GARTEUR Helicopter Cooperative Research

This paper starts with an overview about the general structure of the Group for Aeronautical Research and Technology in EURope (GARTEUR). The focus is on the activities related to rotorcraft which are managed in the GARTEUR Helicopter Group of Responsables (HC GoR). The research activities are carried out in so-called Action Groups. Out of the 5 Action Groups which ended within the last four years results generated in the Helicopter Action Groups HC(AG14) “Methods for Refinement of Structural Dynamic Finite Element Models”, HC(AG15) “Improvement of SPH methods for application to helicopter ditching” and HC(AG16) “Rigid Body and Aeroelastic Rotorcraft-Pilot Coupling” are briefly summarized

The science of clinical practice: disease diagnosis or patient prognosis? Evidence about "what is likely to happen" should shape clinical practice.

BACKGROUND: Diagnosis is the traditional basis for decision-making in clinical practice. Evidence is often lacking about future benefits and harms of these decisions for patients diagnosed with and without disease. We propose that a model of clinical practice focused on patient prognosis and predicting the likelihood of future outcomes may be more useful. DISCUSSION: Disease diagnosis can provide crucial information for clinical decisions that influence outcome in serious acute illness. However, the central role of diagnosis in clinical practice is challenged by evidence that it does not always benefit patients and that factors other than disease are important in determining patient outcome. The concept of disease as a dichotomous 'yes' or 'no' is challenged by the frequent use of diagnostic indicators with continuous distributions, such as blood sugar, which are better understood as contributing information about the probability of a patient's future outcome. Moreover, many illnesses, such as chronic fatigue, cannot usefully be labelled from a disease-diagnosis perspective. In such cases, a prognostic model provides an alternative framework for clinical practice that extends beyond disease and diagnosis and incorporates a wide range of information to predict future patient outcomes and to guide decisions to improve them. Such information embraces non-disease factors and genetic and other biomarkers which influence outcome. SUMMARY: Patient prognosis can provide the framework for modern clinical practice to integrate information from the expanding biological, social, and clinical database for more effective and efficient care

E-infrastructures fostering multi-centre collaborative research into the intensive care management of patients with brain injury

Clinical research is becoming ever more collaborative with multi-centre trials now a common practice. With this in mind, never has it been more important to have secure access to data and, in so doing, tackle the challenges of inter-organisational data access and usage. This is especially the case for research conducted within the brain injury domain due to the complicated multi-trauma nature of the disease with its associated complex collation of time-series data of varying resolution and quality. It is now widely accepted that advances in treatment within this group of patients will only be delivered if the technical infrastructures underpinning the collection and validation of multi-centre research data for clinical trials is improved. In recognition of this need, IT-based multi-centre e-Infrastructures such as the Brain Monitoring with Information Technology group (BrainIT - www.brainit.org) and Cooperative Study on Brain Injury Depolarisations (COSBID - www.cosbid.de) have been formed. A serious impediment to the effective implementation of these networks is access to the know-how and experience needed to install, deploy and manage security-oriented middleware systems that provide secure access to distributed hospital based datasets and especially the linkage of these data sets across sites. The recently funded EU framework VII ICT project Advanced Arterial Hypotension Adverse Event prediction through a Novel Bayesian Neural Network (AVERT-IT) is focused upon tackling these challenges. This chapter describes the problems inherent to data collection within the brain injury medical domain, the current IT-based solutions designed to address these problems and how they perform in practice. We outline how the authors have collaborated towards developing Grid solutions to address the major technical issues. Towards this end we describe a prototype solution which ultimately formed the basis for the AVERT-IT project. We describe the design of the underlying Grid infrastructure for AVERT-IT and how it will be used to produce novel approaches to data collection, data validation and clinical trial design is also presented

Personalized medicine : the impact on chemistry

An effective strategy for personalized medicine requires a major conceptual change in the development and application of therapeutics. In this article, we argue that further advances in this field should be made with reference to another conceptual shift, that of network pharmacology. We examine the intersection of personalized medicine and network pharmacology to identify strategies for the development of personalized therapies that are fully informed by network pharmacology concepts. This provides a framework for discussion of the impact personalized medicine will have on chemistry in terms of drug discovery, formulation and delivery, the adaptations and changes in ideology required and the contribution chemistry is already making. New ways of conceptualizing chemistry’s relationship with medicine will lead to new approaches to drug discovery and hold promise of delivering safer and more effective therapies

Scoping study brief – State of climate information services in East Africa

This brief presents the findings of a scoping study on climate information services in East Africa, conducted as a requirement for the Climate Resilient Agribusiness for Tomorrow (CRAFT) Project, under Work Stream 4 on Enabling Environment for Climate-Smart Agriculture (CSA). The purpose was to ascertain the status of climate information services under the ambit of CSA in each of the three East African countries

The Effect of Epidemiological Cohort Creation on the Machine Learning Prediction of Homelessness and Police Interaction Outcomes Using Administrative Health Care Data

Background: Mental illness can lead to adverse outcomes such as homelessness and police interaction and understanding of the events leading up to these adverse outcomes is important. Predictive models may help identify individuals at risk of such adverse outcomes. Using a fixed observation window cohort with logistic regression (LR) or machine learning (ML) models can result in lower performance when compared with adaptive and parcellated windows. Method: An administrative healthcare dataset was used, comprising of 240,219 individuals in Calgary, Alberta, Canada who were diagnosed with addiction or mental health (AMH) between April 1, 2013, and March 31, 2018. The cohort was followed for 2 years to identify factors associated with homelessness and police interactions. To understand the benefit of flexible windows to predictive models, an alternative cohort was created. Then LR and ML models, including random forests (RF), and extreme gradient boosting (XGBoost) were compared in the two cohorts. Results: Among 237,602 individuals, 0.8% (1,800) experienced first homelessness, while 0.32% (759) reported initial police interaction among 237,141 individuals. Male sex (AORs: H=1.51, P=2.52), substance disorder (AORs: H=3.70, P=2.83), psychiatrist visits (AORs: H=1.44, P=1.49), and drug abuse (AORs: H=2.67, P=1.83) were associated with initial homelessness (H) and police interaction (P). XGBoost showed superior performance using the flexible method (sensitivity =91%, AUC =90% for initial homelessness, and sensitivity =90%, AUC=89% for initial police interaction) Conclusion: This study identified key features associated with initial homelessness and police interaction and demonstrated that flexible windows can improve predictive modeling.Comment: to be published in Frontiers in Digital Health, Health Informatic

Households’ response to economic crisis

This paper studies the economic impact of the current global economic downturn on the household sector. Household budgets can be negatively affected by declines in nominal wages and increases in unemployment. We empirically test this effect for the small open emerging economy. As a result of a lack of individual data on household finances, micro data are simulated. Our analysis clearly shows that there is a significant additional decline in consumption related to an increase in household default rates and unemployment. We find that potential household insolvencies have important implications for the financial system as well as for the macroeconomy.credit cycle; households’ distress; insolvency; household default; aggregate consumption

Improving Quality and Achieving Equity: A Guide for Hospital Leaders

Outlines the need to address racial/ethnic disparities in health care, highlights model practices, and makes step-by-step recommendations on creating a committee, collecting data, setting quality measures, evaluating, and implementing new strategies

New Trends regarding the Operational Risks in Financial Sector

Risks, especially "operational risks" are part of corporate life, they are the essence of financial institutions' activities. Operational risks are complex and often interlinked and have to be managed properly. Today, there is more pressure to avoid operational risks while continuing to improve corporate performance in the new environment. The operational risk management of the future has to be seen in the wider context of globalization and Internet-related technologies. The two major future drivers - globalization and Internet-related technologies - will challenge the firms from financial sector to take on additional and partly new operational risk.operational risk, financial sector, models, trends