Nursing Needs Assessment Tool - Users Guide 2002 (PDF 32 KB)

This assessment tool is designed to assess the registered nursing needs of a person needing long-term care. The tool is designed to encapsulate a systematic approach to assessment whilst at the same time embracing professional decision-making that takes place in the relationship between a nurse and another person. For this reason, the tool takes the assessment through a staged approach, moving from a general ‘narrative’ based assessment of ‘domains’ of care need, to a focused assessment of risk and complexity. åÊ

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.

IgM-Immunofluorescence Test as a Diagnostic Tool for Epidemiologic Studies of Schistosomiasis in Low Endemic Areas

The high sensitivity and the ability to diagnose schistosomiasis in a very early phase after infection have indicated the detection of IgM antibodies to Schistosoma mansoni gut antigens by the immunofluorescence test (IgM-IFT) as a useful serological test for epidemiological studies in low endemic areas. When applied in a follow-up study for two years, higher rates of seroconversion from IFT negative to positive were observed during the summer months, suggesting seasonal transmission of schistosomiasis in the rural area of the municipality of Itariri (São Paulo, Brazil). In each survey, blood samples from about 600 schoolchildren were collected on filter paper and submitted to IgM-IFT. When the blood samples were classified for the IgM antibody levels, according to the intensity of fluorescent reaction observed at fluorescence microscopy, and correlated to the egg counts in the Kato-Katz positive patients, no association was observed. This observation might suggest that the intensity of fluorescence observed in the IgM-IFT, as an indicator of IgM antibody levels, could not be an useful seroepidemiological marker for classifying areas of low endemicity according to degrees of infection.

MAMOT: hidden Markov modeling tool.

Hidden Markov models (HMMs) are probabilistic models that are well adapted to many tasks in bioinformatics, for example, for predicting the occurrence of specific motifs in biological sequences. MAMOT is a command-line program for Unix-like operating systems, including MacOS X, that we developed to allow scientists to apply HMMs more easily in their research. One can define the architecture and initial parameters of the model in a text file and then use MAMOT for parameter optimization on example data, decoding (like predicting motif occurrence in sequences) and the production of stochastic sequences generated according to the probabilistic model. Two examples for which models are provided are coiled-coil domains in protein sequences and protein binding sites in DNA. A wealth of useful features include the use of pseudocounts, state tying and fixing of selected parameters in learning, and the inclusion of prior probabilities in decoding. AVAILABILITY: MAMOT is implemented in C++, and is distributed under the GNU General Public Licence (GPL). The software, documentation, and example model files can be found at http://bcf.isb-sib.ch/mamot

Monitor de servidors JMS

El monitor de servidors JMS és un projecte basat en el disseny i implementacio d'una eina GUI, destinada a programadors i equips de proves que treballin amb la tecnología Java Message Service, multiplataforma i multiservidor, que podrà monitoritzar un nombre variat de servidors JMS des de qualsevol sistema que tingui una màquina virtual de Java instal·lada. L'aplicació té com a principal objectiu visualitzar de forma clara i senzilla l'estat global d'un servidor JMS, mostrant les cues i tòpics creats, juntament amb la possibilitat de realitzar accions sobre les mateixes destinacions (enviament i eliminació de missatges residents al servidor) i la creació de gràfiques sobre el tràfic de missatges.

A noninvasive diagnostic tool to differentiate myocarditis from myocardial infarction: late gadolinium enhanced cardiac magnetic resonance.

Studies in adults have shown that late gadolinium enhanced cardiac magnetic resonance is a safe and noninvasive diagnostic tool which allows one to differentiate myocardial infarction from myocarditis. We believe that it may also be highly useful in the paediatric population for the same purpose.

Biometal muscle to restore atrial transport function in a permanent atrial fibrillation animal model: a potential tool in the treatment of end-stage heart failure.

BACKGROUND: Half of the patients with end-stage heart failure suffer from persistent atrial fibrillation (AF). Atrial kick (AK) accounts for 10-15% of the ejection fraction. A device restoring AK should significantly improve cardiac output (CO) and possibly delay ventricular assist device (VAD) implantation. This study has been designed to assess the mechanical effects of a motorless pump on the right chambers of the heart in an animal model. METHODS: Atripump is a dome-shaped biometal actuator electrically driven by a pacemaker-like control unit. In eight sheep, the device was sutured onto the right atrium (RA). AF was simulated with rapid atrial pacing. RA ejection fraction (EF) was assessed with intracardiac ultrasound (ICUS) in baseline, AF and assisted-AF status. In two animals, the pump was left in place for 4 weeks and then explanted. Histology examination was carried out. The mean values for single measurement per animal with +/-SD were analysed. RESULTS: The contraction rate of the device was 60 per min. RA EF was 41% in baseline, 7% in AF and 21% in assisted-AF conditions. CO was 7+/-0.5 l min(-1) in baseline, 6.2+/-0.5 l min(-1) in AF and 6.7+/-0.5 l min(-1) in assisted-AF status (p<0.01). Histology of the atrium in the chronic group showed chronic tissue inflammation and no sign of tissue necrosis. CONCLUSIONS: The artificial muscle restores the AK and improves CO. In patients with end-stage cardiac failure and permanent AF, if implanted on both sides, it would improve CO and possibly delay or even avoid complex surgical treatment such as VAD implantation.

Report of the Working Group on Sports Sponsorship by the Alcohol Industry

Report of the Working Group on Sports Sponsorship by the Alcohol Industry Click here to download PDF 60KB

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.

Towards the utilization of EEG as a brain imaging tool.

Recent advances in signal analysis have engendered EEG with the status of a true brain mapping and brain imaging method capable of providing spatio-temporal information regarding brain (dys)function. Because of the increasing interest in the temporal dynamics of brain networks, and because of the straightforward compatibility of the EEG with other brain imaging techniques, EEG is increasingly used in the neuroimaging community. However, the full capability of EEG is highly underestimated. Many combined EEG-fMRI studies use the EEG only as a spike-counter or an oscilloscope. Many cognitive and clinical EEG studies use the EEG still in its traditional way and analyze grapho-elements at certain electrodes and latencies. We here show that this way of using the EEG is not only dangerous because it leads to misinterpretations, but it is also largely ignoring the spatial aspects of the signals. In fact, EEG primarily measures the electric potential field at the scalp surface in the same way as MEG measures the magnetic field. By properly sampling and correctly analyzing this electric field, EEG can provide reliable information about the neuronal activity in the brain and the temporal dynamics of this activity in the millisecond range. This review explains some of these analysis methods and illustrates their potential in clinical and experimental applications.

Strongyloides ratti antigenic components recognized by IgE antibodies in immunoblotting as an additional tool for improving the immunodiagnosis in human strongyloidiasis

IgE antibody response in human strongyloidiasis was evaluated by enzyme-linked immunosorbent assay (ELISA) and immunoblotting (IB) using Strongyloides ratti saline extract as heterologous antigen. A total of 50 serum samples of patients who were shedding S. stercoralis larvae in feces (group I, copropositive), 38 of patients with other intestinal parasites (group II), and 38 of subjects with negative results in three parasitologic assays (group III, copronegative) were analyzed. Levels of IgE anti-Strongyloides expressed in ELISA Index (EI) were significantly higher in patients of group I (1.32) than in group II (0.51) and group III (0.81), with positivity rates of 54%, 0%, and 10.5%, respectively. Fifteen S. ratti antigenic components were recognized in IB-IgE by sera of group I, with frequency ranging from 8% to 46%. In group II, only two antigenic bands (101, 81 kDa) were detected in a frequency of 10% and no reactivity was found in group III. Sera with EI values > 1.5 recognized five from 13 specific antigenic bands (70, 63, 61, 44, 7 kDa). It can be concluded that these five antigenic components recognized by IB-IgE using S. ratti antigen might be employed as an additional tool for improving the immunodiagnosis in human strongyloidiasis.