Processionnal à l'usage de Marguerite et Louise de Chabannes, religieuses du couvent des Dominicaines de Poissy.

Ce manuscrit au beau décor enluminé pour Marguerite de Chabannes et Louise de Chabannes reste l'un des rares témoins de la riche bibliothèque du couvent des Dominicaines de Poissy fondé par le roi Philippe le Bel.

Positional therapy for obstructive sleep apnea: an objective measurement of patients' usage and efficacy at home.

BACKGROUND: Positional therapy that prevents patients from sleeping supine has been used for many years to manage positional obstructive sleep apnea (OSA). However, patients' usage at home and the long term efficacy of this therapy have never been objectively assessed. METHODS: Sixteen patients with positional OSA who refused or could not tolerate continuous positive airway pressure (CPAP) were enrolled after a test night study (T0) to test the efficacy of the positional therapy device. The patients who had a successful test night were instructed to use the device every night for three months. Nightly usage was monitored by an actigraphic recorder placed inside the positional device. A follow-up night study (T3) was performed after three months of positional therapy. RESULTS: Patients used the device on average 73.7 ± 29.3% (mean ± SD) of the nights for 8.0 ± 2.0 h/night. 10/16 patients used the device more than 80% of the nights. Compared to the baseline (diagnostic) night, mean apnea-hypopnea index (AHI) decreased from 26.7 ± 17.5 to 6.0 ± 3.4 with the positional device (p<0.0001) during T0 night. Oxygen desaturation (3%) index also fell from 18.4 ± 11.1 to 7.1 ± 5.7 (p = 0.001). Time spent supine fell from 42.8 ± 26.2% to 5.8 ± 7.2% (p < 0.0001). At three months (T3), the benefits persisted with no difference in AHI (p = 0.58) or in time spent supine (p = 0.98) compared to T0 night. The Epworth sleepiness scale showed a significant decrease from 9.4 ± 4.5 to 6.6 ± 4.7 (p = 0.02) after three months. CONCLUSIONS: Selected patients with positional OSA can be effectively treated by a positional therapy with an objective compliance of 73.7% of the nights and a persistent efficacy after three months.

Monitoring network optimisation for spatial data classification using support vector machines

The paper presents a novel method for monitoring network optimisation, based on a recent machine learning technique known as support vector machine. It is problem-oriented in the sense that it directly answers the question of whether the advised spatial location is important for the classification model. The method can be used to increase the accuracy of classification models by taking a small number of additional measurements. Traditionally, network optimisation is performed by means of the analysis of the kriging variances. The comparison of the method with the traditional approach is presented on a real case study with climate data.