CUTANEOUS MANIFESTATIONS OF PRIMARY IMMUNODEFICIENCY DISEASES IN TUNISIAN CHILDREN

Abstract. Skin manifestations are frequent among patients with primary immunodeficiency diseases (PIDs). Their prevalence varies according to the type of immunodeficiency. This review provides the reader with an up-to-date summary of the common dermatologic manifestations of PIDs among Tunisian children. We conducted a prospective study on two hundred and ninety children with immune deficiency. Demographic details (including age, sex, and consanguinity) with personal and family history were recorded. Special attention was paid to cutaneous manifestations. Dermatological involvements were grouped according to the etiology of their most prominent sign. Cutaneous manifestations were found in 164 patients (56.5%). They revealed the diagnosis of PIDs in 71 patients (24.5 %). The mean age at presentation was 21 months. Overall the most prominent cutaneous alterations were infectious. They accounted for 106 cases (36.55%). The most prevalent causes of cutaneous infections were bacterial: 93 cases (32.06%). Immuno-allergic skin diseases were among the common findings in our study. These include eczematous dermatitis found in 62 cases (21.38%). Malignancy related PIDs was seen in a boy with Wiskott Aldrich syndrome. He developed Kaposi’s sarcoma at the age of 14 months. Cutaneous changes are common among children with PIDs. In pediatric patients with failure to thrive, chronic refractory systemic manifestations often present in other family members, recurrent cutaneous infections unresponsive to adequate therapy, atypical forms of eczematous dermatitis or unusual features should arouse the suspicion of PIDs and prompt specialized immunologic consultation should be made

Naive possibilistic classifiers for imprecise or uncertain numerical data

International audienceIn real-world problems, input data may be pervaded with uncertainty. In this paper, we investigate the behavior of naive possibilistic classifiers, as a counterpart to naive Bayesian ones, for dealing with classification tasks in the presence of uncertainty. For this purpose, we extend possibilistic classifiers, which have been recently adapted to numerical data, in order to cope with uncertainty in data representation. Here the possibility distributions that are used are supposed to encode the family of Gaussian probabilistic distributions that are compatible with the considered dataset. We consider two types of uncertainty: (i) the uncertainty associated with the class in the training set, which is modeled by a possibility distribution over class labels, and (ii) the imprecision pervading attribute values in the testing set represented under the form of intervals for continuous data. Moreover, the approach takes into account the uncertainty about the estimation of the Gaussian distribution parameters due to the limited amount of data available. We first adapt the possibilistic classification model, previously proposed for the certain case, in order to accommodate the uncertainty about class labels. Then, we propose an algorithm based on the extension principle to deal with imprecise attribute values. The experiments reported show the interest of possibilistic classifiers for handling uncertainty in data. In particular, the probability-to-possibility transform-based classifier shows a robust behavior when dealing with imperfect data

Possibilistic classifiers for numerical data

International audienceNaive Bayesian Classifiers, which rely on independence hypotheses, together with a normality assumption to estimate densities for numerical data, are known for their simplicity and their effectiveness. However, estimating densities, even under the normality assumption, may be problematic in case of poor data. In such a situation, possibility distributions may provide a more faithful representation of these data. Naive Possibilistic Classifiers (NPC), based on possibility theory, have been recently proposed as a counterpart of Bayesian classifiers to deal with classification tasks. There are only few works that treat possibilistic classification and most of existing NPC deal only with categorical attributes. This work focuses on the estimation of possibility distributions for continuous data. In this paper we investigate two kinds of possibilistic classifiers. The first one is derived from classical or flexible Bayesian classifiers by applying a probability–possibility transformation to Gaussian distributions, which introduces some further tolerance in the description of classes. The second one is based on a direct interpretation of data in possibilistic formats that exploit an idea of proximity between data values in different ways, which provides a less constrained representation of them. We show that possibilistic classifiers have a better capability to detect new instances for which the classification is ambiguous than Bayesian classifiers, where probabilities may be poorly estimated and illusorily precise. Moreover, we propose, in this case, an hybrid possibilistic classification approach based on a nearest-neighbour heuristics to improve the accuracy of the proposed possibilistic classifiers when the available information is insufficient to choose between classes. Possibilistic classifiers are compared with classical or flexible Bayesian classifiers on a collection of benchmarks databases. The experiments reported show the interest of possibilistic classifiers. In particular, flexible possibilistic classifiers perform well for data agreeing with the normality assumption, while proximity-based possibilistic classifiers outperform others in the other cases. The hybrid possibilistic classification exhibits a good ability for improving accuracy

Bioactive Secondary Metabolites from a New Terrestrial Streptomyces sp. TN262

During our search for Streptomyces spp. as new producers of bioactive secondary metabolites, the ethyl acetate extract of the new terrestrial Streptomyces isolate TN262 delivered eight antimicrobially active compounds. They were identified as 1-acetyl-β-carboline (1), tryptophol (2), cineromycin B (3), 2,3-dihydrocineromycin B (4), cyclo-(tyrosylprolyl) (5), 3-(hydroxyacetyl)-indole (6), brevianamide F (7), and cis-cyclo-(l-prolyl-l-leucyl) (8). Three further metabolites were detected in the unpolar fractions using GC–MS and tentatively assigned as benzophenone (9), N-butyl-benzenesulfonamide (10), and hexanedioic acid-bis-(2-ethylhexyl) ester (11). This last compound is known as plasticizer derivatives, but it has never been described from natural sources. In this article, we describe the identification of the new Streptomyces sp. isolate TN262 using its cultural characteristics, the nucleotide sequence of the corresponding 16S rRNA gene and the phylogenetic analysis, followed by optimization, large-scale fermentation, isolation of the bioactive constituents, and determination of their structures. The biological activity of compounds (2), (3), (4), and those of the unpolar fractions was addressed as well

Classification with Belief Decision Trees

Abstract. Decision trees are considered as an efficient technique to express classification knowledge and to use it. However, their most standard algorithms do not deal with uncertainty, especially the cognitive one. In this paper, we develop a method to adapt the decision tree technique to the case where the object’s classes are not exactly known, and where the uncertainty about the class ’ value is represented by a belief function. The adaptation concerns both the construction of the tree and its use to classify new objects characterized by uncertain attribute values.

Inference in directed evidential networks based on the transferable belief model

AbstractInference algorithms in directed evidential networks (DEVN) obtain their efficiency by making use of the represented independencies between variables in the model. This can be done using the disjunctive rule of combination (DRC) and the generalized Bayesian theorem (GBT), both proposed by Smets [Ph. Smets, Belief functions: the disjunctive rule of combination and the generalized Bayesian theorem, International Journal of Approximate Reasoning 9 (1993) 1–35]. These rules make possible the use of conditional belief functions for reasoning in directed evidential networks, avoiding the computations of joint belief function on the product space. In this paper, new algorithms based on these two rules are proposed for the propagation of belief functions in singly and multiply directed evidential networks

Modélisation du Problème Pm | prec | Cmax et Résolution par une méthode Tabou

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