The UK Renal Registry Advanced CKD Study: frequency of incorrect reporting of date of start of RRT

Background: A preliminary review of the UK Renal Registry (UKRR) pre-RRT study data revealed results suggesting that, for some patients, the date of start of renal replacement therapy (RRT), as reported to the UKRR, was incorrect and often significantly later than the true date of start. A more detailed study then aimed to validate a set of criteria to identify patients with an incorrect start date. Methods: Pre-RRT laboratory data were electronically extracted from 8,810 incident RRT patients from 9 UK renal centres. Any patient with a low urea (<15 mmol/L) at the start of RRT or with a substantial improvement in kidney function (either a fall in urea >10 mmol/L or rise in eGFR >2 ml/min/1.73 m) within the two months prior to RRT were considered to potentially have an incorrect date of start. In 4
selected centres, the electronic patient records of all patients flagged were reviewed to validate these criteria.
Results: Of 8,810 patients, 1,616 (18.3%) were flagged by the identification criteria as having a potentially incorrect date of start of RRT, although a single centre accounted for 41% of the total flagged cohort. Of these flagged patients, 61.7% had been assigned an incorrect date of start of haemodialysis (HD), 5.7% had evidence of acute RRT being given before the reported date of start of HD
and 9.2% had evidence of starting peritoneal dialysis exchanges prior to the reported date of start. Of
those flagged, 10.7% had a correct date of start of RRT.
Conclusions: Accurate reporting of RRT episodes is vital for the analysis of time dependent studies such as survival or time to transplantation. A proportion of patients starting RRT were assigned an incorrect start date. In order to improve the accuracy of this reporting the UK Renal Registry
must work with renal centres and clinical staff on improving data input for the start of RRT.

Resolution theory, and static and frequency-dependent cross-talk in piezoresponse force microscopy

Probing the functionality of materials locally by means of scanning probe microscopy (SPM) requires a reliable framework for identifying the target signal and separating it from the effects of surface morphology and instrument non-idealities, e.g. instrumental and topographical cross-talk. Here we develop a linear resolution theory framework in order to describe the cross-talk effects, and apply it for elucidation of frequency-dependent cross-talk mechanisms in piezoresponse force microscopy. The use of a band excitation method allows electromechanical/electrical and mechanical/topographic signals to be unambiguously separated. The applicability of a functional fit approach and multivariate statistical analysis methods for identification of data in band excitation SPM is explored.

Identification of a high-risk haplotype for the dystrobrevin binding protein 1 (DTNBP1) gene in the Irish study of high-density schizophrenia families

A recent report showed significant associations between several SNPs in a previously unknown EST cluster with schizophrenia. (1). The cluster was identified as the human dystrobrevin binding protein 1 gene (DTNBP1) by sequence database comparisons and homology with mouse DTNBP1. (2). However, the linkage disequilibrium (LD) among the SNPs in DTNBP1 as well as the pattern of significant SNP-schizophrenia association was complex. This raised several questions such as the number of susceptibility alleles that may be involved and the size of the region where the actual disease mutation(s) could be located. To address these questions, we performed different single-marker tests on the 12 previously studied and 2 new SNPs in DTNBP1 that were re-scored using an improved procedure, and performed a variety of haplotype analyses. The sample consisted of 268 Irish multiplex families selected for high density of schizophrenia. Results suggested a simple structure where the LD in the target region could be explained by 6 haplotypes that together accounted for 96% of haplotype diversity in the whole sample. From these six, a single high-risk haplotype was identified that showed a significant association with schizophrenia and explained the pattern of significant findings in the analyses with individual markers. This haplotype was 30 kb long, had a large effect, could be measured with two tag SNPs only, had a frequency of 6% in our sample, seemed to be of relatively recent origin in evolutionary terms, and was equally distributed over Ireland. Implications of these findings for follow-up and replication studies are discussed.

Identification of damping in a bridge using a moving instrumented vehicle

In recent years, there has been a significant increase in the number of bridges which are being instrumented and monitored on an ongoing basis. This is in part due to the introduction of bridge management systems designed to provide a high level of protection to the public and early warning if the bridge becomes unsafe. This paper investigates a novel alternative; a low-cost method consisting of the use of a vehicle fitted with accelerometers on its axles to monitor the dynamic behaviour of bridges. A simplified half-car vehicle-bridge interaction model is used in theoretical simulations to test the effectiveness of the approach in identifying the damping ratio of the bridge. The method is tested for a range of bridge spans and vehicle velocities using theoretical simulations and the influences of road roughness, initial vibratory condition of the vehicle, signal noise, modelling errors and frequency matching on the accuracy of the results are investigated.

Identification of the Dynamic Parameters of a Real Bridge under Different Damage Conditions in a Field Experiment

This paper presents the results of a real bridge field experiment in which damage was applied artificially to a steel truss bridge. The aim of this paper is to identify the dynamic parameters of this bridge using conventional techniques and investigate the effect of various damage conditions on those parameters. In the field experiment, acceleration measurements were recorded at a number of locations on the bridge deck. To excite the bridge, a two-axle van was driven across the bridge at constant speed. Dynamic parameters, such as the bridge mode shape, natural frequency and damping constant, are identified from the acceleration signals using existing techniques such as the fast Fourier transform, logarithmic decrement and frequency domain decomposition. The variation of these parameters under the influence of artificially applied damage conditions is investigated in order to evaluate their sensitivity to the bridge damage.

Active singly and dual polarised interwoven spiral frequency selective surfaces

Reconfigurable bi-state interwoven spiral FSSs are explored in this work. Their switching capability is realized by pin diodes that enable the change of the electromagnetic response between transparent and reflecting modes at the specified frequencies in both singly and dual polarised unit cell configurations. The proposed topologies are single layer FSS with their elements acting also as dc current carrying conductors supplying the bias signal for switching pin diodes between the on and off states, thus avoiding the need of external bias lines that can cause parasitic resonances and affect the response at oblique incidence. The presented simulation results show that such active FSSs have potentially good isolation between the transmission and reflection states, while retaining the substantially subwavelength response of the unit cell with large fractional bandwidths (FBWs) inherent to the original passive FSSs.

Material Identification of Loose Particles in Sealed Electronic Devices Using PCA and SVM

The existence of loose particles left inside the sealed electronic devices is one of the main factors affecting the reliability of the whole system. It is important to identify the particle material for analyzing their source. The conventional material identification algorithms mainly rely on time, frequency and wavelet domain features. However, these features are usually overlapped and redundant, resulting in unsatisfactory material identification accuracy. The main objective of this paper is to improve the accuracy of material identification. First, the principal component analysis (PCA) is employed to reselect the nine features extracted from time and frequency domains, leading to six less correlated principal components. And then the reselected principal components are used for material identification using a support vector machine (SVM). Finally, the experimental results show that this new method can effectively distinguish the type of materials including wire, aluminum and tin particles.

Identification of Small Signal Oscillation Mode Parameters from Simulated and Actual PMU Ringdown Data

This paper presents the practical use of Prony Analysis to identify small signal oscillation mode parameters from simulated and actual phasor measurement unit (PMU) ringdown data. A well-known two-area four-machine power system was considered as a study case while the latest PMU ringdown data were collected from a double circuit 275 kV main interconnector on the Irish power system. The eigenvalue analysis and power spectral density were also conducted for the purpose of comparison. The capability of Prony Analysis to identify the mode parameters from three different types of simulated PMU ringdown data has been shown successfully. Furthermore, the results indicate that the Irish power system has dominant frequency modes at different frequencies. However, each mode has good system damping.

An empirical polarization domain channel availability model for cognitive radio

In dynamic spectrum access networks, cognitive radio terminals monitor their spectral environment in order to detect and opportunistically access unoccupied frequency channels. The overall performance of such networks depends on the spectrum occupancy or availability patterns. Accurate knowledge on the channel availability enables optimum performance of such networks in terms of spectrum and energy efficiency. This work proposes a novel probabilistic channel availability model that can describe the channel availability in different polarizations for mobile cognitive radio terminals that are likely to change their orientation during their operation. A Gaussian approximation is used to model the empirical occupancy data that was obtained through a measurement campaign in the cellular frequency bands within a realistic operational scenario.