

Unmanned Aerial Vehicles (UAVs) are a disruptive technology that is transforming a range of industries. Because they operate in the sky, UAVs are able to take advantage of strong Line-of-Sight (LoS) channels for radio propagation, allowing them to communicate over much larger distances than equivalent hardware located at ground level. This has attracted the attention of organisations such as the Irish Defence Forces (DF), with whom we are developing a UAV-based radio relay system as part of the MISTRAL project. This relay system will support digital Very High Frequency (VHF) band communication between ground personnel, while they are deployed on missions. In this paper we report on the initial set of experimental measurements which were carried out to verify the feasibility of VHF signal relaying via UAV. In our experiments, a UAV carrying a lightweight Software-Defined Radio (SDR) receiver is positioned at a height of 500 meters above ground, while two 5W transmitters travel in vehicles on the ground. The SDR receiver measures the received signal power, while the Global Positioning System (GPS) coordinates of the vehicles are logged. This is combined to measure the signal pathloss over distance. Our results show that the signal is received successfully at distances of over 50 kilometers away. While the signals still appear to suffer from a degree of obstacle blockage and multipath effects, these communication ranges are a substantial improvement over the ground communication baseline, and validate the use of UAVs to support wide area emergency communication.Comment: Pre-print of paper presented at the Workshop on Integrating UAVs into 5G and Beyond at IEEE International Conference on Communications 202

A Simpler Machine Learning Model for Acute Kidney Injury Risk Stratification in Hospitalized Patients

Background: Hospitalization-associated acute kidney injury (AKI), affecting one-in-five inpatients, is associated with increased mortality and major adverse cardiac/kidney endpoints. Early AKI risk stratification may enable closer monitoring and prevention. Given the complexity and resource utilization of existing machine learning models, we aimed to develop a simpler prediction model. Methods: Models were trained and validated to predict risk of AKI using electronic health record (EHR) data available at 24 h of inpatient admission. Input variables included demographics, laboratory values, medications, and comorbidities. Missing values were imputed using multiple imputation by chained equations. Results: 26,410 of 209,300 (12.6%) inpatients developed AKI during admission between 13 July 2012 and 11 July 2018. The area under the receiver operating characteristic curve (AUROC) was 0.86 for Random Forest and 0.85 for LASSO. Based on Youden’s Index, a probability cutoff of \u3e0.15 provided sensitivity and specificity of 0.80 and 0.79, respectively. AKI risk could be successfully predicted in 91% patients who required dialysis. The model predicted AKI an average of 2.3 days before it developed. Conclusions: The proposed simpler machine learning model utilizing data available at 24 h of admission is promising for early AKI risk stratification. It requires external validation and evaluation of effects of risk prediction on clinician behavior and patient outcomes

Cell-specific posttranslational events affect functional expression at the plasma membrane but not tetrodotoxin sensitivity of the rat brain IIA sodium channel α-subunit expressed in mammalian cells

The rat brain IIA Na⁺ channel alpha-subunit was expressed and studied in mammalian cells. Cells were infected with a recombinant vaccinia virus (VV) carrying the bacteriophage T7 RNA polymerase gene and were transfected with cDNA encoding the IIA Na⁺ channel α-subunit under control of a T7 promoter. Whole-cell patch-clamp recording showed that functional IIA channels were expressed efficiently (~10 channels/ µm² in approximately 60% of cells) in Chinese hamster ovary (CHO) cells and in neonatal rat ventricular myocytes but were expressed poorly in undifferentiated BC₃H1 cells and failed to express in Ltk⁻ cells. However, voltage-dependent Drosophila Shaker H4 K⁺ channels and Escherichia coli β-galactosidase were expressed efficiently in all four cell types with VV vectors. Because RNA synthesis probably occurs without major differences in the cytoplasm of all infected cell types under the control of the T7 promoter and T7 polymerase, we conclude that cell type-specific expression of the Na⁺ channel probably reflects differences at posttranslational steps. The gating properties of the IIA Na⁺ currents expressed in cardiac myocytes differed from those expressed in CHO cells; most noticeably, the IIA Na⁺ currents displayed more rapid macroscopic inactivation when expressed in cardiac myocytes. These differences also suggest cell- specific posttranslational modifications. IIA channels were blocked by ~90% by 90 nM TTX when expressed either in CHO cells or in cardiac myocytes; the latter also continued to display endogenous TTX- resistant Na⁺ currents. Therefore, the TTX binding site of the channel is not affected by cell-specific modifications and is encoded by the primary amino acid sequence

Mrk 609: resolving the circum-nuclear structure with near-infrared integral field spectroscopy

We present first results of near infrared J and H+K ESO-SINFONI integral field spectroscopy of the composite starburst/Seyfert 1.8 galaxy Mrk 609. The data were taken during the science verification period of SINFONI. We aim to investigate the morphology and excitation conditions within the central 2 kpc. Additional Nobeyama 45 m CO(1-0) data are presented, which we used to estimate the molecular gas mass. The source was selected from a sample of adaptive optics suitable, SDSS/ROSAT based, X-ray bright AGN with redshifts of 0.03 < z < 1. This sample allows for a detailed study of the NIR properties of the nuclear and host environments with high spectral and spatial resolution. Our NIR data reveal a complex emission-line morphology, possibly associated with a nuclear bar seen in the reconstructed continuum images. The detections of [SiVI] and a broad Pa alpha component are clear indicators for the presence of an accreting super-massive black hole at the center of Mrk 609. In agreement with previous observations we find that the circum-nuclear emission is not significantly extincted. The analysis of the high angular resolution rotational-vibrational molecular hydrogen and forbidden [FeII] emission reveals a LINER character of the nucleus. The large H_2 gas mass deduced from the CO(1-0) observation provides the fuel needed to feed the starburst and Seyfert activity in Mrk 609. High angular resolution imaging spectroscopy provides an ideal tool to resolve the nuclear and starburst contribution in active galaxies. We show that Mrk 609 exhibits LINER features, that appear to be hidden in larger aperture visible/NIR spectra.Comment: published by A&A, 19 pages, 16 figures, version with high resolution figures is available via http://www.ph1.uni-koeln.de/~zuther/mrk609.pd

Development and validation of a multivariable risk factor questionnaire to detect oesophageal cancer in 2-week wait patients

INTRODUCTION: Oesophageal cancer is associated with poor health outcomes. Upper GI (UGI) endoscopy is the gold standard for diagnosis but is associated with patient discomfort and low yield for cancer. We used a machine learning approach to create a model which predicted oesophageal cancer based on questionnaire responses. METHODS: We used data from 2 separate prospective cross-sectional studies: the Saliva to Predict rIsk of disease using Transcriptomics and epigenetics (SPIT) study and predicting RIsk of diSease using detailed Questionnaires (RISQ) study. We recruited patients from National Health Service (NHS) suspected cancer pathways as well as patients with known cancer. We identified patient characteristics and questionnaire responses which were most associated with the development of oesophageal cancer. Using the SPIT dataset, we trained seven different machine learning models, selecting the best area under the receiver operator curve (AUC) to create our final model. We further applied a cost function to maximise cancer detection. We then independently validated the model using the RISQ dataset. RESULTS: 807 patients were included in model training and testing, split in a 70:30 ratio. 294 patients were included in model validation. The best model during training was regularised logistic regression using 17 features (median AUC: 0.81, interquartile range (IQR): 0.69-0.85). For testing and validation datasets, the model achieved an AUC of 0.71 (95% CI: 0.61-0.81) and 0.92 (95% CI: 0.88-0.96) respectively. At a set cut off, our model achieved a sensitivity of 97.6% and specificity of 59.1%. We additionally piloted the model in 12 patients with gastric cancer; 9/12 (75%) of patients were correctly classified. CONCLUSIONS: We have developed and validated a risk stratification tool using a questionnaire approach. This could aid prioritising patients at high risk of having oesophageal cancer for endoscopy. Our tool could help address endoscopic backlogs caused by the COVID-19 pandemic

Fate of drugs during wastewater treatment

This is the post-print version of the final paper published in TrAC Trends in Analytical Chemistry. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Recent trends in the determination of pharmaceutical drugs in wastewaters focus on the development of rapid multi-residue methods. This review addresses recent analytical trends in drug determination in environmental matrices used to facilitate fate studies. Analytical requirements for further fate evaluation and tertiary process selection and optimization are also discussed.EPSRC, Northumbrian Water, Anglian Water, Severn Trent Water, Yorkshire Water, and United Utilities