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.

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.

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.

Structural analysis of TCR-ligand interactions studied on H-2Kd-restricted cloned CTL specific for a photoreactive peptide derivative.

To study the interaction of the TCR with its ligand, the complex of a MHC molecule and an antigenic peptide, we modified a TCR contact residue of a H-2Kd-restricted antigenic peptide with photoreactive 4-azidobenzoic acid. The photoreactive group was a critical component of the epitope recognized by CTL clones derived from mice immunized with such a peptide derivative. The majority of these clones expressed V beta 1-encoded beta chains that were paired with J alpha TA28-encoded alpha chains. For one of these TCR, the photoaffinity labeled sites were mapped on the alpha chain as a J alpha TA28-encoded tryptophan and on the beta chain as a residue of the C' strand of V beta 1. Molecular modeling of this TCR suggested the presence of a hydrophobic pocket that harbors this tryptophan as well as a tyrosine on the C' strand of V beta 1 between which the photoreactive side chain inserts. It is concluded that this avid binding principle may account for the preferential selection of V beta 1 and J alpha TA28-encoded TCR.

Identification of new MHC class I-restricted human tumor antigens for immunotherapy

Summary Cancer is a leading cause of morbidity and mortality in Western countries (as an example, colorectal cancer accounts for about 300'000 new cases and 200'000 deaths each year in Europe and in the USA). Despite that many patients with cancer have complete macroscopic clearance of their disease after resection, radiotherapy and/or chemotherapy, many of these patients develop fatal recurrence. Vaccination with immunogenic peptide tumor antigens has shown encouraging progresses in the last decade; immunotherapy might therefore constitute a fourth therapeutic option in the future. We dissect here and critically evaluate the numerous steps of reverse immunology, a forecast procedure to identify antigenic peptides from the sequence of a gene of interest. Bioinformatic algorithms were applied to mine sequence databases for tumor-specific transcripts. A quality assessment of publicly available sequence databanks allowed defining strengths and weaknesses of bioinformatics-based prediction of colon cancer-specific alternative splicing: new splice variants could be identified, however cancer-restricted expression could not be significantly predicted. Other sources of target transcripts were quantitatively investigated by polymerase chain reactions, as cancer-testis genes or reported overexpressed transcripts. Based on the relative expression of a defined set of housekeeping genes in colon cancer tissues, we characterized a precise procedure for accurate normalization and determined a threshold for the definition of significant overexpression of genes in cancers versus normal tissues. Further steps of reverse immunology were applied on a splice variant of the Melan¬A gene. Since it is known that the C-termini of antigenic peptides are directly produced by the proteasome, longer precursor and overlapping peptides encoded by the target sequence were synthesized chemically and digested in vitro with purified proteasome. The resulting fragments were identified by mass spectroscopy to detect cleavage sites. Using this information and based on the available anchor motifs for defined HLA class I molecules, putative antigenic peptides could be predicted. Their relative affinity for HLA molecules was confirmed experimentally with functional competitive binding assays and they were used to search patients' peripheral blood lymphocytes for the presence of specific cytolytic T lymphocytes (CTL). CTL clones specific for a splice variant of Melan-A could be isolated; although they recognized peptide-pulsed cells, they failed to lyse melanoma cells in functional assays of antigen recognition. In the conclusion, we discuss advantages and bottlenecks of reverse immunology and compare the technical aspects of this approach with the more classical procedure of direct immunology, a technique introduced by Boon and colleagues more than 10 years ago to successfully clone tumor antigens.

An empirical study of machine learning techniques applied to image watermarking

A study of how the machine learning technique, known as gentleboost, could improve different digital watermarking methods such as LSB, DWT, DCT2 and Histogram shifting.

Neurofilament triplet proteins are restricted to a subset of neurons in the rat neocortex.

The cellular localisation of neurofilament triplet subunits was investigated in the rat neocortex. A subset of mainly pyramidal neurons showed colocalisation of subunit immunolabelling throughout the neocortex, including labelling with the antibody SMI32, which has been used extensively in other studies of the primate cortex as a selective cellular marker. Neurofilament-labelled neurons were principally localised to two or three cell layers in most cortical regions, but dramatically reduced labelling was present in areas such as the perirhinal cortex, anterior cingulate and a strip of cortex extending from caudal motor regions through the medial parietal region to secondary visual areas. However, quantitative analysis demonstrated a similar proportion (10-20%) of cells with neurofilament triplet labelling in regions of high or low labelling. Combining retrograde tracing with immunolabelling showed that cellular content of the neurofilament proteins was not correlated with the length of projection. Double labelling immunohistochemistry demonstrated that neurofilament content in axons was closely associated with myelination. Analysis of SMI32 labelling in development indicated that content of this epitope within cell bodies was associated with relatively late maturation, between postnatal days 14 and 21. This study is further evidence of a cell type-specific regulation of neurofilament proteins within neocortical neurons. Neurofilament triplet content may be more closely related to the degree of myelination, rather than the absolute length, of the projecting axon.