3 documents found, page 1 of 1
Leveraging machine learning techniques to investigate the pathogenesis of incid...
Elnaggar, J.; Jacobs, C.; Ardizzone, C.; Aaron, K.; Eastlund, I.; Graves, K.; Luo, M.; Tamhane, A.; Long, D.; Laniewski, P.; Herbst-Kralovetz, M.Objective We investigated longitudinal changes in the vaginal microbiota prior to, during, and immediately following the onset of incident bacterial vaginosis (iBV) to investigate its pathogenesis. We used supervised machine learning and artificial neural networks (ANNs) to identify vaginal micro-organisms predictive of future iBV development. Study Design In this prospective cohort study, we performed 16S rRNA...
Incident bacterial vaginosis in a community-based cohort of women
Muzny, C.; Aaron, K.; Tamhane, A.; Long, D.; Van Gerwen, O.; Graves, K.; Eastlund, I.; Elnaggar, J.; Cerca, Nuno; Taylor, C.[Excerpt] OBJECTIVES: ln an ongoing community-based BV pathogenesis study, we evaluated time to incident BV (iBV) as well as select characteristics of women with this infection. METHODS: Non-pregnant women ages 18-45 with normal vaginal microbiota (no Amsel criteria, normal Nugent score), no antibiotic use in the past 14 days, no concurrent STis, and a current male sexual partner were followed for 9 weeks. Part...
MAFTIA: Reference Model and Use Cases
Cachin, C.; Camenisch, J.; Deswarte, Y.; Dobson, J.; Horne, D.; Kursawe, K.; Laprie, J.-C.; Lebraud, J.-C.; Long, D.; McCutcheon, T.; Muller, J.This document constitutes the first deliverable of MAFTIA work package 1. The objective of this work package is to define a consistent framework for ensuring the dependability of distributed applications in the face of a wide class of threats. In particular, the aim is to develop a coherent set of concepts for an architecture that can tolerate deliberately malicious faults, such as intrusions, in applications d...