Le Tamis et le sable

Ce catalogue propose un regard rétrospectif sur le cycle éponyme de trois expositions qui se sont tenues à la Maison Populaire de Montreuil sous la direction d’Ann Lou Vicente, Raphaël Brunel et Antoine Marchand. Dès leur préface (« Sous le visible », p. 6-7), les curateurs convoquent les personnages de Fahrenheit 451 dont la mémoire était la seule garante de la littérature. Ce cycle d’expositions révéle la dimension paradigmatique –quoique cachée– de ces personnages en questionnant la transm..

Channelling figurativity through narrative : the paranarrative in fiction and non-fiction

Contrary to wide-spread assumptions, metaphor in narrative is not a pre-established, extra-textual form appearing in different instances of discourse, but rather an event resulting from a strategic distribution of information in the narrative process. Hence, the appeal to conceptual cultural knowledge is to be considered as a consequence, and not as a prerequisite of metaphor interpretation. By means of the concept of the paranarrative, we highlight the rhetorical interconnectedness of metaphor with other figures of speech (such as metonymy) and we explore the narrative integration of diacritic forms of indirectness. In order to illustrate the terminology that can address these focal concerns, the paper discusses the relation between tropes and narrative, via selected examples from narrative texts (both fictional and non-fictional) written by Juli Zeh, Herta Müller, Jürgen Nieraad, and Siddhartha Mukherjee. As their common denominator, these examples channel through narrative figurative domains considered to be known intuitively to wit: personifications; iconic pars pro toto references to concentration camps; and metaphors for cancer in disease biographies

8 Years of experience in teaching process dynamics and control with control station® software

The paper describes all the steps of a teaching activity dealing with Process Dynamics and Control and focused on the students use of the Control Station® simulation software. After a short software description, the paper discusses the methodology developed for coupling theoretical lecturing and practical PC-lab class, the way of involving students and the use of an interactive software environment to present automatic control of illustrative process plants. These latter comprise unit operations and simple equipment from chemical, biochemical, pharmaceutical and food industries as actual examples of abstract systems and mathematical formalisms introduced for studying processes in the context of dynamics and control. Two Project Works, which were developed by students using Control Station® and discussed by them, are presented as examples. The outcome of this 8-year teaching experience is analyzed on the basis of the number of Project Works annually delivered, the auto-evaluation tests, the final exam scores as well as the relevant answers yearly provided by the students through the Course Evaluation Forms. The final statistical results are positive

An electronic health record based model predicts statin adherence, LDL cholesterol, and cardiovascular disease in the United States Military Health System

<div><p>HMG-CoA reductase inhibitors (or “statins”) are important and commonly used medications to lower cholesterol and prevent cardiovascular disease. Nearly half of patients stop taking statin medications one year after they are prescribed leading to higher cholesterol, increased cardiovascular risk, and costs due to excess hospitalizations. Identifying which patients are at highest risk for not adhering to long-term statin therapy is an important step towards individualizing interventions to improve adherence. Electronic health records (EHR) are an increasingly common source of data that are challenging to analyze but have potential for generating more accurate predictions of disease risk. The aim of this study was to build an EHR based model for statin adherence and link this model to biologic and clinical outcomes in patients receiving statin therapy. We gathered EHR data from the Military Health System which maintains administrative data for active duty, retirees, and dependents of the United States armed forces military that receive health care benefits. Data were gathered from patients prescribed their first statin prescription in 2005 and 2006. Baseline billing, laboratory, and pharmacy claims data were collected from the two years leading up to the first statin prescription and summarized using non-negative matrix factorization. Follow up statin prescription refill data was used to define the adherence outcome (> 80 percent days covered). The subsequent factors to emerge from this model were then used to build cross-validated, predictive models of 1) overall disease risk using coalescent regression and 2) statin adherence (using random forest regression). The predicted statin adherence for each patient was subsequently used to correlate with cholesterol lowering and hospitalizations for cardiovascular disease during the 5 year follow up period using Cox regression. The analytical dataset included 138 731 individuals and 1840 potential baseline predictors that were reduced to 30 independent EHR “factors”. A random forest predictive model taking patient, statin prescription, predicted disease risk, and the EHR factors as potential inputs produced a cross-validated c-statistic of 0.736 for classifying statin non-adherence. The addition of the first refill to the model increased the c-statistic to 0.81. The predicted statin adherence was independently associated with greater cholesterol lowering (correlation = 0.14, p < 1e-20) and lower hospitalization for myocardial infarction, coronary artery disease, and stroke (hazard ratio = 0.84, p = 1.87E-06). Electronic health records data can be used to build a predictive model of statin adherence that also correlates with statins’ cardiovascular benefits.</p></div

Predicted statin adherence and risk of cardiovascular outcomes.

<p>Predicted statin adherence was divided into tertiles of predicted statin adherence. The cumulative event free survival for each tertile of risk from Cox survival model is plotted for hospitalizations for acute myocardial infarction, stroke, coronary artery disease, or a composite of all three. P-values represent results of log-rank testing.</p

Baseline patient and statin characteristics of cohort (N = 138731).

<p>Baseline patient and statin characteristics of cohort (N = 138731).</p

Performance of statin adherence models.

<p>The Receiver operating characteristics (ROC) curves for two models that predict statin adherence defined as percent days covered (PDC) greater than 0.8 during the follow-up period. The results of the risk only model uses random forest modeling and considers baseline demographics, statin prescription characteristics, disease risk predictions, and the ‘factors” resulting from dimension reduction to predict statin adherence. The “risk + first refill” model uses the same predictors as the risk only model but also considers whether or not the first statin prescription was filled and predicts statin adherence for the remaining time period after the first fill. The area represents the area under the ROC curve.</p