Brachytherapy structural shielding calculations using Monte Carlo generated, monoenergetic data

To provide a method for calculating the transmission of any broad photon beam with a known energy spectrum in the range of 20 keV-1090 keV, through concrete and lead, based on the superposition of corresponding monoenergetic data obtained from Monte Carlo simulation

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

Brief communication: landslide motion from cross correlation of UAV-derived morphological attributes

Unmanned aerial vehicles (UAVs) can provide observations of high spatio-temporal resolution to enable operational landslide monitoring. In this research, the construction of digital elevation models (DEMs) and orthomosaics from UAV imagery is achieved using structure-from-motion (SfM) photogrammetric procedures. The study examines the additional value that the morphological attribute of "openness", amongst others, can provide to surface deformation analysis. Image-cross-correlation functions and DEM subtraction techniques are applied to the SfM outputs. Through the proposed integrated analysis, the automated quantification of a landslide's motion over time is demonstrated, with implications for the wider interpretation of landslide kinematics via UAV surveys

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

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

CXCR6 marks a novel subset of T-bet(lo)Eomes(hi) natural killer cells residing in human liver

This work was funded by a Wellcome Trust Investigator Award to MKM funding KS and LP, MRC Career Development Award to CD, MRC Clinical Research Training Fellowship to DP, Wellcome Trust Henry Dale Fellowship to VM

Effect of Advanced Glycation End Products on Human Thyroglobulin's Antigenicity as Identified by the Use of Sera from Patients with Hashimoto's Thyroiditis and Gestational Diabetes Mellitus

Advanced glycation end products (AGEs) are formed on proteins after exposure to high concentrations of glucose and modify protein's immunogenicity. Herein, we investigated whether the modification of thyroglobulin (Tg) by AGEs influences its antigenicity and immunogenicity. Human Tg was incubated in vitro with increasing concentrations of D-glucose-6-phosphate in order to produce Tgs with different AGE content (AGE-Tg). Native Tg and AGE-Tgs were used in ELISA to assess the serum antibody reactivity of two patient groups, pregnant women with gestational diabetes (GDM), and patients with Hashimoto's thyroiditis (HT). We produced in vitro AGE-Tg with low and high AGE content, 13 and 49 AGE units/mg Tg, respectively. All HT patients' sera presented the same antibody reactivity profile against native Tg and AGE-Tgs, indicating that the modification of Tg by AGEs did not alter its antigenicity. Similarly, the GDM patients' sera did not discriminate among the two forms of Tg, native or artificially glycated, suggesting that the modification of Tg by AGEs might not alter its immunogenicity. The modification of Tg by AGEs has no obvious effect on neither its antigenicity nor, most likely, its immunogenicity. It seems that other Tg modifications might account for the production of aTgAbs in patients with GDM