Benchmarking 2D hydraulic models for urban flood simulations

This paper describes benchmark testing of six two-dimensional (2D) hydraulic models (DIVAST, DIVASTTVD, TUFLOW, JFLOW, TRENT and LISFLOOD-FP) in terms of their ability to simulate surface flows in a densely urbanised area. The models are applied to a 1·0 km × 0·4 km urban catchment within the city of Glasgow, Scotland, UK, and are used to simulate a flood event that occurred at this site on 30 July 2002. An identical numerical grid describing the underlying topography is constructed for each model, using a combination of airborne laser altimetry (LiDAR) fused with digital map data, and used to run a benchmark simulation. Two numerical experiments were then conducted to test the response of each model to topographic error and uncertainty over friction parameterisation. While all the models tested produce plausible results, subtle differences between particular groups of codes give considerable insight into both the practice and science of urban hydraulic modelling. In particular, the results show that the terrain data available from modern LiDAR systems are sufficiently accurate and resolved for simulating urban flows, but such data need to be fused with digital map data of building topology and land use to gain maximum benefit from the information contained therein. When such terrain data are available, uncertainty in friction parameters becomes a more dominant factor than topographic error for typical problems. The simulations also show that flows in urban environments are characterised by numerous transitions to supercritical flow and numerical shocks. However, the effects of these are localised and they do not appear to affect overall wave propagation. In contrast, inertia terms are shown to be important in this particular case, but the specific characteristics of the test site may mean that this does not hold more generally

Evaluation of the Northern Territory Library's Libraries and Knowledge Centres Model

Evaluation of the Northern Territory Library's model for Libraries and Knowledge Centres in Indigenous communities

A framework for optimization of diffusion-weighted MRI protocols for large field-of-view abdominal-pelvic imaging in multicenter studies.

PURPOSE: To develop methods for optimization of diffusion-weighted MRI (DW-MRI) in the abdomen and pelvis on 1.5 T MR scanners from three manufacturers and assess repeatability of apparent diffusion coefficient (ADC) estimates in a temperature-controlled phantom and abdominal and pelvic organs in healthy volunteers. METHODS: Geometric distortion, ghosting, fat suppression, and repeatability and homogeneity of ADC estimates were assessed using phantoms and volunteers. Healthy volunteers (ten per scanner) were each scanned twice on the same scanner. One volunteer traveled to all three institutions in order to provide images for qualitative comparison. The common volunteer was excluded from quantitative analysis of the data from scanners 2 and 3 in order to ensure statistical independence, giving n = 10 on scanner 1 and n = 9 on scanners 2 and 3 for quantitative analysis. Repeatability and interscanner variation of ADC estimates in kidneys, liver, spleen, and uterus were assessed using within-patient coefficient of variation (wCV) and Kruskal-Wallis tests, respectively. RESULTS: The coefficient of variation of ADC estimates in the temperature-controlled phantom was 1%-4% for all scanners. Images of healthy volunteers from all scanners showed homogeneous fat suppression and no marked ghosting or geometric distortion. The wCV of ADC estimates was 2%-4% for kidneys, 3%-7% for liver, 6%-9% for spleen, and 7%-10% for uterus. ADC estimates in kidneys, spleen, and uterus showed no significant difference between scanners but a significant difference was observed in liver (p < 0.05). CONCLUSIONS: DW-MRI protocols can be optimized using simple phantom measurements to produce good quality images in the abdomen and pelvis at 1.5 T with repeatable quantitative measurements in a multicenter study

A novel approach to simulate gene-environment interactions in complex diseases

Background: Complex diseases are multifactorial traits caused by both genetic and environmental factors. They represent the major part of human diseases and include those with largest prevalence and mortality (cancer, heart disease, obesity, etc.). Despite a large amount of information that has been collected about both genetic and environmental risk factors, there are few examples of studies on their interactions in epidemiological literature. One reason can be the incomplete knowledge of the power of statistical methods designed to search for risk factors and their interactions in these data sets. An improvement in this direction would lead to a better understanding and description of gene-environment interactions. To this aim, a possible strategy is to challenge the different statistical methods against data sets where the underlying phenomenon is completely known and fully controllable, for example simulated ones. Results: We present a mathematical approach that models gene-environment interactions. By this method it is possible to generate simulated populations having gene-environment interactions of any form, involving any number of genetic and environmental factors and also allowing non-linear interactions as epistasis. In particular, we implemented a simple version of this model in a Gene-Environment iNteraction Simulator (GENS), a tool designed to simulate case-control data sets where a one gene-one environment interaction influences the disease risk. The main aim has been to allow the input of population characteristics by using standard epidemiological measures and to implement constraints to make the simulator behaviour biologically meaningful. Conclusions: By the multi-logistic model implemented in GENS it is possible to simulate case-control samples of complex disease where gene-environment interactions influence the disease risk. The user has full control of the main characteristics of the simulated population and a Monte Carlo process allows random variability. A knowledge-based approach reduces the complexity of the mathematical model by using reasonable biological constraints and makes the simulation more understandable in biological terms. Simulated data sets can be used for the assessment of novel statistical methods or for the evaluation of the statistical power when designing a study

A Neural Network for Stance Phase detection in smart cane users

Slides from conferencePersons with disabilities often rely on assistive devices to carry on their Activities of Daily Living. Deploying sensors on these devices may provide continuous valuable knowledge on their state and condition. Canes are among the most frequently used assistive devices, regularly employed for ambulation by persons with pain on lower limbs and also for balance. Load on canes is reportedly a meaningful condition indicator. Ideally, it corresponds to the time cane users support weight on their lower limb (stance phase). However, in reality, this relationship is not straightforward. We present a Multilayer Perceptron to reliably predict the Stance Phase in cane users using a simple support detection module on commercial canes. The system has been successfully tested on five cane users in care facilities in Spain. It has been optimized to run on a low cost microcontroller.This work has been supported by: Proyectos Puente and programa operativo de empleo juvenil (UMAJI58) and Plan Propio de Investigación at University of Malaga and the Swedish Knowledge Foundation (KKS) through the research profile Embedded Sensor Systems for Health (ESS−H) at Malardalen University, Sweden. Authors would like to ac- knowledge PONIENTE and LOS NARANJOS senior centers for their support during the tests. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Trapped in the prison of the mind: notions of climate-induced (im)mobility decision-making and wellbeing from an urban informal settlement in Bangladesh

The concept of Trapped Populations has until date mainly referred to people ‘trapped’ in environmentally high-risk rural areas due to economic constraints. This article attempts to widen our understanding of the concept by investigating climate-induced socio-psychological immobility and its link to Internally Displaced People’s (IDPs) wellbeing in a slum of Dhaka. People migrated here due to environmental changes back on Bhola Island and named the settlement Bhola Slum after their home. In this way, many found themselves ‘immobile’ after having been mobile—unable to move back home, and unable to move to other parts of Dhaka, Bangladesh, or beyond. The analysis incorporates the emotional and psychosocial aspects of the diverse immobility states. Mind and emotion are vital to better understand people’s (im)mobility decision-making and wellbeing status. The study applies an innovative and interdisciplinary methodological approach combining Q-methodology and discourse analysis (DA). This mixed-method illustrates a replicable approach to capture the complex state of climate-induced (im)mobility and its interlinkages to people’s wellbeing. People reported facing non-economic losses due to the move, such as identity, honour, sense of belonging and mental health. These psychosocial processes helped explain why some people ended up ‘trapped’ or immobile. The psychosocial constraints paralysed them mentally, as well as geographically. More empirical evidence on how climate change influences people’s wellbeing and mental health will be important to provide us with insights in how to best support vulnerable people having faced climatic impacts, and build more sustainable climate policy frameworks

vCJD risk in the Republic of Ireland

BACKGROUND: The Republic of Ireland has the second highest incidence of BSE worldwide. Only a single case of vCJD has been identified to date. METHODS: We estimate the total future number of clinical cases of vCJD using an established mathematical model, and based on infectivity of bovine tissue calculated from UK data and on the relative exposure to BSE contaminated meat. RESULTS: We estimate 1 future clinical case (95% CI 0 – 15) of vCJD in the Republic of Ireland. Irish exposure is from BSE infected indigenous beef products and from imported UK beef products. Additionally, 2.5% of the Irish population was exposed to UK beef through residing in the UK during the 'at-risk' period. The relative proportion of risk attributable to each of these three exposures individually is 2:2:1 respectively. CONCLUSIONS: The low numbers of future vCJD cases estimated in this study is reassuring for the Irish population and for other countries with a similar level of BSE exposure

Crucial Role of the CB3-Region of Collagen IV in PARF-Induced Acute Rheumatic Fever

Acute rheumatic fever (ARF) and rheumatic heart disease are serious autoimmune sequelae to infections with Streptococcus pyogenes. Streptococcal M-proteins have been implicated in ARF pathogenesis. Their interaction with collagen type IV (CIV) is a triggering step that induces generation of collagen-specific auto-antibodies. Electron microscopy of the protein complex between M-protein type 3 (M3-protein) and CIV identified two prominent binding sites of which one is situated in the CB3-region of CIV. In a radioactive binding assay, M3-protein expressing S. pyogenes and S. gordonii bound the CB3-fragment. Detailed analysis of the interactions by surface plasmon resonance measurements and site directed mutagenesis revealed high affinity interactions with dissociation constants in the nanomolar range that depend on the recently described collagen binding motif of streptococcal M-proteins. Because of its role in the induction of disease-related collagen autoimmunity the motif is referred to as “peptide associated with rheumatic fever” (PARF). Both, sera of mice immunized with M3-protein as well as sera from patients with ARF contained anti-CB3 auto-antibodies, indicating their contribution to ARF pathogenesis. The identification of the CB3-region as a binding partner for PARF directs the further approaches to understand the unusual autoimmune pathogenesis of PARF-dependent ARF and forms a molecular basis for a diagnostic test that detects rheumatogenic streptococci

Risk prediction model for knee pain in the Nottingham Community: a Bayesian modeling approach

Background: 25% of the British population over the age of 50 experience knee pain. It can limit physical ability, cause distress and bears significant socioeconomic costs. Knee pain, not knee osteoarthritis (KOA) is the all to common malady. The objectives of this study were to develop and validate the first risk prediction model for incident knee pain in the Nottingham community and validate this internally within the Nottingham cohort and externally within the Osteoarthritis Initiaitve (OAI) Cohort. Methods: 1822 participants at risk for knee pain from the Nottingham community were followed up for 12 years. Of this cohort, 2/3 (n=1203) were used to develop the risk prediction model and 1/3 (n=619) were used to validate the model. Incident knee pain was defined as pain on most days for at least one month in the past 12 months. Predictors were age, gender, body mass index (BMI), pain elsewhere, prior knee injury and knee alignment. Bayesian logistic regression model was used to determine the probability of an odds ratio >1. The Hosmer-Lemeshow x2 statistic (HLS) was used for calibration and receiver operator characteristics (ROC) was used for discrimination. The OAI cohort was used to examine the performance of the model in a secondary care population. Results: A risk prediction model for knee pain incidence was developed using a Bayesian approach. The model had good calibration with HLS of 7.17 (p=0.52) and moderate discriminative abilities (ROC 0.70) in the community. Individual scenarios are given using the model. However, the model had poor calibration (HLS 5866.28, p<0.01) and poor discriminative ability (ROC 0.54) in the OAI secondary care dataset. Conclusion: This is the first risk prediction model for knee pain, irrespective of underlying structural changes of KOA, in the community using a Bayesian modelling approach. The model appears to work well in a community-based population but not in a hospital derived cohort and may provide a convenient tool for primary care to predict the risk of knee pain in the general population