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The emergence of novel infectious agents presents challenges to statistical models of disease transmission. These challenges arise from limited, poor-quality data and an incomplete understanding of the agent. Moreover, outbreaks manifest differently across regions due to various factors, making it imperative for models to factor in regional specifics. In this work, we offer a model that effectively utilizes constrained data resources to estimate disease transmission rates at the local level, especially during the early outbreak phase when primarily infection counts and aggregated local characteristics are accessible. This model merges a pathogen transmission methodology based on daily infection numbers with regression techniques, drawing correlations between disease transmission and local-area factors, such as demographics, health policies, behavior, and even climate, to estimate and forecast daily infections. We incorporate the quasi-score method and an error term to navigate potential data concerns and mistaken assumptions. Additionally, we introduce an online estimator that facilitates real-time data updates, complemented by an iterative algorithm for parameter estimation. This approach facilitates real-time analysis of disease transmission when data quality is suboptimal and knowledge of the infectious pathogen is limited. It is particularly useful in the early stages of outbreaks, providing support for local decision-making.

Chest x-rays are a vital tool in the workup of many patients. Similar to most medical imaging modalities, they are profoundly multi-modal and are capable of visualising a variety of combinations of conditions. There is an ever pressing need for greater quantities of labelled data to develop new diagnostic tools, however this is in direct opposition to concerns regarding patient confidentiality which constrains access through permission requests and ethics approvals. Previous work has sought to address these concerns by creating class-specific GANs that synthesise images to augment training data. These approaches cannot be scaled as they introduce computational trade offs between model size and class number which places fixed limits on the quality that such generates can achieve. We address this concern by introducing latent class optimisation which enables efficient, multi-modal sampling from a GAN and with which we synthesise a large archive of labelled generates. We apply a PGGAN to the task of unsupervised x-ray synthesis and have radiologists evaluate the clinical realism of the resultant samples. We provide an in depth review of the properties of varying pathologies seen on generates as well as an overview of the extent of disease diversity captured by the model. We validate the application of the Fr\'echet Inception Distance (FID) to measure the quality of x-ray generates and find that they are similar to other high resolution tasks. We quantify x-ray clinical realism by asking radiologists to distinguish between real and fake scans and find that generates are more likely to be classed as real than by chance, but there is still progress required to achieve true realism. We confirm these findings by evaluating synthetic classification model performance on real scans. We conclude by discussing the limitations of PGGAN generates and how to achieve controllable, realistic generates., Comment: 18 pages, 8 figures, 2 tables

A custom head-mounted system to track smooth eye movements for control of a mouse cursor is implemented and evaluated. The system comprises a head-mounted infrared camera, an infrared light source, and a computer. Software-based image processing techniques, implemented in Microsoft Visual Studio, OpenCV, and Pupil, detect the pupil position and direction of pupil movement in near real-time. The identified direction is used to determine the desired positioning of the cursor, and the cursor moves towards the target. Two users participated in three tests to quantify the differences between incremental tracking of smooth eye movement resulting from the Vestibulo-ocular Reflex versus step-change tracking of saccadic eye movement. Tracking smooth eye movements was four times more accurate than tracking saccadic eye movements, with an average position resolution of 0.80 cm away from the target. In contrast, tracking saccadic eye movements was measured with an average position resolution of 3.21 cm. Using the incremental tracking of smooth eye movements, the user was able to place the cursor within a target as small as a 9 x 9 pixel square 90 % of the time. However, when using the step change tracking of saccadic eye movements, the user was unable to position the cursor within the 9 x 9 pixel target. The average time for the incremental tracking of smooth eye movements to track a target was 6.45 s, whereas for the step change tracking of saccadic eye movements, it was 2.61 s., Comment: 16 pages, 4 figures, 2 tables

An antibubble is a gas bubble with a liquid droplet inside. Antibubbles have a potential role in ultrasonic imaging, ultrasound-guided drug delivery, and hydrocarbon leakage detection. The liquid core droplets inside antibubbles can be stabilised with a process called Pickering stability. Other systems proposed include spoke crystals to suspend the droplet or endoskeletal structures. In this study, the oscillatory response of microscopic endoskeletal antibubbles to ultrasound has been investigated, primarily to determine whether the presence of an endoskeleton attenuates the oscillation amplitude of the antibubble. Three emulsions containing (anti)bubbles were prepared for evaluation. Each emulsion was pipetted into the observation chamber of a high-speed microscopic observation system. A high-speed camera, operating at 10 million frames per second, was couples to the microscope. During camera recording, the materials were subjected to ultrasound pulses from a laboratory-assembled single-element transducer. The presence of an endoskeleton with droplets hampers the oscillation of antibubbles as compared to identical bubbles without endoskeleton and droplet content., Comment: 6 papers, 4 figures, submitted to the 40th Symposium on UltraSonic Electronics (USE2019)

Obesity is an important concern in public health, and Body Mass Index is one of the useful (and proliferant) measures. We use Convolutional Neural Networks to determine Body Mass Index from photographs in a study with 161 participants. Low data, a common problem in medicine, is addressed by reducing the information in the photographs by generating silhouette images. Results present with high correlation when tested on unseen data., Comment: 7 pages, 4 figures, preprint journal

Various models have been proposed to explain the urine concentrating mechanism in mammals, however uncertainty remains regarding the origin of the energy required for the production of concentrated urine. We propose a novel mechanism for concentrating urine. We postulate that the energy for the concentrating process is derived from the osmotic potentials generated by the separation of afferent blood into protein-rich efferent blood and protein-deplete filtrate. These two streams run in mutual juxtaposition along the length of the nephron and are thus suitably arranged to provide the osmotic potential to concentrate the urine. The proposed model is able to qualitatively explain the production of various urine concentrations under different clinical conditions. An approach to testing the feasibility of the hypothesis is proposed.

We consider the limitations of two techniques for detecting nonlinearity in time series. The first technique compares the original time series to an ensemble of surrogate time series that are constructed to mimic the linear properties of the original. The second technique compares the forecasting error of linear and nonlinear predictors. Both techniques are found to be problematic when the data has a long coherence time; they tend to indicate nonlinearity even for linear time series. We investigate the causes of these difficulties both analytically and with numerical experiments on ``real'' and computer-generated data. In particular, although we do see some initial evidence for nonlinear structure in the SFI dataset E, we are inclined to dismiss this evidence as an artifact of the long coherence time., Comment: 26 pages, Postscript, includes figures embedded in text. This version fixes a very old postscript bug which prevented the paper from being printed out on some machines