Analyse de diagnostics et traitements proposés en pédiatrie ambulatoire libérale et hospitalière. Etude transversale sur une semaine dans le canton de Neuchâtel.

Le type d'affections rencontrées en pédiatrie générale ambulatoire traduit partiellement, d'une part, les compétences du pédiatre qui les prend en charge et, d'autre part, indirectement les besoins de la population en terme de soins pédiatriques. Il est utile de décrire le profil de ces affections pour plusieurs raisons ; en terme de santé publique, une connaissance du recours aux soins pédiatriques permet de mieux planifier la formation des futurs pédiatres en suivant les besoins de la population ; en terme de politique de formation, ces données permettent d'assurer un cursus éducatif de qualité adapté aux types de pathologies pédiatriques locales ; finalement, la description détaillée de l'activité de pédiatrie générale ambulatoire permet aux jeunes médecins de mieux se projeter et de s'identifier à cette profession en tant que futurs pédiatres. Il n'existe actuellement à notre connaissance que peu de données concernant les affections ambulatoires en pédiatrie de premier recours en Suisse romande ; de même, la proportion de consultations de pédiatrie ayant comme motif une pathologie infectieuse ou encore la fréquence du recours à l'antibiothérapie lors de ces dernières est méconnue en Suisse romande.

Advanced mapping of environmental data: Geostatistics, Machine Learning and Bayesian Maximum Entropy

This book combines geostatistics and global mapping systems to present an up-to-the-minute study of environmental data. Featuring numerous case studies, the reference covers model dependent (geostatistics) and data driven (machine learning algorithms) analysis techniques such as risk mapping, conditional stochastic simulations, descriptions of spatial uncertainty and variability, artificial neural networks (ANN) for spatial data, Bayesian maximum entropy (BME), and more.

Neues aus Diagnostik und Therapie der spinalen Erkrankungen [Novel aspects of diagnostics and therapy of spinal cord diseases].

BACKGROUND: Both non-traumatic and traumatic spinal cord injuries have in common that a relatively minor structural lesion can cause profound sensorimotor and autonomous dysfunction. Besides treating the cause of the spinal cord injury the main goal is to restore lost function as far as possible. AIM: This article provides an overview of current innovative diagnostic (imaging) and therapeutic approaches (neurorehabilitation and neuroregeneration) aiming for recovery of function after non-traumatic and traumatic spinal cord injuries. MATERIAL AND METHODS: An analysis of the current scientific literature regarding imaging, rehabilitation and rehabilitation strategies in spinal cord disease was carried out. RESULTS: Novel magnetic resonance imaging (MRI) based techniques (e.g. diffusion-weighted MRI and functional MRI) allow visualization of structural reorganization and specific neural activity in the spinal cord. Robotics-driven rehabilitative measures provide training of sensorimotor function in a targeted fashion, which can even be continued in the homecare setting. From a preclinical point of view, defined stem cell transplantation approaches allow for the first time robust structural repair of the injured spinal cord. CONCLUSION: Besides well-established neurological and functional scores, MRI techniques offer the unique opportunity to provide robust and reliable "biomarkers" for restorative therapeutic interventions. Function-oriented robotics-based rehabilitative interventions alone or in combination with stem cell based therapies represent promising approaches to achieve substantial functional recovery, which go beyond current rehabilitative treatment efforts.

Essential diagnostics - the role of the Department of Biomedical Research of the Royal Tropical Institute in the 21st century

In the light of emerging and overlooked infectious diseases and widespread drug resistance, diagnostics have become increasingly important in supporting surveillance, disease control and outbreak management programs. In many low-income countries the diagnostic service has been a neglected part of health care, often lacking quantity and quality or even non-existing at all. High-income countries have exploited few of their advanced technical abilities for the much-needed development of low-cost, rapid diagnostic tests to improve the accuracy of diagnosis and accelerate the start of appropriate treatment. As is now also recognized by World Healt Organization, investment in the development of affordable diagnostic tools is urgently needed to further our ability to control a variety of diseases that form a major threat to humanity. The Royal Tropical Institute's Department of Biomedical Research aims to contribute to the health of people living in the tropics. To this end, its multidisciplinary group of experts focuses on the diagnosis of diseases that are major health problems in low-income countries. In partnership we develop, improve and evaluate simple and cheap diagnostic tests, and perform epidemiological studies. Moreover, we advice and support others - especially those in developing countries - in their efforts to diagnose infectious diseases.

Extreme Precipitation Modelling Using Geostatistics and Machine Learning Algorithms

The paper presents an approach for mapping of precipitation data. The main goal is to perform spatial predictions and simulations of precipitation fields using geostatistical methods (ordinary kriging, kriging with external drift) as well as machine learning algorithms (neural networks). More practically, the objective is to reproduce simultaneously both the spatial patterns and the extreme values. This objective is best reached by models integrating geostatistics and machine learning algorithms. To demonstrate how such models work, two case studies have been considered: first, a 2-day accumulation of heavy precipitation and second, a 6-day accumulation of extreme orographic precipitation. The first example is used to compare the performance of two optimization algorithms (conjugate gradients and Levenberg-Marquardt) of a neural network for the reproduction of extreme values. Hybrid models, which combine geostatistical and machine learning algorithms, are also treated in this context. The second dataset is used to analyze the contribution of radar Doppler imagery when used as external drift or as input in the models (kriging with external drift and neural networks). Model assessment is carried out by comparing independent validation errors as well as analyzing data patterns.

Cinéma, machine à mondes : essai sur les films à univers multiples

Dans cet ouvrage, l'auteur propose une conceptualisation théorique de la coprésence en un même film de mondes multiples en abordant différents paramètres (hétérogénéité de la facture de l'image, pratiques du montage alterné, typologie des enchâssements, expansion sérielle, etc.) sur la base d'un corpus de films de fiction récents qui appartiennent pour la plupart au genre de la science-fiction (Matrix, Dark City, Avalon, Resident Evil, Avatar,...). Issue de la filmologie, la notion de « diégèse » y est développée à la fois dans le potentiel d'autonomisation dont témoigne la conception mondaine qui semble dominer aujourd'hui à l'ère des jeux vidéo, dans ses liens avec le récit et dans une perspective intermédiale. Les films discutés ont la particularité de mettre en scène des machines permettant aux personnages de passer d'un monde à l'autre : les modes de figuration de ces technologies sont investigués en lien avec les imaginaires du dispositif cinématographique et les potentialité du montage. La comparaison entre les films (Tron et son récent sequel, Totall Recall et son remake) et entre des oeuvres filmiques et littéraires (en particulier les nouvelles de Philip K. Dick et Simlacron 3 de Galouye) constitue un outil d'analyse permettant de saisir la contemporanéité de cette problématique, envisagée sur le plan esthétique dans le contexte de l'imagerie numérique.

L'exploitation des eaux souterraines en Syrie centrale : construction de diagnostics et politiques d'intervention

Syrian dry areas have been for several millennia a place of interaction between human populations and the environment. If environmental constraints and heterogeneity condition the human occupation and exploitation of resources, socio-political, economic and historical elements play a fundamental role. Since the late 1980s, Syrian dry areas are viewed as suffering a serious water crisis, due to groundwater overdraft. The Syrian administration and international development agencies believe that groundwater overexploitation is also leading to a decline of agricultural activities and to poverty increase. Action is thus required to address these problems.However, the overexploitation diagnosis needs to be reviewed. The overexploitation discourse appears in the context of Syria's opening to international organizations and to the market economy. It echoes the international discourse of "global water crisis". The diagnosis is based on national indicators recycling old Soviet data that has not been updated. In the post-Soviet era, the Syrian national water policy seems to abandon large surface water irrigation projects in favor of a strategy of water use rationalization and groundwater conservation in crisis regions, especially in the district of Salamieh.This groundwater conservation policy has a number of inconsistencies. It is justified for the administration and also probably for international donors, since it responds to an indisputable environmental emergency. However, efforts to conserve water are anecdotal or even counterproductive. The water conservation policy appears a posteriori as an extension of the national policy of food self-sufficiency. The dominant interpretation of overexploitation, and more generally of the water crisis, prevents any controversary approach of the status of resources and of the agricultural system in general and thus destroys any attempt to discuss alternatives with respect to groundwater management, allocation, and their inclusion in development programs.A revisited diagnosis of the situation needs to take into account spatial and temporal dimensions of the groundwater exploitation and to analyze the co-evolution of hydrogeological and agricultural systems. It should highlight the adjustments adopted to cope with environmental and economic variability, changes of water availability and regulatory measures enforcements. These elements play an important role for water availability and for the spatial, temporal, sectoral allocation of water resource. The groundwater exploitation in the last century has obviously had an impact on the environment, but the changes are not necessarily catastrophic.The current groundwater use in central Syria increases the uncertainty by reducing the ability of aquifers to buffer climatic changes. However, the climatic factor is not the only source of uncertainty. The high volatility of commodity prices, fuel, land and water, depending on the market but also on the will (and capacity) of the Syrian State to preserve social peace is a strong source of uncertainty. The research should consider the whole range of possibilities and propose alternatives that take into consideration the risks they imply for the water users, the political will to support or not the local access to water - thus involving a redefinition of the economic and social objectives - and finally the ability of international organizations to reconsider pre-established diagnoses.

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.

Devant une plaie des membres inférieurs, quels diagnostics évoquer?

L'ulcère des membres inférieurs se définit comme une plaie située sur la partie déclive des membres inférieurs évoluant depuis plus d'un mois sans tendance à la cicatrisation spontanée. La lenteur de guérison d'un ulcère ne s'explique pas seulement par sa taille et sa profondeur mais également par l'existence d'une pathologie sous-jacente qui doit mener à un traitement spécifique pour induire la guérison de la plaie. La prévalence de l'ulcère des membres inférieurs toutes causes confondues est de 1 % dans la population générale. Une origine vasculaire est retrouvée dans la majorité des cas. On retrouve plus rarement une origine infectieuse, dermatologique, hématologique, tumorale, métabolique, neurologique, iatrogène ou traumatique. Il est essentiel de connaître l'ensemble des pathologies pouvant amener à l'ulcère de jambe afin de pouvoir assurer une prise en charge adaptée qui seule permettra d'amener à la guérison.

Love machine

This poster provides advice on the use of condoms as a method of protection from unplanned pregnancy and sexually transmitted infections (STIs). It also provides contact details for the�Genito Urinary Medicine (GUM) clinics in Northern Ireland.