Active Learning of Very-High Resolution Optical Imagery with SVM: Entropy vs Margin Sampling

An active learning method is proposed for the semi-automatic selection of training sets in remote sensing image classification. The method adds iteratively to the current training set the unlabeled pixels for which the prediction of an ensemble of classifiers based on bagged training sets show maximum entropy. This way, the algorithm selects the pixels that are the most uncertain and that will improve the model if added in the training set. The user is asked to label such pixels at each iteration. Experiments using support vector machines (SVM) on an 8 classes QuickBird image show the excellent performances of the methods, that equals accuracies of both a model trained with ten times more pixels and a model whose training set has been built using a state-of-the-art SVM specific active learning method

Review of the Nursing Homes Support Scheme - Summary of Submissions

When the Nursing Homes Support Scheme, A Fair Deal, was introduced in October 2009, a commitment was made that it would be reviewed after three years. The reason for allowing this period to elapse was to ensure that the Scheme had bedded in and that established and validated trends and statistics would be available in order to inform the work. Public consultation is a key component.   Click here to download PDF 743kb

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Cardiopulmonary support and extracorporeal membrane oxygenation for cardiac assist.

BACKGROUND: Use of cardiopulmonary bypass for emergency resuscitation is not new. In fact, John Gibbon proposed this concept for the treatment of severe pulmonary embolism in 1937. Significant progress has been made since, and two main concepts for cardiac assist based on cardiopulmonary bypass have emerged: cardiopulmonary support (CPS) and extracorporeal membrane oxygenation (ECMO). The objective of this review is to summarize the state of the art in these two technologies. METHODS: Configuration of CPS is now fairly standard. A mobile cart with relatively large wheels allowing for easy transportation carries a centrifugal pump, a back-up battery with a charger, an oxygen cylinder, and a small heating system. Percutaneous cannulation, pump-driven venous return, rapid availability, and transportability are the main characteristics of a CPS system. Cardiocirculatory arrest is a major predictor of mortality despite the use of CPS. In contrast, CPS appears to be a powerful tool for patients in cardiogenic shock before cardiocirculatory arrest, requiring some type of therapeutic procedures, especially repair of anatomically correctable problems or bridging to other mechanical circulatory support systems such as ventricular assist devices. CPS is in general not suitable for long-term applications because of the small-bore cannulas, resulting in significant pressure gradients and eventually hemolysis. RESULTS: In contrast, ECMO can be designed for longer-term circulatory support. This requires large-bore cannulas and specifically designed oxygenators. The latter are either plasma leakage resistent (true membranes) or relatively thrombo-resistant (heparin coated). Both technologies require oxygenator changeovers although the main reason for this is different (clotting for the former, plasma leakage for the latter). Likewise, the tubing within a roller pump has to be displaced and centrifugal pump heads have to be replaced over time. ECMO is certainly the first choice for a circulatory support system in the neonatal and pediatric age groups, where the other assist systems are too bulky. ECMO is also indicated for patients improving on CPS. Septic conditions are, in general, considered as contraindications for ECMO. CONCLUSIONS: Ease of availability and moderate cost of cardiopulmonary bypass-based cardiac support technologies have to be balanced against the significant immobilization of human resources, which is required to make them successful.

Health inequalities: A toolkit to support local conversations

The National Conversation on Health Inequalities is a programme by Public Health England to start a conversation with the public about health inequalities and solutions to reduce these inequalities. This toolkit shows you how to start a conversation in your community.

Healthy Weight Support programme

The aim of this intervention is to assist staff in maintaining a healthy weight through encouragement of more activity and healthy eating

1.5 m for Finglas Addiction Support Team (FAST)

Noel Ahern T.D., Minister of State at the Department of Community, Rural and Gaeltacht Affairs with responsibility for the National Drugs Strategy, has announced funding of €1.5m under the Premises Initiative Fund to Finglas Addiction Support Team (FAST). FAST is a community-based initiative funded by the Department of Community, Rural & Gaeltacht Affairs under the National Drugs Strategy through Finglas/Cabra Local Drugs Task Force.This resource was contributed by The National Documentation Centre on Drug Use.

Etre le frère ou la soeur d'un enfan atteint d'un cancer: un groupe de parole [Being the sibling of a child suffering from cancer: a support group].

This paper offers a reflection on the family life and that of the siblings of a child with cancer. We will present our intervention model developed jointly by the pediatric oncology and the pediatric psychiatry units at the University Hospital CHUV in Lausanne. It is known that siblings show difficulties in dealing with the ambivalent emotions triggered by the sickness of a brother or sister. Their defence mechanisms can be heavy and may have consequences on the child's psycho-affective development and on the dynamics of the whole family. Speech groups allow the siblings to unfold an experience which is often irrepresentable. They also permit remobilization of affects frozen by the illness. This model used since 2006 in our unit responds to the wish to improve the quality of care of heavily sick children.

A-kinase-anchoring protein-Lbc anchors IκB kinase β to support interleukin-6-mediated cardiomyocyte hypertrophy.

In response to stress, the heart undergoes a pathological remodeling process associated with hypertrophy and the reexpression of a fetal gene program that ultimately causes cardiac dysfunction and heart failure. In this study, we show that A-kinase-anchoring protein (AKAP)-Lbc and the inhibitor of NF-κB kinase subunit β (IKKβ) form a transduction complex in cardiomyocytes that controls the production of proinflammatory cytokines mediating cardiomyocyte hypertrophy. In particular, we can show that activation of IKKβ within the AKAP-Lbc complex promotes NF-κB-dependent production of interleukin-6 (IL-6), which in turn enhances fetal gene expression and cardiomyocyte growth. These findings provide a new mechanistic hypothesis explaining how hypertrophic signals are coordinated and conveyed to interleukin-mediated transcriptional reprogramming events in cardiomyocytes.

What do older people with high support needs want and value from life? Involving older people in shaping 'A Better Life'

JRF has recently embarked on a major new programme: 'A Better Life', the central question of which is: 'How can we ensure a better life and better choices for older people who need high levels of support?' JRF now want to commission a project to work with older people with high support needs (current and future generations) and with JRF to ensure that older people with high support needs are at the heart throughout this programme.The deadline for receipt of full proposals is 12 noon on Tuesday 24 November 2009 for decision by 18 December.