Systemic inflammation and residual viraemia in HIV-positive adults on protease inhibitor monotherapy: a cross-sectional study.

Increased levels of markers of systemic inflammation have been associated with serious non-AIDS events even in patients on fully suppressive antiretroviral therapy. We explored residual viremia and systemic inflammation markers in patients effectively treated with ritonavir-boosted protease inhibitor monotherapy (PImono)

The identification and validity of congenital malformation diagnoses in UK electronic health records: A systematic review

PURPOSE: To describe the methods used to identify and validate congenital malformation diagnoses recorded in UK electronic health records, and the results of validation studies. METHODS: Medline and Embase were searched for publications between 1987 and 2019 that involved identifying congenital malformations from UK electronic health records using diagnostic codes. The methods and code-lists used to identify congenital malformations, and the methods and results of validations, were examined. RESULTS: We retrieved 54 eligible studies; 36 identified congenital malformations from primary care data and 18 from secondary care data alone or in combination with birth and/or death records. Identification in secondary care data relied on codes from the 'Q' chapter for congenital malformations in ICD-10. In contrast, studies using primary care data frequently used additional codes outside of the 'P' chapter for congenital malformation diagnoses in Read, although the exact codes used were not always clear. Eight studies validated diagnoses identified in primary care data. The positive predictive value was highest (80-100%) for congenital malformations overall, major malformations, and heart defects although the validity of the reference standard used was often uncertain. It was lowest for neural tube defects (71%) and developmental hip dysplasia (56%). CONCLUSIONS: Studies identifying congenital malformations from primary care data provided limited details about the methods used. The few validation studies were limited to diagnoses recorded in primary care. Further assessments of all measures of validity in both data sources and of other malformation subgroups are needed, using robust reference standards and adhering to reporting guidelines. This article is protected by copyright. All rights reserved

The safety of influenza vaccination in pregnancy: Examining major congenital malformations as potential adverse outcomes using UK electronic health records

The aim of this thesis was to examine the safety of maternal influenza vaccination with respect to major congenital malformations in live-born infants. UK electronic health records from the Clinical Practice Research Datalink were used and work was conducted using linked primary care, hospitalisation and mortality data. The first study systematically reviewed existing methods for identifying congenital malformations in UK electronic health records, and the results of any validation studies. Studies relied on stand-alone primary care or hospitalisation data to identify congenital malformations; none examined linkage between these. Overall, congenital malformations recorded in primary care data had a high positive predictive value (80-100%) but the validity in hospitalisation data was not explored. Methods from these studies informed the development of a comprehensive algorithm to identify major malformations in live-born infants. Using linked primary care, hospitalisation and mortality data, the second study in this thesis demonstrated that just 20% (95% CI, 19-21) of infants with a major malformation had evidence of their condition in both primary care and hospitalisation data. Almost 65% (95% CI, 64-66) only had evidence in hospitalisation data. The third study demonstrated that the overall prevalence of major malformations established in primary care data using this algorithm was slightly higher than published estimates from other studies using UK primary care records (Prevalence ratio, 1.2; 95% CI, 1.2-1.3). Comparisons of linked data with population-based registry data demonstrated a four-fold higher prevalence for major malformations overall in the linked electronic health records (Prevalence ratio, 4.3; 95% CI, 4.1-4.5). This was primarily driven by the high prevalence of some of these conditions in hospitalisation data, which could potentially be explained by nonspecific codes used to record certain malformations that could have related to either major or minor conditions. 5 The fourth study examined the association between the trivalent seasonal inactivated influenza vaccine and major malformations. Among 78,150 live-birth pregnancies, 6,872 (8.8%) were vaccinated in the first trimester whilst 46,669 (59.7%) were unvaccinated throughout pregnancy. There was no evidence to suggest an association between first-trimester vaccination and major malformations recorded in first year of infant life in models adjusted for confounding (HR, 1.06; 99% CI, 0.94-1.19; p=0.23). The fifth study, which examined the safety of the monovalent pandemic inactivated influenza vaccine, showed similar results (HR, 1.02; 99% CI, 0.72-1.46; p=0.86). However, although these vaccine safety studies did not find evidence for an association between vaccination and major malformations, terminations due to foetal anomaly were not included. Therefore, the possibility of an increased risk of the specific subtypes of major malformations typically detected during antenatal scans and subsequently terminated could not be discounted. These results provide additional evidence on the safety of maternal influenza vaccination but highlight the need for further explorations of major malformations among pregnancies that do not result in live-births. The component of this work relating to the methods used to identify major malformations highlights the potential to increase ascertainment through the use of linked data whilst underscoring the need for further studies, particularly in hospitalisation data, to establish the validity of codes used to record these conditions

Handcrafted and learning-based tie point features-comparison using the EuroSDR RPAS benchmark datasets

The identification of accurate and reliable image correspondences is fundamental for Structure-from-Motion (SfM) photogrammetry. Alongside handcrafted detectors and descriptors, recent machine learning-based approaches have shown promising results for tie point extraction, demonstrating matching success under strong perspective and illumination changes, and a general increase of tie point multiplicity. Recently, several methods based on convolutional neural networks (CNN) have been proposed, but few tests have yet been performed under real photogrammetric applications and, in particular, on full resolution aerial and RPAS image blocks that require rotationally invariant features. The research reported here compares two handcrafted (Metashape local features and RootSIFT) and two learning-based methods (LFNet and Key.Net) using the previously unused EuroSDR RPAS benchmark datasets. Analysis is conducted with DJI Zenmuse P1 imagery acquired at Wards Hill quarry in Northumberland, UK. The research firstly extracts keypoints using the aforementioned methods, before importing them into COLMAP for incremental reconstruction. The image coordinates of signalised ground control points (GCPs) and independent checkpoints (CPs) are automatically detected using an OpenCV algorithm, and then triangulated for comparison with accurate geometric ground-truth. The tests showed that learning-based local features are capable of outperforming traditional methods in terms of geometric accuracy, but several issues remain: few deep learning local features are trained to be rotation invariant, significant computational resources are required for large format imagery, and poor performance emerged in cases of repetitive patterns

Evaluation of food photographs assessing the dietary intake of children up to 10 years old

OBJECTIVE: Young children lack basic skills related to recognizing the types of foods they consume and dietary surveys often rely on parents' response. The present study aimed to evaluate how well parents of children aged from 3 months to 10 years perceive images of portions of foods commonly consumed by young children. DESIGN: Pre-weighed, actual food portions (n 2314) were shown to the study participants who were asked to indicate the picture that corresponded to the food in view. Mean differences between picture numbers selected and shown were estimated and compared using unpaired t tests or Tukey-Cramer pairwise comparisons. SETTING: Real-time testing of parents' perception of food images presenting portion sizes consumed by children up to 10 years old. SUBJECTS: A convenience sample of 138 parents/caregivers of young children (69 % females). RESULTS: Individuals selected the correct or adjacent image in about 97 % of the assessments. Images presenting amorphous solids (i.e. pies and pastries with a filling), liquid or semi-liquid dishes (i.e. soups, porridges, fruit and vegetable purées) were more prone to bias. There was no indication that personal characteristics (gender, age, educational background, age, number of offspring) were associated with differences in the way parents/caregivers perceived the food pictures. CONCLUSIONS: Food pictures may not be appropriate to quantify the intake of liquid, semi-liquid or amorphous solid foods in surveys addressing young children and studies evaluating their performance as food portion anchors should ensure the inclusion of several and various amorphous foods in the assessment

URBAN TRAFFIC FLOW ANALYSIS BASED ON DEEP LEARNING CAR DETECTION FROM CCTV IMAGE SERIES

Abstract. Traffic flow analysis is fundamental for urban planning and management of road traffic infrastructure. Automatic number plate recognition (ANPR) systems are conventional methods for vehicle detection and travel times estimation. However, such systems are specifically focused on car plates, providing a limited extent of road users. The advance of open-source deep learning convolutional neural networks (CNN) in combination with freely-available closed-circuit television (CCTV) datasets have offered the opportunities for detection and classification of various road users. The research, presented here, aims to analyse traffic flow patterns through fine-tuning pre-trained CNN models on domain-specific low quality imagery, as captured in various weather conditions and seasons of the year 2018. Such imagery is collected from the North East Combined Authority (NECA) Travel and Transport Data, Newcastle upon Tyne, UK. Results show that the fine-tuned MobileNet model with 98.2% precision, 58.5% recall and 73.4% harmonic mean could potentially be used for a real time traffic monitoring application with big data, due to its fast performance. Compared to MobileNet, the fine-tuned Faster region proposal R-CNN model, providing a better harmonic mean (80.4%), recall (68.8%) and more accurate estimations of car units, could be used for traffic analysis applications that demand higher accuracy than speed. This research ultimately exploits machine learning alogrithms for a wider understanding of traffic congestion and disruption under social events and extreme weather conditions. Document type: Articl