Particularités de la prise en charge des infections chez le patient neutropénique [Special feature in the handling of infectious in neutropenic patients]

Les progrès continus des connaissances réalisés dans le domaine de l'oncohématologie depuis quelques décennies ont permis une amélioration considérable du pronostic de la plupart des formes de cancer. Toutefois, la morbidité et la mortalité attribuables aux infections apparaissent actuellement comme les principaux facteurs limitant l'agressivité des traitements de la maladie cancéreuse, et un meilleur contrôle de ces dernières est devenu l'un des éléments essentiels de la prise en charge de ce type de patients. Une recherche clinique intense a permis d'en identifier les grands principes qui sont exposés dans cet article. Un algorithme thérapeutique susceptible de guider le clinicien face au développement d'un état fébrile, toujours suspect d'infection chez le patient cancéreux neutropénique, est ensuite proposé.

Analysis and evaluation of techniques for the extraction of classes in the ontology learning process

This paper analyzes and evaluates, in the context of Ontology learning, some techniques to identify and extract candidate terms to classes of a taxonomy. Besides, this work points out some inconsistencies that may be occurring in the preprocessing of text corpus, and proposes techniques to obtain good terms candidate to classes of a taxonomy.

S-system parameter estimation for noisy metabolic profiles using newton-flow analysis.

Biochemical systems are commonly modelled by systems of ordinary differential equations (ODEs). A particular class of such models called S-systems have recently gained popularity in biochemical system modelling. The parameters of an S-system are usually estimated from time-course profiles. However, finding these estimates is a difficult computational problem. Moreover, although several methods have been recently proposed to solve this problem for ideal profiles, relatively little progress has been reported for noisy profiles. We describe a special feature of a Newton-flow optimisation problem associated with S-system parameter estimation. This enables us to significantly reduce the search space, and also lends itself to parameter estimation for noisy data. We illustrate the applicability of our method by applying it to noisy time-course data synthetically produced from previously published 4- and 30-dimensional S-systems. In addition, we propose an extension of our method that allows the detection of network topologies for small S-systems. We introduce a new method for estimating S-system parameters from time-course profiles. We show that the performance of this method compares favorably with competing methods for ideal profiles, and that it also allows the determination of parameters for noisy profiles.

Definitional verbal patterns for semantic relation extraction

In this paper we present a description of the role of definitional verbal patterns for the extraction of semantic relations. Several studies show that semantic relations can be extracted from analytic definitions contained in machine-readable dictionaries (MRDs). In addition, definitions found in specialised texts are a good starting point to search for different types of definitions where other semantic relations occur. The extraction of definitional knowledge from specialised corpora represents another interesting approach for the extraction of semantic relations. Here, we present a descriptive analysis of definitional verbal patterns in Spanish and the first steps towards the development of a system for the automatic extraction of definitional knowledge.

Automatic Extraction of Polar Adjectives for the Creation of Polarity Lexicons

Automatic creation of polarity lexicons is a crucial issue to be solved in order to reduce time andefforts in the first steps of Sentiment Analysis. In this paper we present a methodology based onlinguistic cues that allows us to automatically discover, extract and label subjective adjectivesthat should be collected in a domain-based polarity lexicon. For this purpose, we designed abootstrapping algorithm that, from a small set of seed polar adjectives, is capable to iterativelyidentify, extract and annotate positive and negative adjectives. Additionally, the methodautomatically creates lists of highly subjective elements that change their prior polarity evenwithin the same domain. The algorithm proposed reached a precision of 97.5% for positiveadjectives and 71.4% for negative ones in the semantic orientation identification task.

Semantic web adoption: online tools for web evaluation and metadata extraction

This work briefly analyses the difficulties to adopt the Semantic Web, and in particular proposes systems to know the present level of migration to the different technologies that make up the Semantic Web. It focuses on the presentation and description of two tools, DigiDocSpider and DigiDocMetaEdit, designed with the aim of verifYing, evaluating, and promoting its implementation.

A survey on transfer-based approaches to MT using feature structures

Aquest treball és una revisió d'alguns sistemes de Traducció Automàtica que segueixen l'estratègia de Transfer i fan servir estructures de trets com a eina de representació. El treball s'integra dins el projecte MLAP-9315, projecte que investiga la reutilització de les especificacions lingüístiques del projecte EUROTRA per estàndards industrials.

Tandem use of solid-phase extraction and dispersive liquid-liquid microextraction for the determination of mononitrotoluenes in aquatic environment.

Solid-phase extraction (SPE) in tandem with dispersive liquid-liquid microextraction (DLLME) has been developed for the determination of mononitrotoluenes (MNTs) in several aquatic samples using gas chromatography-flame ionization (GC-FID) detection system. In the hyphenated SPE-DLLME, initially MNTs were extracted from a large volume of aqueous samples (100 mL) into a 500-mg octadecyl silane (C(18) ) sorbent. After the elution of analytes from the sorbent with acetonitrile, the obtained solution was put under the DLLME procedure, so that the extra preconcentration factors could be achieved. The parameters influencing the extraction efficiency such as breakthrough volume, type and volume of the elution solvent (disperser solvent) and extracting solvent, as well as the salt addition, were studied and optimized. The calibration curves were linear in the range of 0.5-500 μg/L and the limit of detection for all analytes was found to be 0.2 μg/L. The relative standard deviations (for 0.75 μg/L of MNTs) without internal standard varied from 2.0 to 6.4% (n=5). The relative recoveries of the well, river and sea water samples, spiked at the concentration level of 0.75 μg/L of the analytes, were in the range of 85-118%.

Simple estimation of incident HIV infection rates in notification cohorts based on window periods of algorithms for evaluation of line-immunoassay result patterns.

BACKGROUND: Tests for recent infections (TRIs) are important for HIV surveillance. We have shown that a patient's antibody pattern in a confirmatory line immunoassay (Inno-Lia) also yields information on time since infection. We have published algorithms which, with a certain sensitivity and specificity, distinguish between incident (< = 12 months) and older infection. In order to use these algorithms like other TRIs, i.e., based on their windows, we now determined their window periods. METHODS: We classified Inno-Lia results of 527 treatment-naïve patients with HIV-1 infection < = 12 months according to incidence by 25 algorithms. The time after which all infections were ruled older, i.e. the algorithm's window, was determined by linear regression of the proportion ruled incident in dependence of time since infection. Window-based incident infection rates (IIR) were determined utilizing the relationship 'Prevalence = Incidence x Duration' in four annual cohorts of HIV-1 notifications. Results were compared to performance-based IIR also derived from Inno-Lia results, but utilizing the relationship 'incident = true incident + false incident' and also to the IIR derived from the BED incidence assay. RESULTS: Window periods varied between 45.8 and 130.1 days and correlated well with the algorithms' diagnostic sensitivity (R(2) = 0.962; P<0.0001). Among the 25 algorithms, the mean window-based IIR among the 748 notifications of 2005/06 was 0.457 compared to 0.453 obtained for performance-based IIR with a model not correcting for selection bias. Evaluation of BED results using a window of 153 days yielded an IIR of 0.669. Window-based IIR and performance-based IIR increased by 22.4% and respectively 30.6% in 2008, while 2009 and 2010 showed a return to baseline for both methods. CONCLUSIONS: IIR estimations by window- and performance-based evaluations of Inno-Lia algorithm results were similar and can be used together to assess IIR changes between annual HIV notification cohorts.

VIKI : a semiotic-based system for multilingual knowledge retrieval

Abstract Since its creation, the Internet has permeated our daily life. The web is omnipresent for communication, research and organization. This exploitation has resulted in the rapid development of the Internet. Nowadays, the Internet is the biggest container of resources. Information databases such as Wikipedia, Dmoz and the open data available on the net are a great informational potentiality for mankind. The easy and free web access is one of the major feature characterizing the Internet culture. Ten years earlier, the web was completely dominated by English. Today, the web community is no longer only English speaking but it is becoming a genuinely multilingual community. The availability of content is intertwined with the availability of logical organizations (ontologies) for which multilinguality plays a fundamental role. In this work we introduce a very high-level logical organization fully based on semiotic assumptions. We thus present the theoretical foundations as well as the ontology itself, named Linguistic Meta-Model. The most important feature of Linguistic Meta-Model is its ability to support the representation of different knowledge sources developed according to different underlying semiotic theories. This is possible because mast knowledge representation schemata, either formal or informal, can be put into the context of the so-called semiotic triangle. In order to show the main characteristics of Linguistic Meta-Model from a practical paint of view, we developed VIKI (Virtual Intelligence for Knowledge Induction). VIKI is a work-in-progress system aiming at exploiting the Linguistic Meta-Model structure for knowledge expansion. It is a modular system in which each module accomplishes a natural language processing task, from terminology extraction to knowledge retrieval. VIKI is a supporting system to Linguistic Meta-Model and its main task is to give some empirical evidence regarding the use of Linguistic Meta-Model without claiming to be thorough.

Troubles digestifs associés aux protozoaires et aux helmintes: prise en charge par le médecin de famille [Gastrointestinal complaints associated to helminth and protozoan: management by the general practitioner].

Protozoan and helminthes are frequently associated with persistent digestive complaints, not only in returning travelers from the tropics, but also in industrialized countries. The symptoms are often more vague than those associated to bacterial or viral infections and diarrhea is not always a key feature of the clinical presentation. Three stool examinations and a full blood cells count looking for eosinophilia is the comer stone of the investigations looking for digestive parasites. This article reviews the epidemiology, clinical presentation, diagnostic and management of digestive protozoans and helminthes.

GeoKernels: Modeling of Spatial Data on GeoManifolds

This paper presents a review of methodology for semi-supervised modeling with kernel methods, when the manifold assumption is guaranteed to be satisfied. It concerns environmental data modeling on natural manifolds, such as complex topographies of the mountainous regions, where environmental processes are highly influenced by the relief. These relations, possibly regionalized and nonlinear, can be modeled from data with machine learning using the digital elevation models in semi-supervised kernel methods. The range of the tools and methodological issues discussed in the study includes feature selection and semisupervised Support Vector algorithms. The real case study devoted to data-driven modeling of meteorological fields illustrates the discussed approach.

Differential DNA extraction of challenging simulated sexual assault samples: a Swiss collaborative study.

ABSTRACT: In sexual assault cases, autosomal DNA analysis of gynecological swabs is a challenge, as the presence of a large quantity of female material may prevent the detection of the male DNA. A solution to this problem is differential DNA extraction, but as there are different protocols, it was decided to test their efficiency on simulated casework samples. Four difficult samples were sent to the nine Swiss laboratories active in the forensic genetics. They used their routine protocols to separate the epithelial cell fraction, enriched with the non-sperm DNA, from the sperm fraction. DNA extracts were then sent to the organizing laboratory for analysis. Estimates of male to female DNA ratio without differential DNA extraction ranged from 1:38 to 1:339, depending on the semen used to prepare the samples. After differential DNA extraction, most of the ratios ranged from 1:12 to 9:1, allowing the detection of the male DNA. Compared to direct DNA extraction, cell separation resulted in losses of 94-98% of the male DNA. As expected, more male DNA was generally present in the sperm than in the epithelial cell fraction. However, for about 30% of the samples, the reverse trend was observed. The recovery of male and female DNA was highly variable depending on the laboratories. Experimental design similar to the one used in this study may help for local protocol testing and improvement.