Search

Worldwide, an estimated 461,000 people die from asthma attacks each year. Whilst there remain treatments to alleviate asthma symptoms and reduce deaths, patient deterioration needs to be identified in sufficient time. To prevent asthma deterioration, patients need to be aware of personal and environmental triggers and monitor their asthma symptoms. The aim of this paper is to provide a comprehensive review of the current state-of-the-art wearable sensors and devices that use vital signs for asthma patient monitoring and management. Among all vital signs, breathing rate and airflow sound are key indicators of asthmatic patients’ health that can be measured directly using wearable sensors to provide continuous and constant patient monitoring or indirectly by estimations based on proven algorithms using electrocardiogram (ECG), photoplethysmogram (PPG), and chest movements. ECG and PPG signals are widely used in smart watches and chest bands, enabling easy integration of a more extensive body sensor framework for asthmatic exacerbation prediction. Other vital signs used in asthma patient monitoring include blood oxygen saturation, temperature, blood pressure, verbal sound, and pain responses. The use of wearable vital signs enabled a broad range of wearable sensor application scenarios for asthma monitoring and management.

The lack of data democratization and information leakage from trained models hinder the development and acceptance of robust deep learning-based healthcare solutions. This paper argues that irreversible data encoding can provide an effective solution to achieve data democratization without violating the privacy constraints imposed on healthcare data and clinical models. An ideal encoding framework transforms the data into a new space where it is imperceptible to a manual or computational inspection. However, encoded data should preserve the semantics of the original data such that deep learning models can be trained effectively. This paper hypothesizes the characteristics of the desired encoding framework and then exploits random projections and random quantum encoding to realize this framework for dense and longitudinal or time-series data. Experimental evaluation highlights that models trained on encoded time-series data effectively uphold the information bottleneck principle and hence, exhibit lesser information leakage from trained models.

Electrocardiogram (ECG) and photoplethysmogram (PPG) are commonly used to determine the vital signs of heart rate, respiratory rate, and oxygen saturation in patient monitoring. In addition to simple observation of those summarized indexes, waveform signals can be analyzed to provide deeper insights into disease pathophysiology and support clinical decisions. Such data, generated from continuous patient monitoring from both conventional bedside and low-cost wearable monitors, are increasingly accessible. However, the recorded waveforms suffer from considerable noise and artifacts and, hence, are not necessarily used prior to certain quality control (QC) measures, especially by those with limited programming experience. Various signal quality indices (SQIs) have been proposed to indicate signal quality. To facilitate and harmonize a wider usage of SQIs in practice, we present a Python package, named vital_sqi, which provides a unified interface to the state-of-the-art SQIs for ECG and PPG signals. The vital_sqi package provides with seven different peak detectors and access to more than 70 SQIs by using different settings. The vital_sqi package is designed with pipelines and graphical user interfaces to enable users of various programming fluency to use the package. Multiple SQI extraction pipelines can take the PPG and ECG waveforms and generate a bespoke SQI table. As these SQI scores represent the signal features, they can be input in any quality classifier. The package provides functions to build simple rule-based decision systems for signal segment quality classification using user-defined SQI thresholds. An experiment with a carefully annotated PPG dataset suggests thresholds for relevant PPG SQIs.

Deep neural networks have been integrated into the whole clinical decision procedure which can improve the efficiency of diagnosis and alleviate the heavy workload of physicians. Typical applications include 1) medical report generation, 2) disease classification, and 3) survival prediction. Since most neural networks are supervised, their quality heavily depends on the volume and quality of available labels. However, for novel diseases, e.g., new pandemics or new variants, there are few existing labels. In addition, the acquisition of new pandemic cases to collect sufficient labels for training is time-consuming and is typically unavailable at the early stage. To prepare neural networks for the next pandemic, in this paper, we propose a large language model - Unsupervised Learning from Unlabelled Medical Images and Text (ULUMIT) framework, which can learn broad medical knowledge (e.g., image understanding, text semantics, and clinical phenotypes) from unlabelled data. As a result, when encountering new pandemics, our framework can be rapidly deployed and easily adapted to them with extremely limited labels. Furthermore, ULUMIT supports medical data across visual modality (e.g., chest X-ray and CT) and textual modality (e.g., medical report and free-text clinical note); therefore, it can be used for any clinical task that involves both visual and textual medical data. We demonstrate the effectiveness of our ULUMIT by showing how it would perform using the COVID-19 pandemic ``in replay''. In particular, in the retrospective setting, we test the model on the early COVID-19 datasets; and in the prospective setting, we test the model on the new variant COVID-19-Omicron. The experiments are conducted on 1) three kinds of input medical data, image-only, text-only, and image-text; 2) three kinds of downstream tasks, medical reporting, diagnosis, and prognosis; 3) five public COVID-19 datasets; and 4) three different languages, i.e., English, Chinese, and Spanish. All experiments consistently show that our framework can make accurate and robust COVID-19 decision-support tasks with little labelled data (such as considering information from only one patient), providing an impact on medical data analysis during the early stage of the next pandemic. Besides COVID-19, our framework can be applied to identify 14 common thorax diseases and tuberculosis across five additional public datasets, demonstrating its robustness in generalization and transferability. In brief, our framework achieves state-of-the-art performances on ten datasets.

Dynamic distribution shifts caused by evolving diseases and demographic changes require domain-incremental adaptation of clinical deep learning models. However, this process of adaptation is often accompanied bycatastrophic forgetting, and even the most sophisticated methods are not good enough for clinical applications. This paper studies incremental learning from the perspective ofmode connections, that is, the low-loss paths connecting the minimisers of neural architectures (modes or trained weights) in the parameter space. The paper argues for learning the low-loss paths originating from an existing mode and exploring the learned paths to find an acceptable mode for the new domain. The learned paths, and hence the new domain mode, are afunctionof the existing mode. As a result, unlike traditional incremental learning, the proposed approach is able to exploit information from a deployed model without changing its weights. Pre-COVID and COVID-19 data collected in Oxford University hospitals are used as a case study to demonstrate the need for domain-incremental learning and the advantages of the proposed approach.

Gestational diabetes is a subtype of diabetes that develops during pregnancy.Managing blood glucose (BG) within the healthy physiological range can reduce clinical complications for women with gestational diabetes. In this paper, we developed a Stacked Long and Short-Term Memory (Stacked-LSTM) model to predict before- and after-meal BG levels for women with gestational diabetes. A total of 190396 BG readings from 1110 patients were used for model development, validation and testing under three different prediction schemes: 7 days of BG readings to predict the next 7 or 14 days, and 14 days to predict 14 days. Our results show that the optimized BG schedule uses a 7-day observational window to predict the BG of the next 14 days, achieved accuracies of RMSE = 0.958±0.007, 0.876±0.003, 0.898±0.003, 0.622±0.003, 0.814±0.009, and 0.845±0.005 for the after-breakfast, after-lunch, after-dinner, before-breakfast, before-lunch and before-dinner predictions, respectively. The performance of our models is on par withthe prediction accuracies (RMSE) in benchmark BG prediction models using continuous glucose monitoring (CGM) readings. In conclusion, the Stacked-LSTM model is a promising approach for capturing the patterns in time series data, resulting in accurate predictions of BG levels. Using a deep learning model with routine fingerstick glucose collection is a promising, predictable and low-cost solution for BG monitoring for women with gestational diabetes.

Innovations in digital health and machine learning are changing the path of clinical health and care. People from many different geographies and cultures can benefit from the mobility of wearable devices and smartphones to monitor their health in a ubiquitous manner. This paper focuses on reviewing the digital health and machine learning technologies used in gestational diabetes ̶ a subtype of diabetes that occurs during pregnancy. Despite a large number of patients with gestational diabetes, only a handful of digital health applications have been deployed in clinical practice. This paper reviews sensor technologies in blood glucose monitoring devices and machine learning fused digital health innovations for gestational diabetes monitoring and management in both clinical and commercial settings. It is one of the first comprehensive reviews in this area to the best of our knowledge. In conclusion, there is a need to (1) develop digital health technologies and clinically interpretable machine learning methods for patients with gestational diabetes, assisting health professionals with treatment monitoring and planning; (2) adapt and develop clinically proven devices for patient self-management of health and well-being at the hospital and home settings thereby facilitating timely intervention; and (3) ensure innovations are affordable and sustainable for women everywhere Innovations in digital health and machine learning are changing the path of clinical health and care. People from different geographical locations and cultural backgrounds can benefit from the mobility of wearable devices and smartphones to monitor their health ubiquitously. This paper focuses on reviewing the digital health and machine learning technologies used in gestational diabetes ̶ a subtype of diabetes that occurs during pregnancy. This paper reviews sensor technologies used in blood glucose monitoring devices, digital health innovations and machine learning models for gestational diabetes monitoring and management, in clinical and commercial settings, and discusses future directions. Despite one in six mothers having gestational diabetes, digital health applications were underdeveloped, especially the techniques that can be deployed in clinical practice. There is an urgent need to (1) develop clinically interpretable machine learning methods for patients with gestational diabetes, assisting health professionals with treatment, monitoring, and risk stratification before, during and after their pregnancies; (2) adapt and develop clinically-proven devices for patient self-management of health and well-being at home settings (“virtual ward” and virtual consultation), thereby improving clinical outcomes by facilitating timely intervention; and (3) ensure innovations are affordable and sustainable for all women with different socioeconomic backgrounds and clinical resources.

COVID-19 is unlikely to be the last pandemic that we face. According to an analysis of a global dataset of historical pandemics from 1600 to the present, the risk of a COVID-like pandemic has been estimated as 2.63% annually or a 38% lifetime probability. This rate may double over the coming decades. While we may be unable to prevent future pandemics, we can reduce their impact by investing in preparedness. In this study, we propose RapiD AI : a framework to guide the use of pretrained neural network models as a pandemic preparedness tool to enable healthcare system resilience and effective use of ML during future pandemics. The RapiD AI framework allows us to build highperforming ML models using data collected in the first weeks of the pandemic and provides an approach to adapt the models to the local populations and healthcare needs. The motivation is to enable healthcare systems to overcome data limitations that prevent the development of effective ML in the context of novel diseases. We digitally recreated the first 20 weeks of the COVID-19 pandemic and experimentally demonstrated the RapiD AI framework using domain adaptation and inductive transfer. We (i) pretrain two neural network models (Deep Neural Network and TabNet) on a large Electronic Health Records dataset representative of a general in-patient population in Oxford, UK, (ii) fine-tune using data from the first weeks of the pandemic, and (iii) simulate local deployment by testing the performance of the models on a held-out test dataset of COVID-19 patients. Our approach has demonstrated an average relative/absolute gain of 4.92/4.21% AUC compared to an XGBoost benchmark model trained on COVID-19 data only. Moreover, we show our ability to identify the most useful historical pretraining samples through clustering and to expand the task of deployed models through inductive transfer to meet the emerging needs of a healthcare system without access to large historical pretraining datasets.

Aims The aim of the PULsE-AI trial was to assess the effectiveness of a machine learning risk-prediction algorithm in conjunction with diagnostic testing for identifying undiagnosed atrial fibrillation (AF) in primary care in England. Methods and results Eligible participants (aged ≥30 years without AF diagnosis; n = 23 745) from six general practices in England were randomized into intervention and control arms. Intervention arm participants, identified by the algorithm as high risk of undiagnosed AF (n = 944), were invited for diagnostic testing (n = 256 consented); those who did not accept the invitation, and all control arm participants, were managed routinely. The primary endpoint was the proportion of AF, atrial flutter, and fast atrial tachycardia diagnoses during the trial (June 2019–February 2021) in high-risk participants. Atrial fibrillation and related arrhythmias were diagnosed in 5.63% and 4.93% of high-risk participants in intervention and control arms, respectively {odds ratio (OR) [95% confidence interval (CI)]: 1.15 (0.77–1.73), P = 0.486}. Among intervention arm participants who underwent diagnostic testing (28.1%), 9.41% received AF and related arrhythmia diagnoses [vs. 4.93% (control); OR (95% CI): 2.24 (1.31–3.73), P = 0.003]. Conclusion The AF risk-prediction algorithm accurately identified high-risk participants in both arms. While the proportions of AF and related arrhythmia diagnoses were not significantly different between high-risk arms, intervention arm participants who underwent diagnostic testing were twice as likely to receive arrhythmia diagnoses compared with routine care. The algorithm could be a valuable tool to select primary care groups at high risk of undiagnosed AF who may benefit from diagnostic testing.

Background: Reliable classification of ischemic stroke (IS) etiological subtypes is required in research and clinical practice, but the predictive properties of these subtypes in population studies with incomplete investigations are poorly understood. Aims: To compare the prognosis of etiologically classified IS subtypes and use machine learning (ML) to classify incompletely investigated IS cases. Methods: In a 9-year follow-up of a prospective study of 512,726 Chinese adults, 22,216 incident IS cases, confirmed by clinical adjudication of medical records, were assigned subtypes using a modified Causative Classification System for Ischemic Stroke (CCS) (large artery atherosclerosis (LAA), small artery occlusion (SAO), cardioaortic embolism (CE), or undetermined etiology) and classified by CCS as “evident,” “probable,” or “possible” IS cases. For incompletely investigated IS cases where CCS yielded an undetermined etiology, an ML model was developed to predict IS subtypes from baseline risk factors and screening for cardioaortic sources of embolism. The 5-year risks of subsequent stroke and all-cause mortality (measured using cumulative incidence functions and 1 minus Kaplan–Meier estimates, respectively) for the ML-predicted IS subtypes were compared with etiologically classified IS subtypes. Results: Among 7443 IS subtypes with evident or probable etiology, 66% had SAO, 32% had LAA, and 2% had CE, but proportions of SAO-to-LAA cases varied by regions in China. CE had the highest rates of subsequent stroke and mortality (43.5% and 40.7%), followed by LAA (43.2% and 17.4%) and SAO (38.1% and 11.1%), respectively. ML provided classifications for cases with undetermined etiology and incomplete clinical data (24% of all IS cases; n = 5276), with area under the curves (AUC) of 0.99 (0.99–1.00) for CE, 0.67 (0.64–0.70) for LAA, and 0.70 (0.67–0.73) for SAO for unseen cases. ML-predicted IS subtypes yielded comparable subsequent stroke and all-cause mortality rates to the etiologically classified IS subtypes. Conclusion: This study highlighted substantial heterogeneity in prognosis of IS subtypes and utility of ML approaches for classification of IS cases with incomplete clinical investigations.

IntroductionEstimating COVID-19 cumulative incidence in Africa remains problematic due to challenges in contact tracing, routine surveillance systems and laboratory testing capacities and strategies. We undertook a meta-analysis of population-based seroprevalence studies to estimate SARS-CoV-2 seroprevalence in Africa to inform evidence-based decision making on public health and social measures (PHSM) and vaccine strategy.MethodsWe searched for seroprevalence studies conducted in Africa published 1 January 2020–30 December 2021 in Medline, Embase, Web of Science and Europe PMC (preprints), grey literature, media releases and early results from WHO Unity studies. All studies were screened, extracted, assessed for risk of bias and evaluated for alignment with the WHO Unity seroprevalence protocol. We conducted descriptive analyses of seroprevalence and meta-analysed seroprevalence differences by demographic groups, place and time. We estimated the extent of undetected infections by comparing seroprevalence and cumulative incidence of confirmed cases reported to WHO.PROSPERO: CRD42020183634.ResultsWe identified 56 full texts or early results, reporting 153 distinct seroprevalence studies in Africa. Of these, 97 (63%) were low/moderate risk of bias studies. SARS-CoV-2 seroprevalence rose from 3.0% (95% CI 1.0% to 9.2%) in April–June 2020 to 65.1% (95% CI 56.3% to 73.0%) in July–September 2021. The ratios of seroprevalence from infection to cumulative incidence of confirmed cases was large (overall: 100:1, ranging from 18:1 to 954:1) and steady over time. Seroprevalence was highly heterogeneous both within countries—urban versus rural (lower seroprevalence for rural geographic areas), children versus adults (children aged 0–9 years had the lowest seroprevalence)—and between countries and African subregions.ConclusionWe report high seroprevalence in Africa suggesting greater population exposure to SARS-CoV-2 and potential protection against COVID-19 severe disease than indicated by surveillance data. As seroprevalence was heterogeneous, targeted PHSM and vaccination strategies need to be tailored to local epidemiological situations.

Early detection of COVID-19 is an ongoing area of research that can help with triage, monitoring and general health assessment of potential patients and may reduce operational strain on hospitals that cope with the coronavirus pandemic. Different machine learning techniques have been used in the literature to detect potential cases of coronavirus using routine clinical data (blood tests, and vital signs measurements). Data breaches and information leakage when using these models can bring reputational damage and cause legal issues for hospitals. In spite of this, protecting healthcare models against leakage of potentially sensitive information is an understudied research area. In this study, two machine learning techniques that aim to predict a patient’s COVID-19 status are examined. Using adversarial training, robust deep learning architectures are explored with the aim to protect attributes related to demographic information about the patients. The two models examined in this work are intended to preserve sensitive information against adversarial attacks and information leakage. In a series of experiments using datasets from the Oxford University Hospitals (OUH), Bedfordshire Hospitals NHS Foundation Trust (BH), University Hospitals Birmingham NHS Foundation Trust (UHB), and Portsmouth Hospitals University NHS Trust (PUH), two neural networks are trained and evaluated. These networks predict PCR test results using information from basic laboratory blood tests, and vital signs collected from a patient upon arrival to the hospital. The level of privacy each one of the models can provide is assessed and the efficacy and robustness of the proposed architectures are compared with a relevant baseline. One of the main contributions in this work is the particular focus on the development of effective COVID19 detection models with built-in mechanisms in order to selectively protect sensitive attributes against adversarial attacks. The results on hold-out test set and external validation confirmed that there was no impact on the generalisibility of the model using adversarial learning.

BackgroundPancreatic cancer continues to have an extremely poor prognosis in part due to late diagnosis. 25% of pancreatic cancer patients have a prior diagnosis of diabetes, and hence identifying individuals at risk of pancreatic cancer in those with recently diagnosed type 2 diabetes may be a useful opportunity to identify candidates for screening and early detection. In this study, we will comparatively evaluate regression and machine learning-based clinical prediction models for estimating individual risk of developing pancreatic cancer two years after type 2 diabetes diagnosis.MethodsIn the development dataset, we will include adults aged 30-84 years with incident type-2 diabetes registered with QResearch primary care database. Patients will be followed up from type-2 diabetes diagnosis to first diagnosis of pancreatic cancer as recorded in any one of primary care records, hospital episode statistics, cancer registry data, or death records. Cox-proportional hazards models will be used to develop a risk prediction model for estimating individual risk of developing pancreatic cancer during up to 2 years of follow-up. We will perform variable selection using a combination of clinical and statistical significance approach i.e. HR 1.1 and pInternal-external cross-validation (IECV) framework across geographical regions within England will be used to assess performance and pooled using random effects meta-analysis using: (i) model fit in terms of variation explained by the model Royston & Sauerbrei’s R2D, (ii) calibration slope and calibration-in-the-large, and (iii) discrimination measured in terms of Harrell’s C and Royston & Sauerbrei’s D-statistic.Further, we will evaluate machine learning (ML) approaches for the clinical prediction model using neural networks (NN) and XGBoost. The model predictors and performance of these will be compared with the results of those derived from the regression-based strategy.DiscussionThe proposed study will develop and validate a novel risk prediction model to aid early diagnosis of pancreatic cancer in patients with new-onset diabetes in primary care. With an enhanced decision-risk tool for use at point-of care by general practitioners to assess pancreatic cancer risk, it may improve decision-making so that at-risk patients are rapidly prioritised to aid early diagnosis of pancreatic cancer in patients with newly diagnosed diabetes.

Due to environmental interference and defects in measured objects, measurement signals are frequently affected by unpredictable noise and periodic defects. Moreover, there is a lack of effective methods for accurately distinguishing defect components from measurement signals. In this study, a distribution-based selective optimisation method (SOM) is proposed to mitigate the effects of noise and defect components. The SOM can be seen as a binary- or multiple-class signal classifier based on an error distribution, which can simultaneously eliminate periodic defect components of measurement signals and proceed with signal-fitting regression. The effectiveness, accuracy, and feasibility of the SOM are verified in theoretical and realworld measurement settings. Based on theoretical simulations under various parameter conditions, some criteria for selecting operation variables among a selection of parameter conditions are explained in detail. The proposed method is capable of separating defect components from measurement signals while also achieving a satisfactory fitting curve for the measurement signals. The proposed SOM has broad application prospects in signal processing and defect detection for mechanical measurements, electronic filtering, instrumentation, part maintenance, and other fields.

Mothers with gestational diabetes are at increased risk of giving birth by caesarean section. A standardised assessment method may help to guide in recommendations in planning caesarean birth. We analysed 203 women with gestational diabetes managed in a single centre and developed an aggregate heuristic risk score. Among 155 women who had not had a previous caesarean birth, five risk factors (previous birth, weight gain during pregnancy, mother's height, and glycated haemoglobin and fasting blood glucose results at the beginning of pregnancy) were found associated with primary caesarean birth. Risk of primary caesarean birth in low-risk women (score 0–1) was 13.8%, medium-risk (score 2–3) 24.5% and high risk (score ≥ 4) 66.7%. The area under the receiver operating characteristic (AUROC) for primary caesarean birth prediction is 0.726 ± 0.003. Machine learning models were then deployed on 97 patients to explore the role of temporal blood glucose in predicting caesarean birth, achieving an AUROC of 0.857 ± 0.008. In conclusion, Oxford caesarean prediction score could help clinicians counselling women with gestational diabetes about their individual risk of primary caesarean birth. Temporal blood glucose measurements may improve the prediction subject to further validation.

Rationale. Intensive care units (ICUs) admit the most severely ill patients. Once these patients are discharged from the ICU to a step-down ward, they continue to have their vital signs monitored by nursing staff, with early warning score (EWS) systems being used to identify those at risk of deterioration. Objectives. We report the development and validation of an enhanced continuous scoring system for predicting adverse events, which combines vital signs measured routinely on acute care wards (as used by most EWSs) with a risk score of a future adverse event calculated on discharge from ICU.Methods. A modified Delphi process identified common, and candidate variables frequently collected and stored in electronic records as the basis for a ‘static’ score of the patient’s condition immediately after discharge from the ICU. L1-regularised logistic regression was used to estimate the in-hospital risk of future adverse event. We then constructed a model of physiological normality using vital-sign data from the day of hospital discharge, which is combined with the static score and used continuously to quantify and update the patient’s risk of deterioration throughout their hospital stay. Data from two NHS Foundation Trusts (UK) were used to develop and (externally) validate the model.Measurements and Main Results. A total of 12,394 vital-sign measurements were acquired from 273 patients after ICU discharge for the development set, and 4,831 from 136 patients in the validation cohort. Outcome validation of our model yielded an area under the receiver operating characteristic curve (AUROC) of 0.724 for predicting ICU re-admission or in-hospital death within 24h. It showed an improved performance with respect to other competitive risk scoring systems, including the National EWS (NEWS, 0.653). Conclusion. We showed that a scoring system incorporating data from a patient’s stay in ICU has better performance than commonly-used EWS systems based on vital signs alone.

BACKGROUND Successful management of gestational diabetes mellitus (GDM) reduces the risk of morbidity in women and newborns. A woman’s blood glucose readings and risk factors are used by clinical staff to make decisions regarding the initiation of pharmacological treatment in women with GDM. Mobile health (mHealth) solutions allow the real-time follow-up of women with GDM and allow timely treatment and management. Machine learning offers the opportunity to quickly analyze large quantities of data to automatically flag women at risk of requiring pharmacological treatment. OBJECTIVE The aim of this study is to assess whether data collected through an mHealth system can be analyzed to automatically evaluate the switch to pharmacological treatment from diet-based management of GDM. METHODS We collected data from 3029 patients to design a machine learning model that can identify when a woman with GDM needs to switch to medications (insulin or metformin) by analyzing the data related to blood glucose and other risk factors. RESULTS Through the analysis of 411,785 blood glucose readings, we designed a machine learning model that can predict the timing of initiation of pharmacological treatment. After 100 experimental repetitions, we obtained an average area under the receiver operating characteristic curve of 0.80 (SD 0.02) and an algorithm that allows the flexibility of setting the operating point rather than relying on a static heuristic method, which is currently used in clinical practice. CONCLUSIONS Using real-time data collected via an mHealth system may further improve the timeliness of the intervention and potentially improve patient care. Further real-time clinical testing will enable the validation of our algorithm using real-world data. CLINICALTRIAL

This thesis is not available on this repository until the author agrees to make it public. If you are the author of this thesis and would like to make your work openly available, please contact us: thesis@repository.cam.ac.uk., The Library can supply a digital copy for private research purposes; interested parties should submit the request form here: http://www.lib.cam.ac.uk/collections/departments/digital-content-unit/ordering-images, Please note that print copies of theses may be available for consultation in the Cambridge University Library's Manuscript reading room. Admission details are at http://www.lib.cam.ac.uk/collections/departments/manuscripts-university-archives

BACKGROUND AND OBJECTIVE: Accurate quantification of retinal surface area from ultra-widefield (UWF) images is challenging due to warping produced when the retina is projected onto a two-dimensional plane for analysis. By accounting for this, the authors sought to precisely montage and accurately quantify retinal surface area in square millimeters. PATIENTS AND METHODS: Montages were created using Optos 200Tx (Optos, Dunfermline, U.K.) images taken at different gaze angles. A transformation projected the images to their correct location on a three-dimensional model. Area was quantified with spherical trigonometry. Warping, precision, and accuracy were assessed. RESULTS: Uncorrected, posterior pixels represented up to 79% greater surface area than peripheral pixels. Assessing precision, a standard region was quantified across 10 montages of the same eye (RSD: 0.7%; mean: 408.97 mm 2 ; range: 405.34–413.87 mm 2 ). Assessing accuracy, 50 patients’ disc areas were quantified (mean: 2.21 mm 2 ; SE: 0.06 mm 2 ), and the results fell within the normative range. CONCLUSION: By accounting for warping inherent in UWF images, precise montaging and accurate quantification of retinal surface area in square millimeters were achieved. [ Ophthalmic Surg Lasers Imaging Retina . 2014;45:312-317.]

Football is an important site in the construction and performance of masculinity. In this paper we focus on rugby league and/or Australian Rules Football in the Bega Valley District, News South Wales, Australia. This paper investigates 17 football life narratives of men who play in the competitive rural sports leagues of the Bega Valley. Drawing on the theoretical work of Elspeth Probyn, a visceral approach is used to critically attend to the ambiguities and tensions surrounding the bodies of men who play football. We argue that the affective and emotional relationships of the dynamics of pride and shame are found within spaces sustained by football. We argue that a feminist geographical perspective of the dynamics of pride and shame open up possibilities to challenge normative understandings of football bodies and spaces.

In this paper, bodyboarders who surf the breaks of the Illawarra, New South Wales talk about their surfing experiences. We are particularly interested in leisured exclusions. Bodyboarders’ principal concern appears to be territorialisation of surf breaks and the legitimisation of bodyboarding in a sport where they are disavowed a ‘real’ surfing identity based on shortboarders’ desire to claim superiority. The paper explores how bodyboarders’ experiences of pride and shame articulate with understanding of space, self and masculinities. Alongside the physical environment of the surf break, interpersonal relationships between shortboarders and bodyboarders figure heavily in participant’s negotiation and construction of their gendered identities. We argue how individual bodyboarders negotiate the pride/shame nexus has important implication for understanding gender and territorialisation of the surf.

With a changing climate and increased urbanisation, the occurrence and the impact of flooding is expected to increase significantly. Floods can bring pathogens into homes and cause lingering damp and microbial growth in buildings, with the level of growth and persistence dependent on the volume and chemical and biological content of the flood water, the properties of the contaminating microbes, and the surrounding environmental conditions, including the restoration time and methods, the heat and moisture transport properties of the envelope design, and the ability of the construction material to sustain the microbial growth. The public health risk will depend on the interaction of these complex processes and the vulnerability and susceptibility of occupants in the affected areas. After the 2007 floods in the UK, the Pitt review noted that there is lack of relevant scientific evidence and consistency with regard to the management and treatment of flooded homes, which not only put the local population at risk but also caused unnecessary delays in the restoration effort. Understanding the drying behaviour of flooded buildings in the UK building stock under different scenarios, and the ability of microbial contaminants to grow, persist, and produce toxins within these buildings can help inform recovery efforts. To contribute to future flood management, this paper proposes the use of building simulations and biological models to predict the risk of microbial contamination in typical UK buildings. We review the state of the art with regard to biological contamination following flooding, relevant building simulation, simulation-linked microbial modelling, and current practical considerations in flood remediation. Using the city of London as an example, a methodology is proposed that uses GIS as a platform to integrate drying models and microbial risk models with the local building stock and flood models. The integrated tool will help local governments, health authorities, insurance companies and residents to better understand, prepare for and manage a large-scale flood in urban environments. Keywords: Flood, Pathogen, Hygrothermal, Modelling, GIS, Climate change