Triatoma rubrovaria (Blanchard, 1843) (Hemiptera, Reduviidae, Triatominae) II: trophic resources and ecological observations of five populations collected in the State of Rio Grande do Sul, Brazil

Triatoma rubrovaria has become the most frequently captured triatomine species after the control of T. infestans in the State of Rio Grande do Sul (RS), Brazil. Isoenzymatic and chromatic studies indicate the existence of, at least, two distinct phenotypic patterns of T. rubrovaria in RS. The geographic variation noted through molecular tools may also result in distinct profiles of vectorial potentiality. In order to enhance our understanding of the bionomic knowledge of T. rubrovaria separate batches of the species were collected from different municipalities of RS distant from 72 to 332 km: Santana do Livramento (natural ecotope), Santana do Livramento (artificial ecotope), Santiago (natural ecotope), Canguçu (peridomicile) and Encruzilhada do Sul (natural ecotope). A total of 285 specimens were collected, 85 specimens kept sufficient fecal material in their guts for the precipitin analysis. The results indicated the food eclecticism for this species and the anti-rodent serum showed the highest positivity in most localities. From the total of analyzed samples, only 1.3% of unique positivity for human blood was registered, all of them for Santiago population. This reactivity to human blood may be associated to pastures activities in the field.

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.

Do resources, justice administration practices and federalism have an impact on registered and sentenced crime prevalence ?

This contribution, based on a statistical approach, undertakes to link data on resources (personnel and financial means) and the working of the administration of penal justice (prosecution, sentencing) taking into account the nationality of those prosecuted. In order to be able to distinguish prosecution and sentencing practices of judicial authorities and possible processes of discrimination, diverse sources have been used such as data from court administrations, public finances and police forces, collected by the Swiss Federal Statistical Office and the Swiss Federal administration of finances. The authors discuss discrimination in prosecution and sentencing between Swiss residents and foreigners taking into account localization and resources regarding personnel and public finances.

Comparison of HIV-infected patients' characteristics, healthcare resources use and cost between native and migrant patients

OBJECTIVES: To assess whether patients' characteristics and healthcare resources consumption and costs were different between native and migrant populations in Switzerland. METHODS: All adult patients followed-up in the Swiss HIV-cohort study in our institution during 2000-2003 were considered. Patients' characteristics were retrieved from the cohort database. Hospital and outpatient resource use were extracted from individual charts and valued with 2002 tariffs. RESULTS: The 66 migrants were younger (29 +/- 8 years versus 37 +/- 11, p < 0.001), less often of male gender (38 % versus 70 %, p < 0.001), predominantly infected via heterosexual contact (87 % versus 52 %, p < 0.01), with lower mean CD4 level at enrollment (326 +/- 235 versus 437 +/- 305, p = 0.002) than their 200 native counterparts. Migrants had fewer hospitalizations, more frequent outpatient visits, laboratory tests, and lower total cost of care per year of follow-up (<euro> 2'215 +/- 4'206 versus 4'155 +/- 12'304, p = 0.037). Resource use and costs were significantly higher in people with < 200 CD4 cell counts in both groups. CONCLUSIONS: Migrant population had more advanced disease, more outpatient visits but less hospitalizations, resulting in lower costs of care when compared with native population.

Health inequalities - Vulnerable groups - National key resources

Is part of Health inequalities - Vulnerable groups Has part Vulnerable adults (DH)

Health inequalities - Vulnerable groups - Regional key resources

Has part Understanding the health needs of migrants in the South East region Assault Prevention Data Sharing Toolkit

Health Poverty Index information and resources

This is a collection of HPI resources stored on the SEPHO web site

Antibiotic prescribing resources for health professionals

Antibiotic prescribing resources for health professionals.

Innovative Applications : Open Educational Resources and Mobile Resources Repository for the Instruction of Educational Researchers in Mexico

The movement of Open Educational Resources (OER) is one of the most important trends that are helping education through the Internet worldwide. "Tecnológico de Monterrey" (http://tecvirtual.itesm.mx/) in Mexico, with other Mexican higher education institutions, is creating an Internet/web based repository of OERs and Mobile Resources for the instruction and development of educational researchers at undergraduate, Master's and Doctoral level. There is a lack of open educational resources and material available at the Internet that can help and assist the development and education of educational researchers in Spanish speaking countries. This OER repository is part of a project that is experimenting new technology for the delivery of OERs from one repository (http://catedra.ruv.itesm.mx/) through an indexed OER catalog (http://www.temoa.info/) to mobile devices (Ipod, Iphone, MP3, MP4). This paper presentation will describe and comment about this project: outcomes, best practices, difficulties and technological constraints.

How to foster sharing of educational resources?

The future of OER is highly dependent on the future of education in general. A future that will be determined by major changes in society that demand more people with a higher education and life long learning. Each vision for the long term future needs to take the qualitative and quantitative demands into account. Backcasting from a vision we arrive at useful steps to take, some of which we managed to start up in the form of pilots as part of the European research project Share.TEC.

Open Educational Resources : Experiences of use in a Latin-American context

This paper presents practical experiences using Open educational Resources (OER) for basic and elementary education (K12), educational research and research training on two inter-institutional projects with the collaboration of thirteen higher education institutions and with the support of the Corporación de Universidades para el Desarrollo del Internet (CUDI) and by the Consejo Nacional de Ciencia y Tecnología (CONACYT) of Mexico and hosted by the Tecnológico de Monterrey. The first initiative is titled "Knowledge Hub for K-12 Education" with the main goal of enrich a catalog of Open Educational Resources for basic and elementary education (K-12) for Mexico and Spanish speaking countries in Latin-America. The main goal of the second initiative is to build a collection of Open Educational Resources for Mobile Learning to address the issue of educational research and research training.

Towards a Social Learning Space for Open Educational Resources

We identify a number of meanings of "Open", as part of the motivating rationale for a social media space tuned for learning, called SocialLearn. We discuss why online social learning seems to be emerging so strongly at this point, explore features of social learning, and identify some of the dimensions that we believe characterize the social learning design space, before describing the emerging design concept and implementation.