De l'Abbittibbi-Témiskaming 5

Ouvrage contenant les études suivantes: Jean Laflamme, «Le Marquis de vaudreuil et l'Abitibi-Témiscamingue. Seconde partie: 1724-1731». Benoît-Beaudry Gourd, «Les journaux de l'Abitibi-Témiscamingue 1920-1950. Portrait historique». Dan Glenday, «Thirty years of labour relations in the mining industry in Rouyn-Noranda, Québec, 1934-1964». Pierre Leblond, «Certaines caractéristiques d'un village minier de compagnie: la localité de Joutel en Abitibi.» Noël Savard, «L'environnement et l'industrie minière à Rouyn-Noranda». Maurice Asselin, «Le rôle de la frontière dans les relations entre le Nord-Ouest québécois et l'Ontario». Claude P. Bigue, «La concession du domaine public et l'aménagement des terrains riverains de l'Harricana.

An Iterative Nonribosomal Peptide Synthetase Assembles the Pyrrole-Amide Antibiotic Congocidine in Streptomyces ambofaciens

SummaryCongocidine (netropsin) is a pyrrole-amide (oligopyrrole, oligopeptide) antibiotic produced by Streptomyces ambofaciens. We have identified, in the right terminal region of the S. ambofaciens chromosome, the gene cluster that directs congocidine biosynthesis. Heterologous expression of the cluster and in-frame deletions of 8 of the 22 genes confirm the involvement of this cluster in congocidine biosynthesis. Nine genes can be assigned specific functions in regulation, resistance, or congocidine assembly. In contrast, the biosynthetic origin of the precursors cannot be easily inferred from in silico analyses. Congocidine is assembled by a nonribosomal peptide synthetase (NRPS) constituted of a free-standing module and several single-domain proteins encoded by four genes. The iterative use of its unique adenylation domain, the utilization of guanidinoacetyl-CoA as a substrate by a condensation domain, and the control of 4-aminopyrrole-2-carboxylate polymerization constitute the most original features of this NRPS

Diagnostic accuracy of ultrasonography, MRI and MR arthrography in the characterisation of rotator cuff disorders: A systematic review and meta-analysis

Background Different diagnostic imaging modalities, such as ultrasonography (US), MRI, MR arthrography (MRA) are commonly used for the characterisation of rotator cuff (RC) disorders. Since the most recent systematic reviews on medical imaging, multiple diagnostic studies have been published, most using more advanced technological characteristics. The first objective was to perform a meta-analysis on the diagnostic accuracy of medical imaging for characterisation of RC disorders. Since US is used at the point of care in environments such as sports medicine, a secondary analysis assessed accuracy by radiologists and nonradiologists. Methods A systematic search in three databases was conducted. Two raters performed data extraction and evaluation of risk of bias independently, and agreement was achieved by consensus. Hierarchical summary receiver-operating characteristic package was used to calculate pooled estimates of included diagnostic studies. Results Diagnostic accuracy of US, MRI and MRA in the characterisation of full-thickness RC tears was high with overall estimates of sensitivity and specificity over 0.90. As for partial RC tears and tendinopathy, overall estimates of specificity were also high (\u3e0.90), while sensitivity was lower (0.67â 0.83). Diagnostic accuracy of US was similar whether a trained radiologist, sonographer or orthopaedist performed it. Conclusions Our results show the diagnostic accuracy of US, MRI and MRA in the characterisation of fullthickness RC tears. Since full thickness tear constitutes a key consideration for surgical repair, this is an important characteristic when selecting an imaging modality for RC disorder. When considering accuracy, cost, and safety, US is the best option

Using Markov Models to Mine Temporal and Spatial Data

Référence du projet ANR BIODIVAGRIM : ANR 07 BDIV 02Markov models represent a powerful way to approach the problem of mining time and spatial signals whose variability is not yet fully understood. In this chapter, we will present a general methodology to mine different kinds of temporal and spatial signals having contrasting properties: continuous or discrete with few or many modalities. This methodology is based on a high order Markov modelling as implemented in a free software: carottAge (Gnu GPL)Les modèles de Markov sont des modèles puissants pour analyser des signaux temporels et spatiaux dont la variabilité n'est pas entièrement comprise. Dans ce chapitre, nous présentons notre méthodologie pour fouiller différentes sortes de signaux ayant des propriétés différentes: signaux continus ou discrets, simples ou composites. Cette méthodologie s'appuie sur des modèles de Markov cachés du second-ordre tels qu'implantés dans la boîte à outils CarottAge (licence Gnu-GPL)

Evaluation of a multivariate syndromic surveillance system for West Nile virus.

Background: Various methods are currently used for the early detection of West Nile virus (WNV) but their output is either not quantitative or does not take into account all available information. Our study aimed to test a multivariate syndromic surveillance system in order to improve early detection of WNV. Method: Weekly time series data on nervous syndromes in horses and mortality in both horses and wild birds were used. Baselines were fitted to the three time series and used to simulate 100 years of surveillance data. WNV outbreaks were simulated and inserted into the baselines based on historical data and expert opinion. Univariate and multivariate syndromic surveillance systems were tested in order to gauge how well they detected the outbreaks; detection was based on an empirical Bayesian approach. The systems’ performances were compared using measures of sensitivity, specificity, and area-under-ROC-curve (AUC). Result: When data sources were considered separately (i.e. univariate systems), the best detection performance was obtained using the dataset of nervous symptoms in horses compared to those of bird and horse mortality (AUCs respectively equal to 0.80, 0.75, and 0.50). A multivariate outbreak detection system that used nervous symptoms in horses and bird mortality generated the best performance (AUC = 0.87). Conclusion: The proposed approach is suitable for performing multivariate syndromic surveillance of WNV outbreaks. This is particularly relevant given that a multivariate surveillance system performed better than a univariate approach. Such a surveillance system could be especially useful in serving as an alert for the possibility of human viral infections. This approach can be also used for other diseases for which multiple sources of evidence are available

Wavelength-scale stationary-wave integrated Fourier-transform spectrometry

Spectrometry is a general physical-analysis approach for investigating light-matter interactions. However, the complex designs of existing spectrometers render them resistant to simplification and miniaturization, both of which are vital for applications in micro- and nanotechnology and which are now undergoing intensive research. Stationary-wave integrated Fourier-transform spectrometry (SWIFTS)-an approach based on direct intensity detection of a standing wave resulting from either reflection (as in the principle of colour photography by Gabriel Lippmann) or counterpropagative interference phenomenon-is expected to be able to overcome this drawback. Here, we present a SWIFTS-based spectrometer relying on an original optical near-field detection method in which optical nanoprobes are used to sample directly the evanescent standing wave in the waveguide. Combined with integrated optics, we report a way of reducing the volume of the spectrometer to a few hundreds of cubic wavelengths. This is the first attempt, using SWIFTS, to produce a very small integrated one-dimensional spectrometer suitable for applications where microspectrometers are essential

Multiple and Variable NHEJ-Like Genes Are Involved in Resistance to DNA Damage in Streptomyces ambofaciens

International audienceNon-homologous end-joining (NHEJ) is a double strand break (DSB) repair pathway which does not require any homologous template and can ligate two DNA ends together. The basic bacterial NHEJ machinery involves two partners: the Ku protein, a DNA end binding protein for DSB recognition and the multifunctional LigD protein composed a ligase, a nuclease and a polymerase domain, for end processing and ligation of the broken ends. In silico analyses performed in the 38 sequenced genomes of Streptomyces species revealed the existence of a large panel of NHEJ-like genes. Indeed, ku genes or ligD domain homologues are scattered throughout the genome in multiple copies and can be distinguished in two categories: the " core " NHEJ gene set constituted of conserved loci and the " variable " NHEJ gene set constituted of NHEJ-like genes present in only a part of the species. In Streptomyces ambofaciens ATCC23877, not only the deletion of " core " genes but also that of " variable " genes led to an increased sensitivity to DNA damage induced by electron beam irradiation. Multiple mutants of ku, ligase or polymerase encoding genes showed an aggravated phenotype compared to single mutants. Biochemical assays revealed the ability of Ku-like proteins to protect and to stimulate ligation of DNA ends. RT-qPCR and GFP fusion experiments suggested that ku-like genes show a growth phase dependent expression profile consistent with their involvement in DNA repair during spores formation and/or germination

Data Mining Using Hidden Markov Models (HMM2) to Detect Heterogeneities into Bacteria Genomes

International audienceThe Streptococcus genus contains both pathogenic bacteria and bacteria used in the food-processing industry. We are developing a statistical segmentation method to identify heterogeneous sequences such as sequences acquired from recent horizontal transfer or genes weakly or strongly expressed. The method is based on second order Hidden Markov Models (HMM2). After an automatic unsupervised training, this method allows to demarcating some particular areas into a genome. After checking the efficiency of such models on various controls and on chimeric sequences generated in silico, we choose a HMM2 (3-mer, 5 states) to analyse the complete genome sequence of S. Thermophilus CNRZ1066 (1.8 Mb). More the 80 atypical segments were extracted and are currently analysed further

Data Mining Using Hidden Markov Models (HMM2) to Detect Heterogeneities into Bacteria Genomes

PosterThe Streptococcus genus contains both pathogenic bacteria and bacteria used in the food-processing industry. We are developing a statistical segmentation method to identify heterogeneous sequences such as sequences acquired from recent horizontal transfer or genes weakly or strongly expressed. The method is based on second order Hidden Markov Models (HMM2). After an automatic unsupervised training, this method allows to demarcating some particular areas into a genome. After checking the efficiency of such models on various controls and on chimeric sequences generated in silico, we choose a HMM2 (3-mer, 5 states) to analyse the complete genome sequence of S. Thermophilus CNRZ1066 (1.8 Mb). More the 80 atypical segments were extracted and are currently analysed further