Is Predominant Clonal Evolution a Common Evolutionary Adaptation to Parasitism in Pathogenic Parasitic Protozoa, Fungi, Bacteria, and Viruses?

We propose that predominant clonal evolution (PCE) in microbial pathogens be defined as restrained recombination on an evolutionary scale, with genetic exchange scarce enough to not break the prevalent pattern of clonal population structure. The main features of PCE are (1) strong linkage disequilibrium, (2) the widespread occurrence of stable genetic clusters blurred by occasional bouts of genetic exchange ('near-clades'), (3) the existence of a "clonality threshold", beyond which recombination is efficiently countered by PCE, and near-clades irreversibly diverge. We hypothesize that the PCE features are not mainly due to natural selection but also chiefly originate from in-built genetic properties of pathogens. We show that the PCE model obtains even in microbes that have been considered as 'highly recombining', such as Neisseria meningitidis, and that some clonality features are observed even in Plasmodium, which has been long described as panmictic. Lastly, we provide evidence that PCE features are also observed in viruses, taking into account their extremely fast genetic turnover. The PCE model provides a convenient population genetic framework for any kind of micropathogen. It makes it possible to describe convenient units of analysis (clones and near-clades) for all applied studies. Due to PCE features, these units of analysis are stable in space and time, and clearly delimited. The PCE model opens up the possibility of revisiting the problem of species definition in these organisms. We hypothesize that PCE constitutes a major evolutionary strategy for protozoa, fungi, bacteria, and viruses to adapt to parasitism

Les consultations nécessitant une prise en charge pédopsychiatrique aux urgences pédiatriques (l expérience du centre hospitalier universitaire de Grenoble)

Les consultations pédopsychiatriques représentent une part non négligeable des consultations aux urgences pédiatriques et nécessitent une prise en charge spécialisée et appropriée, voire urgente quand le pronostic vital du patient est engagé. OBJECTIFS : Caractériser la population d enfants et d adolescents consultant pour motif pédopsychiatrique en 2005 et 2007 aux urgences médicales pédiatriques du CHU de Grenoble. METHODE : Etude descriptive rétrospective sur deux périodes, du 1er janvier au 31 décembre 2005 et 2007, unicentrique. Comparaison des éléments anamnestiques, cliniques et des prises en charge. RESULTATS : 125 patients ont été inclus en 2005 et 183 en 2007 avec une prédominance féminine et un âge médian de 13 ans. Pour l année 2007, les consultants étaient adressés dans plus de la moitié des cas par leur famille excepté en cas d alcoolisation où ils étaient accompagnés par la police ou les pompiers (p<0,001). Les principaux diagnostics retenus étaient les troubles anxio-dépressifs (23,5%), les tentatives de suicide (23,5%) et les troubles du comportement (15,3%), ce dernier diagnostic concernant un nombre significativement plus élevé de garçons (p<0,001). 77,6% des patients ont bénéficié d un avis pédopsychiatrique (tous en cas de tentative de suicide, p<0,001), 53,5% ont été hospitalisés (tous en cas d alcoolisation, p=0,008 et près de 90% en cas de tentative de suicide, p<0,001) et un suivi psychologique a été instauré en ambulatoire dans 72% des cas (instauration plus élevé en cas de tentative de suicide, p=0,005 et en cas de troubles du comportement, p=0,001). CONCLUSION : La prise en charge multidisciplinaire (pédiatrique et psycho-sociale) est essentielle pour les patients présentant des troubles psychiatriques. Des protocoles ont été mis à jour ou créés (alcoolisation, agitation et tentative de suicide) et une conduite à tenir type pour l accueil des enfants présentant des troubles psychiques aux urgences pédiatriques a été proposée.Pedopsychiatric consultations represent a significant part of the consultations to pediatric emergencies and require specialized and appropriate, or even urgent support when the patient threatening is committed. OBJECTIVES: Characterize the population of children and adolescents seeing patient for pedopsychiatric motive in 2005 and 2007 to Grenoble University Hospital pediatric medical emergencies. METHOD: Retrospective descriptive study on two periods, 1 January to 31 December 2005 and 2007, unicentrique. Comparing elements anamnestiques, clinical and support. RESULTS: 125 patients were included in 2005 and 183 in 2007 with a female predominance and a median age of 13. For the year 2007, consultants were addressed in more than half of the cases by their family except for alcohol where they were accompanied by the police or firefighters (p<0, 001). Main diagnoses were anxio-depressive disorders (23.5 %), suicide attempts (23.5 %) and behavior disorders (15.3 %), this last diagnosis concerning a number significantly more high boys (p<0, 001). 77,6 % of patients benefited pedopsychiatric notice (all of attempted suicide, p<0, 001), 53,5 % have been hospitalized (all for alcohol, p = 0, 008 and nearly 90 % of attempted suicide, p<0, 001) and psychological follow-up has been established in ambulatory in 72 % (introduction higher of attempted suicide, p = 0, 005 and for disorders of behavior, p = 0, 001) cases. CONCLUSION: Multidisciplinary support (pediatric and psycho-social) is essential for patients with psychiatric disorders. Protocols have been updated or created (alcohol, agitation and attempted suicide) and a led to hold for the reception of children with mental disabilities to pediatric emergencies has been proposed.GRENOBLE1-BU Médecine pharm. (385162101) / SudocSudocFranceF

Explainable multi-class anomaly detection on functional data

In this paper we describe an approach for anomaly detection and its explainability in multivariate functional data. The anomaly detection procedure consists of transforming the series into a vector of features and using an Isolation forest algorithm. The explainable procedure is based on the computation of the SHAP coefficients and on the use of a supervised decision tree. We apply it on simulated data to measure the performance of our method and on real data coming from industry

Urban Dynamics and Simulation Models

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