Neuropathologie et pathologie moléculaire des gliomes [Neuropathology and molecular pathology of gliomas]

Gliomas are the most frequent primary brain tumours. The WHO classification is essentially based on histological and immunohistochemical criteria. More recently multiple cytogenetic and molecular alterations associated with initiation and progression have been shown and the genetic profiles of tumour entities have been incorporated in the WHO classifiacation. Molecular testing of the MGMT promotor methylation in glioblastoma, predictive for the response to combined radio-/chimiothérapie, and the LOH 1p/19q in oligodendroglial tumours, as prognostic factor supplements the histopathological diagnosis. In the near futur array-based profiling techniques will contribute to a refinement of glioma classification and identify targets for more individualized glioma therapies.

Predicting positive food challenges at the introduction of nuts in sensitised children

Rationale: Children with atopic diseases in early life are frequently found with positive IgE tests to nuts, without a history of previous ingestion. We aimed to identify risk factors for reactions to nuts at their first introduction. Methods: A detailed retrospective case note and database analysis was performed. Inclusion criteria were: patients aged 3 to 16 years who had had a standardized food challenge to peanut and/or tree nuts due to primary sensitisation to the nut (positive specific IgE or SPT). A detailed assessment was performed of factors relating to food challenge outcome with univariate and multivariate logistic regression analysis. Results: There were 98 food challenges (48% peanut, 52% tree nut) with 29 positive, 67 negative and 2 inconclusive challenges. A positive maternal history and a specific IgE > 2 kU/l were strongly associated with a significantly increased risk of a positive food challenge (OR 3.54; 95% CI 1.28 to 9.81; and OR 4.82; 95% CI 1.57 to 14.86; respectively). There was no significant association between the type of nut, age, presence of other food allergies, paternal or sibling atopic history, other atopic conditions or severity of previous reaction to other foods. Conclusions: We have demonstrated an association between the presence of a maternal atopic history and a specific IgE > 2 kU/l, and a significant increase in the likelihood of a positive food challenge in children with primary sensitisation to nuts. Although requiring further prospective validation we suggest these easily identifiable components should be considered when deciding the need for a nut challenge.

Complement Factor B Polymorphism and the Phenotype of Early Age-related Macular Degeneration.

Abstract Purpose: Age-related macular degeneration (AMD) has been associated with a number of polymorphisms in genes in the complement pathway. We examined the potential genotype-phenotype correlation of complement factor B (CFB) (R32Q) polymorphisms in Caucasian patients with AMD. Methods: Data from a Central European cohort of 349 patients with early AMD in at least one eye were analyzed for potential associations of the CFB (R32Q/rs641153) polymorphism with phenotypic features of early AMD. Early AMD was classified according to the International Classification and Grading System into predominant drusen size, largest drusen, drusen covered surface, central or ring-like location, peripheral drusen, and pigmentary changes. The potential association with single nucleotide polymorphisms on CFB (R32Q/rs641153) was evaluated for all patients, corrected for age, sex, and the polymorphisms of CFH (Y402H) and ARMS2 (A69S). Results: CFB (R32Q) polymorphisms showed a significant association with smaller drusen size (largest drusen ≤250 µm, p = 0.021, predominant drusen ≤125 µm, p = 0.016), with smaller surface covered by drusen (≤10%; p = 0.02), and with more frequent occurrence of peripheral drusen (p = 0.007). No association was found for pigmentary changes. Conclusions: The CFB (R32Q) polymorphism was associated with AMD characterized by small drusen only, and appeared to be protective of large drusen (OR 0.48/0.45) and of larger drusen covered area (OR 0.34). Furthermore, peripheral drusen were more frequently found (OR 2.27). This result supports the role of complement components and their polymorphisms in drusen formation and may enable a better understanding of AMD pathogenesis.

Development and evaluation of antibody-MHC/peptide conjugates as a new form of cancer immunotherapy

Summary One of the major goals of cancer immunotherapy is the induction of a specific and effective antitumor cytotoxic T lymphocyte (CTL) response. However, the downregulation of Class I Major Histocompatibility Complexes (MHC) expression and the low level of tumor peptide presentation on tumor cell surface, ás well as the low immunogenicity of tumor specific antigens, limit the effectiveness of anti-tumor CTL responses. On the other hand, monoclonal antibodies, which bind with high affinity to tumor cell surface markers, are powerful tumor targeting tools. However, their capacity to .kill cancer cells is limited and mAb cancer treatments usually require the addition of different form of chemotherapy. The new cancer immunotherapy strategy described herein combines the advantage of the high tumor targeting capacity of monoclonal antibodies (mAb) with the powerful cytotoxicity of CD8 T lymphocytes directed against highly antigenic peptide-MHC complexes. Monoclonal antibody Fab fragments directed against a cell surface tumor associated antigen (TAA) are chemically coupled to soluble MHC class I complexes carrying a highly antigenic peptide. Antibody guided targeting and oligomerization of numerous antigenic class IMHC/peptide complexes on tumor cell surfaces can redirect the cytotoxicity of peptide-specific CD8 T cells towards target cancer cells. After the description of the production of murine anti-tumor xMHC/peptide conjugates in the first part of this thesis, the therapeutic potential of such conjugates were sequentially investigated in different syngeneic tumor mouse models. As a first proof of principle, transgenic OT-1 mice and later CEA transgenic C57BL/6 (B6) mice, adoptively transferred with OT-1 spleen cells and immunized with ovalbumin, were used as a model of high frequency of ova peptide specific T cells. In these mice, growth inhibition and regression of palpable colon carcinoma expressing CEA, were obtained by systemic injection of anti-CEA Fab/H-2Kb/ova peptide conjugates. Next, LCMV virus and influenza virus infection of B6 mice were used as viral models to redirect natural antiviral CTL responses to tumors via conjugates loaded with viral peptides. We showed that in mice infected with the LCMV virus, subcutaneous CEA-expressing tumor cells were inhibited by the H2Db/GP33 restricted anti-viral CTL response when preincubated before grafting with anti-CEA Fab-H-2Db/GP33 peptide conjugates. In mice infected with the influenza virus, lung metastases expressing the HER2 antigen were inhibited by the H-2Db/NP366 restricted CTLs response when preincubated before injection with anti-Her2 Fab-H-2Db/NP366 peptide conjugates. In the last chapter, the stability of the peptide in the anti-CEA Fab-H-2Db/GP33 conjugates was improved by the covalent photocross-link of the GP33 peptide in the H-2Db MHC groove. Thus, LCMV immune mice could reject CEA expressing tumors when treated with systemic injections of anti-CEA FabH-2Db/GP33 cross-linked conjugates. These results are encouraging for the potential application of this strategy in clinic. Such conjugates could be used alone in patients boosted by the relevant virus, or used in combination with existing T cell based ìmmunotherapy. Résumé Une des principales approches utilisées dans l'immunothérapie contre le cancer consiste en l'induction d'une réponse T cytotoxique (CTL) spécifiquement dirigée contre la tumeur. Cependant, le faible niveau d'expression des complexes majeurs d'histocompatibilité de classe I (CMH I) et de présentation des peptides tumoraux à la surface des cellules cancéreuses ainsi que la faible immunogenicité des antigens tumoraux, limitent l'efficacité de la réponse CTL. D'autre part,. l'injection d'anticorps monoclonaux (mAb), se liant avec une haute affinité aux marqueurs de surface des cellules tumorales, a fourni des résultats cliniques encourageant. Cependant l'efficacité de ces mAbs contre des tumeur solides reste limitée et necessite souvent l'addition de chimiotherapie. La nouvelle stratégie thérapeutique décrite dans ce travail associe le fort pouvoir de localisation des anticorps monoclonaux et le fort pouvoir cytotoxique des lymphocytes T CD8+. Des fragments Fab d'anticorps monoclonaux, dirigés contre des antigènes surexprimés à la surface de cellules tumorales, ont été chimiquement couplés à des CMH I solubles, portant un peptide fortement antigénique. Le ciblage et l'oligomérisation à la surface des cellules tumorales de nombreux CMH I présentant un peptide antigénique, va réorienter la cytotoxicité des cellules T CD8+ spécifiques du peptide présenté, vers les cellules tumorales cibles. Après une description de la production de conjugé anti-tumeur x CMH Upeptide dans la première partie de cette thèse, le potentiel thérapeutique de tels conjugés a été successivement étudiés in vivo dans différents modèles de tumeur syngénéiques. Tout d'abord, des souris OT-1 transgéniques, puis des souris C57BL/6 (B6) transférées avec des cellules de rate OT-1 puis immunisées avec l'ovalbumine, ont été employées comme modèle de haute fréquence de cellules T CD8+ spécifiques du peptide ova. Chez ces souris, l'inhibition de la croissance et la régression de nodules palpables de carcinomes exprimant l'antigène caccino embryonaire (ACE), ont été obtenues par l'injection systémique de conjugés anti-ACE Fab/H-2Kb/ova. Par la suite, l'infection de souris B6 par le virus LCMV et par le virus de la grippe, ont été utilisés comme modèles viraux pour redirigées des réponses anti-virales naturelles vers les tumeurs, en utilisant des conjugés chargés avec des peptides viraux. Nous avons montré que .chez les souris infectées par le LCMV, la croissance de carcinome sous-cutané est empêchée par la réponse anti-virale, spécifique du complexe H2Db/GP33, lorsque les cellules tumorales greffées sont pré-incubées avec des conjugés anti-CEA Fab-H-2Db/GP33. Dans le cas de souris infectées par le virus de la grippe, la métastatisation de mélanomes pulmonaires exprimant l'antigène HER-2 est inhibée par la réponse anti-virale spécifique du complexe H-2Db/NP366, après pré-incubation des cellules tumorales avec des conjugés anti-Her2 FabxH-2Db/NP366. Dans le dernier chapitre, la liaison covalente du peptide GP33 dans le complexe H-2Db a amélioré la stabilité des conjugés correspondants et a permis le traitement systémique de souris greffées avec des tumeurs exprimant l'ACE et infectées par le LCMV. L'ensemble de ces résultats sont encourageant pour l'application de cette strategie en clinique. De tels conjugués pourraient être employés seuls ou en combinaison avec des protocols d'immunisation peptidique anti-tumoral. Résumé pour un large public Dans les pays industrialisés, le cancer se situe au deuxième rang des causes de mortalité après les maladies cardiovasculaires. Les principaux traitement de nombreux cancers sont la chirurgie, en association avec la radiothérapie et la chimiothérapie. L'immunothérapie est l'une des nouvelles approches mises en oeuvre pour la lutte contre le cancer. Elle peut être humorale, et s'appuyer alors sur la perfusion d'anticorps monoclonaux dirigés contre des antigènes tumoraux, par exemple les anticorps dirigés contre les protéines oncogéniques Her-2/neu dans le cancer du sein. Ces anticorps ont le grand avantage de spécifiquement se localiser à la tumeur et d'induire la lyse ou d'inhiber la proliferation des cellules tumorales exprimant l'antigène. Certains sont utilisés en clinique pour le traitement de lymphomes, de carcinomes de l'ovaire et du sein ou encore de carcinomes metastatiques du côlon. Cependant l'efficacité de ces anticorps contre des tumeurs solides reste limitée et les traitements exigent souvent d'être combiner avec de la chimiothérapie. L'immunothérapie spécifique peut également être cellulaire et reposer sur une démarche de type vaccinal, consistant à générer des lymphocytes T cytotoxiques (cytotoxic T lymphocytes :CTL) capables de détruire spécifiquement les cellules malignes. Pour obtenir une réponse lymphocytaire T cytotoxique antitumorale, la cellule T doit reconnaître un antigène associé à la tumeur, présenté sous forme de peptide dans un complexe majeur d'histocompatibilité de classe I. Or les cellules tumorales ne presentent pas efficacement les peptides antigèniques, car elles se caractérisent par une diminution ou une absence d'expression des antigènes d'histocompatibilité de classe I, des molécules d'adhésion et des cytokines costimulatrices, et par une faible expression des antigènes associés aux tumeurs. C'est en partie pourquoi, malgré l'induction de fortes réponses CTL specifiquement dirigés contre des antigens tumoraux, les régressions tumorales obtenus grace à ces vaccinations sont relativement rares. Alors que chez les personnes atteintes du cancer on observe l'instauration d'une tolérance immunitaire vis-à-vis de la tumeur, à l'inverse, notre systeme immunitaire reste parfaitement capable de combattre des infection virales classiques, tels que la grippe, qui font aussi appel à une réponse T cytotoxique. Notre groupe de recherche a donc eu l'idee de développer une nouvelle approche thérapeutique où une réponse immunitaire anti-virale très efficace serait redirigée vers les tumeurs par des anticorps monoclonaux. Concrètement, nous avons chimiquement couplés des fragments d'anticorps monoclonaux dirigés contre des antigènes surexprimés à la surface de cellules tumorales, à des CMH I portant un peptide viral antigénique. Les cellules tumorales, ciblées par le fragment anticorps et couvertes d' antigènes viraux présentés par des molécules de CMH I, peuvent ainsi tromper les lymphocytes cytotoxiques anti-viraux qui vont détruire les cellules tumorales comme si elles étaient infectées par le virus. Suite à des résultats prometteurs obtenus in vitro avec différents conjugués anticorps-CMH humain de type HLA.A2/peptide Flu, le but du projet était de tester in vivo des conjugués anticorps-CMH I murins sur des modèles expérimentaux de souris. Tout d'abord, des souris transgéniques pour un recepteur T specifique du peptide ova, puis des transferts adoptifs de ces cellules T specifiques dans des souris immunocompétentes, ont été choisi comme modèle de haute fréquence des cellules T spécifiques, et ont permi de valider le principe de la strategie in vivo. Puis, deux modèles viraux ont été elaboré avec le virus LCMV et le virus Influenza, pour réorienter des réponses antivirales naturelles vers les tumeurs grâce à des conjugés chargés avec des peptides viraux. Nous avons montré la grande capacité de nos conjugués à rediriger des réponses cytotoxiques vers les tumeurs et inhiber la croissance de tumeurs syngénéiques sous cutanés et pulmonaires. Ces résultats d'inhibition tumorales obtenus dans des souris immunocompétentes, grâce à l'injection de conjugués anticorps xCMH/peptide et réorientant deux réponses antivirales différentes vers deux modèles tumoraux syngeneiques, sont encourageant pour l'application de cette nouvelle stratégie en clinique.

AWSum-Combining Classification with Knowledge Aquisition

Many classifiers achieve high levels of accuracy but have limited applicability in real world situations because they do not lead to a greater understanding or insight into the^way features influence the classification. In areas such as health informatics a classifier that clearly identifies the influences on classification can be used to direct research and formulate interventions. This research investigates the practical applications of Automated Weighted Sum, (AWSum), a classifier that provides accuracy comparable to other techniques whilst providing insight into the data. This is achieved by calculating a weight for each feature value that represents its influence on the class value. The merits of this approach in classification and insight are evaluated on a Cystic Fibrosis and Diabetes datasets with positive results.

BIO-INSPIRED COMPUTATIONAL TECHNIQUES APPLIED TO THE CLUSTERING AND VISUALIZATION OF SPATIO-TEMPORAL GEOSPATIAL DATA

The coverage and volume of geo-referenced datasets are extensive and incessantly¦growing. The systematic capture of geo-referenced information generates large volumes¦of spatio-temporal data to be analyzed. Clustering and visualization play a key¦role in the exploratory data analysis and the extraction of knowledge embedded in¦these data. However, new challenges in visualization and clustering are posed when¦dealing with the special characteristics of this data. For instance, its complex structures,¦large quantity of samples, variables involved in a temporal context, high dimensionality¦and large variability in cluster shapes.¦The central aim of my thesis is to propose new algorithms and methodologies for¦clustering and visualization, in order to assist the knowledge extraction from spatiotemporal¦geo-referenced data, thus improving making decision processes.¦I present two original algorithms, one for clustering: the Fuzzy Growing Hierarchical¦Self-Organizing Networks (FGHSON), and the second for exploratory visual data analysis:¦the Tree-structured Self-organizing Maps Component Planes. In addition, I present¦methodologies that combined with FGHSON and the Tree-structured SOM Component¦Planes allow the integration of space and time seamlessly and simultaneously in¦order to extract knowledge embedded in a temporal context.¦The originality of the FGHSON lies in its capability to reflect the underlying structure¦of a dataset in a hierarchical fuzzy way. A hierarchical fuzzy representation of¦clusters is crucial when data include complex structures with large variability of cluster¦shapes, variances, densities and number of clusters. The most important characteristics¦of the FGHSON include: (1) It does not require an a-priori setup of the number¦of clusters. (2) The algorithm executes several self-organizing processes in parallel.¦Hence, when dealing with large datasets the processes can be distributed reducing the¦computational cost. (3) Only three parameters are necessary to set up the algorithm.¦In the case of the Tree-structured SOM Component Planes, the novelty of this algorithm¦lies in its ability to create a structure that allows the visual exploratory data analysis¦of large high-dimensional datasets. This algorithm creates a hierarchical structure¦of Self-Organizing Map Component Planes, arranging similar variables' projections in¦the same branches of the tree. Hence, similarities on variables' behavior can be easily¦detected (e.g. local correlations, maximal and minimal values and outliers).¦Both FGHSON and the Tree-structured SOM Component Planes were applied in¦several agroecological problems proving to be very efficient in the exploratory analysis¦and clustering of spatio-temporal datasets.¦In this thesis I also tested three soft competitive learning algorithms. Two of them¦well-known non supervised soft competitive algorithms, namely the Self-Organizing¦Maps (SOMs) and the Growing Hierarchical Self-Organizing Maps (GHSOMs); and the¦third was our original contribution, the FGHSON. Although the algorithms presented¦here have been used in several areas, to my knowledge there is not any work applying¦and comparing the performance of those techniques when dealing with spatiotemporal¦geospatial data, as it is presented in this thesis.¦I propose original methodologies to explore spatio-temporal geo-referenced datasets¦through time. Our approach uses time windows to capture temporal similarities and¦variations by using the FGHSON clustering algorithm. The developed methodologies¦are used in two case studies. In the first, the objective was to find similar agroecozones¦through time and in the second one it was to find similar environmental patterns¦shifted in time.¦Several results presented in this thesis have led to new contributions to agroecological¦knowledge, for instance, in sugar cane, and blackberry production.¦Finally, in the framework of this thesis we developed several software tools: (1)¦a Matlab toolbox that implements the FGHSON algorithm, and (2) a program called¦BIS (Bio-inspired Identification of Similar agroecozones) an interactive graphical user¦interface tool which integrates the FGHSON algorithm with Google Earth in order to¦show zones with similar agroecological characteristics.

Tricky and terrible T-cell tumors: these are thrilling times for testing: molecular pathology of peripheral T-cell lymphomas.

Peripheral T-cell lymphomas (PTCLs) encompass a group of rare and usually clinically aggressive diseases. The classification and diagnosis of these diseases are compounded by their marked pathological heterogeneity and complex clinical features. With the exception of ALK-positive anaplastic large cell lymphoma (ALCL), which is defined on the basis of ALK rearrangements, genetic features play little role in the definition of other disease entities. In recent years, hitherto unrecognized chromosomal translocations have been reported in small subsets of PTCLs, and genome-wide array-based profiling investigations have provided novel insights into their molecular characteristics. This article summarizes the current knowledge on the best-characterized genetic and molecular alterations underlying the pathogenesis of PTCLs, with a focus on recent discoveries, their relevance to disease classification, and their management implications from a diagnostical and therapeutical perspective.

Pattern recognition ecological niche models fit to presence-only and presence-absence data

1. Identifying the boundary of a species' niche from observational and environmental data is a common problem in ecology and conservation biology and a variety of techniques have been developed or applied to model niches and predict distributions. Here, we examine the performance of some pattern-recognition methods as ecological niche models (ENMs). Particularly, one-class pattern recognition is a flexible and seldom used methodology for modelling ecological niches and distributions from presence-only data. The development of one-class methods that perform comparably to two-class methods (for presence/absence data) would remove modelling decisions about sampling pseudo-absences or background data points when absence points are unavailable. 2. We studied nine methods for one-class classification and seven methods for two-class classification (five common to both), all primarily used in pattern recognition and therefore not common in species distribution and ecological niche modelling, across a set of 106 mountain plant species for which presence-absence data was available. We assessed accuracy using standard metrics and compared trade-offs in omission and commission errors between classification groups as well as effects of prevalence and spatial autocorrelation on accuracy. 3. One-class models fit to presence-only data were comparable to two-class models fit to presence-absence data when performance was evaluated with a measure weighting omission and commission errors equally. One-class models were superior for reducing omission errors (i.e. yielding higher sensitivity), and two-classes models were superior for reducing commission errors (i.e. yielding higher specificity). For these methods, spatial autocorrelation was only influential when prevalence was low. 4. These results differ from previous efforts to evaluate alternative modelling approaches to build ENM and are particularly noteworthy because data are from exhaustively sampled populations minimizing false absence records. Accurate, transferable models of species' ecological niches and distributions are needed to advance ecological research and are crucial for effective environmental planning and conservation; the pattern-recognition approaches studied here show good potential for future modelling studies. This study also provides an introduction to promising methods for ecological modelling inherited from the pattern-recognition discipline.

Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer.

BACKGROUND: A 70-gene signature was previously shown to have prognostic value in patients with node-negative breast cancer. Our goal was to validate the signature in an independent group of patients. METHODS: Patients (n = 307, with 137 events after a median follow-up of 13.6 years) from five European centers were divided into high- and low-risk groups based on the gene signature classification and on clinical risk classifications. Patients were assigned to the gene signature low-risk group if their 5-year distant metastasis-free survival probability as estimated by the gene signature was greater than 90%. Patients were assigned to the clinicopathologic low-risk group if their 10-year survival probability, as estimated by Adjuvant! software, was greater than 88% (for estrogen receptor [ER]-positive patients) or 92% (for ER-negative patients). Hazard ratios (HRs) were estimated to compare time to distant metastases, disease-free survival, and overall survival in high- versus low-risk groups. RESULTS: The 70-gene signature outperformed the clinicopathologic risk assessment in predicting all endpoints. For time to distant metastases, the gene signature yielded HR = 2.32 (95% confidence interval [CI] = 1.35 to 4.00) without adjustment for clinical risk and hazard ratios ranging from 2.13 to 2.15 after adjustment for various estimates of clinical risk; clinicopathologic risk using Adjuvant! software yielded an unadjusted HR = 1.68 (95% CI = 0.92 to 3.07). For overall survival, the gene signature yielded an unadjusted HR = 2.79 (95% CI = 1.60 to 4.87) and adjusted hazard ratios ranging from 2.63 to 2.89; clinicopathologic risk yielded an unadjusted HR = 1.67 (95% CI = 0.93 to 2.98). For patients in the gene signature high-risk group, 10-year overall survival was 0.69 for patients in both the low- and high-clinical risk groups; for patients in the gene signature low-risk group, the 10-year survival rates were 0.88 and 0.89, respectively. CONCLUSIONS: The 70-gene signature adds independent prognostic information to clinicopathologic risk assessment for patients with early breast cancer.

The systematic profiling of false identity documents: method validation and performance evaluation using seizures known to originate from common and different sources

False identity documents constitute a potential powerful source of forensic intelligence because they are essential elements of transnational crime and provide cover for organized crime. In previous work, a systematic profiling method using false documents' visual features has been built within a forensic intelligence model. In the current study, the comparison process and metrics lying at the heart of this profiling method are described and evaluated. This evaluation takes advantage of 347 false identity documents of four different types seized in two countries whose sources were known to be common or different (following police investigations and dismantling of counterfeit factories). Intra-source and inter-sources variations were evaluated through the computation of more than 7500 similarity scores. The profiling method could thus be validated and its performance assessed using two complementary approaches to measuring type I and type II error rates: a binary classification and the computation of likelihood ratios. Very low error rates were measured across the four document types, demonstrating the validity and robustness of the method to link documents to a common source or to differentiate them. These results pave the way for an operational implementation of a systematic profiling process integrated in a developed forensic intelligence model.

Recent advances in remote sensing image processing

Remote sensing image processing is nowadays a mature research area. The techniques developed in the field allow many real-life applications with great societal value. For instance, urban monitoring, fire detection or flood prediction can have a great impact on economical and environmental issues. To attain such objectives, the remote sensing community has turned into a multidisciplinary field of science that embraces physics, signal theory, computer science, electronics, and communications. From a machine learning and signal/image processing point of view, all the applications are tackled under specific formalisms, such as classification and clustering, regression and function approximation, image coding, restoration and enhancement, source unmixing, data fusion or feature selection and extraction. This paper serves as a survey of methods and applications, and reviews the last methodological advances in remote sensing image processing.

Apport du CT-3D dans le diagnostic et le traitement des fractures des plateaux tibiaux [Value of 3D CT in diagnosis and treatment of fractures of the tibial plateau]

A precise classification and an optimal understanding of tibial plateau fractures are the basis of a conservative treatment or adequate surgery. The aim of this prospective study is to determine the contribution of 3D CT to the classification of fractures (comparison with standard X-rays) and as an aid to the surgeon in preoperative planning and surgical reconstruction. Between November 1994 and July 1996, 20 patients presenting 22 tibial plateau fractures were considered in this study. They all underwent surgical treatment. The fractures were classified according to the Müller AO classification. They were all investigated by means of standard X-rays (AP, profile, oblique) and the 3D CT. Analysis of the results has shown the superiority of 3D CT in the planning (easier and more acute), in the classification (more precise), and in the exact assessment of the lesions (quantity of fragments); thereby proving to be of undeniable value of the surgeon.

Differential impact of lacunes and microvascular lesions on poststroke depression.

BACKGROUND AND PURPOSE: Previous studies have postulated that poststroke depression (PSD) might be related to cumulative vascular brain pathology rather than to the location and severity of a single macroinfarct. We performed a detailed analysis of all types of microvascular lesions and lacunes in 41 prospectively documented and consecutively autopsied stroke cases. METHODS: Only cases with first-onset depression <2 years after stroke were considered as PSD in the present series. Diagnosis of depression was established prospectively using DSM-IV criteria for major depression. Neuropathological evaluation included bilateral semiquantitative assessment of microvascular ischemic pathology and lacunes; statistical analysis included Fisher exact test, Mann-Whitney U test, and regression models. RESULTS: Macroinfarct site was not related to the occurrence of PSD for any of the locations studied. Thalamic and basal ganglia lacunes occurred significantly more often in PSD cases. Higher lacune scores in basal ganglia, thalamus, and deep white matter were associated with an increased PSD risk. In contrast, microinfarct and diffuse or periventricular demyelination scores were not increased in PSD. The combined lacune score (thalamic plus basal ganglia plus deep white matter) explained 25% of the variability of PSD occurrence. CONCLUSIONS: The cumulative vascular burden resulting from chronic accumulation of lacunar infarcts within the thalamus, basal ganglia, and deep white matter may be more important than single infarcts in the prediction of PSD.

Machine learning for spatial environmental data. Theory, applications and software

The book presents the state of the art in machine learning algorithms (artificial neural networks of different architectures, support vector machines, etc.) as applied to the classification and mapping of spatially distributed environmental data. Basic geostatistical algorithms are presented as well. New trends in machine learning and their application to spatial data are given, and real case studies based on environmental and pollution data are carried out. The book provides a CD-ROM with the Machine Learning Office software, including sample sets of data, that will allow both students and researchers to put the concepts rapidly to practice.

Artificial evolution on network structures : how time and space influence dynamics

Abstract The main objective of this work is to show how the choice of the temporal dimension and of the spatial structure of the population influences an artificial evolutionary process. In the field of Artificial Evolution we can observe a common trend in synchronously evolv¬ing panmictic populations, i.e., populations in which any individual can be recombined with any other individual. Already in the '90s, the works of Spiessens and Manderick, Sarma and De Jong, and Gorges-Schleuter have pointed out that, if a population is struc¬tured according to a mono- or bi-dimensional regular lattice, the evolutionary process shows a different dynamic with respect to the panmictic case. In particular, Sarma and De Jong have studied the selection pressure (i.e., the diffusion of a best individual when the only selection operator is active) induced by a regular bi-dimensional structure of the population, proposing a logistic modeling of the selection pressure curves. This model supposes that the diffusion of a best individual in a population follows an exponential law. We show that such a model is inadequate to describe the process, since the growth speed must be quadratic or sub-quadratic in the case of a bi-dimensional regular lattice. New linear and sub-quadratic models are proposed for modeling the selection pressure curves in, respectively, mono- and bi-dimensional regu¬lar structures. These models are extended to describe the process when asynchronous evolutions are employed. Different dynamics of the populations imply different search strategies of the resulting algorithm, when the evolutionary process is used to solve optimisation problems. A benchmark of both discrete and continuous test problems is used to study the search characteristics of the different topologies and updates of the populations. In the last decade, the pioneering studies of Watts and Strogatz have shown that most real networks, both in the biological and sociological worlds as well as in man-made structures, have mathematical properties that set them apart from regular and random structures. In particular, they introduced the concepts of small-world graphs, and they showed that this new family of structures has interesting computing capabilities. Populations structured according to these new topologies are proposed, and their evolutionary dynamics are studied and modeled. We also propose asynchronous evolutions for these structures, and the resulting evolutionary behaviors are investigated. Many man-made networks have grown, and are still growing incrementally, and explanations have been proposed for their actual shape, such as Albert and Barabasi's preferential attachment growth rule. However, many actual networks seem to have undergone some kind of Darwinian variation and selection. Thus, how these networks might have come to be selected is an interesting yet unanswered question. In the last part of this work, we show how a simple evolutionary algorithm can enable the emrgence o these kinds of structures for two prototypical problems of the automata networks world, the majority classification and the synchronisation problems. Synopsis L'objectif principal de ce travail est de montrer l'influence du choix de la dimension temporelle et de la structure spatiale d'une population sur un processus évolutionnaire artificiel. Dans le domaine de l'Evolution Artificielle on peut observer une tendence à évoluer d'une façon synchrone des populations panmictiques, où chaque individu peut être récombiné avec tout autre individu dans la population. Déjà dans les année '90, Spiessens et Manderick, Sarma et De Jong, et Gorges-Schleuter ont observé que, si une population possède une structure régulière mono- ou bi-dimensionnelle, le processus évolutionnaire montre une dynamique différente de celle d'une population panmictique. En particulier, Sarma et De Jong ont étudié la pression de sélection (c-à-d la diffusion d'un individu optimal quand seul l'opérateur de sélection est actif) induite par une structure régulière bi-dimensionnelle de la population, proposant une modélisation logistique des courbes de pression de sélection. Ce modèle suppose que la diffusion d'un individu optimal suit une loi exponentielle. On montre que ce modèle est inadéquat pour décrire ce phénomène, étant donné que la vitesse de croissance doit obéir à une loi quadratique ou sous-quadratique dans le cas d'une structure régulière bi-dimensionnelle. De nouveaux modèles linéaires et sous-quadratique sont proposés pour des structures mono- et bi-dimensionnelles. Ces modèles sont étendus pour décrire des processus évolutionnaires asynchrones. Différentes dynamiques de la population impliquent strategies différentes de recherche de l'algorithme résultant lorsque le processus évolutionnaire est utilisé pour résoudre des problèmes d'optimisation. Un ensemble de problèmes discrets et continus est utilisé pour étudier les charactéristiques de recherche des différentes topologies et mises à jour des populations. Ces dernières années, les études de Watts et Strogatz ont montré que beaucoup de réseaux, aussi bien dans les mondes biologiques et sociologiques que dans les structures produites par l'homme, ont des propriétés mathématiques qui les séparent à la fois des structures régulières et des structures aléatoires. En particulier, ils ont introduit la notion de graphe sm,all-world et ont montré que cette nouvelle famille de structures possède des intéressantes propriétés dynamiques. Des populations ayant ces nouvelles topologies sont proposés, et leurs dynamiques évolutionnaires sont étudiées et modélisées. Pour des populations ayant ces structures, des méthodes d'évolution asynchrone sont proposées, et la dynamique résultante est étudiée. Beaucoup de réseaux produits par l'homme se sont formés d'une façon incrémentale, et des explications pour leur forme actuelle ont été proposées, comme le preferential attachment de Albert et Barabàsi. Toutefois, beaucoup de réseaux existants doivent être le produit d'un processus de variation et sélection darwiniennes. Ainsi, la façon dont ces structures ont pu être sélectionnées est une question intéressante restée sans réponse. Dans la dernière partie de ce travail, on montre comment un simple processus évolutif artificiel permet à ce type de topologies d'émerger dans le cas de deux problèmes prototypiques des réseaux d'automates, les tâches de densité et de synchronisation.