Report and papers with guidelines on calibration of urban flood models

Computer modelling offers a sound scientific framework for well-structured analysis and management of urban drainage systems and flooding. Computer models are tools that are expected to simulate the behaviour of the modelled real system with a reasonable level of accuracy. Assurance of accurate representation of reality by a model is obtained through the model calibration. Model calibration is an essential step in modelling. This report present concepts and procedures for calibration and verification of urban flood models. The various stages in the calibration process are presented sequentially. For each stage, a discussion of general concepts is followed by descriptions of process elements. Finally, examples and experiences regarding application of the procedures in the CORFU Barcelona Case Study are presented. Calibration involves not only the adjustment of model parameters but also other activities such as model structural and functional validation, data checking and preparation, sensitivity analysis and model verification, that support and fortify the calibration process as a whole. The objective in calibration is the minimization of differences between model simulated results and observed measurements. This is normally achieved through a manual iterative parameter adjustment process but automatic calibration routines are also available, and combination parameter adjustment methods also exist. The focus of a model calibration exercise is not the same for all types of models. But regardless of the model type, good modelling practice should involve thorough model verification before application. A well-calibrated model can give the assurance that, at least for a range of tested conditions, the model behaves like the real system, and that the model is an accurate and reliable tool that may be used for further analysis. However, calibration could also reveal that the model cannot be calibrated and that the correctness of the model and its suitability as a tool for analysis and management of real-world systems could not be proven. The conceptualisation and simplification of real-world systems and associated processes in modelling inevitably lead to errors and uncertainty. Various modelling components introduce errors such as the input parameters, the model concept, scheme and corresponding model output, and the observed response measurements. Ultimately, the quality of the model as quantified by how much it deviates from reality is an aggregate of the errors that have been brought into it during the modelling process. Thus, it is important to identify the different error sources in a model and also account for and quantify them as part of the modelling.The work described in this publication was supported by the European Community’s Seventh Framework Programme through the grant to the budget of CORFU Collaborative Research on Flood Resilience in Urban Areas, Contract 244047

Predicting deleterious nsSNPs: an analysis of sequence and structural attributes

BACKGROUND: There has been an explosion in the number of single nucleotide polymorphisms (SNPs) within public databases. In this study we focused on non-synonymous protein coding single nucleotide polymorphisms (nsSNPs), some associated with disease and others which are thought to be neutral. We describe the distribution of both types of nsSNPs using structural and sequence based features and assess the relative value of these attributes as predictors of function using machine learning methods. We also address the common problem of balance within machine learning methods and show the effect of imbalance on nsSNP function prediction. We show that nsSNP function prediction can be significantly improved by 100% undersampling of the majority class. The learnt rules were then applied to make predictions of function on all nsSNPs within Ensembl. RESULTS: The measure of prediction success is greatly affected by the level of imbalance in the training dataset. We found the balanced dataset that included all attributes produced the best prediction. The performance as measured by the Matthews correlation coefficient (MCC) varied between 0.49 and 0.25 depending on the imbalance. As previously observed, the degree of sequence conservation at the nsSNP position is the single most useful attribute. In addition to conservation, structural predictions made using a balanced dataset can be of value. CONCLUSION: The predictions for all nsSNPs within Ensembl, based on a balanced dataset using all attributes, are available as a DAS annotation. Instructions for adding the track to Ensembl are a

Health Impacts Model

This report presents the draft outline of the CORFU Health Impacts Model. The model consists of assessing the risk to human health in four steps: Hazard identification Hazard characterisation (or dose-response assessment) Exposure assessment Risk characterisation The health impacts model has four components. The first of these is the risk to human life component, and adapts a model developed in the FLOODsite project to estimate the number of deaths and injuries that could be caused by flooding. The next component relates to waterborne diseases and illnesses that can be assessed by means of a Quantitative Microbial Risk Assessment. Thirdly, the model takes account of other diseases (such as those transmitted by vectors) and suggests the use of relative risk information to estimate the impact of this disease. A similar approach is suggested to consider the mental health impacts of flooding. Finally, the report describes how the health risks could be characterised using the Disability Adjusted Life Year (DALY).The work described in this publication was supported by the European Community’s Seventh Framework Programme through the grant to the budget of CORFU Collaborative Research on Flood Resilience in Urban Areas, Contract 244047

A new flood risk assessment framework for evaluating the effectiveness of policies to improve urban flood resilience

This is the author accepted manuscript. The final version is available from Taylor & Francis via the DOI in this record.To better understand the impacts of flooding such that authorities can plan for adapting measures to cope with future scenarios, we have developed a modified Drivers-Pressures-State-Impact-Response (DPSIR) framework to allow policy makers to evaluate strategies for improving flood resilience in cities. We showed that this framework proved an effective approach to assessing and improving urban flood resilience, albeit with some limitations. This framework has difficulties in capturing all the important relationships in cities, especially with regards to feedbacks. There is therefore a need to develop improved techniques for understanding components and their relationships. While this research showed that risk assessment is possible even at the mega-city scale, new techniques will support advances in this field. Finally, a chain of models engenders uncertainties. However, the resilience approach promoted in this research, is an effective manner to work with uncertainty by providing the capacity to cope and respond to multiple scenariosResearch on the CORFU (Collaborative research on flood resilience in urban areas) project was funded by the European Commission through Framework Programme 7, Grant Number 244047. The work in this paper was partially funded by the PEARL (Preparing for Extreme And Rare events in coastaL regions) project, supported by the European Union's Seventh Framework Programme under Grant Agreement No 603663

A novel martial arts-based virtuality reality intervention modulates pain and the pain neuromatrix in patients with opioid use disorder

Background: Standard-of-care for opioid use disorder (OUD) includes medication and counseling. However, there is an unmet need for complementary approaches to treat OUD patients coping with pain; furthermore, few studies have probed neurobiological features of pain or its management during OUD treatment. This preliminary study examines neurobiological and behavioral effects of a martial arts-based intervention in patients undergoing methadone maintenance treatment (MMT). Methods: Fifteen (11 female) MMT patients completed a virtual reality, therapist-guided martial arts intervention that included breathing and relaxation exercises; sessions were scheduled twice weekly. Assessments included functional magnetic resonance imaging (fMRI) of pain neuromatrix activation and connectivity (pre- and post-intervention), saliva cortisol and C-reactive protein (CRP) at baseline and weeks 4, 8 and 12; and self-reported pain and affective symptoms before and after each intervention session. Results: After each intervention session (relative to pre-session), ratings of pain, opioid craving, anxiety and depression (but not anger) decreased. Saliva cortisol (but not CRP) levels decreased from pre- to post-session. From pre- to post-intervention fMRI assessments, pain task-related left postcentral gyrus (PCG) activation decreased. Higher baseline cortisol levels were associated with greater post-intervention pain task-related insular activation. At baseline, PCG showed positive connectivity with other regions of the pain neuromatrix, but this pattern changed post-intervention. Conclusions: These preliminary findings demonstrate feasibility, therapeutic promise, and brain basis of a martial arts-based intervention for OUD patients undergoing MMT

Predictors of failed attendances in a multi-specialty outpatient centre using electronic databases.

BACKGROUND: Failure to keep outpatient medical appointments results in inefficiencies and costs. The objective of this study is to show the factors in an existing electronic database that affect failed appointments and to develop a predictive probability model to increase the effectiveness of interventions. METHODS: A retrospective study was conducted on outpatient clinic attendances at Tan Tock Seng Hospital, Singapore from 2000 to 2004. 22864 patients were randomly sampled for analysis. The outcome measure was failed outpatient appointments according to each patient's latest appointment. RESULTS: Failures comprised of 21% of all appointments and 39% when using the patients' latest appointment. Using odds ratios from the mutliple logistic regression analysis, age group (0.75 to 0.84 for groups above 40 years compared to below 20 years), race (1.48 for Malays, 1.61 for Indians compared to Chinese), days from scheduling to appointment (2.38 for more than 21 days compared to less than 7 days), previous failed appointments (1.79 for more than 60% failures and 4.38 for no previous appointments, compared with less than 20% failures), provision of cell phone number (0.10 for providing numbers compared to otherwise) and distance from hospital (1.14 for more than 14 km compared to less than 6 km) were significantly associated with failed appointments. The predicted probability model's diagnostic accuracy to predict failures is more than 80%. CONCLUSION: A few key variables have shown to adequately account for and predict failed appointments using existing electronic databases. These can be used to develop integrative technological solutions in the outpatient clinic

Developing the agenda for European Union collaboration on non-communicable diseases research in Sub-Saharan Africa

<p>Abstract</p> <p>Background</p> <p>Health research is increasing in Africa, but most resources are currently chanelled towards infectious diseases and health system development. While infectious diseases remain a heavy burden for some African countries, non-communicable diseases (NCDs) account for more than half of all deaths globally and WHO predicts 27% increase in NCDs in Africa over the next decade. We present findings of a European-Africa consultation on the research agenda for NCDs.</p> <p>Methods</p> <p>A workshop was held in Yaoundé, Cameroon, organized by the Network for the Coordination and Advancement of Sub-Saharan Africa-European Union Science and Technology Cooperation (CAAST-Net). Drawing on initial presentations, a small expert group from academic, clinical, public-health and administrative positions considered research needs in Africa for cardiovascular disease, cancer and diabetes.</p> <p>Results</p> <p>Research in Africa can draw from different environmental and genetic characteristics to understand the causes of the disease, while economic and social factors are important in developing relevant strategies for prevention and treatment. The suggested research needs include better methods for description and recording, clinical studies, understanding cultural impacts, prevention strategies, and the integrated organisation of care. Specific fields proposed for research are listed.</p> <p>Conclusions</p> <p>Our paper contributes to transparency in the process of priority-setting for health research in Africa. Although the European Union Seventh Framework Research Programme prioritises biomedical and clinical research, research for Africa should also address broader social and cultural research and intervention research for greatest impact. Research policy leaders in Africa must engage national governments and international agencies as well as service providers and research communities. None can act effectively alone. Bringing together the different stakeholders, and feeding the results through to the European Union research programme is a valuable contribution of CAAST-Net.</p

siRNA-induced immunostimulation through TLR7 promotes antitumoral activity against HPV-driven tumors in vivo

Oncogene-specific downregulation mediated by RNA interference (RNAi) is a promising avenue for cancer therapy. In addition to specific gene silencing, in vivo RNAi treatment with short interfering RNAs (siRNAs) can initiate immune activation through innate immune receptors including Toll-like receptors, (TLRs) 7 and 8. Two recent studies have shown that activation of innate immunity by addition of tri-phosphate motifs to oncogene-specific siRNAs, or by co-treatment with CpG oligos, can potentiate siRNA antitumor effects. To date, there are no reports on applying such approach against human papillomavirus (HPV)-driven cancers. Here, we characterized the antitumor effects of non-modified siRNAs that can target a specific oncogene and/or recruit the innate immune system against HPV-driven tumors. Following the characterization of silencing efficacy and TLR7 immunostimulatory potential of 15 siRNAs targeting the HPV type 16 E6/E7 oncogenes, we identified a bifunctional siRNA sequence that displayed both potent gene silencing and active immunostimulation effect. In vivo systemic administration of this siRNA resulted in reduced growth of established TC-1 tumors in C57BL/6 mice. Ablation of TLR7 recruitment via 2′O-methyl modification of the oligo backbone reduced these antitumor effects. Further, a highly immunostimulatory, but non-HPV targeting siRNA was also able to exert antitumoral effects although for less prolonged time compared with the bifunctional siRNA. Collectively, our work demonstrates for the first time that siRNA-induced immunostimulation can have antitumoral effects against HPV-driven tumors in vivo, even independent of gene silencing efficacy