Classification of human astrocytic gliomas on the basis of gene expression: a correlated group of genes with angiogenic activity emerges as a strong predictor of subtypes.

The development of targeted treatment strategies adapted to individual patients requires identification of the different tumor classes according to their biology and prognosis. We focus here on the molecular aspects underlying these differences, in terms of sets of genes that control pathogenesis of the different subtypes of astrocytic glioma. By performing cDNA-array analysis of 53 patient biopsies, comprising low-grade astrocytoma, secondary glioblastoma (respective recurrent high-grade tumors), and newly diagnosed primary glioblastoma, we demonstrate that human gliomas can be differentiated according to their gene expression. We found that low-grade astrocytoma have the most specific and similar expression profiles, whereas primary glioblastoma exhibit much larger variation between tumors. Secondary glioblastoma display features of both other groups. We identified several sets of genes with relatively highly correlated expression within groups that: (a). can be associated with specific biological functions; and (b). effectively differentiate tumor class. One prominent gene cluster discriminating primary versus nonprimary glioblastoma comprises mostly genes involved in angiogenesis, including VEGF fms-related tyrosine kinase 1 but also IGFBP2, that has not yet been directly linked to angiogenesis. In situ hybridization demonstrating coexpression of IGFBP2 and VEGF in pseudopalisading cells surrounding tumor necrosis provided further evidence for a possible involvement of IGFBP2 in angiogenesis. The separating groups of genes were found by the unsupervised coupled two-way clustering method, and their classification power was validated by a supervised construction of a nearly perfect glioma classifier.

Hierarchical conditional random fields for parts based models matching

A parts based model is a parametrization of an object class using a collection of landmarks following the object structure. The matching of parts based models is one of the problems where pairwise Conditional Random Fields have been successfully applied. The main reason of their effectiveness is tractable inference and learning due to the simplicity of involved graphs, usually trees. However, these models do not consider possible patterns of statistics among sets of landmarks, and thus they sufffer from using too myopic information. To overcome this limitation, we propoese a novel structure based on a hierarchical Conditional Random Fields, which we explain in the first part of this memory. We build a hierarchy of combinations of landmarks, where matching is performed taking into account the whole hierarchy. To preserve tractable inference we effectively sample the label set. We test our method on facial feature selection and human pose estimation on two challenging datasets: Buffy and MultiPIE. In the second part of this memory, we present a novel approach to multiple kernel combination that relies on stacked classification. This method can be used to evaluate the landmarks of the parts-based model approach. Our method is based on combining responses of a set of independent classifiers for each individual kernel. Unlike earlier approaches that linearly combine kernel responses, our approach uses them as inputs to another set of classifiers. We will show that we outperform state-of-the-art methods on most of the standard benchmark datasets.

Avaluació de l'estàndard SQL : 1999 respecte les característiques orientades a l'objecte que suporta i la seva implementació als SGBD object-relacional

En aquest treball s'intenta fer una síntesi de les especificacions aportades per l'estàndard definit com a SQL: 1999, tot analitzant les ampliacions que fan referència a la nova orientació a l'objecte i a la incorporació de l'herència com a principal element diferenciador.

Catálogo General

El projecte consisteix en el desenvolupament d'una eina especialitzada per a la classificació i recerca de productes. L'aplicació pot ser fàcilment ampliable per a adaptar-la a qualsevol tipus de producte o necessitat futura. Per a la programació d'aquest projecte es va aprofitar la potència i senzillesa que proporciona l'entorn .NET i els seus llenguatges orientats a objecte, en aquest cas VB.

Binary classification of dyslipidemia from the waist-to-hip ratio and body mass index: a comparison of linear, logistic, and CART models.

BACKGROUND: We sought to improve upon previously published statistical modeling strategies for binary classification of dyslipidemia for general population screening purposes based on the waist-to-hip circumference ratio and body mass index anthropometric measurements. METHODS: Study subjects were participants in WHO-MONICA population-based surveys conducted in two Swiss regions. Outcome variables were based on the total serum cholesterol to high density lipoprotein cholesterol ratio. The other potential predictor variables were gender, age, current cigarette smoking, and hypertension. The models investigated were: (i) linear regression; (ii) logistic classification; (iii) regression trees; (iv) classification trees (iii and iv are collectively known as "CART"). Binary classification performance of the region-specific models was externally validated by classifying the subjects from the other region. RESULTS: Waist-to-hip circumference ratio and body mass index remained modest predictors of dyslipidemia. Correct classification rates for all models were 60-80%, with marked gender differences. Gender-specific models provided only small gains in classification. The external validations provided assurance about the stability of the models. CONCLUSIONS: There were no striking differences between either the algebraic (i, ii) vs. non-algebraic (iii, iv), or the regression (i, iii) vs. classification (ii, iv) modeling approaches. Anticipated advantages of the CART vs. simple additive linear and logistic models were less than expected in this particular application with a relatively small set of predictor variables. CART models may be more useful when considering main effects and interactions between larger sets of predictor variables.

Nosology and classification of genetic skeletal disorders: 2010 revision.

Genetic disorders involving the skeletal system arise through disturbances in the complex processes of skeletal development, growth and homeostasis and remain a diagnostic challenge because of their variety. The Nosology and Classification of Genetic Skeletal Disorders provides an overview of recognized diagnostic entities and groups them by clinical and radiographic features and molecular pathogenesis. The aim is to provide the Genetics, Pediatrics and Radiology community with a list of recognized genetic skeletal disorders that can be of help in the diagnosis of individual cases, in the delineation of novel disorders, and in building bridges between clinicians and scientists interested in skeletal biology. In the 2010 revision, 456 conditions were included and placed in 40 groups defined by molecular, biochemical, and/or radiographic criteria. Of these conditions, 316 were associated with mutations in one or more of 226 different genes, ranging from common, recurrent mutations to "private" found in single families or individuals. Thus, the Nosology is a hybrid between a list of clinically defined disorders, waiting for molecular clarification, and an annotated database documenting the phenotypic spectrum produced by mutations in a given gene. The Nosology should be useful for the diagnosis of patients with genetic skeletal diseases, particularly in view of the information flood expected with the novel sequencing technologies; in the delineation of clinical entities and novel disorders, by providing an overview of established nosologic entities; and for scientists looking for the clinical correlates of genes, proteins and pathways involved in skeletal biology. © 2011 Wiley-Liss, Inc.

Avaluació de l'estàndard SQL : 1999 respecte les característiques orientades a l'objecte que suporta i la seva implementació als SGBD object-relational

L'objectiu és estudiar les característiques orientades a l'objecte de l'estàndard SQL: 1999 i posar-les a prova amb un producte comercial que les suporti.

Use of phylogeny to resolve the taxonomy of the widespread and highly polymorphic African giant shrews (Crocidura olivieri group, Crocidurinae, Mammalia).

The aim of this study is to provide a better understanding of the genetic relationships within the widespread and highly polymorphic group of African giant shrews (Crocidura olivieri group). We sequenced 769 base pairs (bp) of the mitochondrial cytochrome b gene and 472 bp of the mitochondrial control region over the entire geographic range from South Africa to Morocco. The analyses reveal four main clades associated with different biomes. The largest clade occurs over a range covering Northwest and Central Africa and includes samples of C. fulvastra, C. olivieri, and C. viaria. The second clade is composed of C. goliath from Gabon, while South African C. flavescens, and C. hirta form two additional clades. On the basis of these results, the validity of some taxa in the C. olivieri group should be re-evaluated.

Cluster kernels for semisupervised classification of VHR urban images

In this paper, we present and apply a semisupervised support vector machine based on cluster kernels for the problem of very high resolution image classification. In the proposed setting, a base kernel working with labeled samples only is deformed by a likelihood kernel encoding similarities between unlabeled examples. The resulting kernel is used to train a standard support vector machine (SVM) classifier. Experiments carried out on very high resolution (VHR) multispectral and hyperspectral images using very few labeled examples show the relevancy of the method in the context of urban image classification. Its simplicity and the small number of parameters involved make it versatile and workable by unexperimented users.

Scene Classification Using a Hybrid Generative/Discriminative Approach

We investigate whether dimensionality reduction using a latent generative model is beneficial for the task of weakly supervised scene classification. In detail, we are given a set of labeled images of scenes (for example, coast, forest, city, river, etc.), and our objective is to classify a new image into one of these categories. Our approach consists of first discovering latent ";topics"; using probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature here applied to a bag of visual words representation for each image, and subsequently, training a multiway classifier on the topic distribution vector for each image. We compare this approach to that of representing each image by a bag of visual words vector directly and training a multiway classifier on these vectors. To this end, we introduce a novel vocabulary using dense color SIFT descriptors and then investigate the classification performance under changes in the size of the visual vocabulary, the number of latent topics learned, and the type of discriminative classifier used (k-nearest neighbor or SVM). We achieve superior classification performance to recent publications that have used a bag of visual word representation, in all cases, using the authors' own data sets and testing protocols. We also investigate the gain in adding spatial information. We show applications to image retrieval with relevance feedback and to scene classification in videos

Efficient generation of human alloantigen-specific CD4+ regulatory T cells from naive precursors by CD40-activated B cells.

CD4(+)CD25(+)Foxp3(+) regulatory T cells (Treg) play an important role in the induction and maintenance of immune tolerance. Although adoptive transfer of bulk populations of Treg can prevent or treat T cell-mediated inflammatory diseases and transplant allograft rejection in animal models, optimal Treg immunotherapy in humans would ideally use antigen-specific rather than polyclonal Treg for greater specificity of regulation and avoidance of general suppression. However, no robust approaches have been reported for the generation of human antigen-specific Treg at a practical scale for clinical use. Here, we report a simple and cost-effective novel method to rapidly induce and expand large numbers of functional human alloantigen-specific Treg from antigenically naive precursors in vitro using allogeneic nontransformed B cells as stimulators. By this approach naive CD4(+)CD25(-) T cells could be expanded 8-fold into alloantigen-specific Treg after 3 weeks of culture without any exogenous cytokines. The induced alloantigen-specific Treg were CD45RO(+)CCR7(-) memory cells, and had a CD4(high), CD25(+), Foxp3(+), and CD62L (L-selectin)(+) phenotype. Although these CD4(high)CD25(+)Foxp3(+) alloantigen-specific Treg had no cytotoxic capacity, their suppressive function was cell-cell contact dependent and partially relied on cytotoxic T lymphocyte antigen-4 expression. This approach may accelerate the clinical application of Treg-based immunotherapy in transplantation and autoimmune diseases.

False phylogenies on wood mice due to cryptic cytochrome-b pseudogene.

The phylogeny and phylogeography of the Old World wood mice (subgenus Sylvaemus, genus Apodemus, Muridae) are well-documented. Nevertheless, the distributions of species, such as A. fulvipectus and A. ponticus remain dubious, as well as their phylogenetic relationships with A. sylvaticus. We analysed samples of Apodemus spp. across Europe using the mitochondrial cytochrome-b gene (cyt-b) and compared the DNA and amino-acid compositions of previously published sequences. The main result stemming from this study is the presence of a well-differentiated lineage of Sylvaemus including samples of various species (A. sylvaticus, A. fulvipectus, A. ponticus) from distant locations, which were revealed to be nuclear copies of the mitochondrial cyt-b. The presence of this cryptic pseudogene in published sequences is supported by different pathways. This has led to important errors in previous molecular trees and hence to partial misinterpretations in the phylogeny of Apodemus.